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.     

The role of abiotic and biotic factors in determining coexistence of multiple pollinators in the yucca–yucca moth mutualism

The determinants of a species' geographic distribution are a combination of both abiotic and biotic factors. Environmental niche modeling of climatic factors has been instrumental in documenting the role of abiotic factors in a species' niche. Integrating this approach with data from species interactions provides a means to assess the relative roles of abiotic and biotic components. Here, we examine whether the high host specificity typically exhibited in the active pollination mutualism between yuccas and yucca moths is the result of differences in climatic niche requirements that limit yucca moth distributions or the result of competition among mutualistic moths that would co‐occur on the same yucca species. We compared the species distribution models of two Tegeticula pollinator moths that use the geographically widespread plant Yucca filamentosa. Tegeticula yuccasella occurs throughout eastern North America whereas T. cassandra is restricted to the southeastern portion of the range, primarily occurring in Florida. Species distribution models demonstrate that T. cassandra is restricted climatically to the southeastern United States and T. yuccasella is predicted to be able to live across all of eastern North America. Data on moth abundances in Florida demonstrate that both moth species are present on Y. filamentosa; however, T. cassandra is numerically dominant. Taken together, the results suggest that moth geographic distributions are heavily influenced by climate, but competition among pollinating congeners will act to restrict populations of moth species that co‐occur.     

Characterization of six microsatellite loci in the African wild silk moth (Gonometa postica,Lasiocampidae)

Six microsatellite markers were developed for the African wild silk moth, Gonometa postica (Lasiocampidae), using an enrichment protocol. The total number of alleles ranged from three to 17 for a sample of 130 individuals across the distribution of the species. Observed levels of heterozygosity ranged from 0.17 to 0.78. Deviation from Hardy–Weinberg equilibrium detected in some loci is probably the result of large interannual population size fluctuations characteristic of this species. No evidence of linkage disequilibrium was detected among loci. These loci will be useful for the inference of demographic processes in a moth species that is of potential economic importance.     

Thorn‐dwelling ants provide antiherbivore defence for camelthorn trees,Vachellia erioloba,in Namibia

Ants are widely employed by plants as an antiherbivore defence. A single host plant can associate with multiple, symbiotic ant species, although usually only a single ant species at a time. Different plant‐ant species may vary in the degree to which they defend their host plant. In Kenya, ant–acacia interactions are well studied, but less is known about systems elsewhere in Africa. A southern African species, Vachellia erioloba, is occupied by thorn‐dwelling ants from three different genera. Unusually, multiple colonies of all these ants simultaneously and stably inhabit trees. We investigated if the ants on V. erioloba (i) deter insect herbivores; (ii) differ in their effectiveness depending on the identity of the herbivore; and (iii) protect the tree against an important herbivore, the larvae of the lepidopteran Gonometa postica. We show that experimental exclusion of ants leads to greater levels of herbivory on trees. The ants inhabiting V. erioloba are an effective deterrent against hemipteran and coleopteran, but not lepidopteran herbivores. Defensive services do not vary among ant species, but only Crematogaster ants exhibit aggression towards G. postica. This highlights the potential of the V. erioloba–ant mutualism for studying ant–plant interactions that involve multiple, simultaneously resident thorn‐dwelling ant species.     

Host plant distributions and climate interact to affect the predicted geographic distribution of a Neotropical termite

Species distribution models (SDMs) have been widely used in the scientific literature. The majority of SDMs use climate data or other abiotic variables to forecast the potential distribution of a species in geographic space. Biotic interactions can affect the predicted spatial distribution of a species in many ways across multiple spatial scales, and incorporating these predictors in an SDM is a current topic in the scientific literature. Constrictotermes cyphergaster is a widely distributed termite in the Neotropics. This termite species nests in plants and more frequently nests in some arboreal species. Thus, this species is an excellent model to evaluate the influence of biotic interactions in SDMs. We evaluate the influences of climate and the geographic distribution of host plants on the potential distribution of C. cyphergaster. Three correlative models (MaxEnt) were built to predict the geographic distribution of the termite: (1) climate data, (2) biotic data (i.e., the geographic distribution of host plants), and (3) climate and biotic data. The models that were generated indicate that the potential geographic distribution of C. cyphergaster is concentrated in the Cerrado and Caatinga regions. In addition, path analysis and multiple regression revealed the importance of the direct effects of biological interactions in the geographic distribution of the termite, while climate affected the distribution of the termite mainly through indirect effects by influencing the geographic distributions of host plants. The current study endorses the importance of including biological interactions in SDMs. We recommend using biotic predictors in SDM studies of insect species, mainly because insects have important environmental services and biotic interaction data can improve the macroecological studies of this group.     

Biotic predictors with phenological information improve range estimates for migrating monarch butterflies in Mexico

Although long-standing theory suggests that biotic variables are only relevant at local scales for explaining the patterns of species' distributions, recent studies have demonstrated improvements to species distribution models (SDMs) by incorporating predictor variables informed by biotic interactions. However, some key methodological questions remain, such as which kinds of interactions are permitted to include in these models, how to incorporate the effects of multiple interacting species, and how to account for interactions that may have a temporal dependence. We addressed these questions in an effort to model the distribution of the monarch butterfly Danaus plexippus during its fall migration (September–November) through Mexico, a region with new monitoring data and uncertain range limits even for this well-studied insect. We estimated species richness of selected nectar plants (Asclepias spp.) and roosting trees (various highland species) for use as biotic variables in our models. To account for flowering phenology, we additionally estimated nectar plant richness of flowering species per month. We evaluated three types of models: climatic variables only (abiotic), plant richness estimates only (biotic) and combined (abiotic and biotic). We selected models with AICc and additionally determined if they performed better than random on spatially withheld data. We found that the combined models accounting for phenology performed best for all three months, and better than random for discriminatory ability but not omission rate. These combined models also produced the most ecologically realistic spatial patterns, but the modeled response for nectar plant richness matched ecological predictions for November only. These results represent the first model-based monarch distributional estimates for the Mexican migration route and should provide foundations for future conservation work. More generally, the study demonstrates the potential benefits of using SDM-derived richness estimates and phenological information for biotic factors affecting species distributions.     

Species distribution models for Peruvian plantcutter improve with consideration of biotic interactions

Biotic interactions have been controversial in distributional ecology, mainly in regards to whether they have effects over broad extents, with the negative view known as the Eltonian noise hypothesis (ENH). In this study, we evaluated the ENH for Phytotoma raimondii, a restricted‐range Peruvian endemic bird species: we developed models based on 1) only abiotic conditions, 2) only host plant distributions, and 3) both abiotic conditions and host plant distributions; models were evaluated with partial receiver operating characteristic test and Akaike information criteria metrics. We rejected the ENH for this case: biotic interactions improved the model. The frequency with which exceptions to the ENH are detected has important implications for distributional ecology and methods for estimating distributions of species.     

Biotic Interactions in the Face of Climate Change: A Comparison of Three Modelling Approaches

Climate change is expected to alter biotic interactions, and may lead to temporal and spatial mismatches of interacting species. Although the importance of interactions for climate change risk assessments is increasingly acknowledged in observational and experimental studies, biotic interactions are still rarely incorporated in species distribution models. We assessed the potential impacts of climate change on the obligate interaction between Aeshna viridis and its egg-laying plant Stratiotes aloides in Europe, based on an ensemble modelling technique. We compared three different approaches for incorporating biotic interactions in distribution models: (1) We separately modelled each species based on climatic information, and intersected the future range overlap (‘overlap approach’). (2) We modelled the potential future distribution of A. viridis with the projected occurrence probability of S. aloides as further predictor in addition to climate (‘explanatory variable approach’). (3) We calibrated the model of A. viridis in the current range of S. aloides and multiplied the future occurrence probabilities of both species (‘reference area approach’). Subsequently, all approaches were compared to a single species model of A. viridis without interactions. All approaches projected a range expansion for A. viridis. Model performance on test data and amount of range gain differed depending on the biotic interaction approach. All interaction approaches yielded lower range gains (up to 667% lower) than the model without interaction. Regarding the contribution of algorithm and approach to the overall uncertainty, the main part of explained variation stems from the modelling algorithm, and only a small part is attributed to the modelling approach. The comparison of the no-interaction model with the three interaction approaches emphasizes the importance of including obligate biotic interactions in projective species distribution modelling. We recommend the use of the ‘reference area approach’ as this method allows a separation of the effect of climate and occurrence of host plant.     

Biotic interactions influence the projected distribution of a specialist mammal under climate change

Aim

To measure the effects of including biotic interactions on climate‐based species distribution models (SDMs) used to predict distribution shifts under climate change. We evaluated the performance of distribution models for an endangered marsupial, the northern bettong (Bettongia tropica), comparing models that used only climate variables with models that also took into account biotic interactions.

Location

North‐east Queensland, Australia.

Methods

We developed separate climate‐based distribution models for the northern bettong, its two main resources and a competitor species. We then constructed models for the northern bettong by including climate suitability estimates for the resources and competitor as additional predictor variables to make climate + resource and climate + resource + competition models. We projected these models onto seven future climate scenarios and compared predictions of northern bettong distribution made by these differently structured models, using a ‘global’ metric, the I similarity statistic, to measure overlap in distribution and a ‘local’ metric to identify where predictions differed significantly.

Results

Inclusion of food resource biotic interactions improved model performance. Over moderate climate changes, up to 3.0 °C of warming, the climate‐only model for the northern bettong gave similar predictions of distribution to the more complex models including interactions, with differences only at the margins of predicted distributions. For climate changes beyond 3.0 °C, model predictions diverged significantly. The interactive model predicted less contraction of distribution than the simpler climate‐only model.

Main conclusions

Distribution models that account for interactions with other species, in particular direct resources, improve model predictions in the present‐day climate. For larger climate changes, shifts in distribution of interacting species cause predictions of interactive models to diverge from climate‐only models. Incorporating interactions with other species in SDMs may be needed for long‐term prediction of changes in distribution of species under climate change, particularly for specialized species strongly dependent on a small number of biotic interactions.     

Parasitoids of the African wild silkmoth,Gonometa postica (Lepidoptera: Lasiocampidae) in the Mwingi forests,Kenya

Gonometa postica Walker produces silk of high quality, but it is affected by parasitoids attack. A study on the parasitism of G. postica larvae and pupae on host and non‐host plants were undertaken for the first and second generations, corresponding to the long (March–May) and short (October–December) rainy seasons in 2006 at six field sites, three each in the Imba and Mumoni forests of Mwingi, eastern Kenya. All freshly spun cocoons of G. postica were sampled at each site from a total of 100 trees of host plants and other non‐host plants where they have migrated before pupation. The cocoons were kept individually in fine net‐sealed plastic vials to determine percentage parasitism. Two dipterans and four hymenopteran larval–pupal parasitoids were identified from the two forests. The most common parasitoids were Palexorista sp. (Diptera: Tachinidae) and Goryphus sp. (Hymenoptera: Ichneumonidae) with parasitism ranging from 1.8 to 32.7% and 2.2 to 7.5%, respectively. Parasitism varied significantly according to host or non‐host plants, seasons and sites. This study indicates that, of the six parasitoid species recovered, only two had a significant impact in reducing the quality of the cocoons.     

The importance of biotic interactions for modelling species distributions under climate change

Aim There is a debate as to whether biotic interactions exert a dominant role in governing species distributions at macroecological scales. The prevailing idea is that climate is the key limiting factor; thus models that use present‐day climate–species range relationships are expected to provide reasonable means to quantify the impacts of climate change on species distributions. However, there is little empirical evidence that biotic interactions would not constrain species distributions at macroecological scales. We examine this idea, for the first time, and provide tests for two null hypotheses: (H₀ 1) – biotic interactions do not exert a significant role in explaining current distributions of a particular species of butterfly (clouded Apollo, Parnassius mnemosyne) in Europe; and (H₀ 2) – biotic interactions do not exert a significant role in predictions of altered species’ ranges under climate change. Location Europe. Methods Generalized additive modelling (GAM) was used to investigate relationships between species and climate; species and host plants; and species and climate + host plants. Because models are sensitive to the variable selection strategies utilised, four alternative approaches were used: AIC (Akaike's Information Criterion), BIC (Bayesian Information Criterion), BRUTO (Adaptive Backfitting), and CROSS (Cross Selection). Results In spite of the variation in the variables selected with different methods, both hypotheses (H₀ 1 and H₀ 2) were falsified, providing support for the proposition that biotic interactions significantly affect both the explanatory and predictive power of bioclimatic envelope models at macro scales. Main conclusions Our results contradict the widely held view that the effects of biotic interactions on individual species distributions are not discernible at macroecological scales. Results are contingent on the species, type of interaction and methods considered, but they call for more stringent evidence in support of the idea that purely climate‐based modelling would be sufficient to quantify the impacts of climate change on species distributions.     

A winner in the Anthropocene: changing host plant distribution explains geographical range expansion in the gulf fritillary butterfly

1. The changing climate is altering species distributions with consequences for population dynamics, resulting in winners and losers in the Anthropocene. 2. Agraulis vanillae, the gulf fritillary butterfly, has expanded its range in the past 100 years in the western U.S.A. Time series analysis is combined with species distribution modelling to investigate factors limiting the distribution of A. vanillae and to predict future shifts under warming scenarios. 3. Time series analyses from the western U.S.A. show that urban development has a positive association with year of colonisation (the host plant Passiflora is an ornamental in gardens). Colonisation was also associated positively and to a lesser extent with winter maximum temperatures, whereas a negative impact of minimum temperatures and precipitation was apparent on population growth rates after establishment. 4. Species distribution models vary by region. In the eastern U.S.A., the butterfly is primarily limited by minimum temperatures in the winter and host availability later in the season. Eastern U.S. projected expansion broadly follows the expectation of poleward distributional shifts, especially for the butterfly's maximum annual extent. Western U.S. distributions are limited by the host plant, which in turn is dependent on urban centres. Projected western U.S. expansion is not limited to a single direction and is driven by urban centres becoming more suitable for the host plant. 5. These results demonstrate the value of combining time series with spatial modelling, at the same time as incorporating biotic interactions, aiming to understand and predict shifting geographical ranges in the Anthropocene.     

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.     

Incorporating biotic factors in species distribution modeling: are interactions with soil microbes important?

It is increasingly recognized that species distributions are driven by both abiotic factors and biotic interactions. Despite much recent work incorporating competition, predation, and mutualism into species distribution models (SDMs), the focus has been confined to aboveground macroscopic interactions. Biotic interactions between plants and soil microbial communities are understudied as potentially important drivers of plant distributions. Some soil bacteria promote plant growth by cycling nutrients, while others are pathogenic; thus they have a high potential for influencing plant occurrence. We investigated the influence of soil bacterial clades on the distributions of bryophytes and 12 vascular plant species in a high elevation talus‐field ecosystem in the Rocky Mountain Front Range, Colorado, USA. We used an information‐theoretic criterion (AICc) modeling approach to compare SDMs with the following different sets of predictors: abiotic variables, abiotic variables and other plant abundances, abiotic variables and soil bacteria clade relative abundances, and a full model with abiotic factors, plant abundances, and bacteria relative abundances. We predicted that bacteria would influence plant distributions both positively and negatively, and that these interactions would improve prediction of plant species distributions. We found that inclusion of either plant or bacteria biotic predictors generally improved the fit, deviance explained, and predictive power of the SDMs, and for the majority of the species, adding information on both other plants and bacteria yielded the best model. Interactions between the modeled species and biotic predictors were both positive and negative, suggesting the presence of competition, parasitism, and facilitation. While our results indicate that plant–plant co‐occurrences are a stronger driver of plant distributions than plant–bacteria co‐occurrences, they also show that bacteria can explain parts of plant distributions that remain unexplained by abiotic and plant predictors. Our results provide further support for including biotic factors in SDMs, and suggest that belowground factors be considered as well.     

Mutualist‐mediated effects on species' range limits across large geographic scales

Understanding the processes determining species range limits is central to predicting species distributions under climate change. Projected future ranges are extrapolated from distribution models based on climate layers, and few models incorporate the effects of biotic interactions on species' distributions. Here, we show that a positive species interaction ameliorates abiotic stress, and has a profound effect on a species' range limits. Combining field surveys of 92 populations, 10 common garden experiments throughout the range, species distribution models and greenhouse experiments, we show that mutualistic fungal endophytes ameliorate drought stress and broaden the geographic range of their native grass host Bromus laevipes by thousands of square kilometres (~ 20% larger) into drier habitats. Range differentiation between fungal‐associated and fungal‐free grasses was comparable to species‐level range divergence of congeners, indicating large impacts on range limits. Positive biotic interactions may be underappreciated in determining species' ranges and species' responses to future climates across large geographic scales.     

Biotic interactions mediate the expansion of black mangrove (Avicennia germinans) into salt marshes under climate change

Many species are expanding their distributions to higher latitudes due to global warming. Understanding the mechanisms underlying these distribution shifts is critical for better understanding the impacts of climate changes. The climate envelope approach is widely used to model and predict species distribution shifts with changing climates. Biotic interactions between species, however, may also influence species distributions, and a better understanding of biotic interactions could improve predictions based solely on climate envelope models. Along the northern Gulf of Mexico coast, USA, subtropical black mangrove (Avicennia germinans) at the northern limit of its distribution grows sympatrically with temperate salt marsh plants in Florida, Louisiana, and Texas. In recent decades, freeze‐free winters have led to an expansion of black mangrove into salt marshes. We examined how biotic interactions between black mangrove and salt marsh vegetation along the Texas coast varied across (i) a latitudinal gradient (associated with a winter‐temperature gradient); (ii) the elevational gradient within each marsh (which creates different marsh habitats); and (iii) different life history stages of black mangroves (seedlings vs. juvenile trees). Each of these variables affected the strength or nature of biotic interactions between black mangrove and salt marsh vegetation: (i) Salt marsh vegetation facilitated black mangrove seedlings at their high‐latitude distribution limit, but inhibited black mangrove seedlings at lower latitudes; (ii) mangroves performed well at intermediate elevations, but grew and survived poorly in high‐ and low‐marsh habitats; and (iii) the effect of salt marsh vegetation on black mangroves switched from negative to neutral as black mangroves grew from seedlings into juvenile trees. These results indicate that the expansion of black mangroves is mediated by complex biotic interactions. A better understanding of the impacts of climate change on ecological communities requires incorporating context‐dependent biotic interactions into species range models.     

Incorporating intraspecific variation into species distribution models improves distribution predictions,but cannot predict species traits for a wide-spread plant species

The most common approach to predicting how species ranges and ecological functions will shift with climate change is to construct correlative species distribution models (SDMs). These models use a species’ climatic distribution to determine currently suitable areas for the species and project its potential distribution under future climate scenarios. A core, rarely tested, assumption of SDMs is that all populations will respond equivalently to climate. Few studies have examined this assumption, and those that have rarely dissect the reasons for intraspecific differences. Focusing on the arctic-alpine cushion plant Silene acaulis, we compared predictive accuracy from SDMs constructed using the species’ full global distribution with composite predictions from separate SDMs constructed using subpopulations defined either by genetic or habitat differences. This is one of the first studies to compare multiple ways of constructing intraspecific-level SDMs with a species-level SDM. We also examine the contested relationship between relative probability of occurrence and species performance or ecological function, testing if SDM output can predict individual performance (plant size) and biotic interactions (facilitation). We found that both genetic- and habitat-informed SDMs are considerably more accurate than a species-level SDM, and that the genetic model substantially differs from and outperforms the habitat model. While SDMs have been used to infer population performance and possibly even biotic interactions, in our system these relationships were extremely weak. Our results indicate that individual subpopulations may respond differently to climate, although we discuss and explore several alternative explanations for the superior performance of intraspecific-level SDMs. We emphasize the need to carefully examine how to best define intraspecific-level SDMs as well as how potential genetic, environmental, or sampling variation within species ranges can critically affect SDM predictions. We urge caution in inferring population performance or biotic interactions from SDM predictions, as these often-assumed relationships are not supported in our study.     

Biotic Interactions Overrule Plant Responses to Climate,Depending on the Species' Biogeography

This study presents an experimental approach to assess the relative importance of climatic and biotic factors as determinants of species'' geographical distributions. We asked to what extent responses of grassland plant species to biotic interactions vary with climate, and to what degree this variation depends on the species'' biogeography. Using a gradient from oceanic to continental climate represented by nine common garden transplant sites in Germany, we experimentally tested whether congeneric grassland species of different geographic distribution (oceanic vs. continental plant range type) responded differently to combinations of climate, competition and mollusc herbivory. We found the relative importance of biotic interactions and climate to vary between the different components of plant performance. While survival and plant height increased with precipitation, temperature had no effect on plant performance. Additionally, species with continental plant range type increased their growth in more benign climatic conditions, while those with oceanic range type were largely unable to take a similar advantage of better climatic conditions. Competition generally caused strong reductions of aboveground biomass and growth. In contrast, herbivory had minor effects on survival and growth. Against expectation, these negative effects of competition and herbivory were not mitigated under more stressful continental climate conditions. In conclusion we suggest variation in relative importance of climate and biotic interactions on broader scales, mediated via species-specific sensitivities and factor-specific response patterns. Our results have important implications for species distribution models, as they emphasize the large-scale impact of biotic interactions on plant distribution patterns and the necessity to take plant range types into account.     

Biotic interactions boost spatial models of species richness

Biotic interactions are known to affect the composition of species assemblages via several mechanisms, such as competition and facilitation. However, most spatial models of species richness do not explicitly consider inter‐specific interactions. Here, we test whether incorporating biotic interactions into high‐resolution models alters predictions of species richness as hypothesised. We included key biotic variables (cover of three dominant arctic‐alpine plant species) into two methodologically divergent species richness modelling frameworks – stacked species distribution models (SSDM) and macroecological models (MEM) – for three ecologically and evolutionary distinct taxonomic groups (vascular plants, bryophytes and lichens). Predictions from models including biotic interactions were compared to the predictions of models based on climatic and abiotic data only. Including plant–plant interactions consistently and significantly lowered bias in species richness predictions and increased predictive power for independent evaluation data when compared to the conventional climatic and abiotic data based models. Improvements in predictions were constant irrespective of the modelling framework or taxonomic group used. The global biodiversity crisis necessitates accurate predictions of how changes in biotic and abiotic conditions will potentially affect species richness patterns. Here, we demonstrate that models of the spatial distribution of species richness can be improved by incorporating biotic interactions, and thus that these key predictor factors must be accounted for in biodiversity forecasts.     

Assessing congruence of opportunistic records and systematic surveys for predicting Hispaniolan mammal species distributions

Comparative assessment of the relative information content of different independent spatial data types is necessary to evaluate whether they provide congruent biogeographic signals for predicting species ranges. Opportunistic occurrence records and systematically collected survey data are available from the Dominican Republic for Hispaniola’s surviving endemic non‐volant mammals, the Hispaniolan solenodon (Solenodon paradoxus) and Hispaniolan hutia (Plagiodontia aedium); opportunistic records (archaeological, historical and recent) exist from across the entire country, and systematic survey data have been collected from seven protected areas. Species distribution models were developed in maxent for solenodons and hutias using both data types, with species habitat suitability and potential country‐level distribution predicted using seven biotic and abiotic environmental variables. Three different models were produced and compared for each species: (a) opportunistic model, with starting model incorporating abiotic‐only predictors; (b) total survey model, with starting model incorporating biotic and abiotic predictors; and (c) reduced survey model, with starting model incorporating abiotic‐only predictors to allow further comparison with the opportunistic model. All models predict suitable environmental conditions for both solenodons and hutias across a broadly congruent, relatively large area of the Dominican Republic, providing a spatial baseline of conservation‐priority landscapes that might support native mammals. Correlation between total and reduced survey models is high for both species, indicating the substantial explanatory power of abiotic variables for predicting Hispaniolan mammal distributions. However, correlation between survey models and opportunistic models is only moderately positive. Species distribution models derived from different data types can provide different predictions about habitat suitability and conservation‐priority landscapes for threatened species, likely reflecting incompleteness and bias in spatial sampling associated with both data types. Models derived using both opportunistic and systematic data must therefore be applied critically and cautiously.