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.

Assessing the Impacts of Future Climate Change on Protected Area Networks: A Method to Simulate Individual Species' Responses

Abstract: Global climate change, along with continued habitat loss and fragmentation, is now recognized as being a major threat to future biodiversity. There is a very real threat to species, arising from the need to shift their ranges in the future to track regions of suitable climate. The Important Bird Area (IBA) network is a series of sites designed to conserve avian diversity in the face of current threats from factors such as habitat loss and fragmentation. However, in common with other networks, the IBA network is based on the assumption that the climate will remain unchanged in the future. In this article, we provide a method to simulate the occurrence of species of conservation concern in protected areas, which could be used as a first-step approach to assess the potential impacts of climate change upon such species in protected areas. We use species-climate response surface models to relate the occurrence of 12 biome-restricted African species to climate data at a coarse (quarter degree-degree latitude-longitude) resolution and then intersect the grid model output with IBA outlines to simulate the occurrence of the species in South African IBAs. Our results demonstrate that this relatively simple technique provides good simulations of current species' occurrence in protected areas. We then use basic habitat data for IBAs along with habitat preference data for the species to reduce over-prediction and further improve predictive ability. This approach can be used with future climate change scenarios to highlight vulnerable species in IBAs in the future and allow practical recommendations to be made to enhance the IBA network and minimize the predicted impacts of climate change.

Physiological effects of climate on distributions of endothermic species

Abstract: Aim Determining the mechanisms underlying climatic limitation of species distributions is essential for understanding responses to current climatic change. Disentangling direct (e.g. physiological) and indirect (e.g. trophic) effects of climate on distributions through occurrence-based modelling is problematic because most species use the same area for both shelter and food acquisition. By focusing on marine birds that breed on land but feed at sea, we exploit a rare opportunity to dissociate direct from indirect climatic effects on endothermic species. Location Coastal Europe. Methods We developed climate-response surfaces (CRS) for 13 seabird species in coastal Europe, linking terrestrial climatic variables considered important for heat transfer with presence/absence data across each species’ entire European breeding range. Agreement between modelled and actual distribution was assessed for jackknifed samples using area under the curve (AUC) of receiver operating characteristic plots. Higher AUC values indicated closer correspondence between observed breeding distribution and terrestrial climate. We assessed the influence of several ecological factors on model performance across species.

Beyond climate envelopes: bio-climate modelling accords with observed 25-year changes in seabird populations of the British Isles

Abstract: Aim: Climate envelope models (CEMs) are used to assess species’ vulnerability to predicted changes in climate, based on their distributions. Extinction risk, however, also depends on demographic parameters. Accordingly, we use CEMs for 18 seabird species to test three hypotheses: (i) population sizes are larger in areas where CEMs fitted using distribution data predict more suitable climate; (ii) the presence of this relationship (Hypothesis i) is related to a species’ foraging ecology; and (iii) species whose distributions and population sizes conformed most closely to indices of climatic suitability in the mid-1980s experienced the largest population changes following climatic change between 1986 and 2010. Location: Europe. Methods: Climate envelope models fitted at a 50-km resolution using European climatic and distribution data were applied using local climatic data to calculate local climatic suitability indices (CSIs) for 18 species within the British Isles. We then investigated the relationship between CSI and population size at a 10-km resolution and related both the presence of this relationship and goodness-of-fit metrics from the European models to changes in population size (1986–2010). Results: Local population sizes were significantly positively related to local CSI in 50% of species, providing support for Hypothesis (i), and these 50% of species were independently considered to be most vulnerable to changes in food availability at sea in support of Hypothesis (ii). Those species whose distributions and populations most closely conformed to indices of climatic suitability showed the least favourable subsequent changes in population size, over a period in which mean climatic suitability decreased for all species, in support of Hypothesis (iii). Main conclusions: Climate influences the population sizes of multiple seabird species in the British Isles. We highlight the potential for outputs of CEMs fitted with coarse resolution occupancy data to provide information on both local abundance and sensitivity to future climate changes.

Explaining patterns of avian diversity and endemicity: climate and biomes of southern Africa over the last 140,000 years

Abstract: Aim Test hypotheses that present biodiversity and endemic species richness are related to climatic stability and/or biome persistence. Location Africa south of 15° S. Methods Seventy eight HadCM3 general circulation model palaeoclimate experiments spanning the last 140,000 years, plus a pre-industrial experiment, were used to calculate measures of climatic variability for 0.5° grid cells. Models were fitted relating distributions of the nine biomes of South Africa, Lesotho and Swaziland to present climate. These models were used to simulate potential past biome distribution and extent for the 78 palaeoclimate experiments, and three measures of biome persistence. Climatic response surfaces were fitted for 690 bird species regularly breeding in the region and used to simulate present species richness for cells of the 0.5° grid. Species richness was evaluated for residents, mobile species (nomadic or partially/altitudinally migrant within the region), and intra-African migrants, and also separately for endemic/near-endemic (hereafter ‘endemic’) species as a whole and those associated with each biome. Our hypotheses were tested by analysing correlations between species richness and climatic variability or biome persistence. Results The magnitude of climatic variability showed clear spatial patterns. Marked changes in biome distributions and extents were projected, although limited areas of persistence were projected for some biomes. Overall species richness was not correlated with climatic variability, although richness of mobile species showed a weak negative correlation. Endemic species richness was significantly negatively correlated with climatic variability. Strongest correlations, however, were positive correlations between biome persistence and richness of endemics associated with individual biomes. Main conclusions Low climatic variability, and especially a degree of stability enabling biome persistence, is strongly correlated with species richness of birds endemic to southern Africa. This probably principally reflects reduced extinction risk for these species where the biome to which they are adapted persisted.

Species distribution models indicate contrasting late‐Quaternary histories for Southern and Northern Hemisphere bird species

Abstract: Aim To test the following hypotheses: that Fynbos species had more extensive distributions at the Last Glacial Maximum (LGM), extending onto the exposed ‘Agulhas Plain’; that genetically distinct British taxa could have persisted through the LGM on adjacent areas of exposed shelf. Location Southern Africa; Europe. Methods Climatic response surfaces were fitted for 14 Fynbos and two European birds. These models were used to hindcast species distributions for palaeoclimates simulated using a fully coupled atmosphere–ocean general circulation model. LGM annual net primary productivity (ANPP) of two plant functional types upon which the European birds depend were simulated using a dynamic vegetation model and compared with potential LGM bird distributions. Results Fynbos birds' potential LGM distributions mostly extended southwards onto the exposed Agulhas Plain and were consistently more extensive than at present. This contrasts with conventional expectations for temperate species based upon Northern Hemisphere evidence. North-west European taxa potentially had LGM ranges on exposed shelf and ice-free areas to the west and south-west of the British Isles ice sheet. This is consistent with available genetic evidence, supporting the long-standing hypothesis that these taxa persisted through the LGM in this region. In both regions, results allow the generation of new testable hypotheses about species evolution and palaeobiogeography. Main conclusions Bird species found today in the Fynbos probably had more extensive ranges under glacial conditions, with the potential ranges of many species extending onto the Agulhas Plain. Bird taxa restricted today to the British Isles probably survived the LGM with limited distributions on exposed shelf and ice-free areas south-west of the British Isles ice sheet. Areas of shelf exposed under glacial conditions are likely to have been important components of glacial distributions of species in both the Northern and Southern Hemisphere. The contrasting history of Northern and Southern Hemisphere species has important conservation implications, especially in relation to conserving intra-specific genetic diversity.

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.