Bio‐ORACLE: a global environmental dataset for marine species distribution modelling

Aim The oceans harbour a great diversity of organisms whose distribution and ecological preferences are often poorly understood. Species distribution modelling (SDM) could improve our knowledge and inform marine ecosystem management and conservation. Although marine environmental data are available from various sources, there are currently no user‐friendly, high‐resolution global datasets designed for SDM applications. This study aims to fill this gap by assembling a comprehensive, uniform, high‐resolution and readily usable package of global environmental rasters. Location Global, marine. Methods We compiled global coverage data, e.g. satellite‐based and in situ measured data, representing various aspects of the marine environment relevant for species distributions. Rasters were assembled at a resolution of 5 arcmin (c. 9.2 km) and a uniform landmask was applied. The utility of the dataset was evaluated by maximum entropy SDM of the invasive seaweed Codium fragile ssp. fragile. Results We present Bio‐ORACLE (ocean rasters for analysis of climate and environment), a global dataset consisting of 23 geophysical, biotic and climate rasters. This user‐friendly data package for marine species distribution modelling is available for download at http://www.bio‐oracle.ugent.be . The high predictive power of the distribution model of C. fragile ssp. fragile clearly illustrates the potential of the data package for SDM of shallow‐water marine organisms. Main conclusions The availability of this global environmental data package has the potential to stimulate marine SDM. The high predictive success of the presence‐only model of a notorious invasive seaweed shows that the information contained in Bio‐ORACLE can be informative about marine distributions and permits building highly accurate species distribution models.     

Testing the ability of species distribution models to infer variable importance

Models of species’ distributions and niches are frequently used to infer the importance of range- and niche-defining variables. However, the degree to which these models can reliably identify important variables and quantify their influence remains unknown. Here we use a series of simulations to explore how well models can 1) discriminate between variables with different influence and 2) calibrate the magnitude of influence relative to an ‘omniscient’ model. To quantify variable importance, we trained generalized additive models (GAMs), Maxent and boosted regression trees (BRTs) on simulated data and tested their sensitivity to permutations in each predictor. Importance was inferred by calculating the correlation between permuted and unpermuted predictions, and by comparing predictive accuracy of permuted and unpermuted predictions using AUC and the continuous Boyce index. In scenarios with one influential and one uninfluential variable, models failed to discriminate reliably between variables when training occurrences were < 8–64, prevalence was > 0.5, spatial extent was small, environmental data had coarse resolution and spatial autocorrelation was low, or when pairwise correlation between environmental variables was |r| > 0.7. When two variables influenced the distribution equally, importance was underestimated when species had narrow or intermediate niche breadth. Interactions between variables in how they shaped the niche did not affect inferences about their importance. When variables acted unequally, the effect of the stronger variable was overestimated. GAMs and Maxent discriminated between variables more reliably than BRTs, but no algorithm was consistently well-calibrated vis-à-vis the omniscient model. Algorithm-specific measures of importance like Maxent's change-in-gain metric were less robust than the permutation test. Overall, high predictive accuracy did not connote robust inferential capacity. As a result, requirements for reliably measuring variable importance are likely more stringent than for creating models with high predictive accuracy.     

Pushing the limits in marine species distribution modelling: lessons from the land present challenges and opportunities

Aim Species distribution models (SDMs) have been used to address a wide range of theoretical and applied questions in the terrestrial realm, but marine‐based applications remain relatively scarce. In this review, we consider how conceptual and practical issues associated with terrestrial SDMs apply to a range of marine organisms and highlight the challenges relevant to improving marine SDMs. Location We include studies from both marine and terrestrial systems that encompass many geographic locations around the globe. Methods We first performed a literature search and analysis of marine and terrestrial SDMs in ISI Web of Science to assess trends and applications. Using knowledge from terrestrial applications, we critically evaluate the application of SDMs in marine systems in the context of ecological factors (dispersal, species interactions, aggregation and ontogenetic shifts) and practical considerations (data quality, alternative modelling approaches and model validation) that facilitate or create difficulties for model application. Results The relative importance of ecological factors to be considered when applying SDMs varies among terrestrial and marine organisms. Correctly incorporating dispersal is frequently considered an important issue for terrestrial models, but because there is greater potential for dispersal in the ocean, it is often less of a concern in marine SDMs. By contrast, ontogenetic shifts and feeding have received little attention in terrestrial SDM applications, but these factors are important to many marine SDMs. Opportunities also exist for applying more advanced SDM approaches in the marine realm, including mechanistic ecophysiological models, where water balance and heat transfer equations are simpler for some marine organisms relative to their terrestrial counterparts. Main conclusions SDMs have generally been under‐utilized in the marine realm relative to terrestrial applications. Correlative SDM methods should be tested on a range of marine organisms, and we suggest further development of methods that address ontogenetic shifts and feeding interactions. We anticipate developments in, and cross‐fertilization between, coupled correlative and process‐based SDMs, mechanistic eco‐physiological SDMs, and spatial population dynamic models for climate change and species invasion applications in particular. Comparisons of the outputs of different model types will provide insight that is useful for improved spatial management of marine species.     

PSDS 2.0: 一个基于GIS和多个模型的生物潜在分布地预测系统

根据对生物分布地预测模型和软件发展现状的分析和总结, 本研究在PSDS 1.0的基础上提出并实现一个基于GIS且具有多个代表性模型的生物分布地预测系统(PSDS 2.0)。PSDS 2.0系统继承了1.0的环境包络和聚类包络模型, 进一步引入了限制因子包络、马氏距离、支持向量机等新模型, 并针对本领域中模型比较与选择的难点增加了迭代交叉验证的多模型选择功能。系统还实现了灵活定制和评估伪负样本的功能, 通过用只需要正样本的I类模型预测的结果对随机产生的伪负样本进行评估, 减小其落入适宜地区的概率, 进一步提高需要正负样本的II类模型的准确率。GIS功能在PSDS 2.0中也得到加强, 被应用于数据准备及结果分析等重要环节。文章最后以白冠长尾雉(Syrmaticus reevesii)为例, 运用PSDS 2.0系统预测其在中国范围内的潜在分布地, 并对各种模型的预测结果进行评估和比较。     

Spatial autocorrelation in predictors reduces the impact of positional uncertainty in occurrence data on species distribution modelling

Aim To investigate the impact of positional uncertainty in species occurrences on the predictions of seven commonly used species distribution models (SDMs), and explore its interaction with spatial autocorrelation in predictors. Methods A series of artificial datasets covering 155 scenarios including different combinations of five positional uncertainty scenarios and 31 spatial autocorrelation scenarios were simulated. The level of positional uncertainty was defined by the standard deviation of a normally distributed zero‐mean random variable. Each dataset included two environmental gradients (predictor variables) and one set of species occurrence sample points (response variable). Seven commonly used models were selected to develop SDMs: generalized linear models, generalized additive models, boosted regression trees, multivariate adaptive regression spline, random forests, genetic algorithm for rule‐set production and maximum entropy. A probabilistic approach was employed to model and simulate five levels of error in the species locations. To analyse the propagation of positional uncertainty, Monte Carlo simulation was applied to each scenario for each SDM. The models were evaluated for performance using simulated independent test data with Cohen’s Kappa and the area under the receiver operating characteristic curve. Results Positional uncertainty in species location led to a reduction in prediction accuracy for all SDMs, although the magnitude of the reduction varied between SDMs. In all cases the magnitude of this impact varied according to the degree of spatial autocorrelation in predictors and the levels of positional uncertainty. It was shown that when the range of spatial autocorrelation in the predictors was less than or equal to three times the standard deviation of the positional error, the models were less affected by error and, consequently, had smaller decreases in prediction accuracy. When the range of spatial autocorrelation in predictors was larger than three times the standard deviation of positional error, the prediction accuracy was low for all scenarios. Main conclusions The potential impact of positional uncertainty in species occurrences on the predictions of SDMs can be understood by comparing it with the spatial autocorrelation range in predictor variables.     

Modelled spatial distribution of marine fish and projected modifications in the North Atlantic Ocean

The objectives of this work were to examine the past, current and potential influence of global climate change on the spatial distribution of some commercially exploited fish and to evaluate a recently proposed new ecological niche model (ENM) called nonparametric probabilistic ecological niche model (NPPEN). This new technique is based on a modified version of the test called Multiple Response Permutation Procedure (MRPP) using the generalized Mahalanobis distance. The technique was applied in the extratropical regions of the North Atlantic Ocean on eight commercially exploited fish species using three environmental parameters (sea surface temperature, bathymetry and sea surface salinity). The numerical procedure and the model allowed a better characterization of the niche (sensu Hutchinson) and an improved modelling of the spatial distribution of the species. Furthermore, the technique appeared to be robust to incomplete or bimodal training sets. Despite some potential limitations related to the choice of the climatic scenarios (A2 and B2), the type of physical model (ECHAM 4) and the absence of consideration of biotic interactions, modelled changes in species distribution explained some current observed shifts in dominance that occurred in the North Atlantic sector, and particularly in the North Sea. Although projected changes suggest a poleward movement of species, our results indicate that some species may not be able to track their climatic envelope and that climate change may have a prominent influence on fish distribution during this century. The phenomenon is likely to trigger locally major changes in the dominance of species with likely implications for socio‐economical systems. In this way, ENMs might provide a new management tool against which changes in the resource might be better anticipated.     

A simple method to estimate the probable distribution of species

Species distribution models (SDMs) are broadly used to predict species distributions from available presence data. However, SDMs results have been criticized for several reasons mainly related to two basic characteristics of most SDMs: 1) general lack of reliable species absence information, 2) the frequent use of an arbitrary geographical extent (GE) or accessible area of the species. These impediments have motivated us to generate a procedure called niche of occurrence (NOO). NOO provides the probable distribution of species (realized niche) relying solely on partial information about presence of species. It operates within a natural geographical extent delimited by available observations and avoids using misleading thresholds to obtain binary presence–absence estimations when the species prevalence is unknown. In this study the main characteristics of NOO are presented, comparing its performance with other recognized and more complex SDMs by using virtual species to avoid the omnipresent error sources of real data sets.     

Testing the utility of species distribution modelling using Random Forests for a species in decline

Habitat suitability estimates derived from species distribution models (SDMs) are increasingly used to guide management of threatened species. Poorly estimating species’ ranges can lead to underestimation of threatened status, undervaluing of remaining habitat and misdirection of conservation funding. We aimed to evaluate the utility of a SDM, similar to the models used to inform government regulation of habitat in our study region, in estimating the contemporary distribution of a threatened and declining species. We developed a presence‐only SDM for the endangered New Holland Mouse (Pseudomys novaehollandiae) across Victoria, Australia. We conducted extensive camera trap surveys across model‐predicted and expert‐selected areas to generate an independent data set for use in evaluating the model, determining confidence in absence data from non‐detection sites with occupancy and detectability modelling. We assessed the predictive capacity of the model at thresholds based on (1) sum of sensitivity and specificity (SSS), and (2) the lowest presence threshold (LPT; i.e. the lowest non‐zero model‐predicted habitat suitability value at which we detected the species). We detected P. novaehollandiae at 40 of 472 surveyed sites, with strong support for the species’ probable absence from non‐detection sites. Based on our post hoc optimised SSS threshold of the SDM, 25% of our detection sites were falsely predicted as non‐suitable habitat and 75% of sites predicted as suitable habitat did not contain the species at the time of our survey. One occupied site had a model‐predicted suitability value of zero, and at the LPT, 88% of sites predicted as suitable habitat did not contain the species at the time of our survey. Our findings demonstrate that application of generic SDMs in both regulatory and investment contexts should be tempered by considering their limitations and currency. Further, we recommend engaging species experts in the extrapolation and application of SDM outputs.     

The difficulty of using species distribution modelling for the conservation of refugee species – the example of European bison

Refugee species have been confined to suboptimal habitat through historic anthropogenic factors. If this is unknown, management might actively conserve these species in suboptimal habitat assuming it represents optimal habitat. Similarly, species distribution modelling (SDM) might misguide conservation management of refugee species by only using presence data from suboptimal habitats. We illustrate this by commenting on a recent SDM for European bison that reconstructed the historic distribution of the species. We challenge the interpretation of this model by suggesting an alternative historic biogeography based on the refugee species concept. We argue that, in the case of refugee species, historic reconstructions using SDM cannot be used as a template for conservation management. Rather, experimental re‐introduction programmes should provide us with population performance and life history data from a range of suboptimal to optimal habitats. Such data could be used in mechanistic niche modelling to predict potential distribution of refugee species.     

Flexible species distribution modelling methods perform well on spatially separated testing data

Aim

To assess whether flexible species distribution models that perform well at nearby testing locations still perform strongly when evaluated on spatially separated testing data.

Location

Australian Wet Tropics (AWT), Ontario, Canada (CAN), north-east New South Wales, Australia (NSW), New Zealand (NZ), five countries of South America (SA), and Switzerland (SWI).

Time period

Most species data were collected between 1950 and 2000.

Major taxa studied

Birds, mammals, plants and reptiles.

Methods

We compared 10 species distribution modelling methods with varying flexibility in terms of the allowed complexity of their fitted functions [boosted regression trees (BRT), generalized additive model (GAM), multivariate adaptive regression splines (MARS), maximum entropy (MaxEnt), support vector machine (SVM), variants of generalized linear model (GLM) and random forest (RF), and an Ensemble model]. We used established practices for model selection to avoid overfitting, including parameter tuning in learning methods. Models were trained on presence–background data for 171 species and tested on presence–absence data. Training and testing data were separated using both random and spatial partitioning, the latter based on 75-km blocks. We calculated the average performance and mean rank of the methods (focussing on the area under the receiver operating characteristic and precision-recall gain curves, and correlation) and assessed the statistical significance of the differences between them.

Results

The ranking of methods did not change when evaluated on spatially separated testing data. Methods with the strongest predictive performance were nonparametric methods known to be flexible. An ensemble formed by averaging predictions of five pre-selected modelling methods was the best model in both random and spatial partitioning, followed by MaxEnt and a variant of random forest.

Main conclusions

Whilst some modellers expect methods limited to simple smooth functions to predict better spatially separated data, we found no evidence of that using blocks of 75 km. We conclude that flexible models that are tuned well enough to avoid overfitting are effective at predicting to spatially distinct areas.     

Combined modelling of distribution and niche in invasion biology: a case study of two invasive Tetramorium ant species

Spatial modelling of species distributions has become an important tool in the study of biological invasions. Here, we examine the utility of combining distribution and ecological niche modelling for retrieving information on invasion processes, based on species occurrence data from native and introduced ranges. Specifically, we discuss questions, concerning (1) the global potential to spread to other ranges, (2) the potential to spread within established invasions, (3) the detectability of niche differences across ranges, and (4) the ability to infer invasion history through data from the introduced range. We apply this approach to two congeneric pavement ants, Tetramorium sp.E (formerly T. caespitum (Linnaeus 1758)) and T. tsushimae Emery 1925, both introduced to North America. We identify (1) the potential of both species to inhabit ranges worldwide, and (2) the potential of T. sp.E and T. tsushimae, to spread to 23 additional US states and to five provinces of Canada, and to 24 additional US states and to one province of Canada, respectively. We confirm that (3) niche modelling can be an effective tool to detect niche shifts, identifying an increased width of T. sp.E and a decreased width of T. tsushimae following introduction, with potential changes in niche position for both species. We make feasible that (4) combined modelling could become an auxiliary tool to reconstruct invasion history, hypothesizing admixture following multiple introductions in North America for T. sp.E, and a single introduction to North America from central Japan, for T. tsushimae. Combined modelling represents a rapid means to formulate testable explanatory hypotheses on invasion patterns and helps approach a standard in predictive invasion research.     

Niche shift in four non‐native estrildid finches and implications for species distribution models

Non‐native species can have severe impacts on ecosystems. Therefore, predictions of potentially suitable areas that are at risk of the establishment of non‐native populations are desirable. In recent years, species distribution models (SDMs) have been widely applied for this purpose. However, the appropriate selection of species records, whether from the native area alone or also from the introduced range, is still a matter of debate. We combined analyses of native and non‐native realized climate niches to understand differences between models based on all locations, as well as on locations from the native range only. Our approach was applied to four estrildid finch species that have been introduced to many regions around the world. Our results showed that SDMs based on location data from native areas alone may underestimate the potential distribution of a given species. The climatic niches of species in their native ranges differed from those of their non‐native ranges. Niche comparisons resulted in low overlap values, indicating considerable niche shifts, at least in the realized niches of these species. All four species have high potential to spread over many tropical and subtropical areas. However, transferring these results to temperate areas has a high degree of uncertainty, and we urge caution when assessing the potential spread of tropical species that have been introduced to higher latitudes.     

Infusing considerations of trophic dependencies into species distribution modelling

Community ecology involves studying the interdependence of species with each other and their environment to predict their geographical distribution and abundance. Modern species distribution analyses characterise species‐environment dependency well, but offer only crude approximations of species interdependency. Typically, the dependency between focal species and other species is characterised using other species’ point occurrences as spatial covariates to constrain the focal species’ predicted range. This implicitly assumes that the strength of interdependency is homogeneous across space, which is not generally supported by analyses of species interactions. This discrepancy has an important bearing on the accuracy of inferences about habitat suitability for species. We introduce a framework that integrates principles from consumer–resource analyses, resource selection theory and species distribution modelling to enhance quantitative prediction of species geographical distributions. We show how to apply the framework using a case study of lynx and snowshoe hare interactions with each other and their environment. The analysis shows how the framework offers a spatially refined understanding of species distribution that is sensitive to nuances in biophysical attributes of the environment that determine the location and strength of species interactions.