Yvonne C. Collingham

Bio: Yvonne C. Collingham is an academic researcher from Durham University. The author has contributed to research in topics: Climate change & Range (biology). The author has an hindex of 27, co-authored 38 publications receiving 10469 citations. Previous affiliations of Yvonne C. Collingham include University of York.

Biodiversity Conservation: Uncertainty in predictions of extinction risk/Effects of changes in climate and land use/Climate change and extinction risk (reply).

Abstract: Thomas et al. reply — We reconsider our estimates of climate-related extinction1 in the light of three questions raised by Thuiller .2, Buckley and Roughgarden 3 and Harte .4. We are able to confirm our original conclusion that climate change represents a major threat to terrestrial species1.

Millennial climatic fluctuations are key to the structure of last glacial ecosystems.

Abstract: Whereas fossil evidence indicates extensive treeless vegetation and diverse grazing megafauna in Europe and northern Asia during the last glacial, experiments combining vegetation models and climate models have to-date simulated widespread persistence of trees. Resolving this conflict is key to understanding both last glacial ecosystems and extinction of most of the mega-herbivores. Using a dynamic vegetation model (DVM) we explored the implications of the differing climatic conditions generated by a general circulation model (GCM) in “normal” and “hosing” experiments. Whilst the former approximate interstadial conditions, the latter, designed to mimic Heinrich Events, approximate stadial conditions. The “hosing” experiments gave simulated European vegetation much closer in composition to that inferred from fossil evidence than did the “normal” experiments. Given the short duration of interstadials, and the rate at which forest cover expanded during the late-glacial and early Holocene, our results demonstrate the importance of millennial variability in determining the character of last glacial ecosystems.

Modelling relationships between species spatial abundance patterns and climate

Abstract: Aim To move towards modelling spatial abundance patterns and to evaluate the relative impacts of climatic change upon species abundances as opposed to range extents. Location Southern Africa, including Lesotho, Namibia, South Africa, Swaziland and Zimbabwe. Methods Quantitative response surface models were fitted for 78 bird species, mostly endemic (68) or near-endemic to the region, to model relationships between species reporting rates (i.e. the proportion of checklists reporting a species for a particular grid cell), as recorded by the Southern African Bird Atlas Project, and four bioclimatic variables derived from climatic data for the period 1961–90. With caution, reporting rates can be used as a proxy for abundance. Models were used to project potential impacts of a series of projected climatic change scenarios upon species abundance patterns and range extents. Results Most models obtained were robust with good predictive power. Projections of potential future abundance patterns indicate that the magnitude of impacts upon a proxy for abundance are greater than those upon range extent for the majority of species (82% by 2071–2100). For most species (74%) both abundance and range extent are projected to decrease by 2100. Impacts are especially severe if species are unable to realize projected range changes; when only the area of a species' simulated present range is considered, overall abundance decreases of more than 80% are projected for 19 (24%) of species examined. Main conclusions Our results indicate that projected climatic changes are likely to elicit greater relative changes in species abundances than range extents. For most species examined changes were decreases, suggesting the impacts upon biodiversity are likely generally to be negative. These results also suggest that previous estimates of the proportion of species at increased risk of extinction as a result of climatic change may, in some cases, be under-estimates.

RESEARCH Density-dependent dispersal and the speed of range expansions

Abstract: Aim The speed of range expansions, be it invasive species colonizing a new area or species tracking a moving climatic niche, critically depends on dispersal. Models for species’ range expansions generally assume dispersal to be independent of local population densities. However, animals often disperse in response to high population size or alternatively may avoid or leave areas of very low population sizes. We explore whether such density dependence in dispersal can safely be ignored when predicting the speed of range expansions. Location Simulation study. Methods We use simulations to examine the effect of different forms of density dependence in emigration and immigration on the speed of range expansions. For emigration, we consider linear and nonlinear forms of positive density dependence, negative density dependence at low population densities and constant emigration rates. For immigration, we consider options where individuals avoid crowded patches, are attracted to the presence of conspecifics or settle independent of local density. Results The speed of range expansion was slowest when emigration was strongly positively related to density (higher emigration at higher densities) and when individuals avoided settling in low-density patches. It tended to be fastest under negatively density-dependent emigration (higher emigration at lower densities). These results were consistent across two different life histories and different levels of carrying capacity. Main conclusions Our results suggest that considering density-dependent dispersal and the mechanisms leading to it are important for correctly predicting species’ rates of spread. Organisms with a tendency to aggregate, for example, by relying on conspecific attraction in settlement and emigrating mainly in response to high local densities, are predicted to be least likely to expand their ranges and most at risk from spatial shifts in their climatic niches.

Density-dependent dispersal and the speed of range expansions.

Abstract: Aim The speed of range expansions, be it invasive species colonizing a new area or species tracking a moving climatic niche, critically depends on dispersal. Models for species' range expansions generally assume dispersal to be independent of local population densities. However, animals often disperse in response to high population size or alternatively may avoid or leave areas of very low population sizes. We explore whether such density dependence in dispersal can safely be ignored when predicting the speed of range expansions. Location Simulation study. Methods We use simulations to examine the effect of different forms of density dependence in emigration and immigration on the speed of range expansions. For emigration, we consider linear and nonlinear forms of positive density dependence, negative density dependence at low population densities and constant emigration rates. For immigration, we consider options where individuals avoid crowded patches, are attracted to the presence of conspecifics or settle independent of local density. Results The speed of range expansion was slowest when emigration was strongly positively related to density (higher emigration at higher densities) and when individuals avoided settling in low-density patches. It tended to be fastest under negatively density-dependent emigration (higher emigration at lower densities). These results were consistent across two different life histories and different levels of carrying capacity. Main conclusions Our results suggest that considering density-dependent dispersal and the mechanisms leading to it are important for correctly predicting species' rates of spread. Organisms with a tendency to aggregate, for example, by relying on conspecific attraction in settlement and emigrating mainly in response to high local densities, are predicted to be least likely to expand their ranges and most at risk from spatial shifts in their climatic niches.

Novel methods improve prediction of species' distributions from occurrence data

Abstract: Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.

Effects of Habitat Fragmentation on Biodiversity

Abstract: ■ Abstract The literature on effects of habitat fragmentation on biodiversity is huge. It is also very diverse, with different authors measuring fragmentation in different ways and, as a consequence, drawing different conclusions regarding both the magnitude and direction of its effects. Habitat fragmentation is usually defined as a landscape-scale process involving both habitat loss and the breaking apart of habitat. Results of empirical studies of habitat fragmentation are often difficult to interpret because (a) many researchers measure fragmentation at the patch scale, not the landscape scale and (b) most researchers measure fragmentation in ways that do not distinguish between habitat loss and habitat fragmentation per se, i.e., the breaking apart of habitat after controlling for habitat loss. Empirical studies to date suggest that habitat loss has large, consistently negative effects on biodiversity. Habitat fragmentation per se has much weaker effects on biodiversity that are at least as likely to be positive as negative. Therefore, to correctly interpret the influence of habitat fragmentation on biodiversity, the effects of these two components of fragmentation must be measured independently. More studies of the independent effects of habitat loss and fragmentation per se are needed to determine the factors that lead to positive versus negative effects of fragmentation per se. I suggest that the term “fragmentation” should be reserved for the breaking apart of habitat, independent of habitat loss.

Predicting species distribution: offering more than simple habitat models.

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.

Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation

Abstract: Accurate modeling of geographic distributions of species is crucial to various applications in ecology and conservation. The best performing techniques often require some parameter tuning, which may be prohibitively time-consuming to do separately for each species, or unreliable for small or biased datasets. Additionally, even with the abundance of good quality data, users interested in the application of species models need not have the statistical knowledge required for detailed tuning. In such cases, it is desirable to use "default settings", tuned and validated on diverse datasets. Maxent is a recently introduced modeling technique, achieving high predictive accuracy and enjoying several additional attractive properties. The performance of Maxent is influenced by a moderate number of parameters. The first contribution of this paper is the empirical tuning of these parameters. Since many datasets lack information about species absence, we present a tuning method that uses presence-only data. We evaluate our method on independently collected high-quality presence-absence data. In addition to tuning, we introduce several concepts that improve the predictive accuracy and running time of Maxent. We introduce "hinge features" that model more complex relationships in the training data; we describe a new logistic output format that gives an estimate of probability of presence; finally we explore "background sampling" strategies that cope with sample selection bias and decrease model-building time. Our evaluation, based on a diverse dataset of 226 species from 6 regions, shows: 1) default settings tuned on presence-only data achieve performance which is almost as good as if they had been tuned on the evaluation data itself; 2) hinge features substantially improve model performance; 3) logistic output improves model calibration, so that large differences in output values correspond better to large differences in suitability; 4) "target-group" background sampling can give much better predictive performance than random background sampling; 5) random background sampling results in a dramatic decrease in running time, with no decrease in model performance.