Ecological niches and potential geographical distributions of Mediterranean fruit fly (Ceratitis capitata) and Natal fruit fly (Ceratitis rosa)

Aim To predict and compare potential geographical distributions of the Mediterranean fruit fly (Ceratitis capitata) and Natal fruit fly (Ceratitis rosa). Location Africa, southern Europe, and worldwide. Methods Two correlative ecological niche modelling techniques, genetic algorithm for rule‐set prediction (GARP) and a technique based on principal components analysis (PCA), were used to predict distributions of the two fly species using distribution records and a set of environmental predictor variables. Results The two species appear to have broadly similar potential ranges in Africa and southern Europe, with much of sub‐Saharan Africa and Madagascar predicted as highly suitable. The drier regions of Africa (central and western regions of southern Africa and Sahelian zone) were identified as being less suitable for C. rosa than for C. capitata. Overall, the proportion of the region predicted to be highly suitable is larger for C. capitata than for C. rosa under both techniques, suggesting that C. capitata may be tolerant of a wider range of climatic conditions than C. rosa. Worldwide, tropical and subtropical regions are highlighted as highly suitable for both species. Differences in overlap of predictions from the two models for these species were observed. An evaluation using independent records from the adventive range for C. capitata and comparison with other predictions suggest that GARP models offer more accurate predictions than PCA models. Main conclusions This study suggests that these species have broadly similar potential distributions worldwide (based on climate), although the potential distribution appears to be broader for C. capitata than for C. rosa. Ceratitis capitata has become invasive throughout the world, whereas C. rosa has not, despite both species having broadly similar potential distributions. Further research into the biology of these species and their ability to overcome barriers is necessary to explain this difference, and to better understand invasion risk.     

Impact of climate change on vector transmission of Trypanosoma cruzi (Chagas, 1909) in North America

Climate change can influence the geographical range of the ecological niche of pathogens by altering biotic interactions with vectors and reservoirs. The distributions of 20 epidemiologically important triatomine species in North America were modelled, comparing the genetic algorithm for rule‐set prediction (GARP) and maximum entropy (MaxEnt), with or without topographical variables. Potential shifts in transmission niche for Trypanosoma cruzi (Trypanosomatida: Trypanosomatidae) (Chagas, 1909) were analysed for 2050 and 2070 in Representative Concentration Pathway (RCP) 4.5 and RCP 8.5. There were no significant quantitative range differences between the GARP and MaxEnt models, but GARP models best represented known distributions for most species [partial‐receiver operating characteristic (ROC) > 1]; elevation was an important variable contributing to the ecological niche model (ENM). There was little difference between niche breadth projections for RCP 4.5 and RCP 8.5; the majority of species shifted significantly in both periods. Those species with the greatest current distribution range are expected to have the greatest shifts. Positional changes in the centroid, although reduced for most species, were associated with latitude. A significant increase or decrease in mean niche elevation is expected principally for Neotropical 1 species. The impact of climate change will be specific to each species, its biogeographical region and its latitude. North American triatomines with the greatest current distribution ranges (Nearctic 2 and Nearctic/Neotropical) will have the greatest future distribution shifts. Significant shifts (increases or decreases) in mean elevation over time are projected principally for the Neotropical species with the broadest current distributions. Changes in the vector exposure threat to the human population were significant for both future periods, with a 1.48% increase for urban populations and a 1.76% increase for rural populations in 2050.     

Shifts in potential geographical distribution of Pterocarya stenoptera under climate change scenarios in China

Climate change poses a serious threat to biodiversity. Predicting the effects of climate change on the distribution of a species' habitat can help humans address the potential threats which may change the scope and distribution of species. Pterocarya stenoptera is a common fast‐growing tree species often used in the ecological restoration of riverbanks and alpine forests in central and eastern China. Until now, the characteristics of the distribution of this species' habitat are poorly known as are the environmental factors that influence its preferred habitat. In the present study, the Maximum Entropy Modeling (Maxent) algorithm and the Genetic Algorithm for Ruleset Production (GARP) were used to establish the models for the potential distribution of this species by selecting 236 sites with known occurrences and 14 environmental variables. The results indicate that both models have good predictive power. Minimum temperature of coldest month (Bio6), mean temperature of warmest quarter (Bio10), annual precipitation (Bio12), and precipitation of driest month (Bio14) were important environmental variables influencing the prediction of the Maxent model. According to the models, the temperate and subtropical regions of eastern China had high environmental suitability for this species, where the species had been recorded. Under each climate change scenario, climatic suitability of the existing range of this species increased, and its climatic niche expanded geographically to the north and higher elevation. GARP predicted a more conservative expansion. The projected spatial and temporal patterns of P. stenoptera can provide reference for the development of forest management and protection strategies.     

Modelling the potential distribution of endangered Prunus africana (Hook.f.) Kalkm. in East Africa

Continued harvesting and climate change are affecting the distributions of many plant species and may lead to numerous extinctions over the next century. Endangered species are likely to be a special concern, but the extent to which they are sensitive to climate is currently unclear. Species distribution modelling, if carefully implemented, can be used to assess climate sensitivity and potential climate change impacts, of tree species. We used MaxEnt algorithm for species distribution modelling to assess the potential distribution and climate change risks for a threatened Prunus africana, in East Africa. Data from different herbaria on its distribution were linked to data on climate to test hypotheses on the factors determining its distribution. Predictive models were developed and projected onto a climate scenario for 2050 to assess climate change risks. Precipitation of driest quarter and annual precipitation appeared to be the main factors influencing its distribution. Climate change was predicted to result in reductions of the species' habitats (e.g. Erasmus et al., Glob. Change Biol. 2002; 8 : 679). Prunus africana distribution is thus highly vulnerable to a warming climate and highlights the fact that both in‐situ and ex‐situ conservation will be a solution to global warming.     

Habitat suitability modelling for species at risk is sensitive to algorithm and scale: A case study of Blanding's turtle,Emydoidea blandingii,in Ontario,Canada

Species distribution modelling (SDM) can help conservation by providing information on the ecological requirements of species at risk. We developed habitat suitability models at multiple spatial scales for a threatened freshwater turtle, Emydoidea blandingii, in Ontario as a case study. We also explored the effect of background data selection and modelling algorithm selection on habitat suitability predictions. We used sighting records, high-resolution land cover data (25 m), and two SDM techniques: boosted regression trees; and maximum entropy modelling. The area under the receiver characteristic operating curve (AUC) for habitat suitability models tested on independent data ranged from 0.878 to 0.912 when using random background and from 0.727 to 0.741 with target-group background. E. blandingii habitat suitability was best predicted by air temperature, wetland area, open water area, road density, and cropland area. Habitat suitability increased with increasing air temperature and wetland area, and decreased with increasing cropland area. Low road density and open water increased habitat suitability, while high levels of either variable decreased habitat suitability. Robust habitat suitability maps for species at risk require using a multi-scale and multi-algorithm approach. If well used, SDM can offer insight on the habitat requirements of species at risk and help guide the development of management plans. Our results suggest that E. blandingii management plans should promote the protection of terrestrial habitat surrounding residential wetlands, halt the building of roads within and adjacent to currently occupied habitat, and identify movement corridors for isolated populations.     

Predicting potential distribution of chestnut phylloxerid (Hemiptera: Phylloxeridae) based on GARP and Maxent ecological niche models

The chestnut phylloxerid, Moritziella castaneivora, has been recently recorded as a forest pest in China. It heavily damaged chestnut trees and has caused serious economic losses in some main chestnut production areas. In order to effectively monitor and manage this pest, it is necessary to investigate its potential geographical distribution worldwide. In this study, we used two ecological niche models, Genetic Algorithm for Rule‐set Production (GARP) and Maximum Entropy (Maxent), along with the geographical distribution of the host plants, Japanese chestnut (Castanea crenata) and Chinese chestnut (Castanea mollissima), to predict the potential geographical distribution of M. castaneivora. The results suggested that the suitable distribution areas based on GARP were general consistent with those based on Maxent, but GARP predicted distribution areas that extended more in size than did Maxent. The results also indicated that the suitable areas for chestnut phylloxerid infestations were mainly restricted to Northeast China (northern Liaoning), East China (southern Shandong, northern Jiangsu and western Anhui), North China (southern Hebei, Beijing and Tianjin), Central China (eastern Hubei and southern Henan), Japan (Kinki, Shikoku and Tohoku) and most parts of the Korean Peninsula. In addition, some provinces of central and western China were predicted to have low suitability or unsuitable areas (e.g. Xinjiang, Qinghai and Tibet). A jackknife test in Maxent showed that the average precipitation in July was the most important environmental variable affecting the distribution of this pest species. Consequently, the study suggests several reasonable regulations and management strategies for avoiding the introduction or invasion of this high‐risk chestnut pest to these potentially suitable areas.     

Does the interpolation accuracy of species distribution models come at the expense of transferability?

Model transferability (extrapolative accuracy) is one important feature in species distribution models, required in several ecological and conservation biological applications. This study uses 10 modelling techniques and nationwide data on both (1) species distribution of birds, butterflies, and plants and (2) climate and land cover in Finland to investigate whether good interpolative prediction accuracy for models comes at the expense of transferability – i.e. markedly worse performance in new areas. Models’ interpolation and extrapolation performance was primarily assessed using AUC (the area under the curve of a receiver characteristic plot) and Kappa statistics, with supplementary comparisons examining model sensitivity and specificity values. Our AUC and Kappa results show that extrapolation to new areas is a greater challenge for all included modelling techniques than simple filling of gaps in a well‐sampled area, but there are also differences among the techniques in the degree of transferability. Among the machine‐learning modelling techniques, MAXENT, generalized boosting methods (GBM), and artificial neural networks (ANN) showed good transferability while the performance of GARP and random forest (RF) decreased notably in extrapolation. Among the regression‐based methods, generalized additive models (GAM) and generalized linear models (GLM) showed good transferability. A desirable combination of good prediction accuracy and good transferability was evident for three modelling techniques: MAXENT, GBM, and GAM. However, examination of model sensitivity and specificity revealed that model types may differ in their tendencies to either increased over‐prediction of presences or absences in extrapolation, and some of the methods show contrasting changes in sensitivity vs specificity (e.g. ANN and GARP). Among the three species groups, the best transferability was seen with birds, followed closely by butterflies, whereas reliable extrapolation for plant species distribution models appears to be a major challenge at least at this scale. Overall, detailed knowledge of the behaviour of different techniques in various study settings and with different species groups is of utmost importance in predictive modelling.     

Exploring consensus in 21st century projections of climatically suitable areas for African vertebrates

Africa is predicted to be highly vulnerable to 21st century climatic changes. Assessing the impacts of these changes on Africa's biodiversity is, however, plagued by uncertainties, and markedly different results can be obtained from alternative bioclimatic envelope models or future climate projections. Using an ensemble forecasting framework, we examine projections of future shifts in climatic suitability, and their methodological uncertainties, for over 2500 species of mammals, birds, amphibians and snakes in sub‐Saharan Africa. To summarize a priori the variability in the ensemble of 17 general circulation models, we introduce a consensus methodology that combines co‐varying models. Thus, we quantify and map the relative contribution to uncertainty of seven bioclimatic envelope models, three multi‐model climate projections and three emissions scenarios, and explore the resulting variability in species turnover estimates. We show that bioclimatic envelope models contribute most to variability, particularly in projected novel climatic conditions over Sahelian and southern Saharan Africa. To summarize agreements among projections from the bioclimatic envelope models we compare five consensus methodologies, which generally increase or retain projection accuracy and provide consistent estimates of species turnover. Variability from emissions scenarios increases towards late‐century and affects southern regions of high species turnover centred in arid Namibia. Twofold differences in median species turnover across the study area emerge among alternative climate projections and emissions scenarios. Our ensemble of projections underscores the potential bias when using a single algorithm or climate projection for Africa, and provides a cautious first approximation of the potential exposure of sub‐Saharan African vertebrates to climatic changes. The future use and further development of bioclimatic envelope modelling will hinge on the interpretation of results in the light of methodological as well as biological uncertainties. Here, we provide a framework to address methodological uncertainties and contextualize results.     

Modelling ecological niches from low numbers of occurrences: assessment of the conservation status of poorly known viverrids (Mammalia, Carnivora) across two continents

The conservation of poorly known species is difficult because of incomplete knowledge on their biology and distribution. We studied the contribution of two ecological niche modelling tools, the Genetic Algorithm for Rule-set Prediction (GARP) and maximum entropy (Maxent), in assessing potential ranges and distributional connectivity among 12 of the least known African and Asian viverrids. The level of agreement between GARP and Maxent predictions was low when < 15 occurrences were available, probably indicating a minimum number below that necessary to obtain models with good predictive power. Unexpectedly, our results suggested that Maxent extrapolated more than GARP in the context of small sample sizes. Predictions were overlapped with current land use and location of protected areas to estimate the conservation status of each species. Our analyses yielded range predictions generally contradicting with extents of occurrence established by the IUCN. We evidenced a high level of disturbance within predicted distributions in West and East Africa, Sumatra, and South-East Asia, and identified within West African degraded lowlands four relatively preserved areas that might be of prime importance for the conservation of rainforest taxa. Knowing whether these species of viverrids may survive in degraded or alternative habitats is of crucial importance for further conservation planning. The level of coverage of species suitable ranges by existing and proposed IUCN reserves was low, and we recommend that the total surface of protected areas be substantially increased on both continents.     

Environmental suitability of new reported localities of the funnelweb spider Macrothele calpeiana: an assessment using potential distribution modelling with presence‐only techniques

Aim The funnelweb spider Macrothele calpeiana is endemic to the southern half of the Iberian Peninsula, but recent occurrence records from localities in Spain, North Africa and other regions of Europe, which are distant from its native populations, suggest human‐mediated dispersal, probably associated with the commercial export of olive trees. The main goal of this study was to assess the environmental suitability of these new records and to discuss the spider’s potential to become an invasive species, mainly in new regions across Central Europe and the Mediterranean Basin. Location Central Europe, Mediterranean Basin. Methods Using presence points from the Iberian native populations of M. calpeiana and a set of climatic variables, four presence‐only algorithms (BIOCLIM, DOMAIN, GARP and Maxent) were applied to model the potential distribution of the spider. The models were transferred to Central Europe and the Mediterranean Basin, and the locations of the new records in both the occupied and potential environmental spaces were screened. Results The four models were generally congruent in predicting the existence of a suitable climate for the species across the Mediterranean Basin, although BIOCLIM and DOMAIN yielded more constrained predictions than GARP and Maxent. Whereas the new records from Central Europe were located far from the occupied and potential climatic spaces, those from the Iberian Peninsula were not. Main conclusions Climatic suitability together with propagule pressure owing to human activities will certainly enhance the opportunities for M. calpeiana to colonize new areas across the Mediterranean Basin. The species has invaded areas beyond its native range, and those new locations located in the Iberian Peninsula and North Africa show environmental suitability for the spider and deserve long‐term monitoring. Although the new locations in Central Europe were not predicted by the climate models and the persistence of the species seems improbable, the possibility of rapid evolution or phenotypic plasticity processes raises the need for caution over the possibility of a future spread of M. calpeiana across Europe. Stronger controls over the transport of trees must be applied, and further studies on the ecology of the spider are imperative to assess the possible impact on the invaded ecosystems.     

Modeling potential invasion range of alien invasive species,Hyptis suaveolens (L.) Poit. in India: Comparison of MaxEnt and GARP

Bushmint (Hyptis suaveolens (L.) Poit.) is one among the world's most noxious weeds. Bushmint is rapidly invading tropical ecosystems across the world, including India, and is major threat to native biodiversity, ecosystems and livelihoods. Knowledge about the likely areas under bushmint invasion has immense importance for taking rapid response and mitigation measures. In the present study, we model the potential invasion range of bushmint in India and investigate prediction capabilities of two popular species distribution models (SDM) viz., MaxEnt (Maximum Entropy) and GARP (Genetic Algorithm for Rule-Set Production). We compiled spatial layers on 22 climatic and non-climatic (soil type and land use land cover) environmental variables at India level and selected least correlated 14 predictor variables. 530 locations of bushmint along with 14 predictor variables were used to predict bushmint distribution using MaxEnt and GARP. We demonstrate the relative contribution of predictor variables and species-environmental linkages in modeling bushmint distribution. A receiver operating characteristic (ROC) curve was used to assess each model's performance and robustness. GARP had a relatively lower area under curve (AUC) score (AUC: 0.75), suggesting its lower ability in discriminating the suitable/unsuitable sites. Relative to GARP, MaxEnt performed better with an AUC value of 0.86. Overall the outputs of MaxEnt and GARP matched in terms of geographic regions predicted as suitable/unsuitable for bushmint in India, however, predictions were closer in the spatial extent in Central India and Western Himalayan foothills compared to North-East India, Chottanagpur and Vidhayans and Deccan Plateau in India.     

利用最大熵模型和规则集遗传算法模型预测孑遗植物裸果木的潜在地理分布及格局

利用野外调查的16个居群分布点和7个环境因子图层, 选择最大熵模型(MAXENT)和规则集遗传算法模型(GARP), 在地理和环境空间上模拟了第三纪孑遗植物裸果木(Gymnocarpos przewalskii)在中国西北地区的潜在分布。结果表明: (1)裸果木的潜在适生区全部集中在西北荒漠区, 其中最佳适生区主要集中在3个区域, 一是河西走廊中部和玉门以西、宁夏北部及内蒙古乌拉特后旗; 二是塔里木盆地西北缘; 三是柴达木盆地西北缘两片极小的高度适生区。裸果木的生态位被确定在一个较广的干旱环境空间: 适生区极端最高气温基本上在29.2-36.8 ℃之间, 极端最低气温在-18.3至-13.4 ℃之间; 年平均降水量40-200 mm; 潜在蒸发率在3-15之间。(2) MAXENT和GARP模型都较好地预测了裸果木的潜在分布, 但GARP产生了相对较大、较连续的潜在分布区, 部分过预测了破碎化生境; 而MAXENT预测到的潜在分布区, 在不同区域具有不同的环境适生性指数, 而且成功地排除了不合理的破碎化分布, 从而更直观地展示了裸果木的潜在分布格局和生态位要求。     

An evaluation of a GARP model as an approach to predicting the spatial distribution of non-vagile invertebrate species

One of the primary goals of any systematic, taxonomic or biodiversity study is the characterization of species distributions. While museum collection data are important for ascertaining distributional ranges, they are often biased or incomplete. The Genetic Algorithm for Rule-set Prediction (GARP) is an ecological niche modelling method based on a genetic algorithm that has been argued to provide an accurate assessment of the spatial distribution of organisms that have dispersal capabilities. The primary objective of this study is to evaluate the accuracy of a GARP model to predict the spatial distribution of a non-invasive, non-vagile invertebrate whose full distributional range was unknown. A GARP predictive model based on seven environmental parameters and 42 locations known from historical museum records for species of the trapdoor spider genus Promyrmekiaphila was produced and subsequently used as a guide for ground truthing the model. The GARP model was neither a significant nor an accurate predictor of spider localities and was outperformed by more simplistic BIOCLIM and GLM models. The isolated nature of Promyrmekiaphila populations mandates that environmental layers and their respective resolutions are carefully chosen for model production. Our results strongly indicate that, for modelling the spatial distribution of low vagility organisms, one should employ a modelling method whose results are more conducive to interpretation than models produced by a 'black box' algorithm such as GARP.     

Predicting the distribution of non‐vagile taxa: a response to McNyset and Blackburn (2006) and re‐evaluation of Stockman et al. (2006)

This paper addresses the issues raised by McNyset and Blackburn (2006 ) in their response to Stockman et al. (2006 ). Re‐evaluation of our published GARP analyses by McNyset and Blackburn showed that a much improved ecological niche model is obtained for predicting the distribution of the trapdoor spider genus Promyrmekiaphila in central/northern California. The improved niche model results in a substantially reduced omission error rate and a predictive model comparable to models obtained using other methods (GLM and BIOCLIM). However, the improved GARP models have a high commission error rate (> 0.75); consequently, the inferences regarding difficulties in modelling non‐vagile taxa drawn by Stockman et al. remain valid. Finally, we discuss other relatively minor criticisms of our study raised by McNyset and Blackburn and issues related to the peer review of our original paper.     

Predicting species distributions: a critical comparison of the most common statistical models using artificial species

Aim To test statistical models used to predict species distributions under different shapes of occurrence–environment relationship. We addressed three questions: (1) Is there a statistical technique that has a consistently higher predictive ability than others for all kinds of relationships? (2) How does species prevalence influence the relative performance of models? (3) When an automated stepwise selection procedure is used, does it improve predictive modelling, and are the relevant variables being selected? Location We used environmental data from a real landscape, the state of California, and simulated species distributions within this landscape. Methods Eighteen artificial species were generated, which varied in their occurrence response to the environmental gradients considered (random, linear, Gaussian, threshold or mixed), in the interaction of those factors (no interaction vs. multiplicative), and on their prevalence (50% vs. 5%). The landscape was then randomly sampled with a large (n = 2000) or small (n = 150) sample size, and the predictive ability of each statistical approach was assessed by comparing the true and predicted distributions using five different indexes of performance (area under the receiver‐operator characteristic curve, Kappa, correlation between true and predictive probability of occurrence, sensitivity and specificity). We compared generalized additive models (GAM) with and without flexible degrees of freedom, logistic regressions (general linear models, GLM) with and without variable selection, classification trees, and the genetic algorithm for rule‐set production (GARP). Results Species with threshold and mixed responses, additive environmental effects, and high prevalence generated better predictions than did other species for all statistical models. In general, GAM outperforms all other strategies, although differences with GLM are usually not significant. The two variable‐selection strategies presented here did not discriminate successfully between truly causal factors and correlated environmental variables. Main conclusions Based on our analyses, we recommend the use of GAM or GLM over classification trees or GARP, and the specification of any suspected interaction terms between predictors. An expert‐based variable selection procedure was preferable to the automated procedures used here. Finally, for low‐prevalence species, variability in model performance is both very high and sample‐dependent. This suggests that distribution models for species with low prevalence can be improved through targeted sampling.     

Predicting the potential invasion of kudzu bug, <Emphasis Type="Italic">Megacopta cribraria</Emphasis> (Heteroptera: Plataspidae), in North and South America and determining its climatic preference

Biological invasions have long placed challenges on ecosystems, agricultural production, and human health. Modeling potential invasion of an introduced organism becomes a critical tool for early management of damaging species, such as kudzu bug, Megacopta cribraria (F.) (Hemiptera:Heteroptera:Plataspidae). Since it was first found in the United States in 2009, kudzu bug has spread rapidly, economically impacted agricultural production, and became a household pest. To better predict the potential invasion of kudzu bug in North and South America, we used the species distribution models Genetic Algorithm for Rule-set Production (GARP) and Maximum Entropy (Maxent). We used the D metric to test for niche equivalency and similarity between native and invaded populations of kudzu bug. We found that kudzu bugs currently occupied unequal environmental space between the two ranges. Therefore, distribution models using GARP and Maxent were constructed using occurrences in both native and invaded ranges. Area under the curve (AUC), true skill statistics (TSS), and omission rate (OR) were used to evaluate and compare the models. Results indicated both models had good performance, but Maxent (AUC?=?0.971, TSS?=?0.946, OR?=?0.019) performed better than GARP (AUC?=?0.922, TSS?=?0.860, OR?=?0.037). This research confirmed the effectiveness of using occurrence data in both ranges to predict potential invasions. Kudzu bugs prefer warm (annual mean temperature around 15 °C) and humid (annual mean precipitation around 1300 mm) regions. Distribution models generated by both methods indicated similar regions with high invasion risk. Management programs that include quarantine and prevention measures are suggested for these regions to avoid outbreaks of kudzu bug.     

Are stacked species distribution models accurate at predicting multiple levels of diversity along a rainfall gradient?

We use observed patterns of species richness and composition of ant communities along a 1000 mm rainfall gradient in northern Australian savanna to assess the accuracy of species richness and turnover predictions derived from stacked species distribution models (S‐SDMs) and constrained by macroecological models (MEMs). We systematically sampled ants at 15 sites at 50 km intervals along the rainfall gradient in 2012 and 2013. Using the observed data, we created MEMs of species richness, composition and turnover. We built distribution models for 135 of the observed species using data from museum collections and online databases. We compared two approaches of stacking SDMs and three modelling algorithms to identify the most accurate way of predicting richness and composition. We then applied the same beta diversity metrics to compare the observed versus predicted patterns. Stacked SDMs consistently over‐predicted local species richness, and there was a mismatch between the observed pattern of richness estimated from the MEM, and the pattern predicted by S‐SDMs. The most accurate richness and turnover predictions occurred when the stacked models were rank‐ordered by their habitat suitability and constrained by the observed MEM richness predictions. In contrast with species richness, the predictions obtained by the MEM of community similarity, composition and turnover matched those predicted by the S‐SDMs. S‐SDMs regulated by MEMs may therefore be a useful tool in predicting compositional patterns despite being unreliable estimators of species richness. Our results highlight that the choice of species distribution model, the stacking method used, and underlying macroecological patterns all influence the accuracy of community assembly predictions derived from S‐SDMS.     

Ecological niche modelling as a technique for assessing threats and setting conservation priorities for Asian slow lorises (Primates: Nycticebus)

Aim Data on geographical ranges are essential when defining the conservation status of a species, and in evaluating levels of human disturbance. Where locality data are deficient, presence‐only ecological niche modelling (ENM) can provide insights into a species’ potential distribution, and can aid in conservation planning. Presence‐only ENM is especially important for rare, cryptic and nocturnal species, where absence is difficult to define. Here we applied ENM to carry out an anthropogenic risk assessment and set conservation priorities for three threatened species of Asian slow loris (Primates: Nycticebus). Location Borneo, Java and Sumatra, Southeast Asia. Methods Distribution models were built using maximum entropy (MaxEnt) ENM. We input 20 environmental variables comprising temperature, precipitation and altitude, along with species locality data. We clipped predicted distributions to forest cover and altitudinal data to generate remnant distributions. These were then applied to protected area (PA) and human land‐use data, using specific criteria to define low‐, medium‐ or high‐risk areas. These data were analysed to pinpoint priority study sites, suitable reintroduction zones and protected area extensions. Results A jackknife validation method indicated highly significant models for all three species with small sample sizes (n = 10 to 23 occurrences). The distribution models represented high habitat suitability within each species’ geographical range. High‐risk areas were most prevalent for the Javan slow loris (Nycticebus javanicus) on Java, with the highest proportion of low‐risk areas for the Bornean slow loris (N. menagensis) on Borneo. Eighteen PA extensions and 23 priority survey sites were identified across the study region. Main conclusions Discriminating areas of high habitat suitability lays the foundations for planning field studies and conservation initiatives. This study highlights potential reintroduction zones that will minimize anthropogenic threats to animals that are released. These data reiterate the conclusion of previous research, showing MaxEnt is a viable technique for modelling species distributions with small sample sizes.     

Effects of climate, disturbance and soil factors on the potential distribution of Liaotung oak (Quercus wutaishanica Mayr) in China

Liaotung oak (strictly named as Quercus wutaishanica Mayr, but usually called Q. liaotungensis Koidz) is the main dominant tree species in deciduous broad-leaved forests and mixed coniferous and broad-leaved forests occupying the warm-temperate zone and temperate zone of China. It plays important roles in soil and water conservation. We collected occurrence data of Liaotung oak together with environmental variables, and used 24 environmental layers as indicators of climate, human disturbance and soil characteristics (at a spatial resolution of 5 arc-min) across China. The genetic algorithm for rule-set prediction (GARP) was used to predict the potential distribution area of Liaotung oak. Forward selection, Monte Carlo permutation tests and variation partitioning methods were used to identify the key environmental factors that determined the distribution pattern of Liaotung oak. The results show that (1) GARP predicts the potential distribution area of Liaotung oak with high accuracy, with areas under the receiver operating characteristic curve (AUC) and Kappa index values being relatively high (0.96 ± 0.01, 0.91 ± 0.01); (2) the highest probability of Liaotung oak occurrence is located mainly in Gansu, Shaanxi, Shanxi, Henan, Shandong, Hebei, Liaoning, Jilin, and Heilongjiang provinces in China; (3) climate and disturbance intensity are predominant in determining the geographical boundary of Liaotung oak, with human footprint and precipitation of the coldest quarter being the most important (both explaining 71% variation) among the factors investigated across continental China. While climate and soil factors play important roles in determining the suitability index of Liaotung oak, soil organic carbon and temperature are the critical factors (both explaining 55% variation) among the factors investigated across the potential distribution area.     

Geographical distributions of spiny pocket mice in South America: insights from predictive models

Aim Predictive models of species’ distributions use occurrence records and environmental data to produce a model of the species’ requirements and a map of its potential distribution. To determine regions of suitable environmental conditions and assess biogeographical questions regarding their ranges, we modelled the potential geographical distributions of two spiny pocket mice (Rodentia: Heteromyidae) in north‐western South America. Location North‐western South America. Methods We used the Genetic Algorithm for Rule‐Set Prediction (GARP), environmental data from GIS maps and georeferenced collection localities from a recent systematic review of Heteromys australis and H. anomalus to produce the models. Results GARP models indicate the potential presence of H. australis throughout mesic montane regions of north‐western South America, as well as in some lowland regions of moderately high precipitation. In contrast, H. anomalus is predicted to occur primarily in drier areas of the Caribbean coast and rain‐shadowed valleys of the Andes. Conclusions The models support the disjunct status of the population of H. australis in the Cordillera de Mérida, but predict a continuous distribution between known populations of H. anomalus in the upper Magdalena Valley and the Caribbean coast. Regions of suitable environmental conditions exist disjunct from known distributional areas for both species, suggesting possible historical restrictions to their ranges. This technique holds wide application to other study systems.