Ecological niche models display nonlinear relationships with abundance and demographic performance across the latitudinal distribution of Astragalus utahensis (Fabaceae)

The potential for ecological niche models (ENMs) to accurately predict species' abundance and demographic performance throughout their geographic distributions remains a topic of substantial debate in ecology and biogeography. Few studies simultaneously examine the relationship between ENM predictions of environmental suitability and both a species' abundance and its demographic performance, particularly across its entire geographic distribution. Yet, studies of this type are essential for understanding the extent to which ENMs are a viable tool for identifying areas that may promote high abundance or performance of a species or how species might respond to future climate conditions. In this study, we used an ensemble ecological niche model to predict climatic suitability for the perennial forb Astragalus utahensis across its geographic distribution. We then examined relationships between projected climatic suitability and field‐based measures of abundance, demographic performance, and forecasted stochastic population growth (λ_s). Predicted climatic suitability showed a J‐shaped relationship with A. utahensis abundance, where low‐abundance populations were associated with low‐to‐intermediate suitability scores and abundance increased sharply in areas of high predicted climatic suitability. A similar relationship existed between climatic suitability and λ_s from the center to the northern edge of the latitudinal distribution. Patterns such as these, where density or demographic performance only increases appreciably beyond some threshold of climatic suitability, support the contention that ENM‐predicted climatic suitability does not necessarily represent a reliable predictor of abundance or performance across large geographic regions.     

Ecological niche models of invasive seaweeds

Ecological niche models (ENMs) are commonly used to calculate habitat suitability from species’ occurrence and macroecological data. In invasive species biology, ENMs can be applied to anticipate whether invasive species are likely to establish in an area, to identify critical routes and arrival points, to build risk maps and to predict the extent of potential spread following an introduction. Most studies using ENMs focus on terrestrial organisms and applications in the marine realm are still relatively rare. Here, we review some common methods to build ENMs and their application in seaweed invasion biology. We summarize methods and concepts involved in the development of niche models, show examples of how they have been applied in studies on algae and discuss the application of ENMs in invasive algae research and to predict effects of climate change on seaweed distributions.     

Is there a correlation between abundance and environmental suitability derived from ecological niche modelling? A meta‐analysis

It is thought that species abundance is correlated with environmental suitability and that environmental variables, scale, and type of model fitting can confound this relationship. We performed a meta‐analysis to 1) test whether species abundance is positively correlated with environmental suitability derived from correlative ecological niche models (ENM), 2) test whether studies encompassing large areas within a species range (> 50%) exhibited higher AS correlations than studies encompassing small areas within a species range (< 50%), 3) assess which modelling method provided higher AS correlation, and 4) compare strength of the AS relationship between studies using only climatic variables and those that used both climatic and other environmental variables to derive suitability. We used correlation coefficients to measure the relationship between abundance and environmental suitability derived from ENM. Each correlation coefficient was considered an effect size in a random‐effects multivariate meta‐analysis. In all cases we found a significantly positive relationship between abundance and suitability. This relationship was consistent regardless of scale of study, ENM method, or set of variables used to derive suitability. There was no difference in strength of correlation between studies focusing on large or small areas within a species’ range or among ENM methods. Studies using other variables in combination with climate exhibited higher AS correlations than studies using only climatic variables. We conclude that occurrence data can be a reasonable proxy for abundance, especially for vertebrates, and the use of local variables increases the strength of the AS relationship. Use of ENMs can significantly decrease survey costs and allow the study of large‐scale abundance patterns using less information. Including only climatic variables in ENM may confound the relationship between abundance and suitability when compared to studies including variables taken locally. However, modelers and conservationists must be aware that high environmental suitability does not always indicate high abundance.     

Climate change likely to reduce orchid bee abundance even in climatic suitable sites

Studies have tested whether model predictions based on species’ occurrence can predict the spatial pattern of population abundance. The relationship between predicted environmental suitability and population abundance varies in shape, strength and predictive power. However, little attention has been paid to the congruence in predictions of different models fed with occurrence or abundance data, in particular when comparing metrics of climate change impact. Here, we used the ecological niche modeling fit with presence–absence and abundance data of orchid bees to predict the effect of climate change on species and assembly level distribution patterns. In addition, we assessed whether predictions of presence–absence models can be used as a proxy to abundance patterns. We obtained georeferenced abundance data of orchid bees (Hymenoptera: Apidae: Euglossina) in the Brazilian Atlantic Forest. Sampling method consisted in attracting male orchid bees to baits of at least five different aromatic compounds and collecting the individuals with entomological nets or bait traps. We limited abundance data to those obtained by similar standard sampling protocol to avoid bias in abundance estimation. We used boosted regression trees to model ecological niches and project them into six climate models and two Representative Concentration Pathways. We found that models based on species occurrences worked as a proxy for changes in population abundance when the output of the models were continuous; results were very different when outputs were discretized to binary predictions. We found an overall trend of diminishing abundance in the future, but a clear retention of climatically suitable sites too. Furthermore, geographic distance to gained climatic suitable areas can be very short, although it embraces great variation. Changes in species richness and turnover would be concentrated in western and southern Atlantic Forest. Our findings offer support to the ongoing debate of suitability–abundance models and can be used to support spatial conservation prioritization schemes and species triage in Atlantic Forest.     

Can species distribution modelling provide estimates of population densities? A case study with jaguars in the Neotropics

Aim To test the prediction that environmental suitability derived from species distribution modelling (SDM) could be a surrogate for jaguar local population density estimates. Location Americas. Methods We used 1409 occurrence records of jaguars to model the distribution of the species using 11 SDM methods. We tested whether models’ suitability is linearly correlated with jaguar population densities estimated from 37 different locations. We evaluated whether the relationship between density and suitability forms a constraint envelope, in which higher densities are found mainly in regions with high suitability, whereas low densities can occur in regions with variable suitability. We tested this using heteroscedasticity test and quantile regressions. Results A positive linear relationship between suitability and jaguar density was found only for four methods [bioclimatic envelope (BIOCLIM), genetic algorithm for rule set production (GARP), maximum entropy (Maxent) and generalized boosting models (GBM)], but with weak explanatory power. BIOCLIM showed the strongest relationship. Variance of suitability for lower densities values was larger than for higher values for many of the SDM models used, but the quantile regression was significantly positive only for BIOCLIM and random forests (RF). RF and GBM provided the most accurate models when measured with the standard SDM evaluation metrics, but possess poor relationship with local density estimates. Main conclusions Results indicate that the relationship between density and suitability could be better described as a triangular constraint envelope than by a straight positive relationship, and some of the SDM methods tested here were able to discriminate regions with high or low local population densities. Low jaguar densities can occur in areas with low or high suitability, whereas high values are restricted to areas where the suitability is greater. In high suitability areas but with low jaguar density estimates, we discuss how extrinsic factors driving abundance could act at local scales and then prevent higher densities that would be expected by the favourable regional environmental conditions.     

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.     

The role of resource dispersion in promoting the co‐occurrence of dominant and subordinate ant species

Dominant competitors govern resource use in many communities, leading to predictions of local exclusion and lower species diversity where dominant species are abundant. However, subordinate and dominant species frequently co‐occur. One mechanism that could facilitate resource sharing and co‐occurrence of dominant and subordinate competitors is fine‐scale resource dispersion. Here, we distributed 6 g of a food resource into 1, 2, 8, 32 or 64 units in small 0.40 m² areas centred on nests of the dominant ant Monomorium sydneyense. We tested three hypotheses. First, we hypothesized that the species richness and abundance of foraging ants would increase with increasing resource dispersion. Accordingly, species richness doubled and total ant abundance was two orders of magnitude higher in high resource dispersion treatments. Secondly, we hypothesized that increasing resource dispersion would reduce competitive interactions such as resource turnover events and lower the probability of food resources being occupied. Substantial support for this hypothesis was observed. Finally, we tested the hypothesis that the foraging time of each species would be proportional to the relative abundance of each species solely in high resource dispersion treatments. Expected and observed foraging times were statistically similar for only the dominant ant M. sydneyense. The subdominant Pheidole rugosula increased its foraging time much more than was expected, while two subordinate ants showed no relationship between observed and expected times. Thus, while increasing resource dispersion significantly increased overall species richness, this increase in co‐occurrence did not correlate with a significant increase in foraging time for the two subordinate species. Rather, changes in resource dispersion appeared to benefit only the subdominant species. Inter‐site variation appeared more important for other subordinate species indetermining co‐occurrence and foraging time. Multiple mechanisms facilitate co‐occurrence and resource sharing in this community, and probably in most other communities.     

Characterizing and predicting species distributions across environments and scales: Argentine ant occurrences in the eye of the beholder

Aim Species distribution models (SDMs) or, more specifically, ecological niche models (ENMs) are a useful and rapidly proliferating tool in ecology and global change biology. ENMs attempt to capture associations between a species and its environment and are often used to draw biological inferences, to predict potential occurrences in unoccupied regions and to forecast future distributions under environmental change. The accuracy of ENMs, however, hinges critically on the quality of occurrence data. ENMs often use haphazardly collected data rather than data collected across the full spectrum of existing environmental conditions. Moreover, it remains unclear how processes affecting ENM predictions operate at different spatial scales. The scale (i.e. grain size) of analysis may be dictated more by the sampling regime than by biologically meaningful processes. The aim of our study is to jointly quantify how issues relating to region and scale affect ENM predictions using an economically important and ecologically damaging invasive species, the Argentine ant (Linepithema humile). Location California, USA. Methods We analysed the relationship between sampling sufficiency, regional differences in environmental parameter space and cell size of analysis and resampling environmental layers using two independently collected sets of presence/absence data. Differences in variable importance were determined using model averaging and logistic regression. Model accuracy was measured with area under the curve (AUC) and Cohen's kappa. Results We first demonstrate that insufficient sampling of environmental parameter space can cause large errors in predicted distributions and biological interpretation. Models performed best when they were parametrized with data that sufficiently sampled environmental parameter space. Second, we show that altering the spatial grain of analysis changes the relative importance of different environmental variables. These changes apparently result from how environmental constraints and the sampling distributions of environmental variables change with spatial grain. Conclusions These findings have clear relevance for biological inference. Taken together, our results illustrate potentially general limitations for ENMs, especially when such models are used to predict species occurrences in novel environments. We offer basic methodological and conceptual guidelines for appropriate sampling and scale matching.     

Genetically informed ecological niche models improve climate change predictions

We examined the hypothesis that ecological niche models (ENMs) more accurately predict species distributions when they incorporate information on population genetic structure, and concomitantly, local adaptation. Local adaptation is common in species that span a range of environmental gradients (e.g., soils and climate). Moreover, common garden studies have demonstrated a covariance between neutral markers and functional traits associated with a species’ ability to adapt to environmental change. We therefore predicted that genetically distinct populations would respond differently to climate change, resulting in predicted distributions with little overlap. To test whether genetic information improves our ability to predict a species’ niche space, we created genetically informed ecological niche models (gENMs) using Populus fremontii (Salicaceae), a widespread tree species in which prior common garden experiments demonstrate strong evidence for local adaptation. Four major findings emerged: (i) gENMs predicted population occurrences with up to 12‐fold greater accuracy than models without genetic information; (ii) tests of niche similarity revealed that three ecotypes, identified on the basis of neutral genetic markers and locally adapted populations, are associated with differences in climate; (iii) our forecasts indicate that ongoing climate change will likely shift these ecotypes further apart in geographic space, resulting in greater niche divergence; (iv) ecotypes that currently exhibit the largest geographic distribution and niche breadth appear to be buffered the most from climate change. As diverse agents of selection shape genetic variability and structure within species, we argue that gENMs will lead to more accurate predictions of species distributions under climate change.     

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.     

Type and spatial structure of distribution data and the perceived determinants of geographical gradients in ecology: the species richness of African birds

Aim Studies exploring the determinants of geographical gradients in the occurrence of species or their traits obtain data by: (1) overlaying species range maps; (2) mapping survey‐based species counts; or (3) superimposing models of individual species’ distributions. These data types have different spatial characteristics. We investigated whether these differences influence conclusions regarding postulated determinants of species richness patterns. Location Our study examined terrestrial bird diversity patterns in 13 nations of southern and eastern Africa, spanning temperate to tropical climates. Methods Four species richness maps were compiled based on range maps, field‐derived bird atlas data, logistic and autologistic distribution models. Ordinary and spatial regression models served to examine how well each of five hypotheses predicted patterns in each map. These hypotheses propose productivity, temperature, the heat–water balance, habitat heterogeneity and climatic stability as the predominant determinants of species richness. Results The four richness maps portrayed broadly similar geographical patterns but, due to the nature of underlying data types, exhibited marked differences in spatial autocorrelation structure. These differences in spatial structure emerged as important in determining which hypothesis appeared most capable of explaining each map's patterns. This was true even when regressions accounted for spurious effects of spatial autocorrelation. Each richness map, therefore, identified a different hypothesis as the most likely cause of broad‐scale gradients in species diversity. Main conclusions Because the ‘true’ spatial structure of species richness patterns remains elusive, firm conclusions regarding their underlying environmental drivers remain difficult. More broadly, our findings suggest that care should be taken to interpret putative determinants of large‐scale ecological gradients in light of the type and spatial characteristics of the underlying data. Indeed, closer scrutiny of these underlying data — here the distributions of individual species — and their environmental associations may offer important insights into the ultimate causes of observed broad‐scale patterns.     

Modelle für den Wandel der Natur

Ecological Modelling – Scenarios of future environmental changes Climate change affects ecosystems at different levels. Changes in species phenology, distribution and interactions are today well described phenomena documenting species responses to increasing temperatures. Environmental niche models (ENMs) have developed as powerful tools to address various questions in macroecology. Aiming at a species environmental niche, statistical modelling can be employed to predict a species' potential occurrence by projecting environmental information recorded at locality records over space and time. In climate change biology, ENMs are used to identify individual species' fates as range expansions or retractions as well as features that affect the structure of species assemblages and species interactions within and across different taxonomic groups. ENMs help to promote the persistence of species by identifying spatial patterns of species richness or endangerment to target conservation priorities. Moreover, they are an essential part of risk assessments to set up preventive measures against non‐native species most likely to adversely impact native ecosystems.     

Testing for local adaptation and evolutionary potential along altitudinal gradients in rainforest Drosophila: beyond laboratory estimates

Predicting how species will respond to the rapid climatic changes predicted this century is an urgent task. Species distribution models (SDMs) use the current relationship between environmental variation and species’ abundances to predict the effect of future environmental change on their distributions. However, two common assumptions of SDMs are likely to be violated in many cases: (i) that the relationship of environment with abundance or fitness is constant throughout a species’ range and will remain so in future and (ii) that abiotic factors (e.g. temperature, humidity) determine species’ distributions. We test these assumptions by relating field abundance of the rainforest fruit fly Drosophila birchii to ecological change across gradients that include its low and high altitudinal limits. We then test how such ecological variation affects the fitness of 35 D. birchii families transplanted in 591 cages to sites along two altitudinal gradients, to determine whether genetic variation in fitness responses could facilitate future adaptation to environmental change. Overall, field abundance was highest at cooler, high‐altitude sites, and declined towards warmer, low‐altitude sites. By contrast, cage fitness (productivity) increased towards warmer, lower‐altitude sites, suggesting that biotic interactions (absent from cages) drive ecological limits at warmer margins. In addition, the relationship between environmental variation and abundance varied significantly among gradients, indicating divergence in ecological niche across the species’ range. However, there was no evidence for local adaptation within gradients, despite greater productivity of high‐altitude than low‐altitude populations when families were reared under laboratory conditions. Families also responded similarly to transplantation along gradients, providing no evidence for fitness trade‐offs that would favour local adaptation. These findings highlight the importance of (i) measuring genetic variation in key traits under ecologically relevant conditions, and (ii) considering the effect of biotic interactions when predicting species’ responses to environmental change.     

A spatio‐temporal framework for efficient inventories of natural resources: A case study with submersed macrophytes

Questions: Does a reduced nutrient load in open water increase species richness and the importance of regional and local site characteristics for species abundance and spatial distribution? Can we build lake‐specific models of macrophyte abundance and distribution based on site characteristics in order to prepare a cost‐efficient framework for future surveys? Location: Lake Constance, 47°39′N, 9°18′E. Methods: Generalized additive models (GAMs) were used to predict the potential distributions of eight species and overall species richness. Submersed macrophyte distribution in 1993 was compared with corresponding data from 1978, when eutrophication was at its maximum. Results: Spatial predictions for eight species and overall species richness were relatively accurate and independent of water chemistry. Depth was confirmed as a main predictor of species distribution, while effective fetch distance was retained in many models. Mineralogical variables of sediment composition represent allogenic and autogenic sediment sources and their east‐west gradient in Lake Constance corresponded to east‐west gradients of species distribution and richness. GAMs appeared more efficient than generalized linear models (GLMs) for modelling species responses to environmental gradients. Conclusions: Reduced trophic status increases species richness and the importance of regional and local site characteristics for species abundance and distribution. Our models represent a spatio‐temporal framework for future lake monitoring purposes and allow the development of effective monitoring; this could be generalized for many ecosystem types and would be particularly efficient for large lakes such as Lake Constance.     

Patterns of tree species richness in relation to environment in southeastern New South Wales,Australia

Abstract We present regression models of species richness for total tree species, two growth forms, rainforest trees (broadleaf evergreens) and eucalypts (sclerophylls), and two large subgenera of Eucalyptus. The correlative models are based on a data set of 166 tree species from 7208 plots in an area of southeastern New South Wales, Australia. Eight environmental variables are used to model the patterns of species richness, four continuous variables (mean annual temperature, rainfall, radiation and plot size), plus four categorical factors (topographic position, lithology, soil nutrient level and rainfall seasonality). Generalized linear modelling with curvilinear and interaction terms, is used to derive the models. Each model shows a significant and differing response to the environmental predictors. Maximum species richness of eucalypts occurs at high temperatures, and intermediate rainfall and radiation conditions on ridges with aseasonal rainfall and intermediate nutrient levels. Maximum richness of rainforest species occurs at high temperatures, intermediate rainfall and low radiation in gullies with summer rainfall and high nutrient levels. The eucalypt subgenera models differ in ways consistent with experimental studies of habitat preferences of the subgenera. Curvilinear and interaction terms are necessary for adequate modelling. Patterns of richness vary widely with taxonomic rank and growth form. Any theories of species diversity should be consistent with these correlative models. The models are consistent with an available energy hypothesis based on actual evapotranspiration. We conclude that studies of species richness patterns should include local (e.g. soil nutrients, topographic position) and regional (e.g. mean annual temperature, annual rainfall) environmental variables before invoking concepts such as niche saturation.     

Assessing plant-nutrient relationships in highly invaded Californian grasslands using non-normal probability distributions

Question: Is native species occurrence related to soil nutrients in highly invaded Californian annual grasslands? What is the best method to analyze this relationship, given that native species occur in very low numbers and are absent from many locations? Location: California, USA. Methods: We investigated the effects of soil characteristics and livestock grazing on native plant occurrence at 40 plots from six sites during the period 2003–2005. Low absolute cover (< 5.8%) of native species resulted in strongly skewed, zero-inflated data sets. To overcome problems in the analysis created by non-normality and correlations within plots, we used GLMs and GLMMs, either with a Poisson or a negative binomial distribution, to analyse native species richness and Nassella pulchra cover. Results: N. pulchra cover was strongly associated with low phosphorus in sandy soils, whereas native species richness was highest in soils with low available nitrogen (high C:N). Conclusion: Under current conditions, phosphorus seems to be a critical factor influencing abundance of N. pulchra. Low fertility soils may provide refugia for native species in highly invaded California grasslands as they may be below a threshold required for non-native annuals to completely dominate. By using non-normal distributions in linear models with random components, we report well fitted models with more accurately tested significant covariates.     

Inferring relationships between native plant diversity and Lonicera japonica in upland forests in north Mississippi,USA

Question: Do anthropogenic disturbances interact with local environmental factors to increase the abundance and frequency of invasive species, which in turn exerts a negative effect on native biodiversity? Location: Mature Quercus‐Carya and Quercus‐Carya‐Pinus (oak‐hickory‐pine) forests in north Mississippi, USA. Methods: We used partial correlation and factor analysis to investigate relationships between native ground cover plant species richness and composition, percent cover of Lonicera japonica, and local and landscape‐level environmental variables and disturbance patterns in mature upland forests. We directly measured vegetation and environmental variables within 34 sampling subplots and quantified the amount of tree cover surrounding our plots using digital color aerial photography. Results: Simple bivariate correlations revealed that high species richness and a high proportion of herbs were associated with low Lonicera japonica cover, moist and sandy uncompacted soils, low disturbance in the surrounding landscape, and periodic prescribed burning. Partial correlations and factor analysis showed that once we accounted for the environmental factors, L japonica cover was the least important predictor of composition and among the least important predictors of species richness. Hence, much of the negative correlation between native species diversity and this invasive species was explained by soil texture and local and landscape‐level land‐use practices. Conclusions: We conclude that negative correlations between the abundance of invasive species and native plant diversity can occur in landscapes with a gradient of human disturbance, regardless of whether there is any negative effect of invasive species on native species.     

Current and future distribution of a parasite with complex life cycle under global change scenarios: Echinococcus multilocularis in Europe

Global change is expected to have complex effects on the distribution and transmission patterns of zoonotic parasites. Modelling habitat suitability for parasites with complex life cycles is essential to further our understanding of how disease systems respond to environmental changes, and to make spatial predictions of their future distributions. However, the limited availability of high quality occurrence data with high spatial resolution often constrains these investigations. Using 449 reliable occurrence records for Echinococcus multilocularis from across Europe published over the last 35 years, we modelled habitat suitability for this parasite, the aetiological agent of alveolar echinococcosis, in order to describe its environmental niche, predict its current and future distribution under three global change scenarios, and quantify the probability of occurrence for each European country. Using a machine learning approach, we developed large-scale (25 × 25 km) species distribution models based on seven sets of predictors, each set representing a distinct biological hypothesis supported by current knowledge of the autecology of the parasite. The best-supported hypothesis included climatic, orographic and land-use/land-cover variables such as the temperature of the coldest quarter, forest cover, urban cover and the precipitation seasonality. Future projections suggested the appearance of highly suitable areas for E. multilocularis towards northern latitudes and in the whole Alpine region under all scenarios, while decreases in habitat suitability were predicted for central Europe. Our spatially explicit predictions of habitat suitability shed light on the complex responses of parasites to ongoing global changes.     

Relationships between survival and habitat suitability of semi‐aquatic mammals

Spatial distribution and habitat selection are integral to the study of animal ecology. Habitat selection may optimize the fitness of individuals. Hutchinsonian niche theory posits the fundamental niche of species would support the persistence or growth of populations. Although niche‐based species distribution models (SDMs) and habitat suitability models (HSMs) such as maximum entropy (Maxent) have demonstrated fair to excellent predictive power, few studies have linked the prediction of HSMs to demographic rates. We aimed to test the prediction of Hutchinsonian niche theory that habitat suitability (i.e., likelihood of occurrence) would be positively related to survival of American beaver (Castor canadensis), a North American semi‐aquatic, herbivorous, habitat generalist. We also tested the prediction of ideal free distribution that animal fitness, or its surrogate, is independent of habitat suitability at the equilibrium. We estimated beaver monthly survival probability using the Barker model and radio telemetry data collected in northern Alabama, United States from January 2011 to April 2012. A habitat suitability map was generated with Maxent for the entire study site using landscape variables derived from the 2011 National Land Cover Database (30‐m resolution). We found an inverse relationship between habitat suitability index and beaver survival, contradicting the predictions of niche theory and ideal free distribution. Furthermore, four landscape variables selected by American beaver did not predict survival. The beaver population on our study site has been established for 20 or more years and, subsequently, may be approaching or have reached the carrying capacity. Maxent‐predicted increases in habitat use and subsequent intraspecific competition may have reduced beaver survival. Habitat suitability‐fitness relationships may be complex and, in part, contingent upon local animal abundance. Future studies of mechanistic SDMs incorporating local abundance and demographic rates are needed.