Ecological models

@misc{elton1927animal11,
    author = "Elton, C. S",
    title = "Animal ecology",
    year = "1927",
    howpublished = "London, Sidgwick and Jackson, 209 p",
    note = "talkorigins\_source = {true}; raw\_reference = {Elton, C. S., 1927, Animal ecology: London, Sidgwick and Jackson, 209 p.}"
}

@article{elton1946competition12,
    author = "Elton, C. S",
    title = "Competition and the structure of ecological communities",
    year = "1946",
    journal = "Journal of Animal Ecology, v. 15, p. 54-68",
    note = "talkorigins\_source = {true}; raw\_reference = {Elton, C. S., 1946, Competition and the structure of ecological communities: Journal of Animal Ecology, v. 15, p. 54-68.}"
}

@article{cole1951population7,
    author = "Cole, L. C",
    title = "Population cycles and random oscillations",
    year = "1951",
    journal = "Journal of Wildlife Management, v. 15, p. 233-251",
    note = "talkorigins\_source = {true}; raw\_reference = {Cole, L. C., 1951, Population cycles and random oscillations: Journal of Wildlife Management, v. 15, p. 233-251.}"
}

@article{cole1954some8,
    author = "Cole, L. C",
    title = "Some features of random cycles",
    year = "1954",
    journal = "Journal of Wildlife Management, v. 18, p. 2-24",
    note = "talkorigins\_source = {true}; raw\_reference = {Cole, L. C., 1954, Some features of random cycles: Journal of Wildlife Management, v. 18, p. 2-24.}"
}

@article{cole1954the9,
    author = "Cole, L. C",
    title = "The population consequences of life history phenonema",
    year = "1954",
    journal = "Quarterly Review of Biology, v. 29, p. 103-137",
    note = "talkorigins\_source = {true}; raw\_reference = {Cole, L. C., 1954, The population consequences of life history phenonema: Quarterly Review of Biology, v. 29, p. 103-137.}"
}

@misc{brower1964birds4,
    author = "Brower, L. P. and Brower, J",
    title = "Birds, butterflies, and plant poisons",
    year = "1964",
    howpublished = "a study in ecological chemistry: Zoologica, v. 49, p. 137-159",
    note = "talkorigins\_source = {true}; raw\_reference = {Brower, L. P., and Brower, J., 1964, Birds, butterflies, and plant poisons: a study in ecological chemistry: Zoologica, v. 49, p. 137-159.}"
}

A model is proposed to account for the level of diversity supported by any ecological community. If we begin with a hypothetical increase in the stability of the physical environment, the following consequences ensue. With greater environmental stability less energy is required for regulatory activities, that is, those which counter the challenges offered by the environment. Therefore, more energy is allocated for net productivity, that is, growth and reproduction. With increased net productivity, larger populations are supported. Larger populations provide more opportunities for the formation of interspecific associations; they also maintain greater genetic variety. Animals in more productive communities are more sedentary so that the species tends to be broken into many semi-isolated populations. As a result, speciation is favored with the interspecies associations providing the new adaptive opportunities. Plants which are pollinated by animals would be in the same situation. The new species tend to be more' specialized and to have, initially, smaller populations. These events are shown in Steps 1 to 5 in figure 1. in the early stages of the evolution of a community, positive feedback mechanisms would operate, ever-increasing the rate of speciation. The evolution of large heterotrophs (animals) would increase the rate of cycling of mineral nutrients, which would augment the net productivity. As more complex food webs developed with the increase in the number of species, community stability would increase, augmenting the stability of the physical environment. The clothing of the earth's surface with larger plants would tend to damp the fluctuations in climate, also increasing stability (Step 6). In the later stages, the tendency toward overspecialization and smaller populations would decrease community stability and provide a negative feedback control on the whole process (Steps 7 and 8). Even under very stable conditions, productivity may be limited directly by the short supply of various factors such as light, water, heat, etc. Population size may also be limited directly by a restriction in the area of suitable habitat or by a larger body size. Any such limitations would result in a lower diversity of species. This model applies only to equilibrium conditions. In certain situations, such as during the short favorable periods in the arctic or desert, diversity may be temporarily increased; care must be exercised in comparing this to the diversity maintained all the year in the wet tropics. We feel that, although some niches are determined by physical variations in the environment, most of the dimensions of the niche are a result of interaction between organisms. For this reason, it is impossible to predict the number of niches (and therefore species) from environmental complexity alone. We also discard the idea that rigorousness per se limits diversity. Lastly, the hypothesis that the tropics are closer to equilibrium while the temperate zone is in a "successional" state of development of diversity is not accepted, for theoretical reasons and for lack of evidence.

@article{doi101086282335,
    author = "Connell, Joseph H. and Orias, Eduardo",
    title = "The Ecological Regulation of Species Diversity",
    year = "1964",
    journal = "The American Naturalist",
    abstract = {A model is proposed to account for the level of diversity supported by any ecological community. If we begin with a hypothetical increase in the stability of the physical environment, the following consequences ensue. With greater environmental stability less energy is required for regulatory activities, that is, those which counter the challenges offered by the environment. Therefore, more energy is allocated for net productivity, that is, growth and reproduction. With increased net productivity, larger populations are supported. Larger populations provide more opportunities for the formation of interspecific associations; they also maintain greater genetic variety. Animals in more productive communities are more sedentary so that the species tends to be broken into many semi-isolated populations. As a result, speciation is favored with the interspecies associations providing the new adaptive opportunities. Plants which are pollinated by animals would be in the same situation. The new species tend to be more' specialized and to have, initially, smaller populations. These events are shown in Steps 1 to 5 in figure 1. in the early stages of the evolution of a community, positive feedback mechanisms would operate, ever-increasing the rate of speciation. The evolution of large heterotrophs (animals) would increase the rate of cycling of mineral nutrients, which would augment the net productivity. As more complex food webs developed with the increase in the number of species, community stability would increase, augmenting the stability of the physical environment. The clothing of the earth's surface with larger plants would tend to damp the fluctuations in climate, also increasing stability (Step 6). In the later stages, the tendency toward overspecialization and smaller populations would decrease community stability and provide a negative feedback control on the whole process (Steps 7 and 8). Even under very stable conditions, productivity may be limited directly by the short supply of various factors such as light, water, heat, etc. Population size may also be limited directly by a restriction in the area of suitable habitat or by a larger body size. Any such limitations would result in a lower diversity of species. This model applies only to equilibrium conditions. In certain situations, such as during the short favorable periods in the arctic or desert, diversity may be temporarily increased; care must be exercised in comparing this to the diversity maintained all the year in the wet tropics. We feel that, although some niches are determined by physical variations in the environment, most of the dimensions of the niche are a result of interaction between organisms. For this reason, it is impossible to predict the number of niches (and therefore species) from environmental complexity alone. We also discard the idea that rigorousness per se limits diversity. Lastly, the hypothesis that the tropics are closer to equilibrium while the temperate zone is in a "successional" state of development of diversity is not accepted, for theoretical reasons and for lack of evidence.},
    url = "https://doi.org/10.1086/282335",
    doi = "10.1086/282335",
    openalex = "W2041192555",
    references = "doi101130mem67v1p461"
}

@misc{dunbar1968ecological10,
    author = "Dunbar, M. J",
    title = "Ecological development in polar regions",
    year = "1968",
    howpublished = "Englewood Cliffs, New Jersey, Prentice-Hall, 119 p",
    note = "talkorigins\_source = {true}; raw\_reference = {Dunbar, M. J., 1968, Ecological development in polar regions: Englewood Cliffs, New Jersey, Prentice-Hall, 119 p.}"
}

@misc{brower1969ecological3,
    author = "Brower, L. P",
    title = "Ecological chemistry",
    year = "1969",
    howpublished = "Scientific American, v. 220, p. 22- 29",
    note = "talkorigins\_source = {true}; raw\_reference = {Brower, L. P., 1969, Ecological chemistry: Scientific American, v. 220, p. 22- 29.}"
}

@book{cody1971ecological6,
    author = "Cody, M. L",
    title = "Ecological aspects of reproduction. Chapter 10, in Farner, D. S., and King, J. R., eds., Avain Biology",
    year = "1971",
    publisher = "New York, Academic Press, v. 1, p. 461-512; 586 pp",
    note = "talkorigins\_source = {true}; raw\_reference = {Cody, M. L., 1971, Ecological aspects of reproduction. Chapter 10, in Farner, D. S., and King, J. R., eds., Avain Biology: New York, Academic Press, v. 1, p. 461-512; 586 pp.}"
}

@misc{arnold1972species1,
    author = "Arnold, S. J",
    title = "Species densities of predators and their prey",
    year = "1972",
    howpublished = "American Naturalist, v. 106, p. 220-236",
    note = "talkorigins\_source = {true}; raw\_reference = {Arnold, S. J., 1972, Species densities of predators and their prey: American Naturalist, v. 106, p. 220-236.}"
}

@phdthesis{caswell1973photosynthetic5,
    author = "Caswell, H. and Reed, F. and Stephenson, S. N. and Werner, P. A",
    title = "Photosynthetic pathways and selective herbivory",
    year = "1973",
    publisher = "a hypothesis: American Naturalist, v. 107, p. 465-480",
    note = "talkorigins\_source = {true}; raw\_reference = {Caswell, H., Reed, F., Stephenson, S. N., and Werner, P. A., 1973, Photosynthetic pathways and selective herbivory: a hypothesis: American Naturalist, v. 107, p. 465-480.}"
}

where NS represents the survivors after Pt parasites have searched for Nt hosts resulting in P+ 1 parasite progenyt. All assumptions about parasite searching behaviour are here contained in the functionf[Pt,Nt]. If we consider the simplest case where the parasite population is specific and synchronized temporally with its host population, we can write the following generalized model for a host-parasite interaction:

@article{doi1023073133,
    author = "Hassell, M. P. and May, Robert M.",
    title = "Stability in Insect Host-Parasite Models",
    year = "1973",
    journal = "Journal of Animal Ecology",
    abstract = "where NS represents the survivors after Pt parasites have searched for Nt hosts resulting in P+ 1 parasite progenyt. All assumptions about parasite searching behaviour are here contained in the functionf[Pt,Nt]. If we consider the simplest case where the parasite population is specific and synchronized temporally with its host population, we can write the following generalized model for a host-parasite interaction:",
    url = "https://doi.org/10.2307/3133",
    doi = "10.2307/3133",
    openalex = "W2011882588"
}

The spatial component of environment, often neglected in modeling of ecological interactions, in general operates to increase species diversity. This arises due to the heterogeneity of the environment, but such heterogeneity can arise in an initially homogeneous environment due to what may be random initial events (e.g., colonization patterns), effects of which are magnified by species interactions. In this way, homogeneous environments may become heterogeneous and heterogeneous environments even more so. In patchy environments, distinct patches are likely to be colonized initially by different species, and thereby a kind of founder effect results whereby individual patches evolve along different paths simply as a consequence of initial colonization patterns. Species which would be unable to invade may nevertheless survive by establishing themselves early and will moreover be found in lower densities in other areas as overflow from their "safe" areas. Spatially continuous environments may evolve toward essentially patchy ones by this kind of process. Overall species richness is expected to be higher in patchy environments but to decrease as the ability of species to migrate becomes large. These results are due to patchiness per se and do not depend on the existence of several kinds of patches, a situation which will tend to reinforce these effects. Diversity is also increased in such environments with spatial extent due to the opportunities for fugitive-type spatio-temporal strategies. In these, local population oscillations provide the salvation for species which are for example competitively inferior or easy victims to predation but which can survive by superior migratory ability and (in patchy environments) talent for recolonization. Again, dependence is on spatial heterogeneity, in addition to temporal heterogeneity; again, this may be externally imposed or the result largely of internal processes. Some gross statistics for these processes, principally patch occupancy fractions, may prove useful for a simplified treatment of colonization-extinction equilibria, as in the approaches of Cohen (1970), Levins and Culver (1971), Horn and MacArthur (1972), and Slatkin (in preparation). For such considerations, however, one cannot assume independence of distributions; and the approach of Cohen (1970) and Slatkin (in preparation), which allows for consideration of covariance, is favored.

@article{doi101086282900,
    author = "Levin, Simon A.",
    title = "Dispersion and Population Interactions",
    year = "1974",
    journal = "The American Naturalist",
    abstract = {The spatial component of environment, often neglected in modeling of ecological interactions, in general operates to increase species diversity. This arises due to the heterogeneity of the environment, but such heterogeneity can arise in an initially homogeneous environment due to what may be random initial events (e.g., colonization patterns), effects of which are magnified by species interactions. In this way, homogeneous environments may become heterogeneous and heterogeneous environments even more so. In patchy environments, distinct patches are likely to be colonized initially by different species, and thereby a kind of founder effect results whereby individual patches evolve along different paths simply as a consequence of initial colonization patterns. Species which would be unable to invade may nevertheless survive by establishing themselves early and will moreover be found in lower densities in other areas as overflow from their "safe" areas. Spatially continuous environments may evolve toward essentially patchy ones by this kind of process. Overall species richness is expected to be higher in patchy environments but to decrease as the ability of species to migrate becomes large. These results are due to patchiness per se and do not depend on the existence of several kinds of patches, a situation which will tend to reinforce these effects. Diversity is also increased in such environments with spatial extent due to the opportunities for fugitive-type spatio-temporal strategies. In these, local population oscillations provide the salvation for species which are for example competitively inferior or easy victims to predation but which can survive by superior migratory ability and (in patchy environments) talent for recolonization. Again, dependence is on spatial heterogeneity, in addition to temporal heterogeneity; again, this may be externally imposed or the result largely of internal processes. Some gross statistics for these processes, principally patch occupancy fractions, may prove useful for a simplified treatment of colonization-extinction equilibria, as in the approaches of Cohen (1970), Levins and Culver (1971), Horn and MacArthur (1972), and Slatkin (in preparation). For such considerations, however, one cannot assume independence of distributions; and the approach of Cohen (1970) and Slatkin (in preparation), which allows for consideration of covariance, is favored.},
    url = "https://doi.org/10.1086/282900",
    doi = "10.1086/282900",
    openalex = "W1968786866",
    references = "doi1010160022519370900925, doi1010160022519372900902, doi101086282272, doi1023071931746"
}

To understand resource partitioning, essentially a community phenomenon, we require a holistic theory that draws upon models at the individual and population level. Yet some investigators are still content mainly to document differences between species, a procedure of only limited interest. Therefore, it may be useful to conclude with a list of questions appropriate for studies of resource partitioning, questions this article has related to the theory in a preliminary way. 1) What is the mechanism of competition? What is the relative importance of predation? Are differences likely to be caused by pressures toward reproductive isolation? 2) Are niches (utilizations) regularly spaced along a single dimension? 3) How many dimensions are important, and is there a tendency for more dimensions to be added as species number increases? 4) Is dimensional separation complementary? 5) Which dimensions are utilized, how do they rank in importance, and why? How do particular dimensions change in rank as species nuimber increases? 6) What is the relation of dimensional separation to difference in phenotypic indicators? To what extent does the functional relation of phenotype to resource characteristics constrain partitioning? 7) What is the distance between mean position of niches, what is the niche standard deviation, and what is the ratio of the two? What is the niche shape?

@article{doi101126science185414527,
    author = "Schoener, Thomas W.",
    title = "Resource Partitioning in Ecological Communities",
    year = "1974",
    journal = "Science",
    abstract = "To understand resource partitioning, essentially a community phenomenon, we require a holistic theory that draws upon models at the individual and population level. Yet some investigators are still content mainly to document differences between species, a procedure of only limited interest. Therefore, it may be useful to conclude with a list of questions appropriate for studies of resource partitioning, questions this article has related to the theory in a preliminary way. 1) What is the mechanism of competition? What is the relative importance of predation? Are differences likely to be caused by pressures toward reproductive isolation? 2) Are niches (utilizations) regularly spaced along a single dimension? 3) How many dimensions are important, and is there a tendency for more dimensions to be added as species number increases? 4) Is dimensional separation complementary? 5) Which dimensions are utilized, how do they rank in importance, and why? How do particular dimensions change in rank as species nuimber increases? 6) What is the relation of dimensional separation to difference in phenotypic indicators? To what extent does the functional relation of phenotype to resource characteristics constrain partitioning? 7) What is the distance between mean position of niches, what is the niche standard deviation, and what is the ratio of the two? What is the niche shape?",
    url = "https://doi.org/10.1126/science.185.4145.27",
    doi = "10.1126/science.185.4145.27",
    openalex = "W2034690972",
    references = "doi101016s0065250408603190, doi101073pnas6951109, doi101086282070, doi101086282146, doi101086282400, doi101086282454, doi101086282505, doi101086282531, doi101111j109583121972tb00690x, doi101111j1469185x1965tb00815x, doi101126science1473655250, doi101126science16338741419, doi101126science1794075759, doi101146annureves02110171002101, doi1023071931600, doi1023071933181, doi1023071933500, doi1023071935534, doi1023071936893, doi1023071942327, doi1023072411924, doi104039ent913857, doi107312pric91844"
}

A population ecology perspective on organization-environment relations is proposed as an alternative to the dominant adaptation perspective. The strength of inertial pressures on organizational structure suggests the application of models that depend on competition and selection in populations of organizations. Several such models as well as issues that arise in attempts to apply them to the organization-environment problem are discussed.

@article{doi101086226424,
    author = "Hannan, Michael T. and Freeman, John H.",
    title = "The Population Ecology of Organizations",
    year = "1977",
    journal = "American Journal of Sociology",
    abstract = "A population ecology perspective on organization-environment relations is proposed as an alternative to the dominant adaptation perspective. The strength of inertial pressures on organizational structure suggests the application of models that depend on competition and selection in populations of organizations. Several such models as well as issues that arise in attempts to apply them to the organization-environment problem are discussed.",
    url = "https://doi.org/10.1086/226424",
    doi = "10.1086/226424",
    openalex = "W3125349408",
    references = "doi1015159780691206912, doi105962bhltitle4489"
}

@article{benton1983dinosaur2,
    author = "Benton, M. J",
    title = "Dinosaur success in the Triassic",
    year = "1983",
    journal = "A noncompetitive ecological model: Quarterly Review of Biology, v. 58, p. 29-55",
    note = "talkorigins\_source = {true}; raw\_reference = {Benton, M. J., 1983, Dinosaur success in the Triassic: A noncompetitive ecological model: Quarterly Review of Biology, v. 58, p. 29-55.}"
}

We describe a general framework for understanding the ecological processes that operate at landscape scales. The composition of habitat types in a landscape and the physiognomic or spatial arrangement of those habitats are the two essential features that are required to describe any landscape. As such, these two features affect four basic ecological processes that can influence population dynamics or community structure. The first two of these processes, landscape complementation and landscape supplementation, occur when individuals move between patches in the landscape to make use of nonsubstitutable and substitutable resources, respectively

@article{doi1023073544901,
    author = "Dunning, John B. and Danielson, Brent J. and Pulliam, H. Ronald",
    title = "Ecological Processes That Affect Populations in Complex Landscapes",
    year = "1992",
    journal = "Oikos",
    abstract = "We describe a general framework for understanding the ecological processes that operate at landscape scales. The composition of habitat types in a landscape and the physiognomic or spatial arrangement of those habitats are the two essential features that are required to describe any landscape. As such, these two features affect four basic ecological processes that can influence population dynamics or community structure. The first two of these processes, landscape complementation and landscape supplementation, occur when individuals move between patches in the landscape to make use of nonsubstitutable and substitutable resources, respectively",
    url = "https://doi.org/10.2307/3544901",
    doi = "10.2307/3544901",
    openalex = "W2014034900",
    references = "doi1010160040580977900429, doi1010160040580986900109, doi101146annureves20110189001131, doi1023071932254, doi1023071935620, doi1023072389612, doi1023073808148"
}

Predicting the distribution of endangered species from habitat data is frequently perceived to be a useful technique. Models that predict the presence or absence of a species are normally judged by the number of prediction errors. These may be of two types: false positives and false negatives. Many of the prediction errors can be traced to ecological processes such as unsaturated habitat and species interactions. Consequently, if prediction errors are not placed in an ecological context the results of the model may be misleading. The simplest, and most widely used, measure of prediction accuracy is the number of correctly classified cases. There are other measures of prediction success that may be more appropriate. Strategies for assessing the causes and costs of these errors are discussed. A range of techniques for measuring error in presence/absence models, including some that are seldom used by ecologists (e.g. ROC plots and cost matrices), are described. A new approach to estimating prediction error, which is based on the spatial characteristics of the errors, is proposed. Thirteen recommendations are made to enable the objective selection of an error assessment technique for ecological presence/absence models.

@article{doi101017s0376892997000088,
    author = "Fielding, Alan H. and Bell, John F.",
    title = "A review of methods for the assessment of prediction errors in conservation presence/absence models",
    year = "1997",
    journal = "Environmental Conservation",
    abstract = "Predicting the distribution of endangered species from habitat data is frequently perceived to be a useful technique. Models that predict the presence or absence of a species are normally judged by the number of prediction errors. These may be of two types: false positives and false negatives. Many of the prediction errors can be traced to ecological processes such as unsaturated habitat and species interactions. Consequently, if prediction errors are not placed in an ecological context the results of the model may be misleading. The simplest, and most widely used, measure of prediction accuracy is the number of correctly classified cases. There are other measures of prediction success that may be more appropriate. Strategies for assessing the causes and costs of these errors are discussed. A range of techniques for measuring error in presence/absence models, including some that are seldom used by ecologists (e.g. ROC plots and cost matrices), are described. A new approach to estimating prediction error, which is based on the spatial characteristics of the errors, is proposed. Thirteen recommendations are made to enable the objective selection of an error assessment technique for ecological presence/absence models.",
    url = "https://doi.org/10.1017/s0376892997000088",
    doi = "10.1017/s0376892997000088",
    openalex = "W2115268776",
    references = "openalexw2077454220"
}

Every form of behaviour is shaped by trial and error. Such stepwise adaptation can occur through individual learning or through natural selection, the basis of evolution. Since the work of Maynard Smith and others, it has been realised how game theory can model this process. Evolutionary game theory replaces the static solutions of classical game theory by a dynamical approach centred not on the concept of rational players but on the population dynamics of behavioural programmes. In this book the authors investigate the nonlinear dynamics of the self-regulation of social and economic behaviour, and of the closely related interactions between species in ecological communities. Replicator equations describe how successful strategies spread and thereby create new conditions which can alter the basis of their success, i.e. to enable us to understand the strategic and genetic foundations of the endless chronicle of invasions and extinctions which punctuate evolution. In short, evolutionary game theory describes when to escalate a conflict, how to elicit cooperation, why to expect a balance of the sexes, and how to understand natural selection in mathematical terms.

@book{doi101017cbo9781139173179,
    author = "Hofbauer, Josef and Sigmund, Karl",
    title = "Evolutionary Games and Population Dynamics",
    year = "1998",
    booktitle = "Cambridge University Press eBooks",
    abstract = "Every form of behaviour is shaped by trial and error. Such stepwise adaptation can occur through individual learning or through natural selection, the basis of evolution. Since the work of Maynard Smith and others, it has been realised how game theory can model this process. Evolutionary game theory replaces the static solutions of classical game theory by a dynamical approach centred not on the concept of rational players but on the population dynamics of behavioural programmes. In this book the authors investigate the nonlinear dynamics of the self-regulation of social and economic behaviour, and of the closely related interactions between species in ecological communities. Replicator equations describe how successful strategies spread and thereby create new conditions which can alter the basis of their success, i.e. to enable us to understand the strategic and genetic foundations of the endless chronicle of invasions and extinctions which punctuate evolution. In short, evolutionary game theory describes when to escalate a conflict, how to elicit cooperation, why to expect a balance of the sexes, and how to understand natural selection in mathematical terms.",
    url = "https://doi.org/10.1017/cbo9781139173179",
    doi = "10.1017/cbo9781139173179",
    openalex = "W2085728653",
    references = "doi101007978146847862422, doi1010160040580977900429, doi101038119012b0, doi101086282272, doi1023071578, doi1023072965538, doi1023074549, doi1023075530, doi105962bhltitle4489"
}

This address deals with just one kind of natural enemy, insect parasitoids, and illustrates some ways in which our understanding of their dynamical interaction with hosts has advanced over the past 25 years or so. Parasitoids comprise some 10% or more of all metazoan species, and largely belong to two families, the Diptera (two-winged flies) and the Hymenoptera (sawflies, bees, wasps and ants). Excellent introductions to the biology of parasitoids can be found in Clausen (1940), Askew (1971) and Godfray (1994). Adult female parasitoids lay one or more of their eggs on, in or close to the body of their host, usually an immature stage of another insect, which is then consumed over a period of days or weeks by the feeding parasitoid larva or larvae. As in most true parasites, all the food necessary to complete development comes from a single host, but like true predators this almost always leads to the death of the host, albeit with a delay until the parasitoid larva is fully developed. Parasitoids have long been popular subjects for ecological study for several reasons. First, they are important for biological pest control and this has stimulated much empirical and theoretical work on the attributes that make parasitoids effective pest control agents. Secondly, parasitoids are ideal subjects for developing relatively simple population models. This is mainly because it is only the adult females that search for hosts, and because the act of finding a host is normally followed by oviposition. The success in finding and attacking hosts therefore closely defines parasitoid reproduction, which means that (i) host–parasitoid models can have a much simpler structure than corresponding predator–prey models in which all predator stages may attack prey with different effectiveness, and (ii) reproduction is less closely defined by prey consumption. Finally, many species of parasitoids and their hosts can readily be cultured in laboratory microcosms, and this has greatly increased the amount of empirical information on host–parasitoid interactions under controlled conditions. This experimental approach to population dynamics has rightly been extolled by Kareiva et al. (1989). Much of the work on the dynamics of host–parasitoid interactions has taken a mechanistic approach. Components of the interaction are investigated using simple experiments, and their dynamical effects examined in a step-wise way within population models. The overall objective is to have a detailed understanding of how the important processes operating in the host and parasitoid life cycles affect the dynamics of the populations. These components may be the fundamental demographic parameters, features of the life histories, effects of other interacting species or other features of the habitat such as patchiness of resources and variability of resource quality. In each case, empirical information is needed to describe the components and define their relationship with key variables such as population density. From this, a description is obtained of each component in an appropriate model framework. Finally, analysis of the parameterised model indicates the dynamical effects of that component. Such a mechanistic approach to population dynamics is demanding of data and requires a particularly close interaction between data and model development. This address illustrates how the dynamics of host–parasitoid interactions may be influenced by three major ecological processes: by spatial patchiness, by interactions with other species, and by metapopulation structure. A common underlying theme is that parasitism does not occur at random and that spatial and other processes lead to aggregated distributions of parasitism amongst the host population. The realisation of how important spatial processes are to population dynamics in general has led to a revolution in the subject (Wiens 1989), and as a result many of the earlier, simple host–parasitoid models are now viewed as rather special limiting cases. As is usually the case, developing the mathematical models has sometimes proved less of a challenge than designing and executing appropriate studies to collect the data. But it is encouraging that there are now several examples where empirical field studies and models have been drawn fairly close together (e.g. Hassell 1980; Jones et al. 1993; Reeve et al. 1994; Murdoch et al. 1996). But problems of spatial scale still pervade ecology (Levin 1992, 1994). The interactions discussed in this paper involve either a single host and parasitoid species interacting together or simple webs, and are discussed at two very different spatial scales. On the one hand, there are local populations characterised by more-or-less complete mixing of individuals at some point(s) during the generation period. On the other hand, there are metapopulations formed by collections of local populations linked by some degree of dispersal each generation between the individual local populations. But first a basic framework is outlined upon which many of the developments in modelling host–parasitoid interactions have been built. A modelling tradition, initiated mainly by entomologists with insect hosts and their parasitoids in mind (Thompson 1924; Nicholson 1933; Varley 1947), assumes that populations have discrete and synchronized generations. This is in contrast to Lotka–Volterra models (Lotka 1925; Volterra 1926), which start with the assumption that the generations of the interacting populations overlap completely and that birth and death processes are continuous. Discrete generations inevitably introduce a one-generation time lag between the act of parasitism and the resulting change in host populations, and it is the presence of these time lags that represent the fundamental difference between the two kinds of model. Although the discrete and continuous frameworks reflect fundamentally different kinds of life cycle, both classes of model have been used to demonstrate how a wide range of comparable features of host–parasitoid interactions influence population dynamics The usual framework for discrete-generation host–parasitoid models is given by N t + 1 = λNtf(Nt, Pt) P t + 1 = cNt[1 - f(Nt, Pt)]eqn 1 where P and N are the population sizes of the searching adult female parasitoids and the susceptible host stage, respectively, in successive generations t and t + 1. In the host equation, the parameter λ is the net finite rate of increase of hosts in the absence of the parasitoids, which may be density-dependent or assumed to be a constant. It depends on the hosts' fecundity, sex ratio, any immigration and emigration, and all host mortalities other than parasitism itself. The function f(Nt,Pt) defines the fraction of the Nt hosts escaping parasitism; one minus this term (within the square brackets in the parasitoid equation) therefore gives the fraction of hosts parasitized. All assumptions about the efficiency of parasitoids at finding and parasitizing hosts are thus contained within this term. Finally, c is the average number of adult female parasitoids emerging from each host parasitized (often assumed to be one, that corresponds to parasitoids with solitary larvae). It depends upon the sex ratio of the parasitoid progeny, any mortality suffered within hosts and any mortalities of the subsequent adult female parasitoids prior to searching for hosts in the next generation. Clearly, these simple equations subsume a huge amount of host and parasitoid biology, and the apparent simplicity of the model is deceptive: to be parameterized for a particular host–parasitoid system requires detailed life table information on both populations (e.g. Hassell 1980; Jones et al. 1993). The best known example of model 1 is that of Nicholson (1933) and Nicholson & Bailey (1935) who explored in depth a model in which the following assumptions about parasitism were made. First, the parasitoids are never egg-limited and encounter hosts in direct proportion to host abundance. The total number of encounters with hosts is therefore given by Nenc=aNtPt, where a is the per capita searching efficiency which Nicholson called the ‘area of discovery’. Secondly, these Nenc encounters are distributed randomly amongst the population of equally susceptible hosts. There is thus either no avoidance of superparasitism or, if the parasitoids can avoid superparasitism, they do so instantaneously without affecting subsequent performance in any way. These two assumptions are at the core of Nicholson's so-called ‘competition curve’ in which the proportion of hosts escaping parasitism is given by the zero term of the Poisson distribution, exp(– aPt), where aPt are the mean encounters per host, Nenc/Nt = aPt. Thus, one minus this zero term is the probability of a host being attacked. Substituting into model 1 gives: N t + 1 = λNt exp(- aPt) P t + 1 = Na = cNt[1 - exp (- aPt)]eqn 2 where Na is the number of hosts that are parasitized irrespective of the number of times they have been encountered. The dynamical properties of the Nicholson–Bailey model are well known. A host–parasitoid equilibrium always exists depending on the values of a, c andλ, and this is always locally unstable with the slightest perturbation leading to oscillations of rapidly increasing amplitude (Fig. 1a). This instability, compared to the neutrally stable Lotka–Volterra model, arises from the one-generation time lags between cause and effect that are inherent in these difference equation models (May 1973) and which enhance the degree that parasitism acts as a delayed density-dependent, or second-order feedback process (Varley 1947; Berryman & Turchin 1997). Numerical simulations showing host (○) and parasitoid (● population oscillations from: (a) the Nicholson–Bailey model with parasitoid searching efficiency, a = 0·068 and the host rate of increase, λ = 2; (b) the negative binomial model. The parameters are the same as in (a), except that parasitism is no longer random (k = 0·6 instead of k→∞). Although unstable oscillations have been observed in a few simple laboratory host–parasitoid and predator–prey experiments (e.g. Burnett 1958; Huffaker 1958; Hassell & May 1988), such instability is hard to reconcile with the results from other laboratory systems in which the interactions are much more stable (e.g. Utida 1957; Huffaker 1958; Huffaker et al. 1963; Fujii 1983; Bonsall & Hassell 1997, 1998; Shimada 1999) (and see Fig. 2 below) and, more generally, with the long-term persistence of natural systems. Nicholson (1947), anticipating the current vogue for metapopulations, suggested one means by which oscillatorily unstable local populations may persist. Assuming that the interaction occurs in distinct and separated areas, the ‘cycle of increase in numbers, followed by … extermination, proceeds independently in different parts of the occupied country; so at all times some groups are increasing and some decreasing in numbers…. Consequently when one considers a large tract of country, the abundance [of both host and parasitoid].. remains more or less constant; whereas in any small area of the same country the fluctuation in numbers ….may be violent.’ Such metapopulation persistence is considered in more detail below. Population dynamics of the bruchid beetle, Callosobruchus chinensis (●) feeding on black eyed beans and its pteromalid parasitoid, Anisopteromalus calandrae (○) in a laboratory system (Hassell & May 1988). (a) A non-patchy system with 50 beans uniformly distributed on the arena floor. The parasitoids are introduced in week 19 and become extinct in week 32, allowing the hosts to increase until checked by resources. (b) A patchy system with 50 beans each in an individual container with restricted access to both hosts and parasitoids. (From Hassell 2000.) There are, however, several other important ways in which the Nicholson–Bailey model can be modified that both add realism and allow the populations to persist (Hassell 2000). Many of these involve elaborating the parasitism function f(·). For example, May (1978) started from the premise that the assumption that hosts are parasitized at random is an unlikely proposition in the real world where host individuals are bound to vary in their spatial location, phenotype, and stage of development. It is much more likely, therefore, that the risk of being parasitized will vary within the host population, leading to an overall distribution of parasitoid attacks that is more aggregated than random. His model, instead of being based on the Poisson distribution, makes the specific assumption that survival from parasitism is described by the zero term of the negative binomial distribution so that f in eqn 1 is given by: where k is the index defining the degree to which the distribution of parasitism amongst the host population is aggregated (most aggregated as k→ 0, becoming random (i.e. Poisson) as k→∞). The properties of this model are importantly different from those of the Nicholson − Bailey model. The equilibria are locally stable provided that the distribution of parasitism is sufficiently aggregated, or, more specifically, if and only if k < 1 (Fig. 1b). The stabilizing effect of small k stems from the way that parasitism is heterogeneously distributed amongst the host population. This is further explored in the next section. No study has had a greater influence in publicising how heterogeneity can affect population dynamics than C.B. Huffaker's classic experiments with predatory and prey mites feeding on oranges (Huffaker 1958; Huffaker et al. 1963). While these experiments tell us little about the detailed mechanisms by which spatial patchiness promotes persistence, they have been the inspiration for the development of many models for spatially structured predator–prey systems (e.g. Hilborn 1975; Caswell 1978; Hastings 1978; Crowley 1979; Nisbet & Gurney 1982). An example from a host–parasitoid experiment is shown in Fig. 2; the interaction is unstable in a more-or-less homogeneous environment, but persists in a relatively stable interaction when the beans, which are the host resource, are confined within small patches. As with Huffaker's mites, increased partitioning of the environment into discrete patches reduced the chances of extinction and allowed the populations to persist at levels well below the host or prey's carrying capacity. The term ‘heterogeneity’ will be used here in a specific way. Following Chesson & Murdoch (1986), it is defined in terms of the variation in risk of parasitism between different individuals in the host population. For example, the Nicholson–Bailey model is recovered if the relative risk of parasitism is uniformly distributed per patch, while the negative binomial model of May (1978) is obtained when the risk is gamma distributed. In this terminology, therefore, a habitat is only ‘heterogeneous’ in so far as it leads to ‘aggregation of risk’ of parasitism between host individuals. The importance of this measure lies in the way that it can be used to quantify the stabilizing effect of an aggregated distribution of parasitism amongst host individuals in a population. One of the most obvious ways in which heterogeneity of risk of parasitism can arise is in a patchy environment where the level of parasitism varies between patches. But it can also arise in quite different ways. For example, host and parasitoid life cycles may not be properly synchronized so that some hosts are less at risk from parasitism, or escape completely, due to the phenological mismatch of life cycles that do not coincide. Or, there may be phenotypic variation between host individuals such that some hosts are able to reduce their risk of parasitism by virtue of their physiology or behaviour. Heterogeneity of risk is thus a pervasive feature of natural interactions. A very clear exposition of how this heterogeneity helps to stabilize host–parasitoid interactions of the form of model is given by Taylor (1993). Let us consider an environment made up of discrete patches of food plants upon which an insect herbivore species with discrete generations feeds. The herbivore is attacked by a specialist parasitoid species whose adults coincide temporally with the susceptible host stage. On emergence, the adult hosts and female parasitoids disperse from their natal patches and move amongst the patches ovipositing. We have, therefore, total populations of Nt hosts and Pt adult female parasitoids distributed amongst the n patches such that within any one patch there are Pi parasitoids searching for Ni hosts. Suppose first that the parasitoids divide themselves evenly amongst the patches, and that within any patch they have a linear functional response, a constant searching efficiency and encounter hosts at random. Percentage parasitism is now the same in each patch in any one generation, and the Nicholson–Bailey model for the whole population is recovered exactly. All host individuals therefore suffer the same risk of parasitism. Such a uniform risk of parasitism across patches is countered by a wealth of empirical evidence. The two laboratory examples in Fig. 3 are clear cases where the searching parasitoids tend to congregate in the patches of high host density. In one case, the resulting pattern of parasitism is positively density-dependent, while in the other, it is just the opposite – parasitism is inversely density-dependent. This difference stems from the different functional responses of the two species in the following way. Trybliographa rapae has a short handling time giving a relatively high maximum attack rate per parasitoid (i.e. high upper asymptotes of their functional responses). The pattern of parasitism thus reflects the distribution of parasitoids, and hence the density-dependent patterns in Fig. 3b. In contrast, the egg parasitoid, Trichogramma evanescens, has a much longer handling time and hence a lower maximum attack rate per female. Individual parasitoids are therefore restricted in their ability to exploit high host density patches and this leads to the inverse density dependence in Fig. 3d. Examples illustrating this mechanism are shown in Fig. 4. Average patterns of time allocation (a, c) and parasitism (b, d) per patch in relation to host density per patch from two laboratory systems. (a, b) Ten females of the cynipid parasitoid, Trybliographa rapae, parasitizing larvae of the cabbage at different within of & Hassell 1988). d) females of the parasitoid, Trichogramma parasitizing eggs of the on at different In contrast to (a, b) parasitism is inversely density-dependent, the for the parasitoids to on the with host (Hassell 1982). Numerical examples showing how the of parasitoids and different kinds of functional can lead to both density-dependent and inverse density-dependent spatial patterns of parasitism. (a) An aggregated distribution of searching parasitoids. (b) kinds of functional defining the attack rate per parasitoid within a from a linear functional with a = and = from a with a = and = The resulting overall parasitism per patch with and corresponding to those in that the more than for the of the parasitoids to cause inverse density (From Hassell 2000.) While data information on the distribution of searching parasitoids and the resulting patterns of parasitism are to collect from the field see 1983; & it is relatively to quantify patterns of parasitism without about the distribution of searching hosts can be from a range of patches, taken to the laboratory and the of parasitism then usually by the next generation or by the different examples in the of & Murdoch and Hassell & direct density-dependent patterns of parasitism, inverse patterns and parasitism with host density per examples are shown in Fig. the of et al. and Hassell et al. the direct and inverse density-dependent patterns arise from and the patterns arise from Examples of field studies showing different spatial patterns of parasitism. (a) parasitism of the larvae of cabbage by the cynipid parasitoid, Trybliographa rapae & Hassell 1988). (b) density-dependent parasitism of eggs by the parasitoid, & parasitism of the scale by the parasitoid, et al. parasitism of the by the parasitoid, for a of the (From & Hassell Much of the in these different patterns has on their importance to population theoretical work with discrete generation interactions to how density-dependent patterns (e.g. Hassell & May Murdoch & 1975; et al. have that both the inverse patterns and the can be equally important for (Hassell Chesson & Murdoch Hassell & May & Murdoch et al. Hassell et al. models on this The simple model framework can be to interactions in an patchy environment as long as f(Nt,Pt) the across all patches, of the fraction of hosts escaping parasitism. The function f(Nt,Pt) therefore depends on both survival from parasitism within patches as well as the spatial distributions of hosts and parasitoids between the n patches. Let us consider the simple of a habitat with n patches as within each of which parasitism is random and by a functional The distribution of hosts and parasitoids from patch to patch is defined by and the of total hosts and total searching parasitoids, respectively, in the patch, so that f is given by: where a is the searching efficiency per patch and is the handling time (Hassell & May specifically, will that the parasitoids in patches of high host density following the simple where is an index of parasitoid and is a constant such that the values to (Hassell & May The can describe a wide range of parasitoid distribution they will be evenly distributed across patches if = 0, and tend to in patches of high host density as in the they will all congregate in the single patch of host density the as complete if < the parasitoid distribution is with their local abundance now inversely with host density per The way that these different patterns can for a linear functional within is shown by the following example in which the hosts are aggregated to a negative binomial is the probability of hosts in a patch from the negative binomial distribution and are the number of parasitoids in a patch with hosts by eqn of this model that sufficiently direct or inverse density-dependent distributions of parasitoids can stabilize the interactions (Fig. while parasitoid leads to local instability the host rate of increase is some a range of dynamics can occur et al. of parasitoid can also have a effect on equilibrium levels (Fig. Parasitoids have the effect in host equilibria when their distribution most closely that of the hosts (i.e. = or in this model leads to host because the parasitoids, are confined to their patches, irrespective of the degree of host The effects of parasitoid on and equilibrium (a) between the degree of of parasitoids, in eqn and the amount of host k a negative binomial distribution of hosts per from model with survival from parasitism, given by eqn total number of patches n = searching efficiency a = 1 and host rate of increase λ = (b) Examples of the host equilibrium level with the degree of parasitoid for three different numbers of patches from model with given by and a = λ = 2 and n = n = and n = Hassell in which are There has been much more on the effects of than because of the it has been assumed to have in population and because have to make the between and population dynamics et al. et al. Godfray 1994). It is now however, that heterogeneity can be at as important a process in population Let us consider a specific example where all the heterogeneity in parasitism is of the spatial distribution of hosts as in Fig. and that this is by the distribution of searching parasitoids being independently aggregated from patch to patch following a gamma We also that within any one patch the parasitoids exploit hosts at random and have a constant per capita searching efficiency (i.e. a linear functional The fraction of hosts escaping parasitism is now given by: where is the gamma probability density function for parasitoids per patch with mean and is a constant the of the density and a is the usual per capita searching efficiency of the In each patch, therefore, host survival by randomly searching parasitoids is given by the zero term of a Poisson distribution with mean et al. Chesson & Murdoch much the same were the parasitoids uniformly distributed across patches but their searching efficiency, a, to a gamma distribution et al. The properties of the model are the populations will be by the heterogeneity as long as there is in the distribution of parasitoids or, more specifically, if < 1. eqn also to (1978) negative binomial model. The index of is now to the degree of of the searching parasitoids & Murdoch et al. Hassell et al. The k < 1 from the negative binomial model, therefore corresponds in this spatially model to < 1. stabilizing properties have also been from models with parasitism by Reeve et al. further that patterns of parasitism are important as a stabilizing The parasitoid described an in which individuals are distributed amongst the patches at the start of each generation, where they then confined irrespective of how the hosts become and or not there are more patches The parasitoids in this are much more in being able to the best patches The large body of on patch stems from the classic paper by in which a a patch when the rate of is reduced to the maximum rate in the environment as a whole (e.g. & & 1997). to parasitoids, this means an distribution of parasitoid searching that the rate of parasitism for the individual searching parasitoids, depending on the spatial distribution of hosts and any and life & & (1978) and & Hassell models for such searching parasitoids. The model individual parasitoids amongst patches, their searching time in relation to the of the host distribution and the of the parasitoid searching efficiency and handling time also the start

@article{doi101046j13652656200000445x,
    author = "Hassell, M. P.",
    title = "Host–parasitoid population dynamics*",
    year = "2000",
    journal = "Journal of Animal Ecology",
    abstract = "This address deals with just one kind of natural enemy, insect parasitoids, and illustrates some ways in which our understanding of their dynamical interaction with hosts has advanced over the past 25 years or so. Parasitoids comprise some 10\% or more of all metazoan species, and largely belong to two families, the Diptera (two-winged flies) and the Hymenoptera (sawflies, bees, wasps and ants). Excellent introductions to the biology of parasitoids can be found in Clausen (1940), Askew (1971) and Godfray (1994). Adult female parasitoids lay one or more of their eggs on, in or close to the body of their host, usually an immature stage of another insect, which is then consumed over a period of days or weeks by the feeding parasitoid larva or larvae. As in most true parasites, all the food necessary to complete development comes from a single host, but like true predators this almost always leads to the death of the host, albeit with a delay until the parasitoid larva is fully developed. Parasitoids have long been popular subjects for ecological study for several reasons. First, they are important for biological pest control and this has stimulated much empirical and theoretical work on the attributes that make parasitoids effective pest control agents. Secondly, parasitoids are ideal subjects for developing relatively simple population models. This is mainly because it is only the adult females that search for hosts, and because the act of finding a host is normally followed by oviposition. The success in finding and attacking hosts therefore closely defines parasitoid reproduction, which means that (i) host–parasitoid models can have a much simpler structure than corresponding predator–prey models in which all predator stages may attack prey with different effectiveness, and (ii) reproduction is less closely defined by prey consumption. Finally, many species of parasitoids and their hosts can readily be cultured in laboratory microcosms, and this has greatly increased the amount of empirical information on host–parasitoid interactions under controlled conditions. This experimental approach to population dynamics has rightly been extolled by Kareiva et al. (1989). Much of the work on the dynamics of host–parasitoid interactions has taken a mechanistic approach. Components of the interaction are investigated using simple experiments, and their dynamical effects examined in a step-wise way within population models. The overall objective is to have a detailed understanding of how the important processes operating in the host and parasitoid life cycles affect the dynamics of the populations. These components may be the fundamental demographic parameters, features of the life histories, effects of other interacting species or other features of the habitat such as patchiness of resources and variability of resource quality. In each case, empirical information is needed to describe the components and define their relationship with key variables such as population density. From this, a description is obtained of each component in an appropriate model framework. Finally, analysis of the parameterised model indicates the dynamical effects of that component. Such a mechanistic approach to population dynamics is demanding of data and requires a particularly close interaction between data and model development. This address illustrates how the dynamics of host–parasitoid interactions may be influenced by three major ecological processes: by spatial patchiness, by interactions with other species, and by metapopulation structure. A common underlying theme is that parasitism does not occur at random and that spatial and other processes lead to aggregated distributions of parasitism amongst the host population. The realisation of how important spatial processes are to population dynamics in general has led to a revolution in the subject (Wiens 1989), and as a result many of the earlier, simple host–parasitoid models are now viewed as rather special limiting cases. As is usually the case, developing the mathematical models has sometimes proved less of a challenge than designing and executing appropriate studies to collect the data. But it is encouraging that there are now several examples where empirical field studies and models have been drawn fairly close together (e.g. Hassell 1980; Jones et al. 1993; Reeve et al. 1994; Murdoch et al. 1996). But problems of spatial scale still pervade ecology (Levin 1992, 1994). The interactions discussed in this paper involve either a single host and parasitoid species interacting together or simple webs, and are discussed at two very different spatial scales. On the one hand, there are local populations characterised by more-or-less complete mixing of individuals at some point(s) during the generation period. On the other hand, there are metapopulations formed by collections of local populations linked by some degree of dispersal each generation between the individual local populations. But first a basic framework is outlined upon which many of the developments in modelling host–parasitoid interactions have been built. A modelling tradition, initiated mainly by entomologists with insect hosts and their parasitoids in mind (Thompson 1924; Nicholson 1933; Varley 1947), assumes that populations have discrete and synchronized generations. This is in contrast to Lotka–Volterra models (Lotka 1925; Volterra 1926), which start with the assumption that the generations of the interacting populations overlap completely and that birth and death processes are continuous. Discrete generations inevitably introduce a one-generation time lag between the act of parasitism and the resulting change in host populations, and it is the presence of these time lags that represent the fundamental difference between the two kinds of model. Although the discrete and continuous frameworks reflect fundamentally different kinds of life cycle, both classes of model have been used to demonstrate how a wide range of comparable features of host–parasitoid interactions influence population dynamics The usual framework for discrete-generation host–parasitoid models is given by N t + 1 = λNtf(Nt, Pt) P t + 1 = cNt[1 - f(Nt, Pt)]eqn 1 where P and N are the population sizes of the searching adult female parasitoids and the susceptible host stage, respectively, in successive generations t and t + 1. In the host equation, the parameter λ is the net finite rate of increase of hosts in the absence of the parasitoids, which may be density-dependent or assumed to be a constant. It depends on the hosts' fecundity, sex ratio, any immigration and emigration, and all host mortalities other than parasitism itself. The function f(Nt,Pt) defines the fraction of the Nt hosts escaping parasitism; one minus this term (within the square brackets in the parasitoid equation) therefore gives the fraction of hosts parasitized. All assumptions about the efficiency of parasitoids at finding and parasitizing hosts are thus contained within this term. Finally, c is the average number of adult female parasitoids emerging from each host parasitized (often assumed to be one, that corresponds to parasitoids with solitary larvae). It depends upon the sex ratio of the parasitoid progeny, any mortality suffered within hosts and any mortalities of the subsequent adult female parasitoids prior to searching for hosts in the next generation. Clearly, these simple equations subsume a huge amount of host and parasitoid biology, and the apparent simplicity of the model is deceptive: to be parameterized for a particular host–parasitoid system requires detailed life table information on both populations (e.g. Hassell 1980; Jones et al. 1993). The best known example of model 1 is that of Nicholson (1933) and Nicholson \& Bailey (1935) who explored in depth a model in which the following assumptions about parasitism were made. First, the parasitoids are never egg-limited and encounter hosts in direct proportion to host abundance. The total number of encounters with hosts is therefore given by Nenc=aNtPt, where a is the per capita searching efficiency which Nicholson called the ‘area of discovery’. Secondly, these Nenc encounters are distributed randomly amongst the population of equally susceptible hosts. There is thus either no avoidance of superparasitism or, if the parasitoids can avoid superparasitism, they do so instantaneously without affecting subsequent performance in any way. These two assumptions are at the core of Nicholson's so-called ‘competition curve’ in which the proportion of hosts escaping parasitism is given by the zero term of the Poisson distribution, exp(– aPt), where aPt are the mean encounters per host, Nenc/Nt = aPt. Thus, one minus this zero term is the probability of a host being attacked. Substituting into model 1 gives: N t + 1 = λNt exp(- aPt) P t + 1 = Na = cNt[1 - exp (- aPt)]eqn 2 where Na is the number of hosts that are parasitized irrespective of the number of times they have been encountered. The dynamical properties of the Nicholson–Bailey model are well known. A host–parasitoid equilibrium always exists depending on the values of a, c andλ, and this is always locally unstable with the slightest perturbation leading to oscillations of rapidly increasing amplitude (Fig. 1a). This instability, compared to the neutrally stable Lotka–Volterra model, arises from the one-generation time lags between cause and effect that are inherent in these difference equation models (May 1973) and which enhance the degree that parasitism acts as a delayed density-dependent, or second-order feedback process (Varley 1947; Berryman \& Turchin 1997). Numerical simulations showing host (○) and parasitoid (● population oscillations from: (a) the Nicholson–Bailey model with parasitoid searching efficiency, a = 0·068 and the host rate of increase, λ = 2; (b) the negative binomial model. The parameters are the same as in (a), except that parasitism is no longer random (k = 0·6 instead of k→∞). Although unstable oscillations have been observed in a few simple laboratory host–parasitoid and predator–prey experiments (e.g. Burnett 1958; Huffaker 1958; Hassell \& May 1988), such instability is hard to reconcile with the results from other laboratory systems in which the interactions are much more stable (e.g. Utida 1957; Huffaker 1958; Huffaker et al. 1963; Fujii 1983; Bonsall \& Hassell 1997, 1998; Shimada 1999) (and see Fig. 2 below) and, more generally, with the long-term persistence of natural systems. Nicholson (1947), anticipating the current vogue for metapopulations, suggested one means by which oscillatorily unstable local populations may persist. Assuming that the interaction occurs in distinct and separated areas, the ‘cycle of increase in numbers, followed by … extermination, proceeds independently in different parts of the occupied country; so at all times some groups are increasing and some decreasing in numbers…. Consequently when one considers a large tract of country, the abundance [of both host and parasitoid].. remains more or less constant; whereas in any small area of the same country the fluctuation in numbers ….may be violent.’ Such metapopulation persistence is considered in more detail below. Population dynamics of the bruchid beetle, Callosobruchus chinensis (●) feeding on black eyed beans and its pteromalid parasitoid, Anisopteromalus calandrae (○) in a laboratory system (Hassell \& May 1988). (a) A non-patchy system with 50 beans uniformly distributed on the arena floor. The parasitoids are introduced in week 19 and become extinct in week 32, allowing the hosts to increase until checked by resources. (b) A patchy system with 50 beans each in an individual container with restricted access to both hosts and parasitoids. (From Hassell 2000.) There are, however, several other important ways in which the Nicholson–Bailey model can be modified that both add realism and allow the populations to persist (Hassell 2000). Many of these involve elaborating the parasitism function f(·). For example, May (1978) started from the premise that the assumption that hosts are parasitized at random is an unlikely proposition in the real world where host individuals are bound to vary in their spatial location, phenotype, and stage of development. It is much more likely, therefore, that the risk of being parasitized will vary within the host population, leading to an overall distribution of parasitoid attacks that is more aggregated than random. His model, instead of being based on the Poisson distribution, makes the specific assumption that survival from parasitism is described by the zero term of the negative binomial distribution so that f in eqn 1 is given by: where k is the index defining the degree to which the distribution of parasitism amongst the host population is aggregated (most aggregated as k→ 0, becoming random (i.e. Poisson) as k→∞). The properties of this model are importantly different from those of the Nicholson − Bailey model. The equilibria are locally stable provided that the distribution of parasitism is sufficiently aggregated, or, more specifically, if and only if k < 1 (Fig. 1b). The stabilizing effect of small k stems from the way that parasitism is heterogeneously distributed amongst the host population. This is further explored in the next section. No study has had a greater influence in publicising how heterogeneity can affect population dynamics than C.B. Huffaker's classic experiments with predatory and prey mites feeding on oranges (Huffaker 1958; Huffaker et al. 1963). While these experiments tell us little about the detailed mechanisms by which spatial patchiness promotes persistence, they have been the inspiration for the development of many models for spatially structured predator–prey systems (e.g. Hilborn 1975; Caswell 1978; Hastings 1978; Crowley 1979; Nisbet \& Gurney 1982). An example from a host–parasitoid experiment is shown in Fig. 2; the interaction is unstable in a more-or-less homogeneous environment, but persists in a relatively stable interaction when the beans, which are the host resource, are confined within small patches. As with Huffaker's mites, increased partitioning of the environment into discrete patches reduced the chances of extinction and allowed the populations to persist at levels well below the host or prey's carrying capacity. The term ‘heterogeneity’ will be used here in a specific way. Following Chesson \& Murdoch (1986), it is defined in terms of the variation in risk of parasitism between different individuals in the host population. For example, the Nicholson–Bailey model is recovered if the relative risk of parasitism is uniformly distributed per patch, while the negative binomial model of May (1978) is obtained when the risk is gamma distributed. In this terminology, therefore, a habitat is only ‘heterogeneous’ in so far as it leads to ‘aggregation of risk’ of parasitism between host individuals. The importance of this measure lies in the way that it can be used to quantify the stabilizing effect of an aggregated distribution of parasitism amongst host individuals in a population. One of the most obvious ways in which heterogeneity of risk of parasitism can arise is in a patchy environment where the level of parasitism varies between patches. But it can also arise in quite different ways. For example, host and parasitoid life cycles may not be properly synchronized so that some hosts are less at risk from parasitism, or escape completely, due to the phenological mismatch of life cycles that do not coincide. Or, there may be phenotypic variation between host individuals such that some hosts are able to reduce their risk of parasitism by virtue of their physiology or behaviour. Heterogeneity of risk is thus a pervasive feature of natural interactions. A very clear exposition of how this heterogeneity helps to stabilize host–parasitoid interactions of the form of model is given by Taylor (1993). Let us consider an environment made up of discrete patches of food plants upon which an insect herbivore species with discrete generations feeds. The herbivore is attacked by a specialist parasitoid species whose adults coincide temporally with the susceptible host stage. On emergence, the adult hosts and female parasitoids disperse from their natal patches and move amongst the patches ovipositing. We have, therefore, total populations of Nt hosts and Pt adult female parasitoids distributed amongst the n patches such that within any one patch there are Pi parasitoids searching for Ni hosts. Suppose first that the parasitoids divide themselves evenly amongst the patches, and that within any patch they have a linear functional response, a constant searching efficiency and encounter hosts at random. Percentage parasitism is now the same in each patch in any one generation, and the Nicholson–Bailey model for the whole population is recovered exactly. All host individuals therefore suffer the same risk of parasitism. Such a uniform risk of parasitism across patches is countered by a wealth of empirical evidence. The two laboratory examples in Fig. 3 are clear cases where the searching parasitoids tend to congregate in the patches of high host density. In one case, the resulting pattern of parasitism is positively density-dependent, while in the other, it is just the opposite – parasitism is inversely density-dependent. This difference stems from the different functional responses of the two species in the following way. Trybliographa rapae has a short handling time giving a relatively high maximum attack rate per parasitoid (i.e. high upper asymptotes of their functional responses). The pattern of parasitism thus reflects the distribution of parasitoids, and hence the density-dependent patterns in Fig. 3b. In contrast, the egg parasitoid, Trichogramma evanescens, has a much longer handling time and hence a lower maximum attack rate per female. Individual parasitoids are therefore restricted in their ability to exploit high host density patches and this leads to the inverse density dependence in Fig. 3d. Examples illustrating this mechanism are shown in Fig. 4. Average patterns of time allocation (a, c) and parasitism (b, d) per patch in relation to host density per patch from two laboratory systems. (a, b) Ten females of the cynipid parasitoid, Trybliographa rapae, parasitizing larvae of the cabbage at different within of \& Hassell 1988). d) females of the parasitoid, Trichogramma parasitizing eggs of the on at different In contrast to (a, b) parasitism is inversely density-dependent, the for the parasitoids to on the with host (Hassell 1982). Numerical examples showing how the of parasitoids and different kinds of functional can lead to both density-dependent and inverse density-dependent spatial patterns of parasitism. (a) An aggregated distribution of searching parasitoids. (b) kinds of functional defining the attack rate per parasitoid within a from a linear functional with a = and = from a with a = and = The resulting overall parasitism per patch with and corresponding to those in that the more than for the of the parasitoids to cause inverse density (From Hassell 2000.) While data information on the distribution of searching parasitoids and the resulting patterns of parasitism are to collect from the field see 1983; \& it is relatively to quantify patterns of parasitism without about the distribution of searching hosts can be from a range of patches, taken to the laboratory and the of parasitism then usually by the next generation or by the different examples in the of \& Murdoch and Hassell \& direct density-dependent patterns of parasitism, inverse patterns and parasitism with host density per examples are shown in Fig. the of et al. and Hassell et al. the direct and inverse density-dependent patterns arise from and the patterns arise from Examples of field studies showing different spatial patterns of parasitism. (a) parasitism of the larvae of cabbage by the cynipid parasitoid, Trybliographa rapae \& Hassell 1988). (b) density-dependent parasitism of eggs by the parasitoid, \& parasitism of the scale by the parasitoid, et al. parasitism of the by the parasitoid, for a of the (From \& Hassell Much of the in these different patterns has on their importance to population theoretical work with discrete generation interactions to how density-dependent patterns (e.g. Hassell \& May Murdoch \& 1975; et al. have that both the inverse patterns and the can be equally important for (Hassell Chesson \& Murdoch Hassell \& May \& Murdoch et al. Hassell et al. models on this The simple model framework can be to interactions in an patchy environment as long as f(Nt,Pt) the across all patches, of the fraction of hosts escaping parasitism. The function f(Nt,Pt) therefore depends on both survival from parasitism within patches as well as the spatial distributions of hosts and parasitoids between the n patches. Let us consider the simple of a habitat with n patches as within each of which parasitism is random and by a functional The distribution of hosts and parasitoids from patch to patch is defined by and the of total hosts and total searching parasitoids, respectively, in the patch, so that f is given by: where a is the searching efficiency per patch and is the handling time (Hassell \& May specifically, will that the parasitoids in patches of high host density following the simple where is an index of parasitoid and is a constant such that the values to (Hassell \& May The can describe a wide range of parasitoid distribution they will be evenly distributed across patches if = 0, and tend to in patches of high host density as in the they will all congregate in the single patch of host density the as complete if < the parasitoid distribution is with their local abundance now inversely with host density per The way that these different patterns can for a linear functional within is shown by the following example in which the hosts are aggregated to a negative binomial is the probability of hosts in a patch from the negative binomial distribution and are the number of parasitoids in a patch with hosts by eqn of this model that sufficiently direct or inverse density-dependent distributions of parasitoids can stabilize the interactions (Fig. while parasitoid leads to local instability the host rate of increase is some a range of dynamics can occur et al. of parasitoid can also have a effect on equilibrium levels (Fig. Parasitoids have the effect in host equilibria when their distribution most closely that of the hosts (i.e. = or in this model leads to host because the parasitoids, are confined to their patches, irrespective of the degree of host The effects of parasitoid on and equilibrium (a) between the degree of of parasitoids, in eqn and the amount of host k a negative binomial distribution of hosts per from model with survival from parasitism, given by eqn total number of patches n = searching efficiency a = 1 and host rate of increase λ = (b) Examples of the host equilibrium level with the degree of parasitoid for three different numbers of patches from model with given by and a = λ = 2 and n = n = and n = Hassell in which are There has been much more on the effects of than because of the it has been assumed to have in population and because have to make the between and population dynamics et al. et al. Godfray 1994). It is now however, that heterogeneity can be at as important a process in population Let us consider a specific example where all the heterogeneity in parasitism is of the spatial distribution of hosts as in Fig. and that this is by the distribution of searching parasitoids being independently aggregated from patch to patch following a gamma We also that within any one patch the parasitoids exploit hosts at random and have a constant per capita searching efficiency (i.e. a linear functional The fraction of hosts escaping parasitism is now given by: where is the gamma probability density function for parasitoids per patch with mean and is a constant the of the density and a is the usual per capita searching efficiency of the In each patch, therefore, host survival by randomly searching parasitoids is given by the zero term of a Poisson distribution with mean et al. Chesson \& Murdoch much the same were the parasitoids uniformly distributed across patches but their searching efficiency, a, to a gamma distribution et al. The properties of the model are the populations will be by the heterogeneity as long as there is in the distribution of parasitoids or, more specifically, if < 1. eqn also to (1978) negative binomial model. The index of is now to the degree of of the searching parasitoids \& Murdoch et al. Hassell et al. The k < 1 from the negative binomial model, therefore corresponds in this spatially model to < 1. stabilizing properties have also been from models with parasitism by Reeve et al. further that patterns of parasitism are important as a stabilizing The parasitoid described an in which individuals are distributed amongst the patches at the start of each generation, where they then confined irrespective of how the hosts become and or not there are more patches The parasitoids in this are much more in being able to the best patches The large body of on patch stems from the classic paper by in which a a patch when the rate of is reduced to the maximum rate in the environment as a whole (e.g. \& \& 1997). to parasitoids, this means an distribution of parasitoid searching that the rate of parasitism for the individual searching parasitoids, depending on the spatial distribution of hosts and any and life \& \& (1978) and \& Hassell models for such searching parasitoids. The model individual parasitoids amongst patches, their searching time in relation to the of the host distribution and the of the parasitoid searching efficiency and handling time also the start",
    url = "https://doi.org/10.1046/j.1365-2656.2000.00445.x",
    doi = "10.1046/j.1365-2656.2000.00445.x",
    openalex = "W2082296409",
    references = "doi101016004058097690040x, doi101086282400, doi101093besa153237, doi101093oso97801985406630010001, doi1015159780691206912, doi1015159780691209418, doi1023071941447, doi1023072389612, doi1023075530, doi102307jctvx5wbbh"
}

Oak gall wasps (Hymenoptera: Cynipidae, Cynipini) are characterized by possession of complex cyclically parthenogenetic life cycles and the ability to induce a wide diversity of highly complex species- and generation-specific galls on oaks and other Fagaceae. The galls support species-rich, closed communities of inquilines and parasitoids that have become a model system in community ecology. We review recent advances in the ecology of oak cynipids, with particular emphasis on life cycle characteristics and the dynamics of the interactions between host plants, gall wasps, and natural enemies. We assess the importance of gall traits in structuring oak cynipid communities and summarize the evidence for bottom-up and top-down effects across trophic levels. We identify major unanswered questions and suggest approaches for the future.

@article{doi101146annurevento47091201145247,
    author = "Stone, Graham N. and Schönrogge, Karsten and Atkinson, Rachel and Bellido, David and Pujade‐Villar, Juli",
    title = "The Population Biology of Oak Gall Wasps (Hymenoptera: Cynipidae)",
    year = "2002",
    journal = "Annual Review of Entomology",
    abstract = "Oak gall wasps (Hymenoptera: Cynipidae, Cynipini) are characterized by possession of complex cyclically parthenogenetic life cycles and the ability to induce a wide diversity of highly complex species- and generation-specific galls on oaks and other Fagaceae. The galls support species-rich, closed communities of inquilines and parasitoids that have become a model system in community ecology. We review recent advances in the ecology of oak cynipids, with particular emphasis on life cycle characteristics and the dynamics of the interactions between host plants, gall wasps, and natural enemies. We assess the importance of gall traits in structuring oak cynipid communities and summarize the evidence for bottom-up and top-down effects across trophic levels. We identify major unanswered questions and suggest approaches for the future.",
    url = "https://doi.org/10.1146/annurev.ento.47.091201.145247",
    doi = "10.1146/annurev.ento.47.091201.145247",
    openalex = "W2114831299",
    references = "doi101006mpev19990614, doi1010160092867494902178, doi101046j13652656199900288x, doi101046j13652656200000445x, doi101093aesa861122, doi101093ee16115, doi101146annureven37010192001541, doi101146annureves11110180000353, doi1023072261636, doi1023072409571, doi107208chicago97802267976700010001"
}

The LandScan Global Population Project produced a world-wide 1998 population database at a 30-by 30-second resolution for estimating ambient populations at risk. Best available census counts were distributed to cells based on probability coefficients which, in turn, were based on road proximity, slope, land cover, and nighttime lights, LandScan 1998 has been completed for the entire world. Verification and validation (V&V) studies were conducted routinely for all regions and more extensively for Israel, Germany, and the southwestern United States. Geographic information systems (GIS) were essential for conflation of diverse input variables, computation of probability coefficients, allocation of population to cells, and reconciliation of cell totals with aggregate (usually province) control totals. Remote sensing was an essential source of two input variables-land cover and nighttime lights-and one ancillary database-high-resolution panchromatic imagery-used in V&V of the population model and resulting LandScan database.

@incollection{doi101201978148226467824,
    title = "LandScan: a global population database for estimating populations at risk",
    year = "2003",
    abstract = "The LandScan Global Population Project produced a world-wide 1998 population database at a 30-by 30-second resolution for estimating ambient populations at risk. Best available census counts were distributed to cells based on probability coefficients which, in turn, were based on road proximity, slope, land cover, and nighttime lights, LandScan 1998 has been completed for the entire world. Verification and validation (V\&V) studies were conducted routinely for all regions and more extensively for Israel, Germany, and the southwestern United States. Geographic information systems (GIS) were essential for conflation of diverse input variables, computation of probability coefficients, allocation of population to cells, and reconciliation of cell totals with aggregate (usually province) control totals. Remote sensing was an essential source of two input variables-land cover and nighttime lights-and one ancillary database-high-resolution panchromatic imagery-used in V\&V of the population model and resulting LandScan database.",
    url = "https://doi.org/10.1201/9781482264678-24",
    doi = "10.1201/9781482264678-24",
    openalex = "W1541771177"
}

▪ Abstract Spatial synchrony refers to coincident changes in the abundance or other time-varying characteristics of geographically disjunct populations. This phenomenon has been documented in the dynamics of species representing a variety of taxa and ecological roles. Synchrony may arise from three primary mechanisms:(a) dispersal among populations, reducing the size of relatively large populations and increasing relatively small ones; (b) congruent dependence of population dynamics on a synchronous exogenous random factor such as temperature or rainfall, a phenomenon known as the “Moran effect”; and (c) trophic interactions with populations of other species that are themselves spatially synchronous or mobile. Identification of the causes of synchrony is often difficult. In addition to intraspecific synchrony, there are many examples of synchrony among populations of different species, the causes of which are similarly complex and difficult to identify. Furthermore, some populations may exhibit complex spatial dynamics such as spiral waves and chaos. Statistical tests based on phase coherence and/or time-lagged spatial correlation are required to characterize these more complex patterns of spatial dynamics fully.

@article{doi101146annurevecolsys34011802132516,
    author = "Liebhold, Andrew M. and Koenig, Walter D. and Bjørnstad, Ottar N.",
    title = "Spatial Synchrony in Population Dynamics",
    year = "2004",
    journal = "Annual Review of Ecology Evolution and Systematics",
    abstract = "▪ Abstract Spatial synchrony refers to coincident changes in the abundance or other time-varying characteristics of geographically disjunct populations. This phenomenon has been documented in the dynamics of species representing a variety of taxa and ecological roles. Synchrony may arise from three primary mechanisms:(a) dispersal among populations, reducing the size of relatively large populations and increasing relatively small ones; (b) congruent dependence of population dynamics on a synchronous exogenous random factor such as temperature or rainfall, a phenomenon known as the “Moran effect”; and (c) trophic interactions with populations of other species that are themselves spatially synchronous or mobile. Identification of the causes of synchrony is often difficult. In addition to intraspecific synchrony, there are many examples of synchrony among populations of different species, the causes of which are similarly complex and difficult to identify. Furthermore, some populations may exhibit complex spatial dynamics such as spiral waves and chaos. Statistical tests based on phase coherence and/or time-lagged spatial correlation are required to characterize these more complex patterns of spatial dynamics fully.",
    url = "https://doi.org/10.1146/annurev.ecolsys.34.011802.132516",
    doi = "10.1146/annurev.ecolsys.34.011802.132516",
    openalex = "W2101968928",
    references = "doi101007b98869"
}

In the last two decades, interest in species distribution models (SDMs) of plants and animals has grown dramatically. Recent advances in SDMs allow us to potentially forecast anthropogenic effects on patterns of biodiversity at different spatial scales. However, some limitations still preclude the use of SDMs in many theoretical and practical applications. Here, we provide an overview of recent advances in this field, discuss the ecological principles and assumptions underpinning SDMs, and highlight critical limitations and decisions inherent in the construction and evaluation of SDMs. Particular emphasis is given to the use of SDMs for the assessment of climate change impacts and conservation management issues. We suggest new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales. Addressing all these issues requires a better integration of SDMs with ecological theory.

@article{doi101111j14610248200500792x,
    author = "Guisan, Antoine and Thuiller, Wilfried",
    title = "Predicting species distribution: offering more than simple habitat models",
    year = "2005",
    journal = "Ecology Letters",
    abstract = "In the last two decades, interest in species distribution models (SDMs) of plants and animals has grown dramatically. Recent advances in SDMs allow us to potentially forecast anthropogenic effects on patterns of biodiversity at different spatial scales. However, some limitations still preclude the use of SDMs in many theoretical and practical applications. Here, we provide an overview of recent advances in this field, discuss the ecological principles and assumptions underpinning SDMs, and highlight critical limitations and decisions inherent in the construction and evaluation of SDMs. Particular emphasis is given to the use of SDMs for the assessment of climate change impacts and conservation management issues. We suggest new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales. Addressing all these issues requires a better integration of SDMs with ecological theory.",
    url = "https://doi.org/10.1111/j.1461-0248.2005.00792.x",
    doi = "10.1111/j.1461-0248.2005.00792.x",
    openalex = "W2123337039",
    references = "doi101016jtree200310013, doi101016jtree200409003, doi101038nature02121, doi101111j001438202004tb00461x, doi101146annurevecolsys271597, doi1023073071998"
}

A common feature of ecological data sets is their tendency to contain many zero values. Statistical inference based on such data are likely to be inefficient or wrong unless careful thought is given to how these zeros arose and how best to model them. In this paper, we propose a framework for understanding how zero-inflated data sets originate and deciding how best to model them. We define and classify the different kinds of zeros that occur in ecological data and describe how they arise: either from 'true zero' or 'false zero' observations. After reviewing recent developments in modelling zero-inflated data sets, we use practical examples to demonstrate how failing to account for the source of zero inflation can reduce our ability to detect relationships in ecological data and at worst lead to incorrect inference. The adoption of methods that explicitly model the sources of zero observations will sharpen insights and improve the robustness of ecological analyses.

@article{doi101111j14610248200500826x,
    author = "Martin, Tara G. and Wintle, Brendan A. and Rhodes, Jonathan R. and Kuhnert, Petra and Field, Scott A. and Low‐Choy, Samantha and Tyre, Andrew J. and Possingham, Hugh P.",
    title = "Zero tolerance ecology: improving ecological inference by modelling the source of zero observations",
    year = "2005",
    journal = "Ecology Letters",
    abstract = "A common feature of ecological data sets is their tendency to contain many zero values. Statistical inference based on such data are likely to be inefficient or wrong unless careful thought is given to how these zeros arose and how best to model them. In this paper, we propose a framework for understanding how zero-inflated data sets originate and deciding how best to model them. We define and classify the different kinds of zeros that occur in ecological data and describe how they arise: either from 'true zero' or 'false zero' observations. After reviewing recent developments in modelling zero-inflated data sets, we use practical examples to demonstrate how failing to account for the source of zero inflation can reduce our ability to detect relationships in ecological data and at worst lead to incorrect inference. The adoption of methods that explicitly model the sources of zero observations will sharpen insights and improve the robustness of ecological analyses.",
    url = "https://doi.org/10.1111/j.1461-0248.2005.00826.x",
    doi = "10.1111/j.1461-0248.2005.00826.x",
    openalex = "W2149914006",
    references = "doi1018900012965820020831131scmise20co2"
}

Summary In recent years the use of species distribution models by ecologists and conservation managers has increased considerably, along with an awareness of the need to provide accuracy assessment for predictions of such models. The kappa statistic is the most widely used measure for the performance of models generating presence–absence predictions, but several studies have criticized it for being inherently dependent on prevalence, and argued that this dependency introduces statistical artefacts to estimates of predictive accuracy. This criticism has been supported recently by computer simulations showing that kappa responds to the prevalence of the modelled species in a unimodal fashion. In this paper we provide a theoretical explanation for the observed dependence of kappa on prevalence, and introduce into ecology an alternative measure of accuracy, the true skill statistic (TSS), which corrects for this dependence while still keeping all the advantages of kappa. We also compare the responses of kappa and TSS to prevalence using empirical data, by modelling distribution patterns of 128 species of woody plant in Israel. The theoretical analysis shows that kappa responds in a unimodal fashion to variation in prevalence and that the level of prevalence that maximizes kappa depends on the ratio between sensitivity (the proportion of correctly predicted presences) and specificity (the proportion of correctly predicted absences). In contrast, TSS is independent of prevalence. When the two measures of accuracy were compared using empirical data, kappa showed a unimodal response to prevalence, in agreement with the theoretical analysis. TSS showed a decreasing linear response to prevalence, a result we interpret as reflecting true ecological phenomena rather than a statistical artefact. This interpretation is supported by the fact that a similar pattern was found for the area under the ROC curve, a measure known to be independent of prevalence. Synthesis and applications. Our results provide theoretical and empirical evidence that kappa, one of the most widely used measures of model performance in ecology, has serious limitations that make it unsuitable for such applications. The alternative we suggest, TSS, compensates for the shortcomings of kappa while keeping all of its advantages. We therefore recommend the TSS as a simple and intuitive measure for the performance of species distribution models when predictions are expressed as presence–absence maps.

@article{doi101111j13652664200601214x,
    author = "Allouche, Omri and Tsoar, Asaf and Kadmon, Ronen",
    title = "Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS)",
    year = "2006",
    journal = "Journal of Applied Ecology",
    abstract = "Summary In recent years the use of species distribution models by ecologists and conservation managers has increased considerably, along with an awareness of the need to provide accuracy assessment for predictions of such models. The kappa statistic is the most widely used measure for the performance of models generating presence–absence predictions, but several studies have criticized it for being inherently dependent on prevalence, and argued that this dependency introduces statistical artefacts to estimates of predictive accuracy. This criticism has been supported recently by computer simulations showing that kappa responds to the prevalence of the modelled species in a unimodal fashion. In this paper we provide a theoretical explanation for the observed dependence of kappa on prevalence, and introduce into ecology an alternative measure of accuracy, the true skill statistic (TSS), which corrects for this dependence while still keeping all the advantages of kappa. We also compare the responses of kappa and TSS to prevalence using empirical data, by modelling distribution patterns of 128 species of woody plant in Israel. The theoretical analysis shows that kappa responds in a unimodal fashion to variation in prevalence and that the level of prevalence that maximizes kappa depends on the ratio between sensitivity (the proportion of correctly predicted presences) and specificity (the proportion of correctly predicted absences). In contrast, TSS is independent of prevalence. When the two measures of accuracy were compared using empirical data, kappa showed a unimodal response to prevalence, in agreement with the theoretical analysis. TSS showed a decreasing linear response to prevalence, a result we interpret as reflecting true ecological phenomena rather than a statistical artefact. This interpretation is supported by the fact that a similar pattern was found for the area under the ROC curve, a measure known to be independent of prevalence. Synthesis and applications. Our results provide theoretical and empirical evidence that kappa, one of the most widely used measures of model performance in ecology, has serious limitations that make it unsuitable for such applications. The alternative we suggest, TSS, compensates for the shortcomings of kappa while keeping all of its advantages. We therefore recommend the TSS as a simple and intuitive measure for the performance of species distribution models when predictions are expressed as presence–absence maps.",
    url = "https://doi.org/10.1111/j.1365-2664.2006.01214.x",
    doi = "10.1111/j.1365-2664.2006.01214.x",
    openalex = "W1541774929",
    references = "doi101111j09067590200503957x"
}

Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature.

@article{doi1018900705391,
    author = "Cutler, D. Richard and Edwards, Thomas C. and Beard, Karen H. and Cutler, Adele and Hess, Kyle T. and Gibson, Jacob and Lawler, Joshua J.",
    title = "RANDOM FORESTS FOR CLASSIFICATION IN ECOLOGY",
    year = "2007",
    journal = "Ecology",
    abstract = "Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature.",
    url = "https://doi.org/10.1890/07-0539.1",
    doi = "10.1890/07-0539.1",
    openalex = "W2139086914",
    references = "openalexw273955616"
}

Connectivity, or the exchange of individuals among marine populations, is a central topic in marine ecology. For most benthic marine species with complex life cycles, this exchange occurs primarily during the pelagic larval stage. The small size of larvae coupled with the vast and complex fluid environment they occupy hamper our ability to quantify dispersal and connectivity. Evidence from direct and indirect approaches using geochemical and genetic techniques suggests that populations range from fully open to fully closed. Understanding the biophysical processes that contribute to observed dispersal patterns requires integrated interdisciplinary approaches that incorporate high-resolution biophysical modeling and empirical data. Further, differential postsettlement survival of larvae may add complexity to measurements of connectivity. The degree to which populations self recruit or receive subsidy from other populations has consequences for a number of fundamental ecological processes that affect population regulation and persistence. Finally, a full understanding of population connectivity has important applications for management and conservation.

@article{doi101146annurevmarine010908163757,
    author = "Cowen, Robert K. and Sponaugle, Su",
    title = "Larval Dispersal and Marine Population Connectivity",
    year = "2008",
    journal = "Annual Review of Marine Science",
    abstract = "Connectivity, or the exchange of individuals among marine populations, is a central topic in marine ecology. For most benthic marine species with complex life cycles, this exchange occurs primarily during the pelagic larval stage. The small size of larvae coupled with the vast and complex fluid environment they occupy hamper our ability to quantify dispersal and connectivity. Evidence from direct and indirect approaches using geochemical and genetic techniques suggests that populations range from fully open to fully closed. Understanding the biophysical processes that contribute to observed dispersal patterns requires integrated interdisciplinary approaches that incorporate high-resolution biophysical modeling and empirical data. Further, differential postsettlement survival of larvae may add complexity to measurements of connectivity. The degree to which populations self recruit or receive subsidy from other populations has consequences for a number of fundamental ecological processes that affect population regulation and persistence. Finally, a full understanding of population connectivity has important applications for management and conservation.",
    url = "https://doi.org/10.1146/annurev.marine.010908.163757",
    doi = "10.1146/annurev.marine.010908.163757",
    openalex = "W2140618008",
    references = "doi1010160169534789900086, doi101046j14610248200300530x, doi101073pnas82113707, doi101093icbicj028, doi101126science11538249, doi101146annurevecolsys271477, doi101146annurevmarine010908163708, doi1018900012965820020831490prhcac20co2"
}

Connectivity among populations and habitats is important for a wide range of ecological processes. Understanding, preserving, and restoring connectivity in complex landscapes requires connectivity models and metrics that are reliable, efficient, and process based. We introduce a new class of ecological connectivity models based in electrical circuit theory. Although they have been applied in other disciplines, circuit-theoretic connectivity models are new to ecology. They offer distinct advantages over common analytic connectivity models, including a theoretical basis in random walk theory and an ability to evaluate contributions of multiple dispersal pathways. Resistance, current, and voltage calculated across graphs or raster grids can be related to ecological processes (such as individual movement and gene flow) that occur across large population networks or landscapes. Efficient algorithms can quickly solve networks with millions of nodes, or landscapes with millions of raster cells. Here we review basic circuit theory, discuss relationships between circuit and random walk theories, and describe applications in ecology, evolution, and conservation. We provide examples of how circuit models can be used to predict movement patterns and fates of random walkers in complex landscapes and to identify important habitat patches and movement corridors for conservation planning.

@article{doi1018900718611,
    author = "McRae, Brad H. and Dickson, Brett G. and Keitt, Timothy H. and Shah, Viral B.",
    title = "USING CIRCUIT THEORY TO MODEL CONNECTIVITY IN ECOLOGY, EVOLUTION, AND CONSERVATION",
    year = "2008",
    journal = "Ecology",
    abstract = "Connectivity among populations and habitats is important for a wide range of ecological processes. Understanding, preserving, and restoring connectivity in complex landscapes requires connectivity models and metrics that are reliable, efficient, and process based. We introduce a new class of ecological connectivity models based in electrical circuit theory. Although they have been applied in other disciplines, circuit-theoretic connectivity models are new to ecology. They offer distinct advantages over common analytic connectivity models, including a theoretical basis in random walk theory and an ability to evaluate contributions of multiple dispersal pathways. Resistance, current, and voltage calculated across graphs or raster grids can be related to ecological processes (such as individual movement and gene flow) that occur across large population networks or landscapes. Efficient algorithms can quickly solve networks with millions of nodes, or landscapes with millions of raster cells. Here we review basic circuit theory, discuss relationships between circuit and random walk theories, and describe applications in ecology, evolution, and conservation. We provide examples of how circuit models can be used to predict movement patterns and fates of random walkers in complex landscapes and to identify important habitat patches and movement corridors for conservation planning.",
    url = "https://doi.org/10.1890/07-1861.1",
    doi = "10.1890/07-1861.1",
    openalex = "W2091202128",
    references = "doi1018900012965820020831131scmise20co2"
}

Abstract Aim To evaluate a suite of species distribution models for their utility as predictors of suitable habitat and as tools for new population discovery of six rare plant species that have both narrow geographical ranges and specialized habitat requirements. Location The Rattlesnake Creek Terrane (RCT) of the Shasta‐Trinity National Forest in the northern California Coast Range of the United States. Methods We used occurrence records from 25 years of US Forest Service botanical surveys, environmental and remotely sensed climate data to model the distributions of the target species across the RCT. The models included generalized linear models (GLM), artificial neural networks (ANN), random forests (RF) and maximum entropy (ME). From the results we generated predictive maps that were used to identify areas of high probability occurrence. We made field visits to the top‐ranked sites to search for new populations of the target species. Results Random forests gave the best results according to area under the curve and Kappa statistics, although ME was in close agreement. While GLM and ANN also gave good results, they were less restrictive and more varied than RF and ME. Cross‐model correlations were the highest for species with the most records and declined with record numbers. Model assessment using a separate dataset confirmed that RF provided the best predictions of appropriate habitat. Use of RF output to prioritize search areas resulted in the discovery of 16 new populations of the target species. Main conclusions Species distribution models, such as RF and ME, which use presence data and information about the background matrix where species do not occur, may be an effective tool for new population discovery of rare plant species, but there does appear to be a lower threshold in the number of occurrences required to build a good model.

@article{doi101111j14724642200900567x,
    author = "Williams, John and Seo, Changwan and Thorne, James H. and Nelson, Julie К. and Erwin, Susan A. and O’Brien, Joshua and Schwartz, Mark W.",
    title = "Using species distribution models to predict new occurrences for rare plants",
    year = "2009",
    journal = "Diversity and Distributions",
    abstract = "Abstract Aim To evaluate a suite of species distribution models for their utility as predictors of suitable habitat and as tools for new population discovery of six rare plant species that have both narrow geographical ranges and specialized habitat requirements. Location The Rattlesnake Creek Terrane (RCT) of the Shasta‐Trinity National Forest in the northern California Coast Range of the United States. Methods We used occurrence records from 25 years of US Forest Service botanical surveys, environmental and remotely sensed climate data to model the distributions of the target species across the RCT. The models included generalized linear models (GLM), artificial neural networks (ANN), random forests (RF) and maximum entropy (ME). From the results we generated predictive maps that were used to identify areas of high probability occurrence. We made field visits to the top‐ranked sites to search for new populations of the target species. Results Random forests gave the best results according to area under the curve and Kappa statistics, although ME was in close agreement. While GLM and ANN also gave good results, they were less restrictive and more varied than RF and ME. Cross‐model correlations were the highest for species with the most records and declined with record numbers. Model assessment using a separate dataset confirmed that RF provided the best predictions of appropriate habitat. Use of RF output to prioritize search areas resulted in the discovery of 16 new populations of the target species. Main conclusions Species distribution models, such as RF and ME, which use presence data and information about the background matrix where species do not occur, may be an effective tool for new population discovery of rare plant species, but there does appear to be a lower threshold in the number of occurrences required to build a good model.",
    url = "https://doi.org/10.1111/j.1472-4642.2009.00567.x",
    doi = "10.1111/j.1472-4642.2009.00567.x",
    openalex = "W2113965979"
}

It is widely acknowledged that species respond to climate change by range shifts. Robust predictions of such changes in species’ distributions are pivotal for conservation planning and policy making, and are thus major challenges in ecological research. Statistical species distribution models (SDMs) have been widely applied in this context, though they remain subject to criticism as they implicitly assume equilibrium, and incorporate neither dispersal, demographic processes nor biotic interactions explicitly. In this study, the effects of transient dynamics and ecological properties and processes on the prediction accuracy of SDMs for climate change projections were tested. A spatially explicit multi‐species dynamic population model was built, incorporating species‐specific and interspecific ecological processes, environmental stochasticity and climate change. Species distributions were sampled in different scenarios, and SDMs were estimated by applying generalised linear models (GLMs) and boosted regression trees (BRTs). Resulting model performances were related to prevailing ecological processes and temporal dynamics. SDM performance varied for different range dynamics. Prediction accuracies decreased when abrupt range shifts occurred as species were outpaced by the rate of climate change, and increased again when a new equilibrium situation was realised. When ranges contracted, prediction accuracies increased as the absences were predicted well. Far‐dispersing species were faster in tracking climate change, and were predicted more accurately by SDMs than short‐dispersing species. BRTs mostly outperformed GLMs. The presence of a predator, and the inclusion of its incidence as an environmental predictor, made BRTs and GLMs perform similarly. Results are discussed in light of other studies dealing with effects of ecological traits and processes on SDM performance. Perspectives are given on further advancements of SDMs and for possible interfaces with more mechanistic approaches in order to improve predictions under environmental change.

@article{doi101111j16000587200905810x,
    author = "Zurell, Damaris and Jeltsch, Florian and Dormann, Carsten F. and Schröder, Boris",
    title = "Static species distribution models in dynamically changing systems: how good can predictions really be?",
    year = "2009",
    journal = "Ecography",
    abstract = "It is widely acknowledged that species respond to climate change by range shifts. Robust predictions of such changes in species’ distributions are pivotal for conservation planning and policy making, and are thus major challenges in ecological research. Statistical species distribution models (SDMs) have been widely applied in this context, though they remain subject to criticism as they implicitly assume equilibrium, and incorporate neither dispersal, demographic processes nor biotic interactions explicitly. In this study, the effects of transient dynamics and ecological properties and processes on the prediction accuracy of SDMs for climate change projections were tested. A spatially explicit multi‐species dynamic population model was built, incorporating species‐specific and interspecific ecological processes, environmental stochasticity and climate change. Species distributions were sampled in different scenarios, and SDMs were estimated by applying generalised linear models (GLMs) and boosted regression trees (BRTs). Resulting model performances were related to prevailing ecological processes and temporal dynamics. SDM performance varied for different range dynamics. Prediction accuracies decreased when abrupt range shifts occurred as species were outpaced by the rate of climate change, and increased again when a new equilibrium situation was realised. When ranges contracted, prediction accuracies increased as the absences were predicted well. Far‐dispersing species were faster in tracking climate change, and were predicted more accurately by SDMs than short‐dispersing species. BRTs mostly outperformed GLMs. The presence of a predator, and the inclusion of its incidence as an environmental predictor, made BRTs and GLMs perform similarly. Results are discussed in light of other studies dealing with effects of ecological traits and processes on SDM performance. Perspectives are given on further advancements of SDMs and for possible interfaces with more mechanistic approaches in order to improve predictions under environmental change.",
    url = "https://doi.org/10.1111/j.1600-0587.2009.05810.x",
    doi = "10.1111/j.1600-0587.2009.05810.x",
    openalex = "W1965759024",
    references = "doi1010020471722146, doi1010079781475734621, doi1010079783319194257, doi101017cbo9780511790942, doi101017s0376892997000088, doi101038nature02121, doi101046j13652656200000445x, doi101109tac19741100705, doi101111j13652656200801390x, doi101111j14610248200500792x, doi105860choice273338"
}

Species distribution models (SDMs) are numerical tools that combine observations of species occurrence or abundance with environmental estimates. They are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time. SDMs are now widely used across terrestrial, freshwater, and marine realms. Differences in methods between disciplines reflect both differences in species mobility and in “established use.” Model realism and robustness is influenced by selection of relevant predictors and modeling method, consideration of scale, how the interplay between environmental and geographic factors is handled, and the extent of extrapolation. Current linkages between SDM practice and ecological theory are often weak, hindering progress. Remaining challenges include: improvement of methods for modeling presence-only data and for model selection and evaluation; accounting for biotic interactions; and assessing model uncertainty.

@article{doi101146annurevecolsys110308120159,
    author = "Elith, Jane and Leathwick, John R.",
    title = "Species Distribution Models: Ecological Explanation and Prediction Across Space and Time",
    year = "2009",
    journal = "Annual Review of Ecology Evolution and Systematics",
    abstract = "Species distribution models (SDMs) are numerical tools that combine observations of species occurrence or abundance with environmental estimates. They are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time. SDMs are now widely used across terrestrial, freshwater, and marine realms. Differences in methods between disciplines reflect both differences in species mobility and in “established use.” Model realism and robustness is influenced by selection of relevant predictors and modeling method, consideration of scale, how the interplay between environmental and geographic factors is handled, and the extent of extrapolation. Current linkages between SDM practice and ecological theory are often weak, hindering progress. Remaining challenges include: improvement of methods for modeling presence-only data and for model selection and evaluation; accounting for biotic interactions; and assessing model uncertainty.",
    url = "https://doi.org/10.1146/annurev.ecolsys.110308.120159",
    doi = "10.1146/annurev.ecolsys.110308.120159",
    openalex = "W2097601813",
    references = "doi1010079780387848587, doi101016jecolmodel200503026, doi101016s0304380000003549, doi101111j001438202004tb00461x, doi101111j13652656200801390x, doi101111j14610248200500792x, doi101111j14610248200801277x, doi101111j20060906759004596x, doi101146annurevecolsys39110707173430, doi1018900012965820020832248esorwd20co2, doi1023071931600, doi1023071941447, doi1023071943577, doi1023072669574, doi1023073802723, doi1023073803117"
}

The pace-of-life syndrome (POLS) hypothesis specifies that closely related species or populations experiencing different ecological conditions should differ in a suite of metabolic, hormonal and immunity traits that have coevolved with the life-history particularities related to these conditions. Surprisingly, two important dimensions of the POLS concept have been neglected: (i) despite increasing evidence for numerous connections between behavioural, physiological and life-history traits, behaviours have rarely been considered in the POLS yet; (ii) the POLS could easily be applied to the study of covariation among traits between individuals within a population. In this paper, we propose that consistent behavioural differences among individuals, or personality, covary with life history and physiological differences at the within-population, interpopulation and interspecific levels. We discuss how the POLS provides a heuristic framework in which personality studies can be integrated to address how variation in personality traits is maintained within populations.

@article{doi101098rstb20100208,
    author = "Réale, Denis and Garant, Dany and Humphries, Murray M. and Bergeron, Patrick and Careau, Vincent and Montiglio, Pierre‐Olivier",
    title = "Personality and the emergence of the pace-of-life syndrome concept at the population level",
    year = "2010",
    journal = "Philosophical Transactions of the Royal Society B Biological Sciences",
    abstract = "The pace-of-life syndrome (POLS) hypothesis specifies that closely related species or populations experiencing different ecological conditions should differ in a suite of metabolic, hormonal and immunity traits that have coevolved with the life-history particularities related to these conditions. Surprisingly, two important dimensions of the POLS concept have been neglected: (i) despite increasing evidence for numerous connections between behavioural, physiological and life-history traits, behaviours have rarely been considered in the POLS yet; (ii) the POLS could easily be applied to the study of covariation among traits between individuals within a population. In this paper, we propose that consistent behavioural differences among individuals, or personality, covary with life history and physiological differences at the within-population, interpopulation and interspecific levels. We discuss how the POLS provides a heuristic framework in which personality studies can be integrated to address how variation in personality traits is maintained within populations.",
    url = "https://doi.org/10.1098/rstb.2010.0208",
    doi = "10.1098/rstb.2010.0208",
    openalex = "W2102429818",
    references = "doi101086282461, doi101093oso97801985406630010001, doi101126science1070315, doi101126science3576198"
}

Statistical species distribution models (SDMs) are widely used to predict the potential changes in species distributions under climate change scenarios. We suggest that we need to revisit the conceptual framework and ecological assumptions on which the relationship between species distributions and environment is based. We present a simple conceptual framework to examine the selection of environmental predictors and data resolution scales. These vary widely in recent papers, with light inconsistently included in the models. Focusing on light as a necessary component of plant SDMs, we briefly review its dependence on aspect and slope and existing knowledge of its influence on plant distribution. Differences in light regimes between north- and south-facing aspects in temperate latitudes can produce differences in temperature equivalent to moves 200 km polewards. Local topography may create refugia that are not recognized in many climate change SDMs using coarse-scale data. We argue that current assumptions about the selection of predictors and data resolution need further testing. Application of these ideas can clarify many issues of scale, extent and choice of predictors, and potentially improve the use of SDMs for climate change modelling of biodiversity.

@article{doi101111j13652699201002416x,
    author = "Austin, Mike P. and Niel, Kimberly P. Van",
    title = "Improving species distribution models for climate change studies: variable selection and scale",
    year = "2010",
    journal = "Journal of Biogeography",
    abstract = "Statistical species distribution models (SDMs) are widely used to predict the potential changes in species distributions under climate change scenarios. We suggest that we need to revisit the conceptual framework and ecological assumptions on which the relationship between species distributions and environment is based. We present a simple conceptual framework to examine the selection of environmental predictors and data resolution scales. These vary widely in recent papers, with light inconsistently included in the models. Focusing on light as a necessary component of plant SDMs, we briefly review its dependence on aspect and slope and existing knowledge of its influence on plant distribution. Differences in light regimes between north- and south-facing aspects in temperate latitudes can produce differences in temperature equivalent to moves 200 km polewards. Local topography may create refugia that are not recognized in many climate change SDMs using coarse-scale data. We argue that current assumptions about the selection of predictors and data resolution need further testing. Application of these ideas can clarify many issues of scale, extent and choice of predictors, and potentially improve the use of SDMs for climate change modelling of biodiversity.",
    url = "https://doi.org/10.1111/j.1365-2699.2010.02416.x",
    doi = "10.1111/j.1365-2699.2010.02416.x",
    openalex = "W1496467185",
    references = "doi101016s0304380000003549, doi101016s0925857400001786, doi101017cbo9780511810602, doi101017cbo9780511845727, doi101046j1466822x200300042x, doi1010970001069419411100000009, doi101111j13652699200601584x, doi101111j14668238200700359x, doi101146annurevecolsys110308120159, doi102307211491"
}

Abstract Aim To demonstrate that multi‐modelling methods have effectively been used to combine static species distribution models (SDM), predicting the geographical pattern of suitable habitat, with dynamic landscape and population models to forecast the impacts of environmental change on species’ status, an important goal of conservation biogeography. Methods Three approaches were considered: (1) incorporating models of species migration to understand the ability of a species to occupy suitable habitat in new locations; (2) linking models of landscape disturbance and succession to models of habitat suitability; and (3) fully linking models of habitat suitability, habitat dynamics and spatially explicit population dynamics. Results Linking species–environment relationships, landscape dynamics and population dynamics in a multi‐modelling framework allows the combined impacts of climate change (affecting species distribution and vital rates) and land cover dynamics (land use change, altered disturbance regimes) on species to be predicted. This approach is only feasible if the life history parameters and habitat requirements of the species are well understood. Main conclusions Forecasts of the impacts of global change on species may be improved by considering multiple causes. A range of methods are available to address the interactions of changing habitat suitability, habitat dynamics and population response that vary in their complexity, realism and data requirements.

@article{doi101111j14724642201000641x,
    author = "Franklin, Janet",
    title = "Moving beyond static species distribution models in support of conservation biogeography",
    year = "2010",
    journal = "Diversity and Distributions",
    abstract = "Abstract Aim To demonstrate that multi‐modelling methods have effectively been used to combine static species distribution models (SDM), predicting the geographical pattern of suitable habitat, with dynamic landscape and population models to forecast the impacts of environmental change on species’ status, an important goal of conservation biogeography. Methods Three approaches were considered: (1) incorporating models of species migration to understand the ability of a species to occupy suitable habitat in new locations; (2) linking models of landscape disturbance and succession to models of habitat suitability; and (3) fully linking models of habitat suitability, habitat dynamics and spatially explicit population dynamics. Results Linking species–environment relationships, landscape dynamics and population dynamics in a multi‐modelling framework allows the combined impacts of climate change (affecting species distribution and vital rates) and land cover dynamics (land use change, altered disturbance regimes) on species to be predicted. This approach is only feasible if the life history parameters and habitat requirements of the species are well understood. Main conclusions Forecasts of the impacts of global change on species may be improved by considering multiple causes. A range of methods are available to address the interactions of changing habitat suitability, habitat dynamics and population response that vary in their complexity, realism and data requirements.",
    url = "https://doi.org/10.1111/j.1472-4642.2010.00641.x",
    doi = "10.1111/j.1472-4642.2010.00641.x",
    openalex = "W1555708599",
    references = "doi101111j16000587200905810x"
}

In order to control pests, a specific management strategy called the threshold policy is proposed, which can be described by Filippov systems (or piecewise smooth systems). The aim of this work is to investigate a variety of bifurcation phenomena of the equilibria and sliding cycles of Filippov two stage structured population models with density dependent per capita birth rates and transition rates from the juvenile class into the adult class. It is shown that interadult competition alone can give rise to multiple sliding segments and multiple pseudoequilibria, whilst interadult and interjuvenile competition together can result in rich sliding bifurcations. As the threshold value varies, local sliding bifurcations including boundary node (saddle), tangency, and pseudo--saddle-node bifurcations occur sequentially, and global sliding bifurcations including buckling bifurcations of the sliding cycles, sliding crossing bifurcations, and pseudohomoclinic bifurcations can be present. Threshold policy control has been shown to be easily implemented and useful in pest management, which can be used to prevent the possibility of multiple pest outbreaks or cause the density of pests to stabilize at a desired level such as an economic threshold.

@article{doi101137110847020,
    author = "Tang, Sanyi and Liang, Juhua and Xiao, Yanni and Cheke, Robert",
    title = "Sliding Bifurcations of Filippov Two Stage Pest Control Models with Economic Thresholds",
    year = "2012",
    journal = "SIAM Journal on Applied Mathematics",
    abstract = "In order to control pests, a specific management strategy called the threshold policy is proposed, which can be described by Filippov systems (or piecewise smooth systems). The aim of this work is to investigate a variety of bifurcation phenomena of the equilibria and sliding cycles of Filippov two stage structured population models with density dependent per capita birth rates and transition rates from the juvenile class into the adult class. It is shown that interadult competition alone can give rise to multiple sliding segments and multiple pseudoequilibria, whilst interadult and interjuvenile competition together can result in rich sliding bifurcations. As the threshold value varies, local sliding bifurcations including boundary node (saddle), tangency, and pseudo--saddle-node bifurcations occur sequentially, and global sliding bifurcations including buckling bifurcations of the sliding cycles, sliding crossing bifurcations, and pseudohomoclinic bifurcations can be present. Threshold policy control has been shown to be easily implemented and useful in pest management, which can be used to prevent the possibility of multiple pest outbreaks or cause the density of pests to stabilize at a desired level such as an economic threshold.",
    url = "https://doi.org/10.1137/110847020",
    doi = "10.1137/110847020",
    openalex = "W2065242464",
    references = "doi101016jmbs200806008"
}

Abstract Aim Advancement in ecological methods predicting species distributions is a crucial precondition for deriving sound management actions. Maximum entropy (MaxEnt) models are a popular tool to predict species distributions, as they are considered able to cope well with sparse, irregularly sampled data and minor location errors. Although a fundamental assumption of MaxEnt is that the entire area of interest has been systematically sampled, in practice, MaxEnt models are usually built from occurrence records that are spatially biased towards better‐surveyed areas. Two common, yet not compared, strategies to cope with uneven sampling effort are spatial filtering of occurrence data and background manipulation using environmental data with the same spatial bias as occurrence data. We tested these strategies using simulated data and a recently collated dataset on Malay civet V iverra tangalunga in Borneo. Location Borneo, Southeast Asia. Methods We collated 504 occurrence records of Malay civets from Borneo of which 291 records were from 2001 to 2011 and used them in the MaxEnt analysis (baseline scenario) together with 25 environmental input variables. We simulated datasets for two virtual species (similar to a range‐restricted highland and a lowland species) using the same number of records for model building. As occurrence records were biased towards north‐eastern Borneo, we investigated the efficacy of spatial filtering versus background manipulation to reduce overprediction or underprediction in specific areas. Results Spatial filtering minimized omission errors (false negatives) and commission errors (false positives). We recommend that when sample size is insufficient to allow spatial filtering, manipulation of the background dataset is preferable to not correcting for sampling bias, although predictions were comparatively weak and commission errors increased. Main Conclusions We conclude that a substantial improvement in the quality of model predictions can be achieved if uneven sampling effort is taken into account, thereby improving the efficacy of species conservation planning.

@article{doi101111ddi12096,
    author = "Kramer‐Schadt, Stephanie and Niedballa, Jürgen and Pilgrim, John D. and Schröder, Boris and Lindenborn, Jana and Reinfelder, Vanessa and Stillfried, Milena and Heckmann, Ilja and Scharf, Anne K. and Augeri, Dave M. and Cheyne, Susan M. and Hearn, Andrew J. and Ross, Joanna and Macdonald, David W. and Mathai, John and Eaton, James A. and Marshall, Andrew J. and Semiadi, Gono and Rustam, Rustam and Bernard, Henry and Alfred, Raymond and Samejima, Hiromitsu and Duckworth, J. W. and Breitenmoser‐Würsten, Christine and Belant, Jerrold L. and Hofer, Heribert and Wilting, Andreas",
    title = "The importance of correcting for sampling bias in MaxEnt species distribution models",
    year = "2013",
    journal = "Diversity and Distributions",
    abstract = "Abstract Aim Advancement in ecological methods predicting species distributions is a crucial precondition for deriving sound management actions. Maximum entropy (MaxEnt) models are a popular tool to predict species distributions, as they are considered able to cope well with sparse, irregularly sampled data and minor location errors. Although a fundamental assumption of MaxEnt is that the entire area of interest has been systematically sampled, in practice, MaxEnt models are usually built from occurrence records that are spatially biased towards better‐surveyed areas. Two common, yet not compared, strategies to cope with uneven sampling effort are spatial filtering of occurrence data and background manipulation using environmental data with the same spatial bias as occurrence data. We tested these strategies using simulated data and a recently collated dataset on Malay civet V iverra tangalunga in Borneo. Location Borneo, Southeast Asia. Methods We collated 504 occurrence records of Malay civets from Borneo of which 291 records were from 2001 to 2011 and used them in the MaxEnt analysis (baseline scenario) together with 25 environmental input variables. We simulated datasets for two virtual species (similar to a range‐restricted highland and a lowland species) using the same number of records for model building. As occurrence records were biased towards north‐eastern Borneo, we investigated the efficacy of spatial filtering versus background manipulation to reduce overprediction or underprediction in specific areas. Results Spatial filtering minimized omission errors (false negatives) and commission errors (false positives). We recommend that when sample size is insufficient to allow spatial filtering, manipulation of the background dataset is preferable to not correcting for sampling bias, although predictions were comparatively weak and commission errors increased. Main Conclusions We conclude that a substantial improvement in the quality of model predictions can be achieved if uneven sampling effort is taken into account, thereby improving the efficacy of species conservation planning.",
    url = "https://doi.org/10.1111/ddi.12096",
    doi = "10.1111/ddi.12096",
    openalex = "W2109735604",
    references = "doi101111j09067590200503957x, doi101146annurevecolsys110308120159"
}

Suggestions of collapse in small herbivore cycles since the 1980s have raised concerns about the loss of essential ecosystem functions. Whether such phenomena are general and result from extrinsic environmental changes or from intrinsic process stochasticity is currently unknown. Using a large compilation of time series of vole abundances, we demonstrate consistent cycle amplitude dampening associated with a reduction in winter population growth, although regulatory processes responsible for cyclicity have not been lost. The underlying syndrome of change throughout Europe and grass-eating vole species suggests a common climatic driver. Increasing intervals of low-amplitude small herbivore population fluctuations are expected in the future, and these may have cascading impacts on trophic webs across ecosystems.

@article{doi101126science1228992,
    author = "Cornulier, Thomas and Yoccoz, Nigel G. and Bretagnolle, Vincent and Brommer, Jon E. and Butet, Alain and Ecke, Frauke and Elston, David A. and Framstad, Erik and Henttonen, Heikki and Hörnfeldt, Birger and Huitu, Otso and Imholt, Christian and Ims, Rolf A. and Jacob, Jens and Jędrzejewska, Bogumiła and Millon, Alexandre and Petty, Steve J. and Pietiäinen, Hannu and Tkadlec, Emil and Zub, Karol and Lambin, Xavier",
    title = "Europe-Wide Dampening of Population Cycles in Keystone Herbivores",
    year = "2013",
    journal = "Science",
    abstract = "Suggestions of collapse in small herbivore cycles since the 1980s have raised concerns about the loss of essential ecosystem functions. Whether such phenomena are general and result from extrinsic environmental changes or from intrinsic process stochasticity is currently unknown. Using a large compilation of time series of vole abundances, we demonstrate consistent cycle amplitude dampening associated with a reduction in winter population growth, although regulatory processes responsible for cyclicity have not been lost. The underlying syndrome of change throughout Europe and grass-eating vole species suggests a common climatic driver. Increasing intervals of low-amplitude small herbivore population fluctuations are expected in the future, and these may have cascading impacts on trophic webs across ecosystems.",
    url = "https://doi.org/10.1126/science.1228992",
    doi = "10.1126/science.1228992",
    openalex = "W2081019150",
    references = "doi1023073546809"
}

To investigate the comparative abilities of six different bioclimatic models in an independent area, utilizing the distribution of eight different species available at a global scale and in Australia. Global scale and Australia. We tested a variety of bioclimatic models for eight different plant species employing five discriminatory correlative species distribution models (SDMs) including Generalized Linear Model (GLM), MaxEnt, Random Forest (RF), Boosted Regression Tree (BRT), Bioclim, together with CLIMEX (CL) as a mechanistic niche model. These models were fitted using a training dataset of available global data, but with the exclusion of Australian locations. The capabilities of these techniques in projecting suitable climate, based on independent records for these species in Australia, were compared. Thus, Australia is not used to calibrate the models and therefore it is as an independent area regarding geographic locations. To assess and compare performance, we utilized the area under the receiver operating characteristic (ROC) curves (AUC), true skill statistic (TSS), and fractional predicted areas for all SDMs. In addition, we assessed satisfactory agreements between the outputs of the six different bioclimatic models, for all eight species in Australia. The modeling method impacted on potential distribution predictions under current climate. However, the utilization of sensitivity and the fractional predicted areas showed that GLM, MaxEnt, Bioclim, and CL had the highest sensitivity for Australian climate conditions. Bioclim calculated the highest fractional predicted area of an independent area, while RF and BRT were poor. For many applications, it is difficult to decide which bioclimatic model to use. This research shows that variable results are obtained using different SDMs in an independent area. This research also shows that the SDMs produce different results for different species; for example, Bioclim may not be good for one species but works better for other species. Also, when projecting a "large" number of species into novel environments or in an independent area, the selection of the "best" model/technique is often less reliable than an ensemble modeling approach. In addition, it is vital to understand the accuracy of SDMs' predictions. Further, while TSS, together with fractional predicted areas, are appropriate tools for the measurement of accuracy between model results, particularly when undertaking projections on an independent area, AUC has been proved not to be. Our study highlights that each one of these models (CL, Bioclim, GLM, MaxEnt, BRT, and RF) provides slightly different results on projections and that it may be safer to use an ensemble of models.

@article{doi101002ece32332,
    author = "Shabani, Farzin and Kumar, Lalit and Ahmadi, Mohsen",
    title = "A comparison of absolute performance of different correlative and mechanistic species distribution models in an independent area",
    year = "2016",
    journal = "Ecology and Evolution",
    abstract = {To investigate the comparative abilities of six different bioclimatic models in an independent area, utilizing the distribution of eight different species available at a global scale and in Australia. Global scale and Australia. We tested a variety of bioclimatic models for eight different plant species employing five discriminatory correlative species distribution models (SDMs) including Generalized Linear Model (GLM), MaxEnt, Random Forest (RF), Boosted Regression Tree (BRT), Bioclim, together with CLIMEX (CL) as a mechanistic niche model. These models were fitted using a training dataset of available global data, but with the exclusion of Australian locations. The capabilities of these techniques in projecting suitable climate, based on independent records for these species in Australia, were compared. Thus, Australia is not used to calibrate the models and therefore it is as an independent area regarding geographic locations. To assess and compare performance, we utilized the area under the receiver operating characteristic (ROC) curves (AUC), true skill statistic (TSS), and fractional predicted areas for all SDMs. In addition, we assessed satisfactory agreements between the outputs of the six different bioclimatic models, for all eight species in Australia. The modeling method impacted on potential distribution predictions under current climate. However, the utilization of sensitivity and the fractional predicted areas showed that GLM, MaxEnt, Bioclim, and CL had the highest sensitivity for Australian climate conditions. Bioclim calculated the highest fractional predicted area of an independent area, while RF and BRT were poor. For many applications, it is difficult to decide which bioclimatic model to use. This research shows that variable results are obtained using different SDMs in an independent area. This research also shows that the SDMs produce different results for different species; for example, Bioclim may not be good for one species but works better for other species. Also, when projecting a "large" number of species into novel environments or in an independent area, the selection of the "best" model/technique is often less reliable than an ensemble modeling approach. In addition, it is vital to understand the accuracy of SDMs' predictions. Further, while TSS, together with fractional predicted areas, are appropriate tools for the measurement of accuracy between model results, particularly when undertaking projections on an independent area, AUC has been proved not to be. Our study highlights that each one of these models (CL, Bioclim, GLM, MaxEnt, BRT, and RF) provides slightly different results on projections and that it may be safer to use an ensemble of models.},
    url = "https://doi.org/10.1002/ece3.2332",
    doi = "10.1002/ece3.2332",
    openalex = "W2476764914",
    references = "doi101111j13652699201002416x"
}

Species distribution modeling is a valuable tool with many applications across ecology and evolutionary biology. The selection of biologically meaningful environmental variables that determine relative habitat suitability is a crucial aspect of the modeling pipeline. The 19 bioclimatic variables from WorldClim are frequently employed, primarily because they are easily accessible and available globally for past, present and future climate scenarios. Yet, the availability of relatively few other comparable environmental datasets potentially limits our ability to select appropriate variables that will most successfully characterize a species’ distribution. We identified a set of 16 climatic and two topographic variables in the literature, which we call the ENVIREM dataset, many of which are likely to have direct relevance to ecological or physiological processes determining species distributions. We generated this set of variables at the same resolutions as WorldClim, for the present, mid‐Holocene, and Last Glacial Maximum (LGM). For 20 North American vertebrate species, we then assessed whether including the ENVIREM variables led to improved species distribution models compared to models using only the existing WorldClim variables. We found that including the ENVIREM dataset in the pool of variables to select from led to substantial improvements in niche modeling performance in 13 out of 20 species. We also show that, when comparing models constructed with different environmental variables, differences in projected distributions were often greater in the LGM than in the present. These variables are worth consideration in species distribution modeling applications, especially as many of the variables have direct links to processes important for species ecology. We provide these variables for download at multiple resolutions and for several time periods at envirem.github.io. Furthermore, we have written the ‘envirem’ R package to facilitate the generation of these variables from other input datasets.

@article{doi101111ecog02880,
    author = "Title, Pascal O. and Bemmels, Jordan B.",
    title = "ENVIREM: an expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling",
    year = "2017",
    journal = "Ecography",
    abstract = "Species distribution modeling is a valuable tool with many applications across ecology and evolutionary biology. The selection of biologically meaningful environmental variables that determine relative habitat suitability is a crucial aspect of the modeling pipeline. The 19 bioclimatic variables from WorldClim are frequently employed, primarily because they are easily accessible and available globally for past, present and future climate scenarios. Yet, the availability of relatively few other comparable environmental datasets potentially limits our ability to select appropriate variables that will most successfully characterize a species’ distribution. We identified a set of 16 climatic and two topographic variables in the literature, which we call the ENVIREM dataset, many of which are likely to have direct relevance to ecological or physiological processes determining species distributions. We generated this set of variables at the same resolutions as WorldClim, for the present, mid‐Holocene, and Last Glacial Maximum (LGM). For 20 North American vertebrate species, we then assessed whether including the ENVIREM variables led to improved species distribution models compared to models using only the existing WorldClim variables. We found that including the ENVIREM dataset in the pool of variables to select from led to substantial improvements in niche modeling performance in 13 out of 20 species. We also show that, when comparing models constructed with different environmental variables, differences in projected distributions were often greater in the LGM than in the present. These variables are worth consideration in species distribution modeling applications, especially as many of the variables have direct links to processes important for species ecology. We provide these variables for download at multiple resolutions and for several time periods at envirem.github.io. Furthermore, we have written the ‘envirem’ R package to facilitate the generation of these variables from other input datasets.",
    url = "https://doi.org/10.1111/ecog.02880",
    doi = "10.1111/ecog.02880",
    openalex = "W2953259387",
    references = "doi101111j13652699201002416x"
}

Critical evaluation of the adequacy of ecological models is urgently needed to enhance their utility in developing theory and enabling environmental managers and policymakers to make informed decisions. Poorly supported management can have detrimental, costly or irreversible impacts on the environment and society. Here, we examine common issues in ecological modelling and suggest criteria for improving modelling frameworks. An appropriate level of process description is crucial to constructing the best possible model, given the available data and understanding of ecological structures. Model details unsupported by data typically lead to over parameterisation and poor model performance. Conversely, a lack of mechanistic details may limit a model's ability to predict ecological systems' responses to management. Ecological studies that employ models should follow a set of model adequacy assessment protocols that include: asking a series of critical questions regarding state and control variable selection, the determinacy of data, and the sensitivity and validity of analyses. We also need to improve model elaboration, refinement and coarse graining procedures to better understand the relevancy and adequacy of our models and the role they play in advancing theory, improving hind and forecasting, and enabling problem solving and management.

@article{doi101111ele12893,
    author = "Getz, Wayne M. and Marshall, Charles R. and Carlson, Colin J. and Giuggioli, Luca and Ryan, Sadie J. and Romañach, Stephanie S. and Boettiger, Carl and Chamberlain, Samuel D. and Larsen, Laurel G. and D’Odorico, Paolo and O’Sullivan, David",
    title = "Making ecological models adequate",
    year = "2017",
    journal = "Ecology Letters",
    abstract = "Critical evaluation of the adequacy of ecological models is urgently needed to enhance their utility in developing theory and enabling environmental managers and policymakers to make informed decisions. Poorly supported management can have detrimental, costly or irreversible impacts on the environment and society. Here, we examine common issues in ecological modelling and suggest criteria for improving modelling frameworks. An appropriate level of process description is crucial to constructing the best possible model, given the available data and understanding of ecological structures. Model details unsupported by data typically lead to over parameterisation and poor model performance. Conversely, a lack of mechanistic details may limit a model's ability to predict ecological systems' responses to management. Ecological studies that employ models should follow a set of model adequacy assessment protocols that include: asking a series of critical questions regarding state and control variable selection, the determinacy of data, and the sensitivity and validity of analyses. We also need to improve model elaboration, refinement and coarse graining procedures to better understand the relevancy and adequacy of our models and the role they play in advancing theory, improving hind and forecasting, and enabling problem solving and management.",
    url = "https://doi.org/10.1111/ele.12893",
    doi = "10.1111/ele.12893",
    openalex = "W2778023581",
    references = "doi1010160021999176900413, doi101016jecolmodel200503026, doi101021j100540a008, doi101038nature02121, doi101046j13652656200000445x, doi101111j14610248200400608x, doi101137s0036144500371907, doi101143ptp33423, doi101214ss1009213726, doi1023071941447, doi107551mitpress32060010001"
}

We formulate a general impulsive reaction-diffusion equation model to describe the population dynamics of species with distinct reproductive and dispersal stages. The seasonal reproduction is modeled by a discrete-time map, while the dispersal is modeled by a reaction-diffusion partial differential equation. Study of this model requires a simultaneous analysis of the differential equation and the recurrence relation. When boundary conditions are hostile we provide critical domain results showing how extinction versus persistence of the species arises, depending on the size and geometry of the domain. We show that there exists an extreme volume size such that if $|\Omega|$ falls below this size the species is driven extinct, regardless of the geometry of the domain. To construct such extreme volume sizes and critical domain sizes, we apply Schwarz symmetrization rearrangement arguments, the classical Rayleigh--Faber--Krahn inequality, and the spectrum of uniformly elliptic operators. The critical domain results provide qualitative insight regarding long-term dynamics for the model. Last, we provide applications of our main results to certain biological reaction-diffusion models regarding marine reserve, terrestrial reserve, insect pest outbreak, and population subject to climate change.

@article{doi10113715m1046666,
    author = "Fazly, Mostafa and Lewis, Mark A. and Wang, Hao",
    title = "On Impulsive Reaction-Diffusion Models in Higher Dimensions",
    year = "2017",
    journal = "SIAM Journal on Applied Mathematics",
    abstract = "We formulate a general impulsive reaction-diffusion equation model to describe the population dynamics of species with distinct reproductive and dispersal stages. The seasonal reproduction is modeled by a discrete-time map, while the dispersal is modeled by a reaction-diffusion partial differential equation. Study of this model requires a simultaneous analysis of the differential equation and the recurrence relation. When boundary conditions are hostile we provide critical domain results showing how extinction versus persistence of the species arises, depending on the size and geometry of the domain. We show that there exists an extreme volume size such that if $|\Omega|$ falls below this size the species is driven extinct, regardless of the geometry of the domain. To construct such extreme volume sizes and critical domain sizes, we apply Schwarz symmetrization rearrangement arguments, the classical Rayleigh--Faber--Krahn inequality, and the spectrum of uniformly elliptic operators. The critical domain results provide qualitative insight regarding long-term dynamics for the model. Last, we provide applications of our main results to certain biological reaction-diffusion models regarding marine reserve, terrestrial reserve, insect pest outbreak, and population subject to climate change.",
    url = "https://doi.org/10.1137/15m1046666",
    doi = "10.1137/15m1046666",
    openalex = "W2963089269",
    references = "doi101016jmbs200806008"
}

@article{doi101016jtree201808001,
    author = "Yates, Katherine L. and Bouchet, Phil J. and Caley, M. Julian and Mengersen, Kerrie and Randin, Christophe F. and Parnell, Stephen and Fielding, Alan H. and Bamford, Andrew J. and Ban, Stephen S. and Barbosa, A. Márcia and Dormann, Carsten F. and Elith, Jane and Embling, Clare B. and Ervin, Gary N. and Fisher, Rebecca and Gould, Susan and Graf, Roland Felix and Gregr, Edward J. and Halpin, Patrick N. and Heikkinen, Risto K. and Heinänen, Stefan and Jones, Alice R. and Krishnakumar, P K and Lauria, Valentina and Lozano‐Montes, Hector and Mannocci, Laura and Mellin, Camille and Mesgaran, Mohsen B. and Amat, Elena Moreno and Mormede, Sophie and Novaczek, Emilie and Oppel, Steffen and Crespo, Guillermo Ortuño and Peterson, A. Townsend and Rapacciuolo, Giovanni and Roberts, Jason J. and Ross, Rebecca E. and Scales, Kylie L. and Schoeman, David S. and Snelgrove, Paul V. R. and Sundblad, Göran and Thuiller, Wilfried and Torres, Leigh G. and Verbruggen, Heroen and Wang, Lifei and Wenger, Seth J. and Whittingham, Mark J. and Zharikov, Yuri and Zurell, Damaris and Sequeira, Ana M. M.",
    title = "Outstanding Challenges in the Transferability of Ecological Models",
    year = "2018",
    journal = "Trends in Ecology \& Evolution",
    url = "https://doi.org/10.1016/j.tree.2018.08.001",
    doi = "10.1016/j.tree.2018.08.001",
    openalex = "W2889489537",
    references = "doi101016jpalaeo201107021, doi101111j16000587200905810x"
}

Species distribution models (SDMs) have rapidly evolved into one of the most widely used tools to answer a broad range of ecological questions, from the effects of climate change to challenges for species management. Current SDMs and their predictions under anthropogenic climate change are, however, often based on free‐air or synoptic temperature conditions with a coarse resolution, and thus fail to capture apparent temperature (cf. microclimate) experienced by living organisms within their habitats. Yet microclimate operates as soon as a habitat can be characterized by a vertical component (e.g. forests, mountains, or cities) or by horizontal variation in surface cover. The mismatch between how we usually express climate (cf. coarse‐grained free‐air conditions) and the apparent microclimatic conditions that living organisms experience has only recently been acknowledged in SDMs, yet several studies have already made considerable progress in tackling this problem from different angles. In this review, we summarize the currently available methods to obtain meaningful microclimatic data for use in distribution modelling. We discuss the issue of extent and resolution, and propose an integrated framework using a selection of appropriately‐placed sensors in combination with both the detailed measurements of the habitat 3D structure, for example derived from digital elevation models or airborne laser scanning, and the long‐term records of free‐air conditions from weather stations. As such, we can obtain microclimatic data with a relevant spatiotemporal resolution and extent to dynamically model current and future species distributions.

@article{doi101111ecog03947,
    author = "Lembrechts, Jonas J. and Nijs, Ivan and Lenoir, Jonathan",
    title = "Incorporating microclimate into species distribution models",
    year = "2018",
    journal = "Ecography",
    abstract = "Species distribution models (SDMs) have rapidly evolved into one of the most widely used tools to answer a broad range of ecological questions, from the effects of climate change to challenges for species management. Current SDMs and their predictions under anthropogenic climate change are, however, often based on free‐air or synoptic temperature conditions with a coarse resolution, and thus fail to capture apparent temperature (cf. microclimate) experienced by living organisms within their habitats. Yet microclimate operates as soon as a habitat can be characterized by a vertical component (e.g. forests, mountains, or cities) or by horizontal variation in surface cover. The mismatch between how we usually express climate (cf. coarse‐grained free‐air conditions) and the apparent microclimatic conditions that living organisms experience has only recently been acknowledged in SDMs, yet several studies have already made considerable progress in tackling this problem from different angles. In this review, we summarize the currently available methods to obtain meaningful microclimatic data for use in distribution modelling. We discuss the issue of extent and resolution, and propose an integrated framework using a selection of appropriately‐placed sensors in combination with both the detailed measurements of the habitat 3D structure, for example derived from digital elevation models or airborne laser scanning, and the long‐term records of free‐air conditions from weather stations. As such, we can obtain microclimatic data with a relevant spatiotemporal resolution and extent to dynamically model current and future species distributions.",
    url = "https://doi.org/10.1111/ecog.03947",
    doi = "10.1111/ecog.03947",
    openalex = "W2892846357",
    references = "doi101002joc1276, doi101002joc5086, doi101038sdata2017122, doi101038sdata2017191, doi101109jproc20102043918, doi101111j14610248200500792x, doi101111j14668238201100686x, doi101126science1150195, doi101146annurevecolsys110308120159, doi101146annureves15110184002141, doi105167uzh139381, lenoir2017climatic"
}

Understanding and predicting the ecological consequences of different management alternatives is becoming increasingly important to support environmental management decisions. Ecological models could contribute to such predictions, but in the past this was often not the case. Ecological models are often developed within research projects but are rarely used for practical applications. In this synthesis paper, we discuss how to strengthen the role of ecological modeling in supporting environmental management decisions with a focus on methodological aspects. We address mainly ecological modellers but also potential users of modeling results. Various modeling approaches can be used to predict the response of ecosystems to anthropogenic interventions, including mechanistic models, statistical models, and machine learning approaches. Regardless of the chosen approach, we outline how to better align the modeling to the decision making process, and identify six requirements that we believe are important to increase the usefulness of ecological models for management support, especially if management decisions need to be justified to the public. These cover: (i) a mechanistic understanding regarding causality, (ii) alignment of model input and output with the management decision, (iii) appropriate spatial and temporal resolutions, (iv) uncertainty quantification, (v) sufficient predictive performance, and (vi) transparent communication. We discuss challenges and synthesize suggestions for addressing these points.

@article{doi101016jecolmodel2019108784,
    author = "Schuwirth, Nele and Borgwardt, Florian and Domisch, Sami and Friedrichs‐Manthey, Martin and Kattwinkel, Mira and Kneis, David and Kuemmerlen, Mathias and Langhans, Simone D. and Martínez‐López, Javier and Vermeiren, Peter",
    title = "How to make ecological models useful for environmental management",
    year = "2019",
    journal = "Ecological Modelling",
    abstract = "Understanding and predicting the ecological consequences of different management alternatives is becoming increasingly important to support environmental management decisions. Ecological models could contribute to such predictions, but in the past this was often not the case. Ecological models are often developed within research projects but are rarely used for practical applications. In this synthesis paper, we discuss how to strengthen the role of ecological modeling in supporting environmental management decisions with a focus on methodological aspects. We address mainly ecological modellers but also potential users of modeling results. Various modeling approaches can be used to predict the response of ecosystems to anthropogenic interventions, including mechanistic models, statistical models, and machine learning approaches. Regardless of the chosen approach, we outline how to better align the modeling to the decision making process, and identify six requirements that we believe are important to increase the usefulness of ecological models for management support, especially if management decisions need to be justified to the public. These cover: (i) a mechanistic understanding regarding causality, (ii) alignment of model input and output with the management decision, (iii) appropriate spatial and temporal resolutions, (iv) uncertainty quantification, (v) sufficient predictive performance, and (vi) transparent communication. We discuss challenges and synthesize suggestions for addressing these points.",
    url = "https://doi.org/10.1016/j.ecolmodel.2019.108784",
    doi = "10.1016/j.ecolmodel.2019.108784",
    openalex = "W2971429758",
    references = "doi101111ele12893"
}

@article{doi101016jeswa201905028,
    author = "Speiser, Jaime L. and Miller, Michael E. and Tooze, Janet A. and Ip, Edward H.",
    title = "A comparison of random forest variable selection methods for classification prediction modeling",
    year = "2019",
    journal = "Expert Systems with Applications",
    url = "https://doi.org/10.1016/j.eswa.2019.05.028",
    doi = "10.1016/j.eswa.2019.05.028",
    openalex = "W2944954104",
    references = "doi1018637jssv036i11"
}

Abstract. Evapotranspiration (ET) is critical in linking global water, carbon and energy cycles. However, direct measurement of global terrestrial ET is not feasible. Here, we first reviewed the basic theory and state-of-the-art approaches for estimating global terrestrial ET, including remote-sensing-based physical models, machine-learning algorithms and land surface models (LSMs). We then utilized 4 remote-sensing-based physical models, 2 machine-learning algorithms and 14 LSMs to analyze the spatial and temporal variations in global terrestrial ET. The results showed that the ensemble means of annual global terrestrial ET estimated by these three categories of approaches agreed well, with values ranging from 589.6 mm yr−1 (6.56×104 km3 yr−1) to 617.1 mm yr−1 (6.87×104 km3 yr−1). For the period from 1982 to 2011, both the ensembles of remote-sensing-based physical models and machine-learning algorithms suggested increasing trends in global terrestrial ET (0.62 mm yr−2 with a significance level of p 0.05), although many of the individual LSMs reproduced an increasing trend. Nevertheless, all 20 models used in this study showed that anthropogenic Earth greening had a positive role in increasing terrestrial ET. The concurrent small interannual variability, i.e., relative stability, found in all estimates of global terrestrial ET, suggests that a potential planetary boundary exists in regulating global terrestrial ET, with the value of this boundary being around 600 mm yr−1. Uncertainties among approaches were identified in specific regions, particularly in the Amazon Basin and arid/semiarid regions. Improvements in parameterizing water stress and canopy dynamics, the utilization of new available satellite retrievals and deep-learning methods, and model–data fusion will advance our predictive understanding of global terrestrial ET.

@article{doi105194hess2414852020,
    author = "Pan, Shufen and Pan, Naiqing and Tian, Hanqin and Friedlingstein, Pierre and Sitch, Stephen and Shi, Hao and Arora, Vivek K. and Haverd, Vanessa and Jain, Atul K. and Kato, Etsushi and Lienert, Sebastian and Lombardozzi, Danica and Nabel, Julia E. M. S. and Ottlé, Catherine and Poulter, Benjamin and Zaehle, Sönke and Running, Steven W.",
    title = "Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling",
    year = "2020",
    journal = "Hydrology and earth system sciences",
    abstract = "Abstract. Evapotranspiration (ET) is critical in linking global water, carbon and energy cycles. However, direct measurement of global terrestrial ET is not feasible. Here, we first reviewed the basic theory and state-of-the-art approaches for estimating global terrestrial ET, including remote-sensing-based physical models, machine-learning algorithms and land surface models (LSMs). We then utilized 4 remote-sensing-based physical models, 2 machine-learning algorithms and 14 LSMs to analyze the spatial and temporal variations in global terrestrial ET. The results showed that the ensemble means of annual global terrestrial ET estimated by these three categories of approaches agreed well, with values ranging from 589.6 mm yr−1 (6.56×104 km3 yr−1) to 617.1 mm yr−1 (6.87×104 km3 yr−1). For the period from 1982 to 2011, both the ensembles of remote-sensing-based physical models and machine-learning algorithms suggested increasing trends in global terrestrial ET (0.62 mm yr−2 with a significance level of p 0.05), although many of the individual LSMs reproduced an increasing trend. Nevertheless, all 20 models used in this study showed that anthropogenic Earth greening had a positive role in increasing terrestrial ET. The concurrent small interannual variability, i.e., relative stability, found in all estimates of global terrestrial ET, suggests that a potential planetary boundary exists in regulating global terrestrial ET, with the value of this boundary being around 600 mm yr−1. Uncertainties among approaches were identified in specific regions, particularly in the Amazon Basin and arid/semiarid regions. Improvements in parameterizing water stress and canopy dynamics, the utilization of new available satellite retrievals and deep-learning methods, and model–data fusion will advance our predictive understanding of global terrestrial ET.",
    url = "https://doi.org/10.5194/hess-24-1485-2020",
    doi = "10.5194/hess-24-1485-2020",
    openalex = "W2969713210"
}

@article{doi101007s00477021021524,
    author = "Alnahit, Ali O. and Mishra, Ashok K. and Khan, Abdul A.",
    title = "Stream water quality prediction using boosted regression tree and random forest models",
    year = "2022",
    journal = "Stochastic Environmental Research and Risk Assessment",
    url = "https://doi.org/10.1007/s00477-021-02152-4",
    doi = "10.1007/s00477-021-02152-4",
    openalex = "W4205243560"
}

Advances in remote sensing imagery and machine learning applications unlock the potential for developing algorithms for species classification at the level of individual tree crowns at unprecedented scales. However, most approaches to date focus on site-specific applications and a small number of taxonomic groups. Little is known about how well these approaches generalize across broader geographic areas and ecosystems. Leveraging field surveys and hyperspectral remote sensing data from the National Ecological Observatory Network (NEON), we developed a continental-extent model for tree species classification that can be applied to the network, including a wide range of US terrestrial ecosystems. We compared the performance of a model trained with data from 27 NEON sites to models trained with data from each individual site, evaluating advantages and challenges posed by training species classifiers at the US scale. We evaluated the effect of geographic location, topography, and ecological conditions on the accuracy and precision of species predictions (72 out of 77 species). On average, the general model resulted in good overall classification accuracy (micro-F1 score), with better accuracy than site-specific classifiers (average individual tree level accuracy of 0.77 for the general model and 0.70 for site-specific models). Aggregating species to the genus-level increased accuracy to 0.83. Regions with more species exhibited lower classification accuracy. Predicted species were more likely to be confused with congeneric and co-occurring species and confusion was highest for trees with structural damage and in complex closed-canopy forests. The model produced accurate estimates of uncertainty, correctly identifying trees where confusion was likely. Using only data from NEON, this single integrated classifier can make predictions for 20% of all tree species found in forest ecosystems across the entire US, which make up to roughly 90% of the upper canopy of the studied ecosystems. This suggests the potential for integrating information from multiple datasets and locations to develop broad scale general models for species classification from hyperspectral imaging.

@article{doi101016jrse2022113264,
    author = "Marconi, Sergio and Weinstein, Ben and Zou, Sheng and Bohlman, Stephanie and Zare, Alina and Singh, Aditya and Stewart, Dylan and Harmon, Ira and Steinkraus, Ashley and White, Ethan P.",
    title = "Continental-scale hyperspectral tree species classification in the United States National Ecological Observatory Network",
    year = "2022",
    journal = "Remote Sensing of Environment",
    abstract = "Advances in remote sensing imagery and machine learning applications unlock the potential for developing algorithms for species classification at the level of individual tree crowns at unprecedented scales. However, most approaches to date focus on site-specific applications and a small number of taxonomic groups. Little is known about how well these approaches generalize across broader geographic areas and ecosystems. Leveraging field surveys and hyperspectral remote sensing data from the National Ecological Observatory Network (NEON), we developed a continental-extent model for tree species classification that can be applied to the network, including a wide range of US terrestrial ecosystems. We compared the performance of a model trained with data from 27 NEON sites to models trained with data from each individual site, evaluating advantages and challenges posed by training species classifiers at the US scale. We evaluated the effect of geographic location, topography, and ecological conditions on the accuracy and precision of species predictions (72 out of 77 species). On average, the general model resulted in good overall classification accuracy (micro-F1 score), with better accuracy than site-specific classifiers (average individual tree level accuracy of 0.77 for the general model and 0.70 for site-specific models). Aggregating species to the genus-level increased accuracy to 0.83. Regions with more species exhibited lower classification accuracy. Predicted species were more likely to be confused with congeneric and co-occurring species and confusion was highest for trees with structural damage and in complex closed-canopy forests. The model produced accurate estimates of uncertainty, correctly identifying trees where confusion was likely. Using only data from NEON, this single integrated classifier can make predictions for 20\% of all tree species found in forest ecosystems across the entire US, which make up to roughly 90\% of the upper canopy of the studied ecosystems. This suggests the potential for integrating information from multiple datasets and locations to develop broad scale general models for species classification from hyperspectral imaging.",
    url = "https://doi.org/10.1016/j.rse.2022.113264",
    doi = "10.1016/j.rse.2022.113264",
    openalex = "W4297920830"
}

OBJECTIVE: To investigate whether structural aspects of primary care units (PCUs) and the work processes of primary care teams are associated with the rate of hospitalizations for primary care-sensitive conditions (HPCSC) in children younger than 5 years of age in Brazil. METHOD: For this longitudinal ecological study, secondary data were obtained from the Brazilian Hospital Information System and from three cycles of the National Program for Access and Quality Improvement in Primary Care (PMAQ-AB) (2012, 2014, 2017/2018). The analysis included 42 916 PCUs. A multilevel random intercept model with fixed slope was used. In the first level, the outcome (HPCSC rates) and explanatory variables (structure and process indicators) aggregated by PCU were analyzed. Social determinants (represented by a stratification criterion combining municipality population and health care management indicators) were entered in the second level. The t test with Bonferroni correction was used to compare indicator means between regions, and multilevel linear regression was used to estimate the correlation coefficients. RESULTS: The HPCSC rate in children younger than 5 years was 62.78/100 thousand population per estimated PCU coverage area. A direct association with the outcome was observed for: participation in one or more PMAQ-AB cycles; team planning; special hours; dedicated pediatric care area; and availability of vaccines. Equipment, materials, supplies, and being a small or medium-size municipality were inversely associated with HPCSC. CONCLUSIONS: HPCSC rates in children below 5 years of age may potentially be reduced through improvements in PCU structure and process indicators and in municipal social determinants.

@article{doi1026633rpsp202263,
    author = "Dos Santos, Danilo Marcelo Araujo and Alves, Cláudia Maria Coelho and Rocha, Thiago Augusto Hernandes and da Silva, Núbia Cristina and Queiroz, Rejane Christine de Sousa and Pinho, Judith Rafaelle Oliveira and Lopes, Clarissa Galvão da Silva and Thomaz, Erika Barbara Abreu Fonseca",
    title = "[Factors associated with hospitalizations for primary care-sensitive conditions in Brazil: an ecological studyFactores asociados a las hospitalizaciones infantiles por afecciones que podrían tratarse en la atención primaria en Brasil: estudio ecológico].",
    year = "2022",
    journal = "Revista panamericana de salud publica = Pan American journal of public health",
    abstract = "OBJECTIVE: To investigate whether structural aspects of primary care units (PCUs) and the work processes of primary care teams are associated with the rate of hospitalizations for primary care-sensitive conditions (HPCSC) in children younger than 5 years of age in Brazil. METHOD: For this longitudinal ecological study, secondary data were obtained from the Brazilian Hospital Information System and from three cycles of the National Program for Access and Quality Improvement in Primary Care (PMAQ-AB) (2012, 2014, 2017/2018). The analysis included 42 916 PCUs. A multilevel random intercept model with fixed slope was used. In the first level, the outcome (HPCSC rates) and explanatory variables (structure and process indicators) aggregated by PCU were analyzed. Social determinants (represented by a stratification criterion combining municipality population and health care management indicators) were entered in the second level. The t test with Bonferroni correction was used to compare indicator means between regions, and multilevel linear regression was used to estimate the correlation coefficients. RESULTS: The HPCSC rate in children younger than 5 years was 62.78/100 thousand population per estimated PCU coverage area. A direct association with the outcome was observed for: participation in one or more PMAQ-AB cycles; team planning; special hours; dedicated pediatric care area; and availability of vaccines. Equipment, materials, supplies, and being a small or medium-size municipality were inversely associated with HPCSC. CONCLUSIONS: HPCSC rates in children below 5 years of age may potentially be reduced through improvements in PCU structure and process indicators and in municipal social determinants.",
    url = "https://pmc.ncbi.nlm.nih.gov/articles/PMC9426956/",
    doi = "10.26633/RPSP.2022.63",
    openalex = "W4293579593",
    pmcid = "PMC9426956",
    pmid = "36060205",
    references = "doi101001jama260121743, doi101136jech568588, doi101186s129130194098x, doi101371journalpone0251905, doi101590010311042018s104, doi10159014138123201521925002015, doi101590141381232018231232122016, doi105123s167949742016000400010, doi105123s167949742017000100018, doi105123s167949742017000200006"
}

Small mammal species play an important role influencing vegetation primary productivity and plant species composition, seed dispersal, soil structure, and as predator and/or prey species. Species which experience population dynamics cycles can, at high population phases, heavily impact agricultural sectors and promote rodent-borne disease transmission. To better understand the drivers behind small mammal distributions and abundances, and how these differ for individual species, it is necessary to characterise landscape variables important for the life cycles of the species in question. In this study, a suite of Earth observation derived metrics quantifying landscape characteristics and dynamics, and in-situ small mammal trapline and transect survey data, are used to generate random forest species distribution models for nine small mammal species for study sites in Narati, China and Sary Mogul, Kyrgyzstan. These species distribution models identify the important landscape proxy variables driving species abundance and distributions, in turn identifying the optimal conditions for each species. The observed relationships differed between species, with the number of landscape proxy variables identified as important for each species ranging from 3 for Microtus gregalis at Sary Mogul, to 26 for Ellobius tancrei at Narati. Results indicate that grasslands were predicted to hold higher abundances of Microtus obscurus, E. tancrei and Marmota baibacina, forest areas hold higher abundances of Myodes centralis and Sorex asper, with mixed forest-grassland boundary areas and areas close to watercourses predicted to hold higher abundances of Apodemus uralensis and Sicista tianshanica. Localised variability in vegetation and wetness conditions, as well as presence of certain habitat types, are also shown to influence these small mammal species abundances. Predictive application of the Random Forest (RF) models identified spatial hot-spots of high abundance, with model validation producing R2 values between 0.670 for M. gregalis transect data at Sary Mogul to 0.939 for E. tancrei transect data at Narati. This enhances previous work whereby optimal habitat was defined simply as presence of a given land cover type, and instead defines optimal habitat via a combination of important landscape dynamic variables, moving from a human-defined to species-defined perspective of optimal habitat. The species distribution models demonstrate differing distributions and abundances of host species across the study areas, utilising the strengths of Earth observation data to improve our understanding of landscape and ecological linkages to small mammal distributions and abundances.

@article{doi101371journalpone0289209,
    author = "Marston, Christopher and Raoul, Francis and Rowland, Clare and Quéré, Jean-Pierre and Feng, Xiaohui and Lin, Renyong and Giraudoux, Patrick",
    title = "Mapping small mammal optimal habitats using satellite-derived proxy variables and species distribution models.",
    year = "2023",
    journal = "PloS one",
    abstract = "Small mammal species play an important role influencing vegetation primary productivity and plant species composition, seed dispersal, soil structure, and as predator and/or prey species. Species which experience population dynamics cycles can, at high population phases, heavily impact agricultural sectors and promote rodent-borne disease transmission. To better understand the drivers behind small mammal distributions and abundances, and how these differ for individual species, it is necessary to characterise landscape variables important for the life cycles of the species in question. In this study, a suite of Earth observation derived metrics quantifying landscape characteristics and dynamics, and in-situ small mammal trapline and transect survey data, are used to generate random forest species distribution models for nine small mammal species for study sites in Narati, China and Sary Mogul, Kyrgyzstan. These species distribution models identify the important landscape proxy variables driving species abundance and distributions, in turn identifying the optimal conditions for each species. The observed relationships differed between species, with the number of landscape proxy variables identified as important for each species ranging from 3 for Microtus gregalis at Sary Mogul, to 26 for Ellobius tancrei at Narati. Results indicate that grasslands were predicted to hold higher abundances of Microtus obscurus, E. tancrei and Marmota baibacina, forest areas hold higher abundances of Myodes centralis and Sorex asper, with mixed forest-grassland boundary areas and areas close to watercourses predicted to hold higher abundances of Apodemus uralensis and Sicista tianshanica. Localised variability in vegetation and wetness conditions, as well as presence of certain habitat types, are also shown to influence these small mammal species abundances. Predictive application of the Random Forest (RF) models identified spatial hot-spots of high abundance, with model validation producing R2 values between 0.670 for M. gregalis transect data at Sary Mogul to 0.939 for E. tancrei transect data at Narati. This enhances previous work whereby optimal habitat was defined simply as presence of a given land cover type, and instead defines optimal habitat via a combination of important landscape dynamic variables, moving from a human-defined to species-defined perspective of optimal habitat. The species distribution models demonstrate differing distributions and abundances of host species across the study areas, utilising the strengths of Earth observation data to improve our understanding of landscape and ecological linkages to small mammal distributions and abundances.",
    url = "https://pmc.ncbi.nlm.nih.gov/articles/PMC10434852/",
    doi = "10.1371/journal.pone.0289209",
    openalex = "W4385945117",
    pmcid = "PMC10434852",
    pmid = "37590218",
    references = "doi101016jtree200505011, doi101023a1010933404324, doi101111j09067590200503957x, doi101111j13652699201002416x, doi1018637jssv036i11, doi1023073235884, doi103390rs12030426, doi10560219780801882210, lenoir2017climatic, openalexw273955616"
}

The common vole is one of the mammalian pests causing more agricultural damage in Europe. Since the late 1970s, this species has invaded the Duero valley in NW Spain, colonizing ca. 5 million ha of agricultural areas of the valley in about 20 years. Once settled in agricultural landscapes, the species experienced cyclic population outbreaks causing crop damages. The major vole population outbreak of 2006–2007 was managed by the Regional Government (Junta de Castilla y León, JCYL) mainly through large-scale application of anticoagulant rodenticides (ARs) and widespread destruction of field margins, natural vegetation patches, and crop stubbles by burning. These actions caused serious damage to regional agrarian biodiversity, including small game species. The coordinated action of scientific institutions and environmental NGOs, with the support of the main Spanish hunting association at a critical time, led to a progressive shift in pest management strategies during subsequent outbreaks, promoting the adoption of biological control and other management techniques causing less environmental damage. Finally, JCYL implemented an IPM program mainly based on biological control, good farming practices, and habitat management. This program has been increasingly adopted in recent years, leading to a marked reduction in chemical control and the complete elimination of burning as a tool of management. Over this period, the scientific knowledge of the species’ ecology has expanded substantially, providing key insights for the development and refinement of IPM strategies. Here, we synthesize this body of knowledge and provide additional recommendations to further improve the current IPM program, which may serve as a model for rodent pest management in other regions worldwide.

@article{doi103390agriculture16050577,
    author = "Viñuela, Javier and Cuéllar-Basterrechea, Carlos and Báscones-Reina, Miriam and Olea, Pedro P and Fernando, Jubete and Domínguez, Julio C. and Jareño, Daniel and Santamaría, Ana Esther and Hernández-Garavís, Lorena and Calero-Riestra, María and Blanca, Fernando and González-Simón, Paula and Paz, Alfonso and García, Jesús T. and Garcés, Fernando",
    title = "Four Decades of Common Vole (Microtus arvalis Pallas 1778) Population Outbreaks in NW Spain: Transition from Environmentally Harmful Practices to Sustainable Integrated Pest Management (IPM)",
    year = "2026",
    journal = "Agriculture",
    abstract = "The common vole is one of the mammalian pests causing more agricultural damage in Europe. Since the late 1970s, this species has invaded the Duero valley in NW Spain, colonizing ca. 5 million ha of agricultural areas of the valley in about 20 years. Once settled in agricultural landscapes, the species experienced cyclic population outbreaks causing crop damages. The major vole population outbreak of 2006–2007 was managed by the Regional Government (Junta de Castilla y León, JCYL) mainly through large-scale application of anticoagulant rodenticides (ARs) and widespread destruction of field margins, natural vegetation patches, and crop stubbles by burning. These actions caused serious damage to regional agrarian biodiversity, including small game species. The coordinated action of scientific institutions and environmental NGOs, with the support of the main Spanish hunting association at a critical time, led to a progressive shift in pest management strategies during subsequent outbreaks, promoting the adoption of biological control and other management techniques causing less environmental damage. Finally, JCYL implemented an IPM program mainly based on biological control, good farming practices, and habitat management. This program has been increasingly adopted in recent years, leading to a marked reduction in chemical control and the complete elimination of burning as a tool of management. Over this period, the scientific knowledge of the species’ ecology has expanded substantially, providing key insights for the development and refinement of IPM strategies. Here, we synthesize this body of knowledge and provide additional recommendations to further improve the current IPM program, which may serve as a model for rodent pest management in other regions worldwide.",
    url = "https://doi.org/10.3390/agriculture16050577",
    doi = "10.3390/agriculture16050577",
    openalex = "W7133347975",
    references = "doi101016jlanplh2025101300"
}