Comparing species distributions modelled from occurrence data and from expert-based range maps. Implication for predicting range shifts with climate change

Species range and climate change risk are often assessed using species distribution models (SDM) that model species niche from presence points and environmental variables and project it in space and time. These presence points frequently originate from occurrence data downloaded from public biodiversity databases, but such data are known to suffer from high biases. There is thus a need to find alternative sources of information to train these models. In this regard, expert-based range maps such as those provided by the International Union for Conservation of Nature (IUCN) have the potential to be used as a source of species presence in a SDM workflow. Here, I compared the predictions of SDM built using true occurrences provided by GBIF or iNaturalist, or using pseudo-occurrences sampled from IUCN expert-based range maps, in current and future climate. I found that the agreement between both types of SDM did not depend on the spatial resolution of environmental data but instead were affected by the number of points sampled from range maps and even more by the spatial congruence between input data. A strong agreement between occurrence data and range maps resulted in very similar SDM outputs, which suggests that expert knowledge can be a valuable alternative source of data to feed SDM and assess potential range shifts when the only available occurrences are biased or fragmentary.     

Differences in spatial predictions among species distribution modeling methods vary with species traits and environmental predictors

Prediction maps produced by species distribution models (SDMs) influence decision‐making in resource management or designation of land in conservation planning. Many studies have compared the prediction accuracy of different SDM modeling methods, but few have quantified the similarity among prediction maps. There has also been little systematic exploration of how the relative importance of different predictor variables varies among model types and affects map similarity. Our objective was to expand the evaluation of SDM performance for 45 plant species in southern California to better understand how map predictions vary among model types, and to explain what factors may affect spatial correspondence, including the selection and relative importance of different environmental variables. Four types of models were tested. Correlation among maps was highest between generalized linear models (GLMs) and generalized additive models (GAMs) and lowest between classification trees and GAMs or GLMs. Correlation between Random Forests (RFs) and GAMs was the same as between RFs and classification trees. Spatial correspondence among maps was influenced the most by model prediction accuracy (AUC) and species prevalence; map correspondence was highest when accuracy was high and prevalence was intermediate (average prevalence for all species was 0.124). Species functional type and the selection of climate variables also influenced map correspondence. For most (but not all) species, climate variables were more important than terrain or soil in predicting their distributions. Environmental variable selection varied according to modeling method, but the largest differences were between RFs and GLMs or GAMs. Although prediction accuracy was equal for GLMs, GAMs, and RFs, the differences in spatial predictions suggest that it may be important to evaluate the results of more than one model to estimate the range of spatial uncertainty before making planning decisions based on map outputs. This may be particularly important if models have low accuracy or if species prevalence is not intermediate.     

Do consensus models outperform individual models? Transferability evaluations of diverse modeling approaches for an invasive moth

Transferability is key to many of the most novel and interesting applications of ecological niche models, such that maximizing predictive power of model transfers is crucial. Here, we explored consensus methods as a means of reducing uncertainty and improving model transferability in anticipating the potential distribution of an invasive moth (Hyphantria cunea). Individual native-range niche models were calibrated using seven modelling algorithms and four environmental datasets, representing different degrees of dimensionality, spatial correlation, and ecological relevance, and showing different degrees of climate niche expansion. Four consensus methods were used to combine individual niche models; we assessed transferability of consensus models and the individual models used to generate them. The results suggested that ideal criteria for environmental variable selection vary among algorithms, as different algorithms showed different sensitivities to spatial dimensionality and correlation. Consensus models reflected the central tendency of individual models, and reduced uncertainty by consolidating consistency across individual models, but did not outperform individual models. The question of whether interpolation accuracy comes at the expense of transferability suggests caution in planning methodologies for processing niche models to predict invasive potential. These explorations outline approaches by which to reduce uncertainty and improve niche model transferability with vital implications for ensemble forecasting.     

Ecological Niche Transferability Using Invasive Species as a Case Study

Species distribution modeling is widely applied to predict invasive species distributions and species range shifts under climate change. Accurate predictions depend upon meeting the assumption that ecological niches are conserved, i.e., spatially or temporally transferable. Here we present a multi-taxon comparative analysis of niche conservatism using biological invasion events well documented in natural history museum collections. Our goal is to assess spatial transferability of the climatic niche of a range of noxious terrestrial invasive species using two complementary approaches. First we compare species’ native versus invasive ranges in environmental space using two distinct methods, Principal Components Analysis and Mahalanobis distance. Second we compare species’ native versus invaded ranges in geographic space as estimated using the species distribution modeling technique Maxent and the comparative index Hellinger’s I. We find that species exhibit a range of responses, from almost complete transferability, in which the invaded niches completely overlap with the native niches, to a complete dissociation between native and invaded ranges. Intermediate responses included expansion of dimension attributable to either temperature or precipitation derived variables, as well as niche expansion in multiple dimensions. We conclude that the ecological niche in the native range is generally a poor predictor of invaded range and, by analogy, the ecological niche may be a poor predictor of range shifts under climate change. We suggest that assessing dimensions of niche transferability prior to standard species distribution modeling may improve the understanding of species’ dynamics in the invaded range.     

Scaling of species distribution models across spatial resolutions and extents along a biogeographic gradient. The case of the Iberian mole Talpa occidentalis

Scaling is a key process in modelling approaches since it allows for translating information from one scale to another. However, the success of this procedure may depend on ‘source’ and ‘target’ scales, but also on the biogeographic/ecological context of the study area. We aimed to quantify the performance and success of scaling species distribution model (SDM) predictions across spatial resolution and extent along a biogeographic gradient using the Iberian mole as study case. We ran separate MaxEnt models at two extents (national and regional) using independent datasets (species locations and environmental predictors) collected at 10 km and 50 m resolutions respectively. Model performance and success of scaling SDMs were quantified on the basis of accuracy measures and spatial predictions. Complementarily, we calculated marginality and tolerance as indicators of habitat availability and niche truncation along the biogeographic gradient. Model performance increased with resolution and extent, as well as from north to south (mainly for high resolution models). When regional models were validated at different scales, their performance reduced severely, particularly in the case of coarse resolution models (some of them performed worse than random). However, when the 10 km‐national model was downscaled within regions, it performed better (AUC_test: 0.82, 0.85 and 0.55 respectively for Galicia, Madrid and Granada) than models specifically calibrated within each region at 10 km (0.47, 0.65, 0.44). Indeed, it also had a better accuracy when projected at 50 m (0.77, 0.91, 0.79) than models fitted at that resolution (0.62, 0.83, 0.96) in two of the three cases. The success of scaling model predictions decreased along the biogeographic gradient, being these differences associated to niche truncation. Models representing non‐truncated niches were more successfully scaled across resolutions and extents (particularly in areas not offering all possible habitats for species), which has important implications for SDM applications.     

Niche variability and its consequences for species distribution modeling

When species distribution models (SDMs) are used to predict how a species will respond to environmental change, an important assumption is that the environmental niche of the species is conserved over evolutionary time-scales. Empirical studies conducted at ecological time-scales, however, demonstrate that the niche of some species can vary in response to environmental change. We use habitat and locality data of five species of stream fishes collected across seasons to examine the effects of niche variability on the accuracy of projections from Maxent, a popular SDM. We then compare these predictions to those from an alternate method of creating SDM projections in which a transformation of the environmental data to similar scales is applied. The niche of each species varied to some degree in response to seasonal variation in environmental variables, with most species shifting habitat use in response to changes in canopy cover or flow rate. SDMs constructed from the original environmental data accurately predicted the occurrences of one species across all seasons and a subset of seasons for two other species. A similar result was found for SDMs constructed from the transformed environmental data. However, the transformed SDMs produced better models in ten of the 14 total SDMs, as judged by ratios of mean probability values at known presences to mean probability values at all other locations. Niche variability should be an important consideration when using SDMs to predict future distributions of species because of its prevalence among natural populations. The framework we present here may potentially improve these predictions by accounting for such variability.     

未来气候变化下两种红景天植物的脆弱性

气候变化对全球的物种多样性有深远影响, 尤其是对高山物种多样性。研究未来气候变化下物种的灭绝风险对生物多样性保护具有重要的意义。本文针对青藏高原的2种重要药用植物大花红景天(Rhodiola crenulata)和菊叶红景天(R. chrysanthemifolia), 利用气候生态位因子分析法研究了它们对气候变化的敏感性、暴露性和脆弱性, 讨论了2种“共享社会经济途径” (SSP2-45和SSP5-85)情景下的未来气候对这2个物种脆弱性的影响。同时计算了2种红景天的气候生态位的边缘性和特化性, 通过主成分分析法对其气候生态位进行了二维可视化, 并分析了它们的气候变化脆弱性与气候生态位之间的关系。结果表明, 未来气候变化情景下2种红景天在其分布区都显示出西部脆弱性高而东部脆弱性低的特征, 而脆弱性都表现为较低的横断山脉地区将成为其未来气候避难所。2种红景天在SSP5-85气候情景下的脆弱性高于SSP2-45, 资源和能源密集型社会经济途径(即SSP5-85)将会增大物种的灭绝风险。此外, 被《中国物种红色名录》评估为无危的菊叶红景天的气候变化脆弱性反而大于被评估为濒危的大花红景天。生态位因子分析结果表明大花红景天的生态位边缘性和特化性都低于菊叶红景天, 研究推断同地区不同物种的气候变化脆弱性主要由物种的气候生态位决定。     

Modelling species distributions without using species distributions: the cane toad in Australia under current and future climates

Accurate predictions of the potential distribution of range-shifting species are required for effective management of invasive species, and for assessments of the impact of climate change on native species. Range-shifting species pose a challenge for traditional correlative approaches to range prediction, often requiring the extrapolation of complex statistical associations into novel environmental space. Here we take an alternative approach that does not use species occurrence data, but instead captures the fundamental niche of a species by mechanistically linking key organismal traits with spatial data using biophysical models. We demonstrate this approach with a major invasive species, the cane toad Bufo marinus in Australia, assessing the direct climatic constraints on its ability to move, survive, and reproduce. We show that the current range can be explained by thermal constraints on the locomotor potential of the adult stage together with limitations on the availability of water for the larval stage. Our analysis provides a framework for biologically grounded predictions of the potential for cane toads to expand their range under current and future climate scenarios. More generally, by quantifying spatial variation in physiological constraints on an organism, trait-based approaches can be used to investigate the range-limits of any species. Assessments of spatial variation in the physiological constraints on an organism may also provide a mechanistic basis for forecasting the rate of range expansion and for understanding a species' potential to evolve at range-edges. Mechanistic approaches thus have broad application to process-based ecological and evolutionary models of range-shift.     

Niche shift can impair the ability to predict invasion risk in the marine realm: an illustration using Mediterranean fish invaders

Climatic niche conservatism, the tendency of species‐climate associations to remain unchanged across space and time, is pivotal for forecasting the spread of invasive species and biodiversity changes. Indeed, it represents one of the key assumptions underlying species distribution models (SDMs), the main tool currently available for predicting range shifts of species. However, to date, no comprehensive assessment of niche conservatism is available for the marine realm. We use the invasion by Indo‐Pacific tropical fishes into the Mediterranean Sea, the world's most invaded marine basin, to examine the conservatism of the climatic niche. We show that tropical invaders may spread far beyond their native niches and that SDMs do not predict their new distributions better than null models. Our results suggest that SDMs may underestimate the potential spread of invasive species and call for prudence in employing these models in order to forecast species invasion and their response to environmental change.     

An assessment of the effect of data partitioning on the performance of modelling algorithms for habitat suitability for ticks

A comparison of the performance of five modelling methods using presence/absence (generalized additive models, discriminant analysis) or presence-only (genetic algorithm for rule-set prediction, ecological niche factor analysis, Gower distance) data for modelling the distribution of the tick species Boophilus decoloratus (Koch, 1844) (Acarina: Ixodidae) at a continental scale (Africa) using climate data was conducted. This work explicitly addressed the usefulness of clustering using the normalized difference vegetation index (NDVI) to split original records and build partial models for each region (cluster) as a method of improving model performance. Models without clustering have a consistently lower performance (as measured by sensitivity and area under the curve [AUC]), although presence/absence models perform better than presence-only models. Two cluster-related variables, namely, prevalence (commonness of tick records in the cluster) and marginality (the relative position of the climate niche occupied by the tick in relation to that available in the cluster) greatly affect the performance of each model (P < 0.05). Both sensitivity and AUC are better for NDVI-derived clusters where the tick is more prevalent or its marginality is low. However, the total size of the cluster or its fragmentation (measured by Shannon's evenness index) did not affect the performance of models. Models derived separately for each cluster produced the best output but resulted in a patchy distribution of predicted occurrence. The use of such a method together with weighting procedures based on prevalence and marginality as derived from populations at each cluster produced a slightly lower predictive performance but a better estimation of the continental distribution of the tick. Therefore, cluster-derived models are able to effectively capture restricting conditions for different tick populations at a regional level. It is concluded that data partitioning is a powerful method with which to describe the climate niche of populations of a tick species, as adapted to local conditions. The use of this methodology greatly improves the performance of climate suitability models.     

Disregarding the edaphic dimension in species distribution models leads to the omission of crucial spatial information under climate change: the case of Quercus pubescens in France

Species distribution modelling is an easy, persuasive and useful tool for anticipating species distribution shifts under global change. Numerous studies have used only climate variables to predict future potential species range shifts and have omitted environmental factors important for determining species distribution. Here, we assessed the importance of the edaphic dimension in the niche‐space definition of Quercus pubescens and in future spatial projections under global change over the metropolitan French forest territory. We fitted two species distribution models (SDM) based on presence/absence data (111 013 plots), one calibrated from climate variables only (mean temperature of January and climatic water balance of July) and the other one from both climate and edaphic (soil pH inferred from plants) variables. Future predictions were conducted under two climate scenarios (PCM B2 and HadCM3 A2) and based on 100 simulations using a cellular automaton that accounted for seed dispersal distance, landscape barriers preventing migration and unsuitable land cover. Adding the edaphic dimension to the climate‐only SDM substantially improved the niche‐space definition of Q. pubescens, highlighting an increase in species tolerance in confronting climate constraints as the soil pH increased. Future predictions over the 21st century showed that disregarding the edaphic dimension in SDM led to an overestimation of the potential distribution area, an underestimation of the spatial fragmentation of this area, and prevented the identification of local refugia, leading to an underestimation of the northward shift capacity of Q. pubescens and its persistence in its current distribution area. Spatial discrepancies between climate‐only and climate‐plus‐edaphic models are strengthened when seed dispersal and forest fragmentation are accounted for in predicting a future species distribution area. These discrepancies highlight some imprecision in spatial predictions of potential distribution area of species under climate change scenarios and possibly wrong conclusions for conservation and management perspectives when climate‐only models are used.     

Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches

Aim This study compares the direct, macroecological approach (MEM) for modelling species richness (SR) with the more recent approach of stacking predictions from individual species distributions (S‐SDM). We implemented both approaches on the same dataset and discuss their respective theoretical assumptions, strengths and drawbacks. We also tested how both approaches performed in reproducing observed patterns of SR along an elevational gradient. Location Two study areas in the Alps of Switzerland. Methods We implemented MEM by relating the species counts to environmental predictors with statistical models, assuming a Poisson distribution. S‐SDM was implemented by modelling each species distribution individually and then stacking the obtained prediction maps in three different ways – summing binary predictions, summing random draws of binomial trials and summing predicted probabilities – to obtain a final species count. Results The direct MEM approach yields nearly unbiased predictions centred around the observed mean values, but with a lower correlation between predictions and observations, than that achieved by the S‐SDM approaches. This method also cannot provide any information on species identity and, thus, community composition. It does, however, accurately reproduce the hump‐shaped pattern of SR observed along the elevational gradient. The S‐SDM approach summing binary maps can predict individual species and thus communities, but tends to overpredict SR. The two other S‐SDM approaches – the summed binomial trials based on predicted probabilities and summed predicted probabilities – do not overpredict richness, but they predict many competing end points of assembly or they lose the individual species predictions, respectively. Furthermore, all S‐SDM approaches fail to appropriately reproduce the observed hump‐shaped patterns of SR along the elevational gradient. Main conclusions Macroecological approach and S‐SDM have complementary strengths. We suggest that both could be used in combination to obtain better SR predictions by following the suggestion of constraining S‐SDM by MEM predictions.     

Biotic and abiotic variables show little redundancy in explaining tree species distributions

Abiotic factors such as climate and soil determine the species fundamental niche, which is further constrained by biotic interactions such as interspecific competition. To parameterize this realized niche, species distribution models (SDMs) most often relate species occurrence data to abiotic variables, but few SDM studies include biotic predictors to help explain species distributions. Therefore, most predictions of species distributions under future climates assume implicitly that biotic interactions remain constant or exert only minor influence on large‐scale spatial distributions, which is also largely expected for species with high competitive ability. We examined the extent to which variance explained by SDMs can be attributed to abiotic or biotic predictors and how this depends on species traits. We fit generalized linear models for 11 common tree species in Switzerland using three different sets of predictor variables: biotic, abiotic, and the combination of both sets. We used variance partitioning to estimate the proportion of the variance explained by biotic and abiotic predictors, jointly and independently. Inclusion of biotic predictors improved the SDMs substantially. The joint contribution of biotic and abiotic predictors to explained deviance was relatively small (~9%) compared to the contribution of each predictor set individually (~20% each), indicating that the additional information on the realized niche brought by adding other species as predictors was largely independent of the abiotic (topo‐climatic) predictors. The influence of biotic predictors was relatively high for species preferably growing under low disturbance and low abiotic stress, species with long seed dispersal distances, species with high shade tolerance as juveniles and adults, and species that occur frequently and are dominant across the landscape. The influence of biotic variables on SDM performance indicates that community composition and other local biotic factors or abiotic processes not included in the abiotic predictors strongly influence prediction of species distributions. Improved prediction of species' potential distributions in future climates and communities may assist strategies for sustainable forest management.     

An evaluation of four threshold selection methods in species occurrence modelling with random forest: Case studies with Davidia involucrata and Cunninghamia lanceolata

Aims Predictive species distribution models (SDMs) are increasingly applied in resource assessment, environmental conservation and biodiversity management. However, most SDM models often yield a predicted probability (suitability) surface map. In conservation and environmental management practices, the information presented as species presence/absence (binary) may be more practical than presented as probability or suitability. Therefore, a threshold is needed to transform the probability or suitability data to presence/absence data. However, little is known about the effects of different threshold-selection methods on model performance and species range changes induced by future climate. Of the numerous SDM models, random forest (RF) can produce probabilistic and binary species distribution maps based on its regression and classification algorisms, respectively. Studies dealing with the comparative test of the performances of RF regression and classification algorisms have not been reported.
Methods Here, the RF was used to simulate the current and project the future potential distributions of Davidia involucrata and Cunninghamia lanceolata. Then, four threshold-setting methods (Default 0.5, MaxKappa, MaxTSS and MaxACC) were selected and used to transform modelled probabilities of occurrence into binary predictions of species presence and absence. Lastly, we investigated the difference in model performance among the threshold selection methods by using five model accuracy measures (Kappa, TSS, Overall accuracy, Sensitivity and Specificity). We also used the map similarity measure, Kappa, for a cell-by-cell comparison of similarities and differences of distribution map under current and future climates.
Important findings We found that the choice of threshold method altered estimates of model performance, species habitat suitable area and species range shifts under future climate. The difference in selected threshold cut-offs among the four threshold methods was significant for D. involucrata, but was not significant for C. lanceolata. Species’ geographic ranges changed (area change and shifting distance) in response to climate change, but the projections of the four threshold methods did not differ significantly with respect to how much or in which direction, but they did differ against RF classification predictions. The pairwise similarity analysis of binary maps indicated that spatial correspondence among prediction maps was the highest between the MaxKappa and the MaxTSS, and lowest between RF classification algorism and the four threshold-setting methods. We argue that the MaxTSS and the MaxKappa are promising methods for threshold selection when RF regression algorism is used for the distribution modeling of species. This study also provides promising insights to our understanding of the uncertainty of threshold selection in species distribution modeling.     

Climatic associations of British species distributions show good transferability in time but low predictive accuracy for range change

Conservation planners often wish to predict how species distributions will change in response to environmental changes. Species distribution models (SDMs) are the primary tool for making such predictions. Many methods are widely used; however, they all make simplifying assumptions, and predictions can therefore be subject to high uncertainty. With global change well underway, field records of observed range shifts are increasingly being used for testing SDM transferability. We used an unprecedented distribution dataset documenting recent range changes of British vascular plants, birds, and butterflies to test whether correlative SDMs based on climate change provide useful approximations of potential distribution shifts. We modelled past species distributions from climate using nine single techniques and a consensus approach, and projected the geographical extent of these models to a more recent time period based on climate change; we then compared model predictions with recent observed distributions in order to estimate the temporal transferability and prediction accuracy of our models. We also evaluated the relative effect of methodological and taxonomic variation on the performance of SDMs. Models showed good transferability in time when assessed using widespread metrics of accuracy. However, models had low accuracy to predict where occupancy status changed between time periods, especially for declining species. Model performance varied greatly among species within major taxa, but there was also considerable variation among modelling frameworks. Past climatic associations of British species distributions retain a high explanatory power when transferred to recent time--due to their accuracy to predict large areas retained by species--but fail to capture relevant predictors of change. We strongly emphasize the need for caution when using SDMs to predict shifts in species distributions: high explanatory power on temporally-independent records--as assessed using widespread metrics--need not indicate a model's ability to predict the future.     

Effects of ecohydrological variables on current and future ranges,local suitability patterns,and model accuracy in big sagebrush

Forecasting of species and ecosystem responses to novel conditions, including climate change, is one of the major challenges facing ecologists at the start of the 21st century. Climate change studies based on species distribution models (SDMs) have been criticized because they extend correlational relationships beyond the observed data. Here, we compared conventional climate‐based SDMs against ecohydrological SDMs that include information from process‐based simulations of water balance. We examined the current and future distribution of Artemisia tridentata (big sagebrush) representing sagebrush ecosystems, which are widespread in semiarid western North America. For each approach, we calculated ensemble models from nine SDM methods and tested accuracy of each SDM with a null distribution. Climatic conditions included current conditions for 1970–1999 and two IPCC projections B1 and A2 for 2070–2099. Ecohydrological conditions were assessed by simulating soil water balance with SOILWAT, a daily time‐step, multiple layer, mechanistic, soil water model. Under current conditions, both climatic and ecohydrological SDM approaches produced comparable sagebrush distributions. Overall, sagebrush distribution is forecasted to decrease, with larger decreases under the A2 than under the B1 scenario and strong decreases in the southern part of the range. Increases were forecasted in the northern parts and at higher elevations. Both SDM approaches produced accurate predictions. However, the ecohydrological SDM approach was slightly less accurate than climatic SDMs (?1% in AUC, ?4% in Kappa and TSS) and predicted a higher number of habitat patches than observed in the input data. Future predictions of ecohydrological SDMs included an increased number of habitat patches whereas climatic SDMs predicted a decrease. This difference is important for understanding landscape‐scale patterns of sagebrush ecosystems and management of sagebrush obligate species for future conditions. Several mechanisms can explain the diverging forecasts; however, we need better insights into the consequences of different datasets for SDMs and how these affect our understanding of future trajectories.     

Ensemble forecasting of species distributions

Concern over implications of climate change for biodiversity has led to the use of bioclimatic models to forecast the range shifts of species under future climate-change scenarios. Recent studies have demonstrated that projections by alternative models can be so variable as to compromise their usefulness for guiding policy decisions. Here, we advocate the use of multiple models within an ensemble forecasting framework and describe alternative approaches to the analysis of bioclimatic ensembles, including bounding box, consensus and probabilistic techniques. We argue that, although improved accuracy can be delivered through the traditional tasks of trying to build better models with improved data, more robust forecasts can also be achieved if ensemble forecasts are produced and analysed appropriately.     

Potential impacts of climate change on geographical distribution of three primary vectors of African Trypanosomiasis in Tanzania’s Maasai Steppe: G. m. morsitans,G. pallidipes and G. swynnertoni

In the Maasai Steppe, public health and economy are threatened by African Trypanosomiasis, a debilitating and fatal disease to livestock (African Animal Trypanosomiasis -AAT) and humans (Human African Trypanosomiasis—HAT), if not treated. The tsetse fly is the primary vector for both HAT and AAT and climate is an important predictor of their occurrence and the parasites they carry. While understanding tsetse fly distribution is essential for informing vector and disease control strategies, existing distribution maps are old and were based on coarse spatial resolution data, consequently, inaccurately representing vector and disease dynamics necessary to design and implement fit-for-purpose mitigation strategies. Also, the assertion that climate change is altering tsetse fly distribution in Tanzania lacks empirical evidence. Despite tsetse flies posing public health risks and economic hardship, no study has modelled their distributions at a scale needed for local planning. This study used MaxEnt species distribution modelling (SDM) and ecological niche modeling tools to predict potential distribution of three tsetse fly species in Tanzania’s Maasai Steppe from current climate information, and project their distributions to midcentury climatic conditions under representative concentration pathways (RCP) 4.5 scenarios. Current climate results predicted that G. m. morsitans, G. pallidipes and G swynnertoni cover 19,225 km², 7,113 km² and 32,335 km² and future prediction indicated that by the year 2050, the habitable area may decrease by up to 23.13%, 12.9% and 22.8% of current habitable area, respectively. This information can serve as a useful predictor of potential HAT and AAT hotspots and inform surveillance strategies. Distribution maps generated by this study can be useful in guiding tsetse fly control managers, and health, livestock and wildlife officers when setting surveys and surveillance programs. The maps can also inform protected area managers of potential encroachment into the protected areas (PAs) due to shrinkage of tsetse fly habitats outside PAs.     

Benchmarking novel approaches for modelling species range dynamics

Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species’ range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process‐based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process‐based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species’ response to climate change but also emphasize several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches operational for large numbers of species.