Multispectral canopy reflectance improves spatial distribution models of Amazonian understory species

Species distribution models are required for the research and management of biodiversity in the hyperdiverse tropical forests, but reliable and ecologically relevant digital environmental data layers are not always available. We here assess the usefulness of multispectral canopy reflectance (Landsat) relative to climate data in modelling understory plant species distributions in tropical rainforests. We used a large dataset of quantitative fern and lycophyte species inventories across lowland Amazonia as the basis for species distribution modelling (SDM). As predictors, we used CHELSA climatic variables and canopy reflectance values from a recent basin-wide composite of Landsat TM/ETM+ images both separately and in combination. We also investigated how species accumulate over sites when environmental distances were expressed in terms of climatic or surface reflectance variables. When species accumulation curves were constructed such that differences in Landsat reflectance among the selected plots were maximised, species accumulated faster than when climatic differences were maximised or plots were selected in a random order. Sixty-nine species were sufficiently frequent for species distribution modelling. For most of them, adequate SDMs were obtained whether the models were based on CHELSA data only, Landsat data only or both combined. Model performance was not influenced by species’ prevalence or abundance. Adding Landsat-based environmental data layers overall improved the discriminatory capacity of SDMs compared to climate-only models, especially for soil specialist species. Our results show that canopy surface reflectance obtained by multispectral sensors can provide studies of tropical ecology, as exemplified by SDMs, much higher thematic (taxonomic) detail than is generally assumed. Furthermore, multispectral datasets complement the traditionally used climatic layers in analyses requiring information on environmental site conditions. We demonstrate the utility of freely available, global remote sensing data for biogeographical studies that can aid conservation planning and biodiversity management.     

Exploring the relationship between land cover and the distribution of water beetle species (Coleoptera) at the regional scale

The advent of remote-sensed satellite land cover data has provided the opportunity to assess the relationship between invertebrate species distributions and individual land cover types. Water beetle species occur in habitats within specific land cover types and the relationship between the distribution of water beetle species and land covers at the regional scale was investigated using records of 154 species from 1018 sites in north-east England. The land covers of tilled land and urban in the lowlands and of shrub heath and heath grassland in the upland areas proved to be most important in explaining the distribution of species. There were both positive and negative associations between some species and other covers such as woodland and the coast. However, a considerable number of species, generally those with a large number of records, showed no strong relationships with any land cover types. The integration of water beetle species recording data and remote-sensed land cover data as a basis for predicting and monitoring both species distribution and environmental change is discussed.     

Habitat Diversity in the Conservation of the Grassland Auchenorrhyncha (Homoptera: Cercopidae,Cicadellidae, Cixidae,Delphacidae) of Northern Britain

Definition of northern British grassland Auchenorrhyncha habitats was carried out using a classification based on analysis of data from 351 sites, involving 121 species, located between Greater Manchester and northern Scotland. Ten habitats were identified showing little influence of geographical position and exhibiting a basic upland-lowland trend. Other factors influencing habitat and species assemblage distribution were soil water, vegetation structure and land cover. An analysis of the species data with satellite-derived land cover data indicated that the lowland covers of tilled land, coast and urban and the upland covers of heath grassland and shrub heath were most important in affecting both species and assemblage distribution. The large-scale survey of grassland sites provided new information on both the ecology and distribution of individual Auchenorrhyncha species. Some were limited to specific habitat types but a considerable number were generalist species found in most or all of the 10 habitat types but showing preferences within upland to lowland or wet to dry site gradients. The ability to generate a subtle grassland Auchenorrhyncha habitat classification with large-scale survey results from standardised and reproducible sampling increases the potential for using habitat diversity for the conservation of grassland Auchenorrhyncha. Habitat preservation would also ensure that species richness (biodiversity) is maintained and that the habitats of rare species are conserved.     

Application of Predictive Distribution Modelling to Invertebrates: Odonata in South Africa

The application of distributional modelling techniques to invertebrates has seldom been explored, primarily due to a lack in adequate distributional data for these taxa. Here, we have selected a simple modelling approach for the generation of distribution maps from a limited dataset, as a first step to the atlassing of Odonata in South Africa. The BIOCLIM-type approach was selected for this purpose, as it requires minimal data for model building and validation procedures. BIOCLIM partitions an area climatically prior to survey, and predicts species distributions on a bioclimatic basis. Conservative deterministic models were developed using point presence/absence data for each of the regions' 160 described species. These models were validated by cross-validation, and the Jaccard coefficient of similarity was used as an index of model performance. A sensitivity analysis investigated the influence of extreme values and errors in the data on predictive ability. Models identified disjunct distribution patterns and accurately predicted the restricted ranges of habitat-specialist species. However, models overstated the distribution of habitat generalists and species with distinct outlier records. For accurate predictions of broad-ranging species, it is suggested that a probabilistic approach be adopted. Nevertheless, basic distribution patterns generated through this conservative approach can be further applied to the investigation of species richness and issues relating to conservation, such as reserve design. The BIOCLIM-type approach provided a means of predicting species distributions, allowing for broad-scale atlassing and thereby providing the first step towards Odonata conservation in South Africa.     

The effect of positional error on fine scale species distribution models increases for specialist species

Species occurrences inherently include positional error. Such error can be problematic for species distribution models (SDMs), especially those based on fine-resolution environmental data. It has been suggested that there could be a link between the influence of positional error and the width of the species ecological niche. Although positional errors in species occurrence data may imply serious limitations, especially for modelling species with narrow ecological niche, it has never been thoroughly explored. We used a virtual species approach to assess the effects of the positional error on fine-scale SDMs for species with environmental niches of different widths. We simulated three virtual species with varying niche breadth, from specialist to generalist. The true distribution of these virtual species was then altered by introducing different levels of positional error (from 5 to 500 m). We built generalized linear models and MaxEnt models using the distribution of the three virtual species (unaltered and altered) and a combination of environmental data at 5 m resolution. The models’ performance and niche overlap were compared to assess the effect of positional error with varying niche breadth in the geographical and environmental space. The positional error negatively impacted performance and niche overlap metrics. The amplitude of the influence of positional error depended on the species niche, with models for specialist species being more affected than those for generalist species. The positional error had the same effect on both modelling techniques. Finally, increasing sample size did not mitigate the negative influence of positional error. We showed that fine-scale SDMs are considerably affected by positional error, even when such error is low. Therefore, where new surveys are undertaken, we recommend paying attention to data collection techniques to minimize the positional error in occurrence data and thus to avoid its negative effect on SDMs, especially when studying specialist species.     

Modelling invasion for a habitat generalist and a specialist plant species

Predicting suitable habitat and the potential distribution of invasive species is a high priority for resource managers and systems ecologists. Most models are designed to identify habitat characteristics that define the ecological niche of a species with little consideration to individual species' traits. We tested five commonly used modelling methods on two invasive plant species, the habitat generalist Bromus tectorum and habitat specialist Tamarix chinensis , to compare model performances, evaluate predictability, and relate results to distribution traits associated with each species. Most of the tested models performed similarly for each species; however, the generalist species proved to be more difficult to predict than the specialist species. The highest area under the receiver-operating characteristic curve values with independent validation data sets of B. tectorum and T. chinensis was 0.503 and 0.885, respectively. Similarly, a confusion matrix for B. tectorum had the highest overall accuracy of 55%, while the overall accuracy for T. chinensis was 85%. Models for the generalist species had varying performances, poor evaluations, and inconsistent results. This may be a result of a generalist's capability to persist in a wide range of environmental conditions that are not easily defined by the data, independent variables or model design. Models for the specialist species had consistently strong performances, high evaluations, and similar results among different model applications. This is likely a consequence of the specialist's requirement for explicit environmental resources and ecological barriers that are easily defined by predictive models. Although defining new invaders as generalist or specialist species can be challenging, model performances and evaluations may provide valuable information on a species' potential invasiveness.     

Variable sensitivity of plant communities in Iceland to experimental warming

Facing an increased threat of rapid climate change in cold‐climate regions, it is important to understand the sensitivity of plant communities both in terms of degree and direction of community change. We studied responses to 3–5 years of moderate experimental warming by open‐top chambers in two widespread but contrasting tundra communities in Iceland. In a species‐poor and nutrient‐deficient moss heath, dominated by Racomitrium lanuginosum, mean daily air temperatures at surface were 1–2°C higher in the warmed plots than the controls whereas soil temperatures tended to be lower in the warmed plots throughout the season. In a species‐rich dwarf shrub heath on relatively rich soils at a cooler site, dominated by Betula nana and R. lanuginosum, temperature changes were in the same direction although more moderate. In the moss heath, there were no detectable community changes while significant changes were detected in the dwarf shrub heath: the abundance of deciduous and evergreen dwarf shrubs significantly increased (>50%), bryophytes decreased (18%) and canopy height increased (100%). Contrary to some other studies of tundra communities, we detected no changes in species richness or other diversity measures in either community and the abundance of lichens did not change. It is concluded that the sensitivity of Icelandic tundra communities to climate warming varies greatly depending on initial conditions in terms of species diversity, dominant species, soil and climatic conditions as well as land‐use history.     

Spatial analysis of a subalpine heath woodland

The horizontal spatial patterns of heath species have implications in dynamic community modelling, fire behaviour modelling and ecological interpretation. The distribution pattern of the dominant woody species in a subalpine heath woodland near Kiandra, New South Wales was analysed as spatial point processes. The species analysed were Bossiaea foliosa, Grevillea australis, Hakea lissosperma and Oxylobium ellipticum, and both large (>2m) and small (<2 m) snowgums (Eucalyptus pauciflora) They all exhibited significant aggregation at scales ranging from 1 to 15 m. Bivariate spatial analyses of these species revealed significant negative association between G. australis and H. lissosperma, between G. australis and O. ellipticum, and between O. ellipticum and H. lissosperma, B. foliosa was independently distributed with respect to other shrub species. There was some evidence for negative association between small snowgums and shrub species, though small snowgums were positively associated with large snowgums. The joint spatial distribution of the individuals of all shrub species was also aggregated. A Poisson cluster process was developed and tested to model the joint spatial pattern of the shrub stratum.     

Modelling the occurrence of threatened plant species in taiga landscapes: methodological and ecological perspectives

Aim Understanding the spatial patterns of species distribution and predicting the occurrence of high biological diversity and rare species are central themes in biogeography and environmental conservation. The aim of this study was to model and scrutinize the relative contributions of climate, topography, geology and land‐cover factors to the distributions of threatened vascular plant species in taiga landscapes in northern Finland. Location North‐east Finland, northern Europe. Methods The study was performed using a data set of 28 plant species and environmental variables at a 25‐ha resolution. Four different stepwise selection algorithms [Akaike information criterion (AIC), Bayesian information criterion (BIC), adaptive backfitting, cross selection] with generalized additive models (GAMs) were fitted to identify the main environmental correlates for species occurrences. The accuracies of the distribution models were evaluated using fourfold cross‐validation based on the area under the curve (AUC) derived from receiver operating characteristic plots. The GAMs were tentatively extrapolated to the whole study area and species occurrence probability maps were produced using GIS techniques. The effect of spatial autocorrelation on the modelling results was also tested by including autocovariate terms in the GAMs. Results According to the AUC values, the model performance varied from fair to excellent. The AIC algorithm provided the highest mean performance (mean AUC = 0.889), whereas the lowest mean AUC (0.851) was obtained from BIC. Most of the variation in the distribution of threatened plant species was related to growing degree days, temperature of the coldest month, water balance, cover of mire and mean elevation. In general, climate was the most powerful explanatory variable group, followed by land cover, topography and geology. Inclusion of the autocovariate only slightly improved the performance of the models and had a minor effect on the importance of the environmental variables. Main conclusions The results confirm that the landscape‐scale distribution patterns of plant species can be modelled well on the basis of environmental parameters. A spatial grid system with several environmental variables derived from remote sensing and GIS data was found to produce useful data sets, which can be employed when predicting species distribution patterns over extensive areas. Landscape‐scale maps showing the predicted occurrences of individual or multiple threatened plant species may provide a useful basis for focusing field surveys and allocating conservation efforts.     

Microbial ecosystems are dominated by specialist taxa

Abundance and specificity are two key characteristics of species distribution and biodiversity. Theories of species assembly aim to reproduce the empirical joint patterns of specificity and abundance, with the goal to explain patterns of biodiversity across habitats. The specialist‐generalist paradigm predicts that specialists should have a local advantage over generalists and thus be more abundant. We developed a specificity index to analyse abundance–specificity relationships in microbial ecosystems. By analysing microbiota spanning 23 habitats from three very different data sets covering a wide range of sequencing depths and environmental conditions, we find that habitats are consistently dominated by specialist taxa, resulting in a strong, positive correlation between abundance and specificity. This finding is consistent over several levels of taxonomic aggregation and robust to errors in abundance measures. The relationship explains why shallow sequencing captures similar β‐diversity as deep sequencing, and can be sufficient to capture the habitat‐specific functions of microbial communities.     

Inclusion of local environmental conditions alters high-latitude vegetation change predictions based on bioclimatic models

Current predictions of how species will respond to climate change are typically based on coarse-grained climate surfaces utilizing bioclimate envelope modelling. However, the suitability of environmental conditions for a given species might result from a variety of factors including some unrelated to climate. To address this issue, we investigated whether the inclusion of topographical and soil information in bioclimatic envelope models would significantly alter predictions of climate change—induced fine-scale tree and shrub species range size changes at the tree-limit in subarctic Europe. Using generalized additive models and data on current climate and species distributions and three different climate scenarios for the period 2040–2069, we developed predictions of the currently suitable area and potential range size changes of seven tree and shrub species in an area of 1,100 km² at a resolution of 1-ha. The inclusion of topography and soil information increased the predictive accuracy of climate-only models for all studied species. The predicted changes in species distribution volumes were contradictory, and the predicted occurrences varied greatly depending on the model used. Our results therefore support the arguments that vegetation responses to climate change can be influenced by local environmental conditions and that attention should be paid to the combined effects of these factors. We conclude that disregarding local topography and soil conditions in bioclimatic models may result in biased projections of range expansions and the associated colonization, extinction and turnover assessments.     

Species distribution modelling for the people: unclassified landsat TM imagery predicts bird occurrence at fine resolutions

Aim

Assessing the influence of land cover in species distribution modelling is limited by the availability of fine‐resolution land‐cover data appropriate for most species responses. Remote‐sensing technology offers great potential for predicting species distributions at large scales, but the cost and required expertise are prohibitive for many applications. We test the usefulness of freely available raw remote‐sensing reflectance data in predicting species distributions of 40 commonly occurring bird species in western Oregon.

Location

Central Coast Range, Cascade and Klamath Mountains Oregon, USA.

Methods

Information on bird observations was collected from 4598 fixed‐radius point counts. Reflectance data were obtained using 30‐m resolution Landsat imagery summarized at scales of 150, 500, 1000 and 2000 m. We used boosted regression tree (BRT) models to analyse relationships between distributions of birds and reflectance values and evaluated prediction performance of the models using area under the receiver operating characteristic curve (AUC) values.

Results

Prediction success of models using all reflectance values was high (mean AUC = 0.79 ± 0.10 SD). Further, model performance using individual reflectance bands exceeded those that used only Normalized Difference Vegetation Index (NDVI). The relative influence of band 4 predictors was highest, indicating the importance of variables associated with vegetation biomass and photosynthetic activity. Across spatial scales, the average influence of predictors at the 2000 m scale was greatest.

Main Conclusions

We demonstrate that unclassified remote‐sensing imagery can be used to produce species distribution models with high prediction success. Our study is the first to identify general patterns in the usefulness of spectral reflectances for species distribution modelling of multiple species. We conclude that raw Landsat Thematic Mapper data will be particularly useful in species distribution models when high‐resolution predictions are required, including habitat change detection studies, identification of fine‐scale biodiversity hotspots and reserve design.

     

Numerical and dietary responses of a predator community in a temperate zone of Europe

The generalist predation hypothesis predicts that the functional responses of generalist predator species should be quicker than those of specialist predators and have a regulating effect on vole populations. New interpretations of their role in temperate ecosystems have, however, reactivated a debate suggesting generalist predators may have a destabilizing effect under certain conditions (e.g. landscape homogeneity, low prey diversity, temporary dominance of 1 prey species associated with a high degree of dietary specialization). We studied a rich predator community dominated by generalist carnivores ( Martes spp., Vulpes vulpes, Felis catus ) over a 6 yr period in farmland and woodland in France. The most frequent prey were small rodents (mostly Microtus arvalis , a grassland species, and Apodemus spp., a woodland species). Alternative prey were diverse and dominated by lagomorphs ( Oryctolagus cuniculus, Lepus europeus ). We detected a numerical response among specialist carnivores but not among generalist predators. The dietary responses of generalist predators were fairly complex and most often dependent on variation in density of at least 1 prey species. These results support the generalist predation hypothesis. We document a switch to alternative prey, an increase of diet diversity, and a decrease of diet overlap between small and medium-sized generalists during the low density phase of M. arvalis . In this ecosystem, the high density phases of small mammal species are synchronous and cause a temporary specializing of several generalist predator species. This rapid functional response may indicate the predominant role of generalists in low amplitude population cycles of voles observed in some temperate areas.     

Vkgetation analysis along a successional gradient from heath to oak forest

At Hjelm Hede in Denmark where an oak shrub is invading into a heathland the successional process has been studied on poor sandy outwashed plain. The study used a transect through a successional gradient from open heath to oak shrub where vegetation, major topsoil parameters and tree characteristics were analysed. The study enables us with spatial variation of key parameters to combine numerous detailed studies from the same site into an overall picture of changing vegetation and soil parameters reflecting the succession, which has been documented previously. The data were analysed by the use of Detrended Correspondence Analysis (DCA). Along the gradient from heath to forest the thick mor layer of the heathland soil decomposed, the CM ratio decreased from 25 to 15, pH increased and ammonium became available for the plants during a time span of approximately 120 years. The vegetation on the open heath was typical Danish inland heath vegetation dominated by Calluna vulgaris, Empetrum nigrum and Deschampsiaflexuosa. Under the young oak shrub the vegetation was dominated by Deschampsia Jexuosa, Trientalis europaea , and Maianthemum bijiolium. In the oldest and most forest like parts of the shrub a vegetation dominated by Holcus mollis, Anemone nemomsa and Stellaria holostea was found. Woody chamaephytes were replaced first by hemicryptophytes and later by geophytes. The vegetation's Ellenberg values indicated an increase in nitrogen availability and a decrease in acidity and light availability through secondary succession.     

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

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

Host and habitat specialization of avian malaria in Africa

Studies of both vertebrates and invertebrates have suggested that specialists, as compared to generalists, are likely to suffer more serious declines in response to environmental change. Less is known about the effects of environmental conditions on specialist versus generalist parasites. Here, we study the evolutionary strategies of malaria parasites (Plasmodium spp.) among different bird host communities. We determined the parasite diversity and prevalence of avian malaria in three bird communities in the lowland forests in Cameroon, highland forests in East Africa and fynbos in South Africa. We calculated the host specificity index of parasites to examine the range of hosts parasitized as a function of the habitat and investigated the phylogenetic relationships of parasites. First, using phylogenetic and ancestral reconstruction analyses, we found an evolutionary tendency for generalist malaria parasites to become specialists. The transition rate at which generalists become specialists was nearly four times as great as the rate at which specialists become generalists. We also found more specialist parasites and greater parasite diversity in African lowland rainforests as compared to the more climatically variable habitats of the fynbos and the highland forests. Thus, with environmental changes, we anticipate a change in the distribution of both specialist and generalist parasites with potential impacts on bird communities.     

A phylogenetically-informed trait-based analysis of range change in the vascular plant flora of Britain

Species distributions are changing, and knowing whether certain character traits predispose species to decline or increase during times of environmental change can shed light on the main drivers of distribution change. Here we conduct a trait-based analysis of range change in the flora of Britain since the 1930s using some of the best plant distribution and trait data available in Europe. We use phylogenetically-informed models based on a recently published, dated, species level plant phylogeny. Traits associated with habitat specialism and competitive ability were related to range change, with more competitive habitat generalists faring better than habitat specialists. We attribute this result to the greater ability of generalists to adapt to environmental perturbation, but also to the negative impacts of agricultural intensification on the flora of Britain, in particular the loss of open, dry habitats. We discovered spatial variation in the main drivers of plant range change and find support for previous evidence that agricultural intensification has been a major driver of distribution change in the flora of Britain over the past 70 years, particularly in southern England.     

Species pool structure determines the level of generalism of island parasitoid faunas

Aim To examine whether island parasitoid faunas are biased towards generalists when compared with the mainland and their species pool, and to evaluate the effects of climate, island characteristics and regional factors on the relative proportions of idiobionts (i.e. generalists) and koinobionts (i.e. specialists) of two parasitic wasp families, Braconidae and Ichneumonidae. Location Seventy‐three archipelagos distributed world‐wide. Methods We used data on the distribution and biology obtained from a digital catalogue and several literature sources. We related level of generalism, measured as the ratio between the number of idiobiont and koinobiont species, to climatic, physiographic and regional factors using generalized linear models. We compared models by means of Akaike weighting, and evaluated the spatial structure of their residuals. We used partial regressions to determine whether the final models account for all latitudinal structure in the level of generalism. Results Islands host comparatively more idiobionts than continental areas. Although there is a latitudinal gradient in the level of generalism of island faunas correlating with both environmental factors and island characteristics, the most important determinant of island community structure is their source pool. This effect is stronger for ichneumonids, where generalism is higher in the Indomalayan region, arguably due to the higher diversity of endophytic hosts in its large rain forests. Main conclusions The level of generalism of island parasitoid faunas is largely constrained by regional factors, namely by the structure of the species pool, which emphasizes the importance of including regional processes in our understanding of the functioning of ecological communities. The fact that generalist species are more predominant in islands with a large cover of rain forests pinpoints the importance of the indirect effects of ecological requirements on community structure, highlighting the complex nature of geographical gradients of diversity.     

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

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