Using n‐dimensional hypervolumes for species distribution modelling: A response to Qiao et al. ()

Hypervolume approaches are used to quantify functional diversity and quantify environmental niches for species distribution modelling. Recently, Qiao et al. ( 2016 ) criticized our geometrical kernel density estimation (KDE) method for measuring hypervolumes. They used a simulation analysis to argue that the method yields high error rates and makes biased estimates of fundamental niches. Here, we show that (a) KDE output depends in useful ways on dataset size and bias, (b) other species distribution modelling methods make equally stringent but different assumptions about dataset bias, (c) simulation results presented by Qiao et al. ( 2016 ) were incorrect, with revised analyses showing performance comparable to other methods, and (d) hypervolume methods are more general than KDE and have other benefits for niche modelling. As a result, our KDE method remains a promising tool for species distribution modelling.     

Predicting the potential invasive range of light brown apple moth (Epiphyas postvittana) using biologically informed and correlative species distribution models

The light brown apple moth (Epiphyas postvittana) is a highly polyphagous species that has invaded several geographic regions across the globe and has stimulated substantial concern over possible impacts for agriculture in the US. We aimed to predict the potential geographic range of E. postvittana to better understand the threat of this species in the US and globally. We used the mechanistic simulation modelling method CLIMEX and the correlative niche modelling method Maxent to predict the geographic distribution of E. postvittana in its native range and globally and tested model projections using known invasion data. Different predictor variable data sets and threshold dependent and independent measures of environmental suitability were considered in model evaluation. Models accurately predicted known invasive localities of E. postvittana across the globe. Overall predictions of environmental suitability were largely congruent across models, although there were some notable differences. Ephiphyas postvittana clearly has the potential to establish in many regions of the globe, although some previous analyses of the potential distribution of this species appear overly pessimistic. Additional studies of the biology of this species in invaded areas, including interactions with natural enemies and the capacity to adapt to novel climatic conditions, are ultimately needed to more fully understand its potential economic and environmental impacts.     

Use of space and home range characteristics of Lepilemur mittermeieri,an endangered sportive lemur endemic to the Ampasindava peninsula,north‐west Madagascar

Lepilemur mittermeieri, a little‐studied sportive lemur of north‐west Madagascar, endemic to the Ampasindava Peninsula, faces habitat loss through forest degradation and rapid fragmentation. Understanding its habitat requirement is the first step toward preservation of this threatened forest‐dependent species. In this study, we gathered data on the use of space and home range characteristics of L. mittermeieri. We studied individuals from early March to the end of June 2015 and 2016, in three sites of the Ampasindava peninsula. We radio‐tracked 15 individuals to obtain detailed information on the size and location of home ranges (around 450 hr of tracking). Direct observation and morphometric measurements provided additional data sets. Both kernel density estimation (KDE) and minimum convex polygon (MCP) methods yielded similar home range sizes (an average of 2.01 ha with KDE method and 1.96 ha with MCP method). We did not find differences in home range size between males and females, with respect to forest type or proximity to the forest edge. Home ranges overlapped and individuals showed low levels of territoriality. We highlighted a sexually‐dimorphic trait: males have longer upper canine than females. Our results constitute the first set of ecological information on Lepilemur mittermeieri and could be the basis for a conservation strategy for this endangered species with a very small distribution area.     

Similarities between Line Fishing and Baited Stereo-Video Estimations of Length-Frequency: Novel Application of Kernel Density Estimates

Age structure data is essential for single species stock assessments but length-frequency data can provide complementary information. In south-western Australia, the majority of these data for exploited species are derived from line caught fish. However, baited remote underwater stereo-video systems (stereo-BRUVS) surveys have also been found to provide accurate length measurements. Given that line fishing tends to be biased towards larger fish, we predicted that, stereo-BRUVS would yield length-frequency data with a smaller mean length and skewed towards smaller fish than that collected by fisheries-independent line fishing. To assess the biases and selectivity of stereo-BRUVS and line fishing we compared the length-frequencies obtained for three commonly fished species, using a novel application of the Kernel Density Estimate (KDE) method and the established Kolmogorov–Smirnov (KS) test. The shape of the length-frequency distribution obtained for the labrid Choerodon rubescens by stereo-BRUVS and line fishing did not differ significantly, but, as predicted, the mean length estimated from stereo-BRUVS was 17% smaller. Contrary to our predictions, the mean length and shape of the length-frequency distribution for the epinephelid Epinephelides armatus did not differ significantly between line fishing and stereo-BRUVS. For the sparid Pagrus auratus, the length frequency distribution derived from the stereo-BRUVS method was bi-modal, while that from line fishing was uni-modal. However, the location of the first modal length class for P. auratus observed by each sampling method was similar. No differences were found between the results of the KS and KDE tests, however, KDE provided a data-driven method for approximating length-frequency data to a probability function and a useful way of describing and testing any differences between length-frequency samples. This study found the overall size selectivity of line fishing and stereo-BRUVS were unexpectedly similar.     

Correlation between genetic diversity and environmental suitability: taking uncertainty from ecological niche models into account

The hindcast of shifts in the geographical ranges of species as estimated by ecological niche modelling (ENM) has been coupled with phylogeographical patterns, allowing the inference of past processes that drove population differentiation and genetic variability. However, more recently, some studies have suggested that maps of environmental suitability estimated by ENM may be correlated to species' abundance, raising the possibility of using environmental suitability to infer processes related to population demographic dynamics and genetic variability. In both cases, one of the main problems is that there is a wide variation in ENM development methods and climatic models. In this study, we analyse the relationship between heterozygosity (He) and environmental suitability from multiple ENMs for 25 population estimates for Dipteryx alata, a widely distributed, endemic tree species of the Cerrado region of central Brazil. We propose a new approach for generating a statistical distribution of correlations under randomly generated ENM. The confidence intervals from these distributions indicate how model selection with different properties affects the ability to detect a correlation of interest (e.g. the correlation between He and suitability). Additionally, our approach allows us to explore which particular ensemble of ENMs produces the better result for finding an association between environmental suitability and He. Caution is necessary when choosing a method or a climatic data set for modelling geographical distributions, but the new approach proposed here provides a conservative way to evaluate the ability of ensembles to detect patterns of interest.     

Ecological genomics meets community‐level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation

Local adaptation is a central feature of most species occupying spatially heterogeneous environments, and may factor critically in responses to environmental change. However, most efforts to model the response of species to climate change ignore intraspecific variation due to local adaptation. Here, we present a new perspective on spatial modelling of organism–environment relationships that combines genomic data and community‐level modelling to develop scenarios regarding the geographic distribution of genomic variation in response to environmental change. Rather than modelling species within communities, we use these techniques to model large numbers of loci across genomes. Using balsam poplar (Populus balsamifera) as a case study, we demonstrate how our framework can accommodate nonlinear responses of loci to environmental gradients. We identify a threshold response to temperature in the circadian clock gene GIGANTEA‐5 (GI5), suggesting that this gene has experienced strong local adaptation to temperature. We also demonstrate how these methods can map ecological adaptation from genomic data, including the identification of predicted differences in the genetic composition of populations under current and future climates. Community‐level modelling of genomic variation represents an important advance in landscape genomics and spatial modelling of biodiversity that moves beyond species‐level assessments of climate change vulnerability.     

Potential distribution of vulnerable Entandrophragma angolense (Welw.) C. DC. (Meliaceae) in East Africa

The various human‐induced threats imposed on nature have recently triggered the study of species' distributions. We developed potential suitability models using two algorithms for a threatened African mahogany, Entandrophragma angolense, in three East African countries; Kenya, Tanzania and Uganda. The effect of features selection and modelling algorithm selection on potential suitability predictions was explored. Occurrence records and high‐resolution environmental data were used. The two species distribution modelling techniques were genetic algorithm rule for prediction; and maximum entropy modelling. With Maxent, the area under the receiver characteristic operating curve (AUC) for potential distribution models tested on independent data ranged from 0.942 to 0.972 when using automatic features and from 0.974 to 0.666 with target or specific features. With GARP, AUC for potential distribution models ranged from 0.591 to 0.736 with all rule types and from 0.388 to 0.805 for specific rule types (Tables  1  and 2 ). The area under the E. angolense potential suitability was best predicted by soil, rainfall and aspect using GARP. Potential suitability increased with increasing aspect and decreased with increasing slope. Low rainfall and elevation increased potential suitability, while high levels of either variable decreased potential suitability. Potential suitability maps for vulnerable species require using a multi‐algorithm, fine scale data approach and incorporation of environmental variables like soil, slope, land use and elevation. Species distribution models can offer insight on the distribution requirements of vulnerable species and help guide the development of management plans. Results of this study suggest that E. angolense management plans should promote the protection of terrestrial forests surrounding water bodies including Mabira forest in Uganda.     

Bio‐ORACLE: a global environmental dataset for marine species distribution modelling

Aim The oceans harbour a great diversity of organisms whose distribution and ecological preferences are often poorly understood. Species distribution modelling (SDM) could improve our knowledge and inform marine ecosystem management and conservation. Although marine environmental data are available from various sources, there are currently no user‐friendly, high‐resolution global datasets designed for SDM applications. This study aims to fill this gap by assembling a comprehensive, uniform, high‐resolution and readily usable package of global environmental rasters. Location Global, marine. Methods We compiled global coverage data, e.g. satellite‐based and in situ measured data, representing various aspects of the marine environment relevant for species distributions. Rasters were assembled at a resolution of 5 arcmin (c. 9.2 km) and a uniform landmask was applied. The utility of the dataset was evaluated by maximum entropy SDM of the invasive seaweed Codium fragile ssp. fragile. Results We present Bio‐ORACLE (ocean rasters for analysis of climate and environment), a global dataset consisting of 23 geophysical, biotic and climate rasters. This user‐friendly data package for marine species distribution modelling is available for download at http://www.bio‐oracle.ugent.be . The high predictive power of the distribution model of C. fragile ssp. fragile clearly illustrates the potential of the data package for SDM of shallow‐water marine organisms. Main conclusions The availability of this global environmental data package has the potential to stimulate marine SDM. The high predictive success of the presence‐only model of a notorious invasive seaweed shows that the information contained in Bio‐ORACLE can be informative about marine distributions and permits building highly accurate species distribution models.     

The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling

Predicting which species will occur together in the future, and where, remains one of the greatest challenges in ecology, and requires a sound understanding of how the abiotic and biotic environments interact with dispersal processes and history across scales. Biotic interactions and their dynamics influence species' relationships to climate, and this also has important implications for predicting future distributions of species. It is already well accepted that biotic interactions shape species' spatial distributions at local spatial extents, but the role of these interactions beyond local extents (e.g. 10 km² to global extents) are usually dismissed as unimportant. In this review we consolidate evidence for how biotic interactions shape species distributions beyond local extents and review methods for integrating biotic interactions into species distribution modelling tools. Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents. We demonstrate this with examples from within and across trophic groups. A range of species distribution modelling tools is available to quantify species environmental relationships and predict species occurrence, such as: (i) integrating pairwise dependencies, (ii) using integrative predictors, and (iii) hybridising species distribution models (SDMs) with dynamic models. These methods have typically only been applied to interacting pairs of species at a single time, require a priori ecological knowledge about which species interact, and due to data paucity must assume that biotic interactions are constant in space and time. To better inform the future development of these models across spatial scales, we call for accelerated collection of spatially and temporally explicit species data. Ideally, these data should be sampled to reflect variation in the underlying environment across large spatial extents, and at fine spatial resolution. Simplified ecosystems where there are relatively few interacting species and sometimes a wealth of existing ecosystem monitoring data (e.g. arctic, alpine or island habitats) offer settings where the development of modelling tools that account for biotic interactions may be less difficult than elsewhere.     

Using maximum entropy to predict the potential distribution of an invasive freshwater snail

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Biogeography of the Amazon molly: ecological niche and range limits of an asexual hybrid species

Aim To understand the relative contributions of environmental factors, dispersal limitations and the presence of sperm donors in determining the distribution of the Amazon molly (Poecilia formosa), a sperm‐dependent unisexual fish species of hybrid origin. To explore niche similarities and/or differences between the hybrid and parental species. To evaluate whether large‐scale abiotic factors can explain a successful introduction of both P. formosa and Poecilia latipinna. Location South‐east United States, Mexico and Central America. Methods We used abiotic variables in ecological niche modelling (ENM) to identify regions with suitable conditions for the presence of the Amazon molly and its two parental species (P. latipinna and Poecilia mexicana). We also used a recently developed metric to calculate the degree of niche overlap between the hybrid and its parental species. Results ENM produced highly significant models [all area under the curve (AUC) > 0.99 for the three species]. Annual mean temperature and minimum temperature of the coldest month were the variables that best explained the distribution of the Amazon molly. With the exception of south Florida, few areas beyond the known distribution of the species were predicted to have suitable environmental conditions. The hybrid species niche overlaps partially with the parental species. However, given the available data, it is neither more similar nor more different than expected by chance. Main conclusions Two different processes are acting to limit the distribution of P. formosa. At the northern limit, although a sperm donor species is present further north, suitable environmental conditions are absent from nearby locations. At the southern limit, a sperm donor species is present and areas with good environmental conditions are present at nearby locations, suggesting that dispersal ability is the limiting factor. We found that the hybrid species overlaps in a similar way with both parental species while still having its own niche identity. This result may be explained by the fact that hybrid species inherit characteristics of two ecologically divergent species, which can result in intermediate or even transgressive phenotypes. These results support recent work on the role of hybridization in diversification.     

Ecological niche modelling as a technique for assessing threats and setting conservation priorities for Asian slow lorises (Primates: Nycticebus)

Aim Data on geographical ranges are essential when defining the conservation status of a species, and in evaluating levels of human disturbance. Where locality data are deficient, presence‐only ecological niche modelling (ENM) can provide insights into a species’ potential distribution, and can aid in conservation planning. Presence‐only ENM is especially important for rare, cryptic and nocturnal species, where absence is difficult to define. Here we applied ENM to carry out an anthropogenic risk assessment and set conservation priorities for three threatened species of Asian slow loris (Primates: Nycticebus). Location Borneo, Java and Sumatra, Southeast Asia. Methods Distribution models were built using maximum entropy (MaxEnt) ENM. We input 20 environmental variables comprising temperature, precipitation and altitude, along with species locality data. We clipped predicted distributions to forest cover and altitudinal data to generate remnant distributions. These were then applied to protected area (PA) and human land‐use data, using specific criteria to define low‐, medium‐ or high‐risk areas. These data were analysed to pinpoint priority study sites, suitable reintroduction zones and protected area extensions. Results A jackknife validation method indicated highly significant models for all three species with small sample sizes (n = 10 to 23 occurrences). The distribution models represented high habitat suitability within each species’ geographical range. High‐risk areas were most prevalent for the Javan slow loris (Nycticebus javanicus) on Java, with the highest proportion of low‐risk areas for the Bornean slow loris (N. menagensis) on Borneo. Eighteen PA extensions and 23 priority survey sites were identified across the study region. Main conclusions Discriminating areas of high habitat suitability lays the foundations for planning field studies and conservation initiatives. This study highlights potential reintroduction zones that will minimize anthropogenic threats to animals that are released. These data reiterate the conclusion of previous research, showing MaxEnt is a viable technique for modelling species distributions with small sample sizes.     

Tracing the origin of disjunct distributions: a case of biogeographical convergence in Pyrgus butterflies

Aim To study the biogeographical factors responsible for the current disjunct distributions of two closely related species of butterflies (Pyrgus cinarae and Pyrgus sidae, Lepidoptera: Hesperioidea). Both species have small populations in the Iberian Peninsula that are isolated by more than 1000 km from their nearest conspecifics. Because these species possess similar ecological preferences and geographical distributions, they are excellent candidates for congruent biogeographical histories. Location The Palaearctic region, with a special focus on the Mediterranean peninsulas as glacial refugia. Methods We integrated phylogeography and population genetic analyses with ecological niche modelling. The mitochondrial gene cytochrome c oxidase subunit 1 (COI) and the non‐coding nuclear marker internal transcribed spacer 2 (ITS2) were analysed for 62 specimens of P. cinarae and for 80 of P. sidae to infer phylogeography and to date the origin of disjunct distributions. Current and ancestral [Last Glacial Maximum using MIROC (Model for Interdisciplinary Research on Climate) and CCSM (Community Climate System Model) circulation models] distribution models were calculated with Maxent . Using present climatic conditions, we delimited the ecological space for each species. Results The genetic structure and potential ancestral distribution of the two species were markedly different. While the Iberian population of P. cinarae had an old origin (c. 1 Ma), that of P. sidae was closely related to French and Italian lineages (which jointly diverged from eastern populations c. 0.27 Ma). Ecological niche modelling showed that minor differences in the ecological preferences of the two species seem to account for their drastically different distributional response to the last glacial to post‐glacial environmental conditions. Although the potential distribution of P. cinarae was largely unaffected by climate change, suitable habitat for P. sidae strongly shifted in both elevation and latitude. This result might explain the early origin of the disjunct distribution of P. cinarae, in contrast to the more recent disjunction of P. sidae. Main conclusions We show that convergent biogeographical patterns can be analysed with a combination of genetic and ecological niche modelling data. The results demonstrate that species with similar distributional patterns and ecology may still have different biogeographical histories, highlighting the importance of including the temporal dimension when studying biogeographical patterns.     

A critical assessment of two species distribution models: a case study of the vervet monkey (Cercopithecus aethiops)

Aim Species distribution models are invaluable tools in biogeographical, ecological and applied biological research, but specific concerns have been raised in relation to different modelling techniques in terms of their validity. Here we compare two fundamentally different approaches to species distribution modelling, one based on simple occurrence data where the lack of an ecological framework has been criticized, and the other firmly based in socio‐ecological theory but requiring highly detailed behavioural information that is often limited in availability. Location (Sub‐Saharan) Africa. Methods We used two distinct techniques to predict the realized distribution of a model species, the vervet monkey (Cercopithecus aethiops Linnaeus, 1758). A maximum entropy model was produced taking 13 environmental variables and presence‐only data from 174 sites throughout Africa as input, with an additional 58 sites retained to test the model. A time‐budget model considering the same environmental variables was constructed from detailed behavioural data on 20 groups representing 14 populations, with presence‐only data from the remaining 218 sites reserved to test model predictions on vervet monkey occurrence. Both models were further validated against a reference species distribution map as drawn up by the African Mammals Databank. Results Both models performed well, with the time budget and maximum entropy algorithms correctly predicting vervet monkey presence at 78.4% and 91.4% of their respective test sites. Similarly, the time‐budget model correctly predicted presence and absence at 87.4% of map pixels against the reference distribution map, and the maximum entropy model achieved a success rate of 81.8%. Finally, there was a high level of agreement (81.6%) between the presence–absence maps produced by the two models, and the environmental variables identified as most strongly driving vervet monkey distribution were the same in both models. Main conclusions The time‐budget and maximum entropy models produced accurate and remarkably similar species distribution maps, despite fundamental differences in their conceptual and methodological approaches. Such strong convergence not only provides support for the credibility of current results, but also relieves concerns about the validity of the two modelling approaches.     

Finessing atlas data for species distribution models

Aim The spatial resolution of species atlases and therefore resulting model predictions are often too coarse for local applications. Collecting distribution data at a finer resolution for large numbers of species requires a comprehensive sampling effort, making it impractical and expensive. This study outlines the incorporation of existing knowledge into a conventional approach to predict the distribution of Bonelli’s eagle (Aquila fasciata) at a resolution 100 times finer than available atlas data. Location Malaga province, Andalusia, southern Spain. Methods A Bayesian expert system was proposed to utilize the knowledge from distribution models to yield the probability of a species being recorded at a finer resolution (1 × 1 km) than the original atlas data (10 × 10 km). The recorded probability was then used as a weight vector to generate a sampling scheme from the species atlas to enhance the accuracy of the modelling procedure. The maximum entropy for species distribution modelling (MaxEnt) was used as the species distribution model. A comparison was made between the results of the MaxEnt using the enhanced and, the random sampling scheme, based on four groups of environmental variables: topographic, climatic, biological and anthropogenic. Results The models with the sampling scheme enhanced by an expert system had a higher discriminative capacity than the baseline models. The downscaled (i.e. finer scale) species distribution maps using a hybrid MaxEnt/expert system approach were more specific to the nest locations and were more contrasted than those of the baseline model. Main conclusions The proposed method is a feasible substitute for comprehensive field work. The approach developed in this study is applicable for predicting the distribution of Bonelli’s eagle at a local scale from a national‐level occurrence data set; however, the usefulness of this approach may be limited to well‐known species.     

Fine scale waterbody data improve prediction of waterbird occurrence despite coarse species data

While modelling habitat suitability and species distribution, ecologists must deal with issues related to the spatial resolution of species occurrence and environmental data. Indeed, given that the spatial resolution of species and environmental datasets range from centimeters to hundreds of kilometers, it underlines the importance of choosing the optimal combination of resolutions to achieve the highest possible modelling prediction accuracy. We evaluated how the spatial resolution of land cover/waterbody datasets (meters to 1 km) affect waterbird habitat suitability models based on atlas data (grid cell of 12 × 11 km). We hypothesized that the area, perimeter and number of waterbodies computed from high resolution datasets would explain distributions of waterbirds better because coarse resolution datasets omit small waterbodies affecting species occurrence. Specifically, we investigated which spatial resolution of waterbodies better explain the distribution of seven waterbirds nesting on ponds/lakes with areas ranging from 0.1 ha to hundreds of hectares. Our results show that the area and perimeter of waterbodies derived from high resolution datasets (raster data with 30 m resolution, vector data corresponding with map scale 1:10 000) explain the distribution of the waterbirds better than those calculated using less accurate datasets despite the coarse grain of the species data. Taking into account the spatial extent (global vs regional) of the datasets, we found the Global Inland Waterbody Dataset to be the most suitable for modelling distribution of waterbirds. In general, we recommend using land cover data of a resolution sufficient to capture the smallest patches of the habitat suitable for a given species’ presence for both fine and coarse grain habitat suitability and distribution modelling.     

Integrating detection probabilities in species distribution models of amphibians breeding in Mediterranean temporary ponds

Aim The imperfect detection of species may lead to erroneous conclusions about species–environment relationships. Accuracy in species detection usually requires temporal replication at sampling sites, a time‐consuming and costly monitoring scheme. Here, we applied a lower‐cost alternative based on a double‐sampling approach to incorporate the reliability of species detection into regression‐based species distribution modelling. Location Doñana National Park (south‐western Spain). Methods Using species‐specific monthly detection probabilities, we estimated the detection reliability as the probability of having detected the species given the species‐specific survey time. Such reliability estimates were used to account explicitly for data uncertainty by weighting each absence. We illustrated how this novel framework can be used to evaluate four competing hypotheses as to what constitutes primary environmental control of amphibian distribution: breeding habitat, aestivating habitat, spatial distribution of surrounding habitats and/or major ecosystems zonation. The study was conducted on six pond‐breeding amphibian species during a 4‐year period. Results Non‐detections should not be considered equivalent to real absences, as their reliability varied considerably. The occurrence of Hyla meridionalis and Triturus pygmaeus was related to a particular major ecosystem of the study area, where suitable habitat for these species seemed to be widely available. Characteristics of the breeding habitat (area and hydroperiod) were of high importance for the occurrence of Pelobates cultripes and Pleurodeles waltl. Terrestrial characteristics were the most important predictors of the occurrence of Discoglossus galganoi and Lissotriton boscai, along with spatial distribution of breeding habitats for the last species. Main conclusions We did not find a single best supported hypothesis valid for all species, which stresses the importance of multiscale and multifactor approaches. More importantly, this study shows that estimating the reliability of non‐detection records, an exercise that had been previously seen as a naïve goal in species distribution modelling, is feasible and could be promoted in future studies, at least in comparable systems.     

Inferring evolutionary scenarios with geostatistics and geographical information systems for the viperid snakes Vipera latastei and Vipera monticola

The present study aimed to infer evolutionary scenarios for Vipera latastei and Vipera monticola in the Iberian Peninsula and the Maghreb through the identification of spatial patterns in morphological character variation and biogeographic patterns in morphological variability distribution. Ten morphological traits from 630 vipers were analysed with geostatistic and ecological niche modelling in a geographical information system. Interpolation by Kriging was used to generate surfaces of morphological variation, which were combined with spatial principal components analysis (SPCA). Putative morphological differentiated groups generated by SPCA maps were tested with discriminant function analysis (DFA). Maximum entropy modelling and nine environmental variables were used to identify factors limiting the distribution of groups and areas for their potential occurrence. Groups supported by DFA were: Western Iberia, Eastern Iberia, Rif plus Middle Atlas, Algeria, and High Atlas. Their distribution is influenced by common environmental factors such as precipitation. Areas of probable sympatry between Iberian groups matched the morphological clines observed by geostatistics tools. Geographic variation patterns in V. latastei‐monticola are probably due to vicariant separation of Iberian and African populations during the opening of the Strait of Gibraltar, and population refugia during the Quaternary glaciations with secondary contact. The taxonomic status of northern Morocco and Algerian groups should be further investigated. We conclude that geostatistics and niche‐modelling tools are adequate to infer morphological variability across wide geographic ranges of species. © 2008 The Linnean Society of London, Biological Journal of the Linnean Society, 2008, 95 , 790–806.     

Improving species distribution models using biotic interactions: a case study of parasites,pollinators and plants

Biotic interactions have been considered as an important factor to be included in species distribution modelling, but little is known about how different types of interaction or different strategies for modelling affect model performance. This study compares different methods for including interspecific interactions in distribution models for bees, their brood parasites, and the plants they pollinate. Host–parasite interactions among bumble bees (genus Bombus: generalist pollinators and brood parasites) and specialist plant–pollinator interactions between Centris bees and Krameria flowers were used as case studies. We used 7 different modelling algorithms available in the BIOMOD R package. For Bombus, the inclusion of interacting species distributions generally increased model predictive accuracy. The improvement was better when the interacting species was included with its raw distribution rather than with its modeled suitability. However, incorporating the distributions of non‐interacting species sometimes resulted in similarly increased model accuracy despite their being no significance of any interaction for the distribution. For the Centris‐Krameria system the best strategy for modelling biotic interactions was to include the interacting species model‐predicted values. However, the results were less consistent than those for Bombus species, and most models including biotic interactions showed no significant improvement over abiotic models. Our results are consistent with previous studies showing that biotic interactions can be important in structuring species distributions at regional scales. However, correlations between species distributions are not necessarily indicative of interactions. Therefore, choosing the correct biotic information, based on biological and ecological knowledge, is critical to improve the accuracy of species distribution models and forecast distribution change.