Understanding niche shifts: using current and historical data to model the invasive redlegged earth mite,Halotydeus destructor

Aim Niche conservatism is key to understanding species responses to environmental stress such as climate change or arriving in new geographical space such as biological invasion. Halotydeus destructor is an important agricultural pest in Australia and has been the focus of extensive surveys that suggest this species has undergone a niche shift to expand its invasive range inland to hotter and drier environments. We employ modern correlative modelling methods to examine niche conservatism in H. destructor and highlight ecological differences between historical and current distributions. Location Australia and South Africa. Methods We compile comprehensive distribution data sets for H. destructor, representing the native range in South Africa, its invasive range in Australia in the 1960s (40 yr post‐introduction) and its current range in Australia. Using MAXENT, we build correlative models and reciprocally project them between South Africa and Australia and investigate range expansion with models constructed for historical and current data sets. We use several recently developed model exploration tools to examine the climate similarity between native and invasive ranges and subsequently examine climatic variables that limit distributions. Results The invasive niche of H. destructor in Australia transgresses the native niche in South Africa, and the species has expanded in Australia beyond what is predicted from the native distribution. Our models support the notion that H. destructor has undergone a more recent range shift into hotter and drier inland areas of Australia since establishing a stable distribution in the 1960s. Main conclusions Our use of historical and current data highlights that invasion is an ongoing dynamic process and demonstrates that once a species has reached an established range, it may still expand at a later stage. We also show that model exploration tools help understand factors influencing the range of invasive species. The models generate hypotheses about adaptive shifts in H. destructor.     

virtualspecies,an R package to generate virtual species distributions

virtualspecies is a freely available package for R designed to generate virtual species distributions, a procedure increasingly used in ecology to improve species distribution models. This package combines the existing methodological approaches with the objective of generating virtual species distributions with increased ecological realism. The package includes 1) generating the probability of occurrence of a virtual species from a spatial set of environmental conditions (i.e. environmental suitability), with two different approaches; 2) converting the environmental suitability into presence–absence with a probabilistic approach; 3) introducing dispersal limitations in the realised virtual species distributions and 4) sampling occurrences with different biases in the sampling procedure. The package was designed to be extremely flexible, to allow users to simulate their own defined species–environment relationships, as well as to provide a fine control over every simulation parameter. The package also includes a function to generate random virtual species distributions. We provide a simple example in this paper showing how increasing ecological realism of the virtual species impacts the predictive performance of species distribution models. We expect that this new package will be valuable to researchers willing to test techniques and protocols of species distribution models as well as various biogeographical hypotheses.     

Co‐occurrence patterns of trees along macro‐climatic gradients and their potential influence on the present and future distribution of Fagus sylvatica L.

Aim During recent and future climate change, shifts in large‐scale species ranges are expected due to the hypothesized major role of climatic factors in regulating species distributions. The stress‐gradient hypothesis suggests that biotic interactions may act as major constraints on species distributions under more favourable growing conditions, while climatic constraints may dominate under unfavourable conditions. We tested this hypothesis for one focal tree species having three major competitors using broad‐scale environmental data. We evaluated the variation of species co‐occurrence patterns in climate space and estimated the influence of these patterns on the distribution of the focal species for current and projected future climates. Location Europe. Methods We used ICP Forest Level 1 data as well as climatic, topographic and edaphic variables. First, correlations between the relative abundance of European beech (Fagus sylvatica) and three major competitor species (Picea abies, Pinus sylvestris and Quercus robur) were analysed in environmental space, and then projected to geographic space. Second, a sensitivity analysis was performed using generalized additive models (GAM) to evaluate where and how much the predicted F. sylvatica distribution varied under current and future climates if potential competitor species were included or excluded. We evaluated if these areas coincide with current species co‐occurrence patterns. Results Correlation analyses supported the stress‐gradient hypothesis: towards favourable growing conditions of F. sylvatica, its abundance was strongly linked to the abundance of its competitors, while this link weakened towards unfavourable growing conditions, with stronger correlations in the south and at low elevations than in the north and at high elevations. The sensitivity analysis showed a potential spatial segregation of species with changing climate and a pronounced shift of zones where co‐occurrence patterns may play a major role. Main conclusions Our results demonstrate the importance of species co‐occurrence patterns for calibrating improved species distribution models for use in projections of climate effects. The correlation approach is able to localize European areas where inclusion of biotic predictors is effective. The climate‐induced spatial segregation of the major tree species could have ecological and economic consequences.     

An evaluation of behavior inferences from Bayesian state‐space models: A case study with the Pacific walrus

State‐space models offer researchers an objective approach to modeling complex animal location data sets, and state‐space model behavior classifications are often assumed to have a link to animal behavior. In this study, we evaluated the behavioral classification accuracy of a Bayesian state‐space model in Pacific walruses using Argos satellite tags with sensors to detect animal behavior in real time. We fit a two‐state discrete‐time continuous‐space Bayesian state‐space model to data from 306 Pacific walruses tagged in the Chukchi Sea. We matched predicted locations and behaviors from the state‐space model (resident, transient behavior) to true animal behavior (foraging, swimming, hauled out) and evaluated classification accuracy with kappa statistics (κ) and root mean square error (RMSE). In addition, we compared biased random bridge utilization distributions generated with resident behavior locations to true foraging behavior locations to evaluate differences in space use patterns. Results indicated that the two‐state model fairly classified true animal behavior (0.06 ≤ κ ≤ 0.26, 0.49 ≤ RMSE ≤ 0.59). Kernel overlap metrics indicated utilization distributions generated with resident behavior locations were generally smaller than utilization distributions generated with true foraging behavior locations. Consequently, we encourage researchers to carefully examine parameters and priors associated with behaviors in state‐space models, and reconcile these parameters with the study species and its expected behaviors.     

Using avatar species to model the potential distribution of emerging invaders

Aim Anticipating the potential distributions of emerging invasive species is complicated by the tendency for species distribution models to perform better when both native and invasive range data are available for model development. If invasive range data are lacking, species models are liable to under‐estimate distributions for emerging invaders, particularly for species that are not at equilibrium with their native range environment due to historical factors, dispersal limitation and/or ecological interactions. We demonstrate the potential to use well‐quantified niche shifts from established ‘avatar’ (i.e. the remote or virtual manifestation of an entity) invaders to develop plausible distributions for data‐poor emerging invaders contingent on niche shifts of similar magnitude or character. Location Global. Methods Using the globally invasive crayfishes Pacifastacus leniusculus and Procambarus clarkii as our avatar invaders, we quantify how niche position, size and structure differs between native and total ranges using Mahalanobis distance (a measure of multivariate similarity) and the climate predictors of annual minimum and maximum air temperature. We then generalize patterns of niche shift from these species to the emerging crayfish invader Cherax quadricarinatus. Results Some patterns of niche shifts were similar for Pacifastacus leniusculus and Procambarus clarkii, but niche shifts were of considerably greater magnitude for P. clarkii. When a native range model for C. quadricarinatus was modified with generalized niche shifts similar to Pacifastacus leniusculus and Procambarus clarkii, the potential global distribution for this species increased considerably, including many areas not identified by the native range model. Main conclusions We illustrate the potential to use avatar invaders to provide cautionary, niche shift‐assuming species distribution models for emerging invaders. Many theoretical and applied implications of the avatar species concept require additional investigation, including the development of frameworks to select appropriate avatar species and evaluate the performance of avatar‐derived models for emerging invaders. Despite these research needs, we believe this concept will have considerable utility for predicting vulnerability to invasion by data‐poor species; this is a critical management need because shifting pathways of introduction and climate change will produce many novel, emerging invasive species in the future.     

Causal or spurious relationship? Climate and the distribution of Phelsuma geckos on Grand Comoro Island

The volcanic island of Grand Comoro, Malagasy biogeographic region, is inhabited by three species of Phelsuma day geckos; two island‐endemic taxa (Phelsuma comorensis and Phelsuma v‐nigra comoraegrandensis) and the introduced Phelsuma dubia. Phelsuma comorensis is restricted to elevations of greater than 150 m above sea level on the northern of the island's two volcanoes and is the only Phelsuma above 300 m. The other species are widespread at low elevations but also reach levels above 900 m at the southern volcano. To investigate these divergent distribution patterns, we used environmental niche models based on climate and habitat data and tested whether predicted climate change may influence species distributions. Analyses of niche overlap did not show significant differences between present‐day and predicted future potential distributions of any Phelsuma species studied, which could be seen as an indicator of resilience towards climate change. Climate models reflected the restricted distribution of P. comorensis with precipitation of the wettest month detected as most important variable, whereas habitat models predicted an island‐wide distribution. While climate appears to determine the distribution of P. comorensis, we propose isolation by migration barriers as an alternative and discuss the detection of causal versus spurious relationships in ecological niche models.     

Ecological niche modeling re‐examined: A case study with the Darwin's fox

Many previous studies have attempted to assess ecological niche modeling performance using receiver operating characteristic (ROC) approaches, even though diverse problems with this metric have been pointed out in the literature. We explored different evaluation metrics based on independent testing data using the Darwin's Fox (Lycalopex fulvipes) as a detailed case in point. Six ecological niche models (ENMs; generalized linear models, boosted regression trees, Maxent, GARP, multivariable kernel density estimation, and NicheA) were explored and tested using six evaluation metrics (partial ROC, Akaike information criterion, omission rate, cumulative binomial probability), including two novel metrics to quantify model extrapolation versus interpolation (E‐space index I) and extent of extrapolation versus Jaccard similarity (E‐space index II). Different ENMs showed diverse and mixed performance, depending on the evaluation metric used. Because ENMs performed differently according to the evaluation metric employed, model selection should be based on the data available, assumptions necessary, and the particular research question. The typical ROC AUC evaluation approach should be discontinued when only presence data are available, and evaluations in environmental dimensions should be adopted as part of the toolkit of ENM researchers. Our results suggest that selecting Maxent ENM based solely on previous reports of its performance is a questionable practice. Instead, model comparisons, including diverse algorithms and parameterizations, should be the sine qua non for every study using ecological niche modeling. ENM evaluations should be developed using metrics that assess desired model characteristics instead of single measurement of fit between model and data. The metrics proposed herein that assess model performance in environmental space (i.e., E‐space indices I and II) may complement current methods for ENM evaluation.     

Mutualism influences species distribution predictions for a bromeliad‐breeding anuran under climate change

Ecological niche models, or species distribution models, have been widely used to identify potentially suitable areas for species in future climate change scenarios. However, there are inherent errors to these models due to their inability to evaluate species occurrence influenced by non‐climatic factors. With the intuit to improve the modelling predictions for a bromeliad‐breeding treefrog (Phyllodytes melanomystax, Hylidae), we investigate how the climatic suitability of bromeliads influences the distribution model for the treefrog in the context of baseline and 2050 climate change scenarios. We used point occurrence data on the frog and the bromeliad (Vriesea procera, Bromeliaceae) to generate their predicted distributions based on baseline and 2050 climates. Using a consensus of five algorithms, we compared the accuracy of the models and the geographic predictions for the frog generated from two modelling procedures: (i) a climate‐only model for P. melanomystax and V. procera; and (ii) a climate‐biotic model for P. melanomystax, in which the climatic suitability of the bromeliad was jointly considered with the climatic variables. Both modelling approaches generated strong and similar predictive power for P. melanomystax, yet climate‐biotic modelling generated more concise predictions, particularly for the year 2050. Specifically, because the predicted area of the bromeliad overlaps with the predictions for the treefrog in the baseline climate, both modelling approaches produce reasonable similar predicted areas for the anuran. Alternatively, due to the predicted loss of northern climatically suitable areas for the bromeliad by 2050, only the climate‐biotic models provide evidence that northern populations of P. melanomystax will likely be negatively affected by 2050.     

Color vision models: Some simulations,a general n‐dimensional model,and the colourvision R package

The development of color vision models has allowed the appraisal of color vision independent of the human experience. These models are now widely used in ecology and evolution studies. However, in common scenarios of color measurement, color vision models may generate spurious results. Here I present a guide to color vision modeling (Chittka (1992, Journal of Comparative Physiology A, 170, 545) color hexagon, Endler & Mielke (2005, Journal Of The Linnean Society, 86, 405) model, and the linear and log‐linear receptor noise limited models (Vorobyev & Osorio 1998, Proceedings of the Royal Society B, 265, 351; Vorobyev et al. 1998, Journal of Comparative Physiology A, 183, 621)) using a series of simulations, present a unified framework that extends and generalize current models, and provide an R package to facilitate the use of color vision models. When the specific requirements of each model are met, between‐model results are qualitatively and quantitatively similar. However, under many common scenarios of color measurements, models may generate spurious values. For instance, models that log‐transform data and use relative photoreceptor outputs are prone to generate spurious outputs when the stimulus photon catch is smaller than the background photon catch; and models may generate unrealistic predictions when the background is chromatic (e.g. leaf reflectance) and the stimulus is an achromatic low reflectance spectrum. Nonetheless, despite differences, all three models are founded on a similar set of assumptions. Based on that, I provide a new formulation that accommodates and extends models to any number of photoreceptor types, offers flexibility to build user‐defined models, and allows users to easily adjust chromaticity diagram sizes to account for changes when using different number of photoreceptors.     

Unraveling the habitat preferences of two closely related bumble bee species in Eastern Europe

Co‐occurrence of closely related species is often explained through resource partitioning, where key morphological or life‐history traits evolve under strong divergent selection. In bumble bees (genus Bombus), differences in tongue lengths, nest sites, and several life‐history traits are the principal factors in resource partitioning. However, the buff‐tailed and white‐tailed bumble bee (Bombus terrestris and B. lucorum respectively) are very similar in morphology and life history, but their ranges nevertheless partly overlap, raising the question how they are ecologically divergent. What little is known about the environmental factors determining their distributions stems from studies in Central and Western Europe, but even less information is available about their distributions in Eastern Europe, where different subspecies occur. Here, we aimed to disentangle the broad habitat requirements and associated distributions of these species in Romania and Bulgaria. First, we genetically identified sampled individuals from many sites across the study area. We then not only computed species distributions based on presence‐only data, but also expanded on these models using relative abundance data. We found that B. terrestris is a more generalist species than previously thought, but that B. lucorum is restricted to forested areas with colder and wetter climates, which in our study area are primarily found at higher elevations. Both vegetation parameters such as annual mean Leaf Area Index and canopy height, as well as climatic conditions, were important in explaining their distributions. Although our models based on presence‐only data suggest a large overlap in their respective distributions, results on their relative abundance suggest that the two species replace one another across an environmental gradient correlated to elevation. The inclusion of abundance enhances our understanding of the distribution of these species, supporting the emerging recognition of the importance of abundance data in species distribution modeling.     

Movement Behavior of Native and Invasive Small Mammals Shows Logging May Facilitate Invasion in a Tropical Rain Forest

Invasive species pose one of the greatest threats to biodiversity. This study investigates the extent to which human disturbance to natural ecosystems facilitates the spread of non‐native species, focusing on a small mammal community in selectively logged rain forest, Sabah, Borneo. The microhabitat preferences of the invasive Rattus rattus and three native species of small mammal were examined in three‐dimensional space by combining the spool‐and‐line technique with a novel method for quantifying fine‐scale habitat selection. These methods allowed the detection of significant differences for each species between the microhabitats used compared with alternative, available microhabitats that were avoided. Rattus rattus showed the greatest preference for heavily disturbed habitats, and in contrast to two native small mammals of the genus Maxomys, R. rattus showed high levels of arboreal behavior, frequently leaving the forest floor and traveling through the understory and midstory forest strata. This behavior may enable R. rattus to effectively utilize the complex three‐dimensional space of the lower strata in degraded forests, which is characterized by dense vegetation. The behavioral flexibility of R. rattus to operate in both terrestrial and arboreal space may facilitate its invasion into degraded forests. Human activities that generate heavily disturbed habitats preferred by R. rattus may promote the establishment of this invasive species in tropical forests in Borneo, and possibly elsewhere. We present this as an example of a synergistic effect, whereby forest disturbance directly threatens biodiversity and indirectly increases the threat posed by invasive species, creating habitat conditions that facilitate the establishment of non‐native fauna.     

Incorporating biotic interactions in the distribution models of African wild silk moths (Gonometa species,Lasiocampidae) using different representations of modelled host tree distributions

Biotic interactions influence species niches and may thus shape distributions. Nevertheless, species distribution modelling has traditionally relied exclusively on environmental factors to predict species distributions, while biotic interactions have only seldom been incorporated into models. This study tested the ability of incorporating biotic interactions, in the form of host plant distributions, to increase model performance for two host‐dependent lepidopterans of economic interest, namely the African silk moth species, Gonometa postica and Gonometa rufobrunnea (Lasiocampidae). Both species are dependent on a small number of host tree species for the completion of their life cycle. We thus expected the host plant distribution to be an important predictor of Gonometa distributions. Model performance of a species distribution model trained only on abiotic predictors was compared to four species distribution models that additionally incorporated biotic interactions in the form of four different representations of host plant distributions as predictors. We found that incorporating the moth–host plant interactions improved G. rufobrunnea model performance for all representations of host plant distribution, while for G. postica model performance only improved for one representation of host plant distribution. The best performing representation of host plant distribution differed for the two Gonometa species. While these results suggest that incorporating biotic interactions into species distribution models can improve model performance, there is inconsistency in which representation of the host tree distribution best improves predictions. Therefore, the ability of biotic interactions to improve species distribution models may be context‐specific, even for species which have obligatory interactions with other organisms.     

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.     

Hydrologic refugia,plants, and climate change

Climate, physical landscapes, and biota interact to generate heterogeneous hydrologic conditions in space and over time, which are reflected in spatial patterns of species distributions. As these species distributions respond to rapid climate change, microrefugia may support local species persistence in the face of deteriorating climatic suitability. Recent focus on temperature as a determinant of microrefugia insufficiently accounts for the importance of hydrologic processes and changing water availability with changing climate. Where water scarcity is a major limitation now or under future climates, hydrologic microrefugia are likely to prove essential for species persistence, particularly for sessile species and plants. Zones of high relative water availability – mesic microenvironments – are generated by a wide array of hydrologic processes, and may be loosely coupled to climatic processes and therefore buffered from climate change. Here, we review the mechanisms that generate mesic microenvironments and their likely robustness in the face of climate change. We argue that mesic microenvironments will act as species‐specific refugia only if the nature and space/time variability in water availability are compatible with the ecological requirements of a target species. We illustrate this argument with case studies drawn from California oak woodland ecosystems. We posit that identification of hydrologic refugia could form a cornerstone of climate‐cognizant conservation strategies, but that this would require improved understanding of climate change effects on key hydrologic processes, including frequently cryptic processes such as groundwater flow.     

Predicting species distributions from checklist data using site‐occupancy models

Aim (1) To increase awareness of the challenges induced by imperfect detection, which is a fundamental issue in species distribution modelling; (2) to emphasize the value of replicate observations for species distribution modelling; and (3) to show how ‘cheap’ checklist data in faunal/floral databases may be used for the rigorous modelling of distributions by site‐occupancy models. Location Switzerland. Methods We used checklist data collected by volunteers during 1999 and 2000 to analyse the distribution of the blue hawker, Aeshna cyanea (Odonata, Aeshnidae), a common dragonfly in Switzerland. We used data from repeated visits to 1‐ha pixels to derive ‘detection histories’ and apply site‐occupancy models to estimate the ‘true’ species distribution, i.e. corrected for imperfect detection. We modelled blue hawker distribution as a function of elevation and year and its detection probability of elevation, year and season. Results The best model contained cubic polynomial elevation effects for distribution and quadratic effects of elevation and season for detectability. We compared the site‐occupancy model with a conventional distribution model based on a generalized linear model, which assumes perfect detectability (p = 1). The conventional distribution map looked very different from the distribution map obtained using site‐occupancy models that accounted for the imperfect detection. The conventional model underestimated the species distribution by 60%, and the slope parameters of the occurrence–elevation relationship were also underestimated when assuming p = 1. Elevation was not only an important predictor of blue hawker occurrence, but also of the detection probability, with a bell‐shaped relationship. Furthermore, detectability increased over the season. The average detection probability was estimated at only 0.19 per survey. Main conclusions Conventional species distribution models do not model species distributions per se but rather the apparent distribution, i.e. an unknown proportion of species distributions. That unknown proportion is equivalent to detectability. Imperfect detection in conventional species distribution models yields underestimates of the extent of distributions and covariate effects that are biased towards zero. In addition, patterns in detectability will erroneously be ascribed to species distributions. In contrast, site‐occupancy models applied to replicated detection/non‐detection data offer a powerful framework for making inferences about species distributions corrected for imperfect detection. The use of ‘cheap’ checklist data greatly enhances the scope of applications of this useful class of models.     

Predictive Mapping of Species Richness and Plant Species' Distributions of a Peruvian Fog Oasis Along an Altitudinal Gradient

Tropical arid to semi‐arid ecosystems are nearly as diverse as more humid forests and occupy large parts of the tropics. In comparison, however, they are vastly understudied. For instance, fog precipitation alone supports a unique vegetation formation, locally termed lomas, on coastal mountains in the Peruvian desert. To effectively protect these highly endemic and threatened ecosystems, we must increase our understanding of their diversity patterns in relation to environmental factors. Consequently, we recorded all vascular species from 100 random 4 × 4 m plots on the fog‐exposed southern slope of the mountain Mongón. We used topographic and remotely sensed covariates in statistical models to generate spatial predictions of alpha diversity and plant species' distribution probabilities. Altitude was the most important predictor in all models and may represent fog moisture levels. Other significant covariates in the models most likely refer also to water availability but on a finer spatial scale. Additionally, model‐based clustering revealed five altitudinal vegetation zones. This study contributes to a better spatial understanding of the biodiversity and spatial arrangement of vegetation belts of the largely unknown but highly unique lomas formations. Furthermore, mapping species richness and plant species' distributions could support a long‐needed lomas strategic conservation scheme.     

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.     

Climate and geochemistry as drivers of eucalypt diversification in Australia

Eucalypts cover most of Australia. Here, we investigate the relative contribution of climate and geochemistry to the distribution and diversity of eucalypts. Using geostatistics, we estimate major element concentrations, pH, and electrical conductivity at sites where eucalypts have been recorded. We compare the median predicted geochemistry and reported substrate for individual species that appear associated with extreme conditions; this provides a partial evaluation of the predictions. We generate a site‐by‐species matrix by aggregating observations to the centroids of 100‐km‐wide grid cells, calculate diversity indices, and use numerical ecology methods (ordination, variation partitioning) to investigate the ecology of eucalypts and their response to climatic and geochemical gradients. We find that β‐diversity coincides with variations in climatic and geochemical patterns. Climate and geochemistry together account for less than half of the variation in eucalypt species assemblages across Australia but for greater than 80% in areas of high species richness. Climate is more important than geochemistry in explaining eucalypts species distribution and change in assemblages across Australia as a whole but there are correlations between the two sets of environmental variables. Many individual eucalypt species and entire taxonomic sections (Aromatica, Longistylus of subgenus Eucalyptus, Dumaria, and Liberivalvae of subgenus Symphyomyrtus) have distributions affected strongly by geochemistry. We conclude that eucalypt diversity is driven by steep geochemical gradients that have arisen as climate patterns have fluctuated over Australia over the Cenozoic, generally aridifying since the Miocene. The diversification of eucalypts across Australia is thus an excellent example of co‐evolution of landscapes and biota in space and time and challenges accepted notions of macroecology.     

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.     

A trait‐based approach for predicting species responses to environmental change from sparse data: how well might terrestrial mammals track climate change?

Estimating population spread rates across multiple species is vital for projecting biodiversity responses to climate change. A major challenge is to parameterise spread models for many species. We introduce an approach that addresses this challenge, coupling a trait‐based analysis with spatial population modelling to project spread rates for 15 000 virtual mammals with life histories that reflect those seen in the real world. Covariances among life‐history traits are estimated from an extensive terrestrial mammal data set using Bayesian inference. We elucidate the relative roles of different life‐history traits in driving modelled spread rates, demonstrating that any one alone will be a poor predictor. We also estimate that around 30% of mammal species have potential spread rates slower than the global mean velocity of climate change. This novel trait‐space‐demographic modelling approach has broad applicability for tackling many key ecological questions for which we have the models but are hindered by data availability.