Can niche‐based distribution models outperform spatial interpolation?

Aim Distribution modelling relates sparse data on species occurrence or abundance to environmental information to predict the population of a species at any point in space. Recently, the importance of spatial autocorrelation in distributions has been recognized. Spatial autocorrelation can be categorized as exogenous (stemming from autocorrelation in the underlying variables) or endogenous (stemming from activities of the organism itself, such as dispersal). Typically, one asks whether spatial models explain additional variability (endogenous) in comparison to a fully specified habitat model. We turned this question around and asked: can habitat models explain additional variation when spatial structure is accounted for in a fully specified spatially explicit model? The aim was to find out to what degree habitat models may be inadvertently capturing spatial structure rather than true explanatory mechanisms. Location We used data from 190 species of the North American Breeding Bird Survey covering the conterminous United States and southern Canada. Methods We built 13 different models on 190 bird species using regression trees. Our habitat‐based models used climate and landcover variables as independent variables. We also used random variables and simulated ranges to validate our results. The two spatially explicit models included only geographical coordinates or a contagion term as independent variables. As another angle on the question of mechanism vs. spatial structure we pitted a model using related bird species as predictors against a model using randomly selected bird species. Results The spatially explicit models outperformed the traditional habitat models and the random predictor species outperformed the related predictor species. In addition, environmental variables produced a substantial R² in predicting artificial ranges. Main conclusions We conclude that many explanatory variables with suitable spatial structure can work well in species distribution models. The predictive power of environmental variables is not necessarily mechanistic, and spatial interpolation can outperform environmental explanatory variables.     

Performance evaluation of cetacean species distribution models developed using generalized additive models and boosted regression trees

Species distribution models (SDMs) are important management tools for highly mobile marine species because they provide spatially and temporally explicit information on animal distribution. Two prevalent modeling frameworks used to develop SDMs for marine species are generalized additive models (GAMs) and boosted regression trees (BRTs), but comparative studies have rarely been conducted; most rely on presence‐only data; and few have explored how features such as species distribution characteristics affect model performance. Since the majority of marine species BRTs have been used to predict habitat suitability, we first compared BRTs to GAMs that used presence/absence as the response variable. We then compared results from these habitat suitability models to GAMs that predict species density (animals per km²) because density models built with a subset of the data used here have previously received extensive validation. We compared both the explanatory power (i.e., model goodness of fit) and predictive power (i.e., performance on a novel dataset) of the GAMs and BRTs for a taxonomically diverse suite of cetacean species using a robust set of systematic survey data (1991–2014) within the California Current Ecosystem. Both BRTs and GAMs were successful at describing overall distribution patterns throughout the study area for the majority of species considered, but when predicting on novel data, the density GAMs exhibited substantially greater predictive power than both the presence/absence GAMs and BRTs, likely due to both the different response variables and fitting algorithms. Our results provide an improved understanding of some of the strengths and limitations of models developed using these two methods. These results can be used by modelers developing SDMs and resource managers tasked with the spatial management of marine species to determine the best modeling technique for their question of interest.     

Building better wildlife-habitat models

Wildlife-habitat models are an important tool in wildlife management today, and by far the majority of these predict aspects of species distribution (abundance or presence) as a proxy measure of habitat quality. Unfortunately, few are tested on independent data., and of those that are, few show useful predictive skill. We demonstrate that six critical assumptions underlie distribution based wildlife-habitat models, all of which must be valid for the model to predict habitat quality. We outline these assumptions in a meta-model, and discuss methods for their validation. Even where all six assumptions show a high level of validity, there is still a strong likelihood that the model will not predict habitat quality. However, the meta-model does suggest habitat quality can be predicted more accurately if distributional data are ignored, and variables more indicative of habitat quality are modelled instead.     

Essential krill species habitat resolved by seasonal upwelling and ocean circulation models within the large marine ecosystem of the California Current System

Due to their global distribution, high biomass and energy content, euphausiids (krill) are important prey for many mid and upper trophic level marine organisms. Understanding drivers of krill habitat is essential for forecasting range shifts, and to better understand the response of krill predators to climate change. We hypothesized that the distribution and abundance of krill species derived from ecosystem surveys in spring/summer relates to geomorphic features, coastal upwelling during the preceding winter, and spring mesoscale oceanographic conditions. To test this hypothesis, we used boosted regression trees with environmental data and ocean model output to quantify the habitat associations of two primary krill species (Euphausia pacifica and Thysanoessa spinifera) in the central California Current Ecosystem from 2002 to 2018. Models confirmed the neritic distribution of T. spinifera and pelagic, outer slope association of E. pacifica (deviance explained ~35%). Distribution of these species were influenced by depth and bottom rugosity; chlorophyll-a concentrations and increased winter upwelling conditions; and spring surface currents and wind stress. Thysanoessa spinifera and E. pacifica abundance responded negatively (positively) to warm (cold) climate events, confirming known relationships. As an independent evaluation of krill models, observations of krill predator (seabirds, marine mammals) distribution indicated they were present within habitats of predicted high krill species abundance. Our framework indicates species-specific habitat relationships for these foundational forage species and their negative response to large-scale climate variations, such as El Niño and marine heatwave conditions. The approach can be easily transferred to other ecosystems or krill species that respond to similar ocean and climate forcing.     

Bayesian joint models with INLA exploring marine mobile predator–prey and competitor species habitat overlap

Understanding spatial physical habitat selection driven by competition and/or predator–prey interactions of mobile marine species is a fundamental goal of spatial ecology. However, spatial counts or density data for highly mobile animals often (1) include excess zeros, (2) have spatial correlation, and (3) have highly nonlinear relationships with physical habitat variables, which results in the need for complex joint spatial models. In this paper, we test the use of Bayesian hierarchical hurdle and zero‐inflated joint models with integrated nested Laplace approximation (INLA), to fit complex joint models to spatial patterns of eight mobile marine species (grey seal, harbor seal, harbor porpoise, common guillemot, black‐legged kittiwake, northern gannet, herring, and sandeels). For each joint model, we specified nonlinear smoothed effect of physical habitat covariates and selected either competing species or predator–prey interactions. Out of a range of six ecologically important physical and biologic variables that are predicted to change with climate change and large‐scale energy extraction, we identified the most important habitat variables for each species and present the relationships between these bio/physical variables and species distributions. In particular, we found that net primary production played a significant role in determining habitat preferences of all the selected mobile marine species. We have shown that the INLA method is well‐suited for modeling spatially correlated data with excessive zeros and is an efficient approach to fit complex joint spatial models with nonlinear effects of covariates. Our approach has demonstrated its ability to define joint habitat selection for both competing and prey–predator species that can be relevant to numerous issues in the management and conservation of mobile marine species.     

Predicting krill swarm characteristics important for marine predators foraging off East Antarctica

Open ocean predator‐prey interactions are often difficult to interpret because of a lack of information on prey fields at scales relevant to predator behaviour. Hence, there is strong interest in identifying the biological and physical factors influencing the distribution and abundance of prey species, which may be of broad predictive use for conservation planning and evaluating effects of environmental change. This study focuses on a key Southern Ocean prey species, Antarctic krill Euphausia superba, using acoustic observations of individual swarms (aggregations) from a large‐scale survey off East Antarctica. We developed two sets of statistical models describing swarm characteristics, one set using underway survey data for the explanatory variables, and the other using their satellite remotely sensed analogues. While survey data are in situ and contemporaneous with the swarm data, remotely sensed data are all that is available for prediction and inference about prey distribution in other areas or at other times. The fitted models showed that the primary biophysical influences on krill swarm characteristics included daylight (solar elevation/radiation) and proximity to the Antarctic continental slope, but there were also complex relationships with current velocities and gradients. Overall model performance was similar regardless of whether underway or remotely sensed predictors were used. We applied the latter models to generate regional‐scale spatial predictions using a 10‐yr remotely‐sensed time series. This retrospective modelling identified areas off east Antarctica where relatively dense krill swarms were consistently predicted during austral mid‐summers, which may underpin key foraging areas for marine predators. Spatiotemporal predictions along Antarctic predator satellite tracks, from independent studies, illustrate the potential for uptake into further quantitative modelling of predator movements and foraging. The approach is widely applicable to other krill‐dependent ecosystems, and our findings are relevant to similar efforts examining biophysical linkages elsewhere in the Southern Ocean and beyond.     

Logistic regression models for predicting occurrence of terrestrial molluscs in southern Sweden – importance of environmental data quality and model complexity

We compared the performance of four logistic regression models of different complexity with different environmental data quality, in predicting the occurrence of 49 terrestrial mollusc species in southern Sweden. Performance of models derived from an explanatory data set was evaluated on a confirmatory data set. The overall predictive success of our models (>80% for the three best model approaches), is as good as in other studies, despite the fact that we had to transform a text database into quantitative habitat variables. Simple models (no variable interactions), with forward selection, and detailed habitat data (from field visits) showed the best overall predictive success (mean=84.8%). From comparisons of model approaches, we conclude that data quality (map‐derived data vs habitat mapping) had a stronger impact than model complexity on model performance. However, most of these models showed relatively low values (mean=0.29) for Kappa (statistic for model evaluation), suggesting that the models need to be improved before they would be applied. Predictive success was strongly associated with species incidence but also Kappa was positively correlated with species incidence in univariate tests. Predictive success for true absences was negatively correlated with predictive success for true presences (R²=0.69) and most models failed to give a good prediction of both categories. Models for species with a high incidence in “Open dry sites” or “Mesic interior forests” had a better performance than expected, suggesting that occurrences of species with preference for “narrow” habitats are most easy to predict. Tree layer variables (openness and species abundance) were included in 48 of the 49 final predictive models, suggesting that these variables were good “indicators” of habitat conditions for ground‐living molluscs. Twenty‐four species models included distance to coast and altitude, and we interpret these associations as partly being related to differences in climate. In the final models, true presences (36.9% correctly classified) were much more difficult to predict than true absences (89.7% correct). Possible explanations might be that important habitat variables (e.g. chemical variables and site history) were not included. On the other hand, all suitable sites would not be expected to be occupied due to dynamics in local extinctions (meta‐population theory).     

Habitat models for a riparian carabid beetle: their validity and applicability in the evaluation of river bank management

In order to assess the management success of river rehabilitation measurements it is necessary to have representative target species and objective statistical methods. In this study we, tested the validity of habitat suitability models for the riparian carabid beetle Bembidion velox in the evaluation of river bank management along the River Elbe, Germany. On the basis of seven independent data sets from different sites and years we have proven the robustness of logistic regression models with respect to their explanatory and predictive power and their applicability in the field. All models had robust explanatory power and described a strong association of B. velox with semi-terrestrial sandy open soil habitats. Transfers of model results for adult beetles to their larvae and vice versa were highly significant with “sand content” and “stem distance” as the main habitat factors for both life stages. To broaden the local explanatory power towards general predictions we performed model cross-validation in space and time. Spatial transfers produced models with excellent discrimination properties, measured by Area Under Curve (AUC) values of Receiver Operating Characteristics (ROC) plots, independent of sampling designs and trapping methodology. However, the applicability of habitat models for B. velox is defined by the validity period, as the availability of suitable habitats for this species is highly temporally variable and dependent on water level. Model transfers between species also demonstrated that the chosen target species is representative for carabids with similar distribution patterns, as the single species model had high predictive power for the occurrence of a multi-species carabid group.     

Prey diversity as a driver of resource partitioning between river‐dwelling fish species

Although food resource partitioning among sympatric species has often been explored in riverine systems, the potential influence of prey diversity on resource partitioning is little known. Using empirical data, we modeled food resource partitioning (assessed as dietary overlap) of coexisting juvenile Atlantic salmon (Salmo salar) and alpine bullhead (Cottus poecilopus). Explanatory variables incorporated into the model were fish abundance, benthic prey diversity and abundance, and several dietary metrics to give a total of seventeen potential explanatory variables. First, a forward stepwise procedure based on the Akaike information criterion was used to select explanatory variables with significant effects on food resource partitioning. Then, linear mixed‐effect models were constructed using the selected explanatory variables and with sampling site as a random factor. Food resource partitioning between salmon and bullhead increased significantly with increasing prey diversity, and the variation in food resource partitioning was best described by the model that included prey diversity as the only explanatory variable. This study provides empirical support for the notion that prey diversity is a key driver of resource partitioning among competing species.     

A new method for dealing with residual spatial autocorrelation in species distribution models

Species distribution modelling (SDM) is a widely used tool and has many applications in ecology and conservation biology. Spatial autocorrelation (SAC), a pattern in which observations are related to one another by their geographic distance, is common in georeferenced ecological data. SAC in the residuals of SDMs violates the ‘independent errors’ assumption required to justify the use of statistical models in modelling species’ distributions. The autologistic modelling approach accounts for SAC by including an additional term (the autocovariate) representing the similarity between the value of the response variable at a location and neighbouring locations. However, autologistic models have been found to introduce bias in the estimation of parameters describing the influence of explanatory variables on habitat occupancy. To address this problem we developed an extension to the autologistic approach by calculating the autocovariate on SAC in residuals (the RAC approach). Performance of the new approach was tested on simulated data with a known spatial structure and on strongly autocorrelated mangrove species’ distribution data collected in northern Australia. The RAC approach was implemented as generalized linear models (GLMs) and boosted regression tree (BRT) models. We found that the BRT models with only environmental explanatory variables can account for some SAC, but applying the standard autologistic or RAC approaches further reduced SAC in model residuals and substantially improved model predictive performance. The RAC approach showed stronger inferential performance than the standard autologistic approach, as parameter estimates were more accurate and statistically significant variables were accurately identified. The new RAC approach presented here has the potential to account for spatial autocorrelation while maintaining strong predictive and inferential performance, and can be implemented across a range of modelling approaches.     

The prediction of macrophyte species occurrence in Swiss ponds

The study attempted to model the abundance of aquatic plant species recorded in a range of ponds in Switzerland. A stratified sample of 80 ponds, distributed all over the country, provided input data for model development. Of the 154 species recorded, 45 were selected for modelling. A total of 14 environmental parameters were preselected as candidate explanatory variables. Two types of statistical tools were used to explore the data and to develop the predictive models: linear regression (LR) and generalized additive models (GAMs). Six LR species models had a reasonable predictive ability (30–50% of variance explained by the selected predictors). There was a gradient in the quality of the 45 GAM models. Ten species models exhibited both a good fit and statistical robustness: Lemna minor, Phragmites australis, Lysimachia vulgaris, Galium palustre, Lysimachia nummularia, Iris pseudacorus, Lythrum salicaria, Lycopus europaeus, Phalaris arundinacea, Alisma plantago-aquatica, Schoenoplectus lacustris, Carex nigra. Altitude appeared to be a key explanatory variable in most of the species models. In some cases, the degree to which the shore was shaded, connectivity between water bodies, pond area, mineral nitrogen levels, pond age, pond depth, and the extent of agriculture or pasture in the catchment were selected as additional explanatory variables. The species models demonstrated that it is possible to predict species abundance of aquatic macrophytes and that each species responded individually to distinct environmental variables.     

Using unclassified continuous remote sensing data to improve distribution models of red-listed plant species

Remote sensing (RS) data may play an important role in the development of cost-effective means for modelling, mapping, planning and conserving biodiversity. Specifically, at the landscape scale, spatial models for the occurrences of species of conservation concern may be improved by the inclusion of RS-based predictors, to help managers to better meet different conservation challenges. In this study, we examine whether predicted distributions of 28 red-listed plant species in north-eastern Finland at the resolution of 25 ha are improved when advanced RS-variables are included as unclassified continuous predictor variables, in addition to more commonly used climate and topography variables. Using generalized additive models (GAMs), we studied whether the spatial predictions of the distribution of red-listed plant species in boreal landscapes are improved by incorporating advanced RS (normalized difference vegetation index, normalized difference soil index and Tasseled Cap transformations) information into species-environment models. Models were fitted using three different sets of explanatory variables: (1) climate-topography only; (2) remote sensing only; and (3) combined climate-topography and remote sensing variables, and evaluated by four-fold cross-validation with the area under the curve (AUC) statistics. The inclusion of RS variables improved both the explanatory power (on average 8.1 % improvement) and cross-validation performance (2.5 %) of the models. Hybrid models produced ecologically more reliable distribution maps than models using only climate-topography variables, especially for mire and shore species. In conclusion, Landsat ETM+ data integrated with climate and topographical information has the potential to improve biodiversity and rarity assessments in northern landscapes, especially in predictive studies covering extensive and remote areas.     

Distribution and coexistence of shrews in patchy landscapes: A field test of multiple hypotheses

Despite the important role of shrews (Soricomorpha: Soricidae) in the functioning of ecosystems, as predators and prey, the effects of habitat loss and fragmentation on this guild of mammals are still unclear. We studied the distribution of 5 species (the greater white toothed shrew Crocidura leucodon; the lesser white toothed shrew Crocidura suaveolens; the pigmy shrew Sorex minutus; the Appennine shrew Sorex samniticus and the Etruscan shrew Suncus etruscus) in a fragmented landscape in central Italy.Shrews were trapped with pitfall traps made from plastic water bottles, the number of traps increased with patch size. A total of 170 individuals, of 5 species of shrews were captured. Shrews were widely distributed in our study area, however patch occupancy was determined mainly by vegetation and geometrical characteristics of the patches. Our data supports the hypotheses that patterns of habitat selection and the dynamics of seasonal abundance (habitat and temporal partitioning between similarly sized species) reduce competitive pressure, thus allowing coexistence of shrews in relatively species-rich assemblages, for such small amounts of habitat. The most important outcome of our results is the crucial role played by vegetation structure in determining distribution patterns. These results strongly suggest that measurements of the vegetation structure of habitat patches should always be included as explanatory variables when studying the distribution of shrews in fragmented landscapes.     

物种分布模型在海洋潜在生境预测的应用研究进展

海洋生物的栖息分布与环境要素的关联性一直是海洋生态学研究的热点之一.近年来,物种分布模型被广泛应用于预测海洋物种分布、潜在适宜性生境评价等研究,为保护海洋生物多样性、防治外来物种入侵及制定渔业管理措施等提供了一条有效途径.物种分布模型主要包括生境适宜性指数模型、机理模型和统计模型.本文对物种分布模型的理论基础进行了归纳和总结,回顾了物种分布模型在预测海洋物种潜在地理分布研究中的开发与应用,重点介绍了不同类型统计模型在海洋物种潜在分布预测中的研究实例.比较各种选取变量和模型验证方法,认为赤池信息准则对于选取模型变量具有优势,Kappa系数和受试者操作特征曲线下面积在验证模型精度中应用最广泛.阐述了物种分布模型存在的问题及未来发展趋势,随着海洋生物生理机制研究的进一步深入,机理模型将是今后物种分布模型发展的重点.     

Habitat modeling used to predict relative abundance of bobcats in Iowa

We combined observations of bobcats (Lynx rufus) from bowhunters with remotely-sensed data to build models that describe habitat and relative abundance of this species in the agricultural landscape of Iowa, USA. We calculated landscape composition and configuration from publicly available land cover, census, road, hydrologic, and elevation data. We used multiple regression models to examine county-level associations between several explanatory variables and relative abundance of bobcats reported by surveyed bowhunters in each county. The most influential explanatory variables in the models were metrics associated with the presence of grassland, including Conservation Reserve, along with configuration of this perennial habitat with forests, although human population density and abundance of eastern cottontails (Sylvilagus floridanus) also correlated with abundance of bobcats. Validation of predictions against 3 years of independent data provided confidence in the models, with 66% of predictions within 1 bobcat/1,000 hunter-hours and 95% within 5 bobcats/1,000 hunter-hours of observed values. Once we accounted for landscape differences, no residual spatial trend was evident, despite relatively recent bobcat recolonization of Iowa. Models suggested that future range expansion of the bobcat population may be possible in some northern Iowa counties where habitat composition is similar to counties in southern Iowa where bobcats are abundant. Results from the county-level model have been useful to the Iowa Department of Natural Resources in evaluating the expansion of this once rare species and for delineating harvest opportunities. © 2011 The Wildlife Society.     

Evaluating models to assess the distribution of Buxus balearica in southern Spain

Question: Which is the best model to predict the habitat distribution of Buxus balearica Lam. in southern Spain? Location: Málaga and Granada, Spain, across an area of 38 180 km². Methods: Prediction models based on 17 environmental variables were tested. Six methods were compared: multivariate adaptive regression spline (MARS), maximum entropy approach to modelling species' distributions (Maxent), two generic algorithms based on environmental metrics dissimilarity (BIOCLIM and DOMAIN), Genetic Algorithm for Rule‐set Prediction (GARP), and supervised learning methods based on generalized linear classifiers (support vector machines, SVMs). To test the predictive power of the models we used the Kappa index. Results: Maxent most accurately predicted the habitat distribution of B. balearica, followed by MARS models. The other models tested yielded lower accuracy values. A comparison of the predictive power of the models revealed that climate variables made the highest contributions among the environmental variables studied. The variables that made the lowest contributions were the insolation models. To examine the sensitivity of the models to a reduction in the number of variables, a test showed that accuracy of over 0.90 was maintained by applying just three climatic variables (spring rainfall, mean temperature of the warmest month, and mean temperature of the coldest month). Maps derived from the algorithms of all models tested coincided well with the known distribution of the species. Conclusions: Model habitat prediction is a preliminary step towards highlighting areas of high habitat suitability of B. balearica. These data support the results of previous research, which show that MaxEnt is the best technique for modelling species distributions with small sample sizes.     

Juvenile fishes in macrophyte beds: influence of food resources, habitat structure and body size

Mean juvenile fish abundance and fish frequency in a large lowland river during low discharge largely differed among the unvegetated and three morphologically contrasted macrophyte habitats. Single separate models revealed that juvenile fish distribution was largely influenced by trophic variables. With the exception of Leuciscus cephalus , which responded mainly to physical variables (depth and substratum), multiple regression models emphasized the importance of trophic variables for fish distribution. For Blicca bjoerkna , L. cephalus and Lepomis gibbosus , habitat shifts with respect to prey size were apparent; small juvenile fishes mainly responded to small zooplankton abundance, whereas large individuals were more influenced by the abundance of large zooplankton. Whatever the species, predictions from multiple regression models were always better for large individuals. Small juvenile fishes appeared to be less affected by the habitat variables measured, and exhibited more uniform spatial distribution. The relative importance of trophic resources and habitat physical structure among macrophyte types for fish-habitat relationships is discussed, and the necessity of quantifying habitat structural complexity is emphasized.     

Tight spatial coupling of a marine predator with soniferous fishes: Using joint modelling to aid in ecosystem approaches to management

Aim

Understanding the distribution of marine organisms is essential for effective management of highly mobile marine predators that face a variety of anthropogenic threats. Recent work has largely focused on modelling the distribution and abundance of marine mammals in relation to a suite of environmental variables. However, biotic interactions can largely drive distributions of these predators. We aim to identify how biotic and abiotic variables influence the distribution and abundance of a particular marine predator, the bottlenose dolphin (Tursiops truncatus), using multiple modelling approaches and conducting an extensive literature review.

Location

Western North Atlantic continental shelf.

Methods

We combined widespread marine mammal and fish and invertebrate surveys in an ensemble modelling approach to assess the relative importance and capacity of the environment and other marine species to predict the distribution of both coastal and offshore bottlenose dolphin ecotypes. We corroborate the modelled results with a systematic literature review on the prey of dolphins throughout the region to help explain patterns driven by prey availability, as well as reveal new ones that may not necessarily be a predator–prey relationship.

Results

We find that coastal bottlenose dolphin distributions are associated with one family of fishes, the Sciaenidae, or drum family, and predictions slightly improve when using only fish versus only environmental variables. The literature review suggests that this tight coupling is likely a predator–prey relationship. Comparatively, offshore dolphin distributions are more strongly related to environmental variables, and predictions are better for environmental-only models. As revealed by the literature review, this may be due to a mismatch between the animals caught in the fish and invertebrate surveys and the predominant prey of offshore dolphins, notably squid.

Main Conclusions

Incorporating prey species into distribution models, especially for coastal bottlenose dolphins, can help inform ecological relationships and predict marine predator distributions.     

Distribution of medium- to large-sized African mammals based on habitat suitability models

The knowledge of the areas inhabited by a species within its distribution range and the connections among patches are critical pieces of information for successful conservation actions. The internal structure of the extent of occurrence (EO) of a species is almost always unknown, even for “well-known” flagship species. We developed a methodology to infer the area of occupancy (AO) within the EO of a species using the limited available data. We present here the results of a three years project funded by European Union to develop high-resolution models of habitat suitability for 281 medium- to large-sized African mammals across the whole continent. The existing literature was reviewed and all data on the geographic distribution and environmental preferences of the selected species were collected. For each species, these data were then expressed in terms of key variables available as GIS layers at a resolution of 1 km² over the entire African continent. The AO of each species was obtained merging the information on the ecological needs of the species and the values of ecological variables over the region identified as EO. The habitat suitability models were evaluated through direct field work in four countries (Morocco, Cameroon, Uganda, Botswana) chosen as representatives of the environmental and species diversity of Africa. More than 81% of models had positive true skill statistics (TSS) values, indicating models performing better than random. Rigorous modeling procedures supported by ad-hoc field evaluation allowed the production of high-resolution habitat suitability models useful for conservation applications.     

The generality of habitat suitability models: A practical test with two insect groups

For the design and declaration of conservation areas as well as for planning habitat management it is important to quantitatively know the habitat preferences of the focal species. To take into account the requirements of as many species as possible, it would be of great advantage if one would either (i) find one or several species whose habitat requirements cover those of a large number of other species or if one could (ii) identify a common set of habitat parameters that is important for the occurrence of many species. Ideally such common habitat parameters should be easy to measure. Only then they may be of practical value in applied conservation biology.In this study, we compared the habitat preferences of different insect species (grasshoppers, bush crickets, butterflies, moths) in the same region by applying identical methods. To identify common explanatory variables that predict the occurrence probability of these species, we first tested the transferability of the specific ‘species models’ to other species within the same insect group. We tested how well the incidence of one species can be predicted by the occurrence probability of another species. The ‘best’ models within each group were then tested for transferability between the different groups. Additionally, we tested the predictive power of the predictor variable ‘habitat type’ as an easy and often available measure for conservation practice.Although in the different ‘species models’ different key factors determine habitat suitability, some models were successfully transferred and were able to reasonably predict the distribution of other species. The habitat preferences of the burnet moth Zygaena carniolica were particularly well suited for the prediction of suitable habitats for all other species. In addition, the predictor variable ‘habitat type’ played a dominant role in all models. Models using this aggregated predictor variable may well predict suitable habitat for all species.