Species Distribution Modelling Techniques Research Articles

Conservation challenges regarding species status assessments in biogeographically complex regions: examples from overexploited reptiles of Indonesia

AbstractIUCN Red List assessments are important for conservation and management initiatives. However, status assessments are often challenging because of poor sampling between biogeographical regions. Researchers sometimes assess poorly known species, which can have unforeseen ramifications, including the trade of rare and cryptic species under common species names. Here, we address this issue in relation to economically important reptile species in Indonesia. We reviewed examples of single species categorized as Least Concern for which the assessments probably encompassed multiple closely related species. We also examined Red List assessments that utilized species distribution modelling techniques, and identified biogeography as a major barrier to using such methods. To test how biogeography may affect status assessments we used our own model lizard system from Indonesia, taking an integrative phylogeographical approach to quantify status assessments under contrasting scenarios. We show that failure to account for biogeographical breaks leads to significant variation in Red List status. Our model system fluctuates from Least Concern to Endangered, depending upon whether biogeographical boundaries are considered in taxonomic evaluations. We identify Sauria (lizards) and Serpentes (snakes) as major lineages requiring taxonomic and conservation attention in Indonesia. We also make the following recommendations: (1) Indonesia's trade quotas should further subdivide management zones to account for gaps in taxonomic evaluations; (2) genetic sampling should be considered a high priority during wildlife exportation processes from poorly studied geographical areas; and (3) continuation of thorough biological inventory is critical for conservation initiatives across heterogeneous mountain and island landscapes.

Modeling the distribution of a rare Amazonian odonate in relation to future deforestation

The advance of the deforestation frontier in the Amazon forest, the largest tropical forest and one of the richest ecosystems in the world, has threatened several plant and animal species. A lack of good biogeographical information of their distributions and a shortage of basic knowledge on their ecology hinder the proper evaluation of the vulnerability of those species. We used species distribution modeling techniques to fill these gaps and to estimate the vulnerability of a forest-dwelling odonate endemic from the Amazon, Diastatops nigra. We used the MaxEnt algorithm and compared the efficiency of this method in relation to the type of environmental data set (climate-only and climate+hydrographic environmental variables). We also estimated the decrease in extension of occurrence of D. nigra in relation to a recently developed model for future deforestation also produced with the MaxEnt approach. Predicted suitable areas were isolated patches in the central Amazon and many peripheral areas. In general, those areas had stable climates with low seasonality in rainfall. The Amazon deforestation frontier is expanding mainly from the south. The core area of D. nigra distribution is in the central Amazon, so in the short-term projection, the main threat for this species was not the deforestation itself. However, deforestation may extirpate some peripheral populations of this species and increase isolation among those patches of suitable areas. We suggest the use of this model for prioritizing future odonate inventories targeting the other species of the group.

Shallow environmental gradients put inland species at risk: Insights and implications from predicting future distributions ofEucalyptusspecies in South Western Australia

As climate changes, tree decline in Mediterranean-type ecosystems is increasing worldwide, often due to decreased effective precipitation and increased drought and heat stress, and has recently been observed in coastal species of the iconic Eucalyptus (Myrtaceae) genus in the biodiversity hotspot of south-west Western Australia. To investigate how this drought-related decline is likely to continue in the future, we used species distribution modelling techniques to generate broad-scale predictions of future distribution patterns under three distinct projected climate change scenarios. In a moderate climate change scenario, suitable habitat for all species was predicted to decrease by, on average, 73% by the year 2100, with most receding into southern areas of their current distribution. Although the most severe Eucalyptus declines in south-west Western Australia have been observed in near-coastal regions, our predictions suggest that inland species are at greater risk from climate change, with six inland species predicted to lose 95% of their suitable habitat in a moderate change scenario. This is due to the shallow environmental gradients of inland regions causing larger spatial shifts of environmental envelopes, which is likely to be relevant in many regions of the world. The knowledge gained suggests that future research and conservation efforts in south-west Western Australia and elsewhere should avoid focussing disproportionately on coastal regions for reasons of convenience and proximity to population centres, and properly address the inland region where the biggest future impacts may occur.

Influence of past and future climate changes on the distribution of three Southeast Asian murine rodents

AbstractAimWe tested the influence of Pleistocene climatic fluctuations and the potential effect of future climate change on Southeast Asian small mammal distributions using two forest‐dwelling (Leopoldamys herberti and Leopoldamys sabanus) and one karst (Leopoldamys neilli) endemic rodent species as models.LocationSoutheast Asia.MethodsWe used presence–absence data of genetically identified individuals, bioclimatic variables and species distribution modelling techniques to predict potential distributions of the three studied species under current, past [Last Interglacial (LIG) and Last Glacial Maximum (LGM)] and future conditions. We applied a variety of modelling techniques and then used consensus techniques to draw up robust maps of potential distribution ranges at all stages.ResultsAccording to our models, these three Leopoldamys species did not experience significant range contraction during the LGM. Our models revealed substantial range contraction during the LIG for L. herberti in northern Indochina, while its distribution expanded in southern Indochina. Evidence of a southward range expansion during that period was also obtained for L. neilli, whereas L. sabanus remained widely distributed in insular Southeast Asia but experienced a range contraction on the Thai‐Malay Peninsula. The two future climate change scenarios used predicted that large climatically suitable areas would still be available in the future for the three species.Main conclusionsOur model predictions contradict the well‐established hypothesis that Southeast Asian forest‐dwelling species were confined to small refugia during the LGM. Moreover, our results suggest that some Southeast Asian taxa may have been distributed in their refugial state during the LIG rather than the LGM. This could be because of vegetation changes that may have occurred at that time as a result of the increased seasonality observed during the LIG. These Pleistocene refugia may have been localized in northern Indochina but our study also revealed that southern Indochina could provide major potential refugia.

Modelling the potential spatial distribution of mosquito species using three different techniques.

BackgroundModels for the spatial distribution of vector species are important tools in the assessment of the risk of establishment and subsequent spread of vector-borne diseases. The aims of this study are to define the environmental conditions suitable for several mosquito species through species distribution modelling techniques, and to compare the results produced with the different techniques.MethodsThree different modelling techniques, i.e., non-linear discriminant analysis, random forest and generalised linear model, were used to investigate the environmental suitability in the Netherlands for three indigenous mosquito species (Culiseta annulata, Anopheles claviger and Ochlerotatus punctor). Results obtained with the three statistical models were compared with regard to: (i) environmental suitability maps, (ii) environmental variables associated with occurrence, (iii) model evaluation.ResultsThe models indicated that precipitation, temperature and population density were associated with the occurrence of Cs. annulata and An. claviger, whereas land surface temperature and vegetation indices were associated with the presence of Oc. punctor. The maps produced with the three different modelling techniques showed consistent spatial patterns for each species, but differences in the ranges of the predictions. Non-linear discriminant analysis had lower predictions than other methods. The model with the best classification skills for all the species was the random forest model, with specificity values ranging from 0.89 to 0.91, and sensitivity values ranging from 0.64 to 0.95.ConclusionsWe mapped the environmental suitability for three mosquito species with three different modelling techniques. For each species, the maps showed consistent spatial patterns, but the level of predicted environmental suitability differed; NLDA gave lower predicted probabilities of presence than the other two methods. The variables selected as important in the models were in agreement with the existing knowledge about these species. All model predictions had a satisfactory to excellent accuracy; best accuracy was obtained with random forest. The insights obtained can be used to gain more knowledge on vector and non-vector mosquito species. The output of this type of distribution modelling methods can, for example, be used as input for epidemiological models of vector-borne diseases.

Ecological niche and potential distribution of Anopheles arabiensis in Africa in 2050.

BackgroundThe future distribution of malaria in Africa is likely to be much more dependent on environmental conditions than the current distribution due to the effectiveness of indoor and therapeutic anti-malarial interventions, such as insecticide-treated nets (ITNs), indoor residual spraying for mosquitoes (IRS), artemisinin-combination therapy (ACT), and intermittent presumptive treatment (IPT). Future malaria epidemiology is therefore expected to be increasingly dominated by Anopheles arabiensis, which is the most abundant exophagic mosquito competent to transmit Plasmodium falciparum and exhibits a wide geographic range.MethodsTo map the potential distribution of An. arabiensis in Africa, ecological niche models were fit to 20th century collection records. Many common species distribution modelling techniques aim to discriminate species habitat from the background distribution of environments. Since these methods arguably result in unnecessarily large Type I and Type II errors, LOBAG-OC was used to identify the niche boundary using only data on An. arabiensis occurrences. The future distribution of An. arabiensis in Africa was forecasted by projecting the fit model onto maps of simulated climate change following three climate change scenarios.ResultsEcological niche modelling revealed An. arabiensis to be a climate generalist in the sense that it can occur in most of Africa’s contemporary environmental range. Under three climate change scenarios, the future distribution of An. arabiensis is expected to be reduced by 48%-61%. Map differences between baseline and projected climate suggest that habitat reductions will be especially extensive in Western and Central Africa; portions of Botswana, Namibia, and Angola in Southern Africa; and portions of Sudan, South Sudan, Somalia, and Kenya in East Africa. The East African Rift Valley and Eastern Coast of Africa are expected to remain habitable. Some modest gains in habitat are predicted at the margins of the current range in South Sudan, South Africa, and Angola.ConclusionIn summary, these results suggest that the future potential distribution of An. arabiensis in Africa is likely to be smaller than the contemporary distribution by approximately half as a result of climate change. Agreement among the three modelling scenarios suggests that this outcome is robust to a wide range of potential climate futures.

Influence of land use and climate on recent forest expansion: a case study in the Eurosiberian–Mediterranean limit of north‐west Spain

Summary In Mediterranean mountainous areas, forests have expanded in recent decades because traditional management practices have been abandoned or reduced. However, understanding the ecological mechanisms behind landscape change is a complex undertaking because the influence of land use may be reinforced or constrained by abiotic factors such as climate. In this work, we evaluated their combined effects on recent forest expansion across climatic, topographic and management gradients. We used orthorectified aerial photographs from the second half of the twentieth century (1956, 1974, 1983, 1990 and 2004) to monitor changes in forest distribution in a set of 20 head‐water basins in the Cantabrian Mountains of north‐west Spain, at the Eurosiberian–Mediterranean limit. In particular, we evaluated the role of land‐use history (comparing natural vs. anthropic basins) and microclimate (comparing shaded vs. sunny aspects) of forest gain/loss rates and spatial distribution shifts. Finally, we applied Species Distribution Modelling techniques (MaxEnt and BIOMOD) in the stated scenarios of land‐use history and microclimate, to assess habitat suitability for forest expansion on the basis of topography, soil properties and mesoclimatic variables. Forest cover increased from 10.72% in 1956 to 27.67% in 2004 in the area. The rate of expansion was significantly higher in natural basins and, particularly, on shaded slopes. In all cases, the mean elevation of new forest patches increased during the study period, which was particularly evident on natural sunny slopes. The performance of the models and the magnitude of the effects varied across land‐use histories and microclimatic conditions. Soil properties and temperature and precipitation in late spring and early summer were the main drivers of forest expansion in modelling exercises, although expansion rates and upward altitudinal shifts were primarily controlled by land‐use history and the biogeographic origin of the forests. Synthesis. The combination of monitoring and modelling techniques used in this work contributed to the understanding of forest expansion in cultural systems, indicating that ecological succession is not a homogeneous process, but varies spatially due to human and abiotic constraints since historical times.

Projected range contractions of European protected oceanic montane plant communities: focus on climate change impacts is essential for their future conservation.

Global climate is rapidly changing and while many studies have investigated the potential impacts of this on the distribution of montane plant species and communities, few have focused on those with oceanic montane affinities. In Europe, highly sensitive bryophyte species reach their optimum occurrence, highest diversity and abundance in the north-west hyperoceanic regions, while a number of montane vascular plant species occur here at the edge of their range. This study evaluates the potential impact of climate change on the distribution of these species and assesses the implications for EU Habitats Directive-protected oceanic montane plant communities. We applied an ensemble of species distribution modelling techniques, using atlas data of 30 vascular plant and bryophyte species, to calculate range changes under projected future climate change. The future effectiveness of the protected area network to conserve these species was evaluated using gap analysis. We found that the majority of these montane species are projected to lose suitable climate space, primarily at lower altitudes, or that areas of suitable climate will principally shift northwards. In particular, rare oceanic montane bryophytes have poor dispersal capacity and are likely to be especially vulnerable to contractions in their current climate space. Significantly different projected range change responses were found between 1) oceanic montane bryophytes and vascular plants; 2) species belonging to different montane plant communities; 3) species categorised according to different biomes and eastern limit classifications. The inclusion of topographical variables in addition to climate, significantly improved the statistical and spatial performance of models. The current protected area network is projected to become less effective, especially for specialised arctic-montane species, posing a challenge to conserving oceanic montane plant communities. Conservation management plans need significantly greater focus on potential climate change impacts, including models with higher-resolution species distribution and environmental data, to aid these communities' long-term survival.

Legume diversity as indicator for botanical diversity on Sundaland, South East Asia

The Global Legume Diversity Assessment (GLDA) proposes the legume family (Fabaceae or Leguminosae) – one of the largest and economically important plant families – as a target for a global botanical diversity assessment project. Where in the Neotropics and Africa legumes dominate the rain forest in terms of diversity and abundance, the Dipterocarpaceae claim this role in South East Asia and on Sundaland in particular. This raises the question whether legumes are an indicator for overall botanical diversity on Sundaland? To answer this question we use the largest compiled database of collection records of the region and species distribution modelling techniques. As a proxy for total botanical diversity we selected seven plant families; Dipterocarpaceae, Ericaceae, Fagaceae, Lauraceae, Moraceae, Myristicaceae, and Sapindaceae. Although the legumes were the most diverse family, the predictive power of legume diversity for overall botanical diversity was poor. This related to the fact that the other seven selected families largely represent trees, whereas legume species more equally represent all different growth forms. After assigning individual legume species to different growth habits (tree, liana, herb, miscellaneous) we were able to predict 78% of the variance in botanical diversity on Sundaland. The lianas represent the single growth habit that best predicted (66%) the variance in botanical diversity. The herb- and miscellaneous growth habits had an inverse relationship to botanical diversity. Legumes can be used as a predictor of overall botanical diversity in tropical and seasonal rain forests, but the relationship should be fitted for different biogeographic regions individually.

Modeling Forest Species Distributions in a Human-Dominated Landscape in Northeastern, USA

Mapping forest species distributions is notoriously difficult in human-dominated mixed temperate forest environments. The goal of this study is to evaluate the effectiveness of species distribution modeling techniques for estimating the distribution of forest canopy species on Cape Cod, Massachusetts, USA. Binary maps of estimated presence/absence were produced for four canopy species using a classification tree approach and a generalized linear modeling approach, both including and excluding a spatial dependence term, and the results are evaluated using several assessment measures. Secondary goals of the study are to examine the influence of past land use on species distributions at the landscape scale and to consider the effect of explicitly including information on spatial dependence. Findings suggest that these techniques are broadly applicable in such human-dominated landscapes, but that complex disturbance histories introduce significant challenges.

Intraspecific variation buffers projected climate change impacts onPinus contorta

Species distribution modeling (SDM) is an important tool to assess the impact of global environmental change. Many species exhibit ecologically relevant intraspecific variation, and few studies have analyzed its relevance for SDM. Here, we compared three SDM techniques for the highly variable species Pinus contorta. First, applying a conventional SDM approach, we used MaxEnt to model the subject as a single species (species model), based on presence–absence observations. Second, we used MaxEnt to model each of the three most prevalent subspecies independently and combined their projected distributions (subspecies model). Finally, we used a universal growth transfer function (UTF), an approach to incorporate intraspecific variation utilizing provenance trial tree growth data. Different model approaches performed similarly when predicting current distributions. MaxEnt model discrimination was greater (AUC – species model: 0.94, subspecies model: 0.95, UTF: 0.89), but the UTF was better calibrated (slope and bias – species model: 1.31 and −0.58, subspecies model: 1.44 and −0.43, UTF: 1.01 and 0.04, respectively). Contrastingly, for future climatic conditions, projections of lodgepole pine habitat suitability diverged. In particular, when the species' intraspecific variability was acknowledged, the species was projected to better tolerate climatic change as related to suitable habitat without migration (subspecies model: 26% habitat loss or UTF: 24% habitat loss vs. species model: 60% habitat loss), and given unlimited migration may increase amount of suitable habitat (subspecies model: 8% habitat gain or UTF: 12% habitat gain vs. species model: 51% habitat loss) in the climatic period 2070–2100 (SRES A2 scenario, HADCM3). We conclude that models derived from within-species data produce different and better projections, and coincide with ecological theory. Furthermore, we conclude that intraspecific variation may buffer against adverse effects of climate change. A key future research challenge lies in assessing the extent to which species can utilize intraspecific variation under rapid environmental change.

Potential distribution of vulnerable Entandrophragma angolense (Welw.) C. DC. (Meliaceae) in East Africa

AbstractThe various human‐induced threats imposed on nature have recently triggered the study of species' distributions. We developed potential suitability models using two algorithms for a threatened African mahogany, Entandrophragma angolense, in three East African countries; Kenya, Tanzania and Uganda. The effect of features selection and modelling algorithm selection on potential suitability predictions was explored. Occurrence records and high‐resolution environmental data were used. The two species distribution modelling techniques were genetic algorithm rule for prediction; and maximum entropy modelling. With Maxent, the area under the receiver characteristic operating curve (AUC) for potential distribution models tested on independent data ranged from 0.942 to 0.972 when using automatic features and from 0.974 to 0.666 with target or specific features. With GARP, AUC for potential distribution models ranged from 0.591 to 0.736 with all rule types and from 0.388 to 0.805 for specific rule types (Tables and ). The area under the E. angolense potential suitability was best predicted by soil, rainfall and aspect using GARP. Potential suitability increased with increasing aspect and decreased with increasing slope. Low rainfall and elevation increased potential suitability, while high levels of either variable decreased potential suitability. Potential suitability maps for vulnerable species require using a multi‐algorithm, fine scale data approach and incorporation of environmental variables like soil, slope, land use and elevation. Species distribution models can offer insight on the distribution requirements of vulnerable species and help guide the development of management plans. Results of this study suggest that E. angolense management plans should promote the protection of terrestrial forests surrounding water bodies including Mabira forest in Uganda.

Selecting thresholds for the prediction of species occurrence with presence‐only data

AbstractAimSpecies distribution models have been widely used to tackle ecological, evolutionary and conservation problems. Most species distribution modelling techniques produce continuous suitability predictions, but many real applications (e.g. reserve design, species invasion and climate change impact assessment) and model evaluations require binary outputs, and thresholds are needed for these transformations. Although there are many threshold selection methods for presence/absence data, it is unclear whether these are suitable for presence‐only data. In this paper, we investigate mathematically and empirically which of the existing threshold selection methods can be used confidently with presence‐only data.LocationWe used real spatially explicit environmental data derived from the western part of the state of Victoria, south‐eastern Australia, and simulated species distributions within this area.MethodsThirteen existing threshold selection methods were investigated mathematically to see whether the same threshold can be produced using either presence/absence data or presence‐only data. We further adopted a simulation approach, created many virtual species with differing prevalences in a real landscape in south‐eastern Australia, generated data sets with different proportions of pseudo‐absences, built eight types of models with four modelling techniques, and investigated the behaviours of four threshold selection methods in these situations.ResultsThree threshold selection methods were not affected by pseudo‐absences, including max SSS (which is based on maximizing the sum of sensitivity and specificity), the prevalence of model training data and the mean predicted value of a set of random points. Max SSS produced higher sensitivity in most cases and higher true skill statistic and kappa in many cases than the other methods. The other methods produced different thresholds from presence‐only data to those determined from presence/absence data.Main conclusionsMax SSS is a promising method for threshold selection when only presence data are available.

CLIMATE CHANGE IMPACTS ON WOODLAND SPECIES: IMPLICATIONS FOR THE CONSERVATION OF WOODLAND HABITATS IN IRELAND

Global climate is rapidly changing and, while many studies have investigated the potential impacts of climate change on the distribution of woodland species in the wider European context, few have focused on the impacts of climate change on the distribution of woodland species in Ireland. This study evaluates the potential impact of climate change on the distribution of species characteristic of woodlands and assesses the implications for the conservation of woodlands in Ireland. Only some 2% of the land area of Ireland is covered in semi-natural woodlands, including communities which are conservation priorities such as bog woodland, alluvial forests and Taxus baccata (Yew) woods. We applied an ensemble of species distribution modelling techniques, using atlas data for 104 vascular plant, bryophyte, bird and butterfly species from Ireland, baseline climate data (1961-1990) and projected future climate data (HadCM3 GCM; 2031-2060), at a 10km² resolution. Of the 104 species, 44 modelled well in at least 6 of the 8 models. Under a scenario of unlimited dispersal, almost 50% of species which modelled well were projected to lose climate space, with the largest range contraction projected for the butterfly Aphantopus hyperantus (Ringlet; — 47.7%). The remainder were projected to have increased climate space, with Thecla betulae (Brown hairstreak) and Frángula alnus (Alder buckthorn) having the largest projected increases of 886% and 532% of their current ranges. When a scenario of limited long-range dispersal was considered, all species were projected to lose some of their current range, from a decrease of 52.4% for Carex strigosa (thin-spiked woodsedge) to a decrease of 10.9% for Saxífraga spathularis (St Patrick's cabbage). These results have significant implications for the future of woodlands in Ireland. Conservation management plans for these habitats need greater focus on potential climate change impacts in order to ensure these communities' long-term survival.

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 (25m), 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.

Comparative interpretation of count, presence–absence and point methods for species distribution models

Summary1. The need to understand the processes shaping population distributions has resulted in a vast increase in the diversity of spatial wildlife data, leading to the development of many novel analytical techniques that are fit‐for‐purpose. One may aggregate location data into spatial units (e.g. grid cells) and model the resulting counts or presence–absences as a function of environmental covariates. Alternatively, the point data may be modelled directly, by combining the individual observations with a set of random or regular points reflecting habitat availability, a method known as a use‐availability design (or, alternatively a presence – pseudo‐absence or case–control design).2. Although these spatial point, count and presence–absence methods are widely used, the ecological literature is not explicit about their connections and how their parameter estimates and predictions should be interpreted. The objective of this study is to recapitulate some recent statistical results and illustrate that under certain assumptions, each method can be motivated by the same underlying spatial inhomogeneous Poisson point process (IPP) model in which the intensity function is modelled as a log‐linear function of covariates.3. The Poisson likelihood used for count data is a discrete approximation of the IPP likelihood. Similarly, the presence–absence design will approximate the IPP likelihood, but only when spatial units (i.e. pixels) are extremely small (Electric Journal of Statistics, 2010, 4, 1151–1201). For larger pixel sizes, presence–absence designs do not differentiate between one or multiple observations within each pixel, hence leading to information loss.4. Logistic regression is often used to estimate the parameters of the IPP model using point data. Although the response variable is defined as 0 for the availability points, these zeros do not serve as true absences as is often assumed; rather, their role is to approximate the integral of the denominator in the IPP likelihood (The Annals of Applied Statistics, 2010, 4, 1383–1402). Because of this common misconception, the estimated exponential function of the linear predictor (i.e. the resource selection function) is often assumed to be proportional to occupancy. Like IPP and count models, this function is proportional to the expected density of observations.5. Understanding these (dis‐)similarities between different species distribution modelling techniques should improve biological interpretation of spatial models and therefore advance ecological and methodological cross‐fertilization.

Measuring ecological niche overlap from occurrence and spatial environmental data

ABSTRACTAim Concerns over how global change will influence species distributions, in conjunction with increased emphasis on understanding niche dynamics in evolutionary and community contexts, highlight the growing need for robust methods to quantify niche differences between or within taxa. We propose a statistical framework to describe and compare environmental niches from occurrence and spatial environmental data.Location Europe, North America and South America.Methods The framework applies kernel smoothers to densities of species occurrence in gridded environmental space to calculate metrics of niche overlap and test hypotheses regarding niche conservatism. We use this framework and simulated species with pre‐defined distributions and amounts of niche overlap to evaluate several ordination and species distribution modelling techniques for quantifying niche overlap. We illustrate the approach with data on two well‐studied invasive species.Results We show that niche overlap can be accurately detected with the framework when variables driving the distributions are known. The method is robust to known and previously undocumented biases related to the dependence of species occurrences on the frequency of environmental conditions that occur across geographical space. The use of a kernel smoother makes the process of moving from geographical space to multivariate environmental space independent of both sampling effort and arbitrary choice of resolution in environmental space. However, the use of ordination and species distribution model techniques for selecting, combining and weighting variables on which niche overlap is calculated provide contrasting results.Main conclusions The framework meets the increasing need for robust methods to quantify niche differences. It is appropriate for studying niche differences between species, subspecies or intra‐specific lineages that differ in their geographical distributions. Alternatively, it can be used to measure the degree to which the environmental niche of a species or intra‐specific lineage has changed over time.

Combining environmental gradients to explain and predict the structure of demersal fish distributions

AbstractAim Species distribution modelling was used to investigate relationships between benthic environmental variables and patterns of demersal fish species distributions using fish occurrence data collected from spatially sparse point sampling. Our goals were to determine: (1) the relative importance of different environmental variables, (2) whether accurate predictions can be developed from these models, and (3) how a comparison of the models against known species ecology can be used to assess model relevance and to provide further insights that could drive future studies of these species.Location The Recherche Archipelago, southern Western Australia.Methods Fish distribution data were collected using baited remote underwater video systems (BRUVS): environmental characteristics (substrate type, macroalgal type and presence of sessile biota) were derived from the video footage; and water depth was measured with a depth sounder. Two species distribution modelling techniques were used to explore and quantify the contribution of environmental characteristics to distribution models of 10 temperate marine fish species.Results Substrate type (reef, sand, cobble) was the most influential variable, and water depth and macroalgal type influenced the probable occurrence of species even over the same substrate type. The probable occurrences of all but one fish species were predicted very successfully, with observed presences being predicted correctly with accuracies > 76%. These predictions were possible due to strong associations between these species and benthic features recorded in this study (substrate type, depth and macroalgal type).Main conclusions The results demonstrate that the combined influence of multiple environmental gradients must be considered to further develop our understanding of how the environment structures demersal fish distributions. The species distribution models not only agreed with the known ecology of the species examined in detail, but also provided more information on the strength of each environmental attribute and interactions between attributes. These interactions may have previously been misinterpreted as, for example, a depth‐driven response. Additional drivers of distribution that were previously not considered, such as the presence of macroalgae, were also found to have influence. This information thus provides a better description of a species’ niche, which is of value to both species management and conservation.

Predicted insect diversity declines under climate change in an already impoverished region

Being ectotherms, insects are predicted to suffer more severely from climate change than warm-blooded animals. We forecast possible changes in diversity and composition of butterflies, grasshoppers and dragonflies in Belgium under increasingly severe climate change scenarios for the year 2100. Two species distribution modelling techniques (Generalised Linear Models and Generalised Additive Models), were combined via a conservative version of the ensemble forecasting strategy to predict present-day and future species distributions, considering the species as potentially present only if both modelling techniques made such a prediction. All models applied were fair to good, according to the AUC (area under the curve of the receiver operating characteristic plot), sensitivity and specificity model performance measures based on model evaluation data. Butterfly and grasshopper diversity were predicted to decrease significantly in all scenarios and species-rich locations were predicted to move towards higher altitudes. Dragonfly diversity was predicted to decrease significantly in all scenarios, but dragonfly-rich locations were predicted to move upwards only in the less severe scenarios. The largest turnover rates were predicted to occur at higher altitudes for butterflies and grasshoppers, but at intermediate altitudes for dragonflies. Our results highlight the challenge of building conservation strategies under climate change, because the changes in the sites important for different groups will not overlap, increasing the area needed for protection. We advocate that possible conservation and policy measures to mitigate the potentially strong impacts of climate change on insect diversity in Belgium should be much more pro-active and flexible than is the case presently.

Dangers of predicting bird species distributions in response to land‐cover changes

Land-cover changes from the last decades are leading to important declines in habitat quality, giving rise to changes in bird species distribution all over the world. However, land-cover changes result from a variety of different processes, and it is not clear how effective species distribution models are in capturing species responses to these changes. In this study, we evaluated our ability to predict the effects of land-cover changes on shifts in species distributions at large spatial and temporal scales using Mediterranean landscapes and early-successional, open-habitat birds as study models. Based on presence-absence data from the second Catalan Breeding Bird Atlas (1999-2002), we applied six different species distribution modeling techniques for 10 bird species using climate, topographic, and land-cover data as predictor variables. Then we back-projected the models on land-cover conditions from 1980 to evaluate the projections with field observation data from the first Catalan Breeding Bird Atlas (1975-1983). Finally, we assessed if, in addition to changes in habitat suitability resulting from land-cover shifts, descriptors of fire impact contributed to further explain species distribution dynamics: colonization and local extinction. We developed accurate model projections of current and past global patterns of species distribution, but our ability to predict species distribution dynamics was reduced. Colonization dynamics were generally more strongly related to fire descriptors than to changes in overall habitat suitability derived from land-cover changes. Our results warn of the dangers of projecting species distribution models onto future conditions if processes behind species distribution dynamics are not explicitly included. Consideration of ecologically meaningful processes for species (i.e., fire disturbance) when modeling species' distribution might contribute to a better explanation of species' colonization dynamics.