virtualspecies is a freely available package for R designed to generate virtual species distributions, a procedure increasingly used in ecology to improve species distribution models. This package ...combines the existing methodological approaches with the objective of generating virtual species distributions with increased ecological realism. The package includes 1) generating the probability of occurrence of a virtual species from a spatial set of environmental conditions (i.e. environmental suitability), with two different approaches; 2) converting the environmental suitability into presence–absence with a probabilistic approach; 3) introducing dispersal limitations in the realised virtual species distributions and 4) sampling occurrences with different biases in the sampling procedure. The package was designed to be extremely flexible, to allow users to simulate their own defined species–environment relationships, as well as to provide a fine control over every simulation parameter. The package also includes a function to generate random virtual species distributions. We provide a simple example in this paper showing how increasing ecological realism of the virtual species impacts the predictive performance of species distribution models. We expect that this new package will be valuable to researchers willing to test techniques and protocols of species distribution models as well as various biogeographical hypotheses.
While large‐scale monitoring, early detection and control can greatly reduce desert locust invasions, global change is most likely to affect conditions that promote the transition from solitary to ...gregarious populations. Although climate change scenarios point to an increase in aridity and further desertification in vast areas of Africa, some regions that have been at the origin of past outbreaks are likely to see a reversed trend (i.e., increase in frequency and intensity of rains), potentially favoring the formation of swarms. This makes reinforcing early detection and keeping a sustained monitoring effort in place even more important under climate change.
Species distribution models (SDMs) have become one of the major predictive tools in ecology. However, multiple methodological choices are required during the modelling process, some of which may have ...a large impact on forecasting results. In this context, virtual species, i.e. the use of simulations involving a fictitious species for which we have perfect knowledge of its occurrence–environment relationships and other relevant characteristics, have become increasingly popular to test SDMs. This approach provides for a simple virtual ecologist framework under which to test model properties, as well as the effects of the different methodological choices, and allows teasing out the effects of targeted factors with great certainty. This simplification is therefore very useful in setting up modelling standards and best practice principles. As a result, numerous virtual species studies have been published over the last decade. The topics covered include differences in performance between statistical models, effects of sample size, choice of threshold values, methods to generate pseudo‐absences for presence‐only data, among many others. These simulations have therefore already made a great contribution to setting best modelling practices in SDMs. Recent software developments have greatly facilitated the simulation of virtual species, with at least three different packages published to that effect. However, the simulation procedure has not been homogeneous, which introduces some subtleties in the interpretation of results, as well as differences across simulation packages. Here we 1) review the main contributions of the virtual species approach in the SDM literature; 2) compare the major virtual species simulation approaches and software packages; and 3) propose a set of recommendations for best simulation practices in future virtual species studies in the context of SDMs.
The discriminating capacity (i.e. ability to correctly classify presences and absences) of species distribution models (SDMs) is commonly evaluated with metrics such as the area under the receiving ...operating characteristic curve (AUC), the Kappa statistic and the true skill statistic (TSS). AUC and Kappa have been repeatedly criticized, but TSS has fared relatively well since its introduction, mainly because it has been considered as independent of prevalence. In addition, discrimination metrics have been contested because they should be calculated on presence–absence data, but are often used on presence-only or presence-background data. Here, we investigate TSS and an alternative set of metrics—similarity indices, also known as F-measures. We first show that even in ideal conditions (i.e. perfectly random presence–absence sampling), TSS can be misleading because of its dependence on prevalence, whereas similarity/F-measures provide adequate estimations of model discrimination capacity. Second, we show that in real-world situations where sample prevalence is different from true species prevalence (i.e. biased sampling or presence-pseudoabsence), no discrimination capacity metric provides adequate estimation of model discrimination capacity, including metrics specifically designed for modelling with presence-pseudoabsence data. Our conclusions are twofold. First, they unequivocally impel SDM users to understand the potential shortcomings of discrimination metrics when quality presence–absence data are lacking, and we recommend obtaining such data. Second, in the specific case of virtual species, which are increasingly used to develop and test SDM methodologies, we strongly recommend the use of similarity/F-measures, which were not biased by prevalence, contrary to TSS.
The desert locust is an agricultural pest that is able to switch from a harmless solitarious stage, during recession periods, to swarms of gregarious individuals that disperse long distances and ...affect areas from western Africa to India during outbreak periods. Large outbreaks have been recorded through centuries, and the Food and Agriculture Organization keeps a long‐term, large‐scale monitoring survey database in the area. However, there is also a much less known subspecies that occupies a limited area in Southern Africa. We used large‐scale climatic and occurrence data of the solitarious phase of each subspecies during recession periods to understand whether both subspecies climatic niches differ from each other, what is the current potential geographical distribution of each subspecies, and how climate change is likely to shift their potential distribution with respect to current conditions. We evaluated whether subspecies are significantly specialized along available climate gradients by using null models of background climatic differences within and between southern and northern ranges and applying niche similarity and niche equivalency tests. The results point to climatic niche conservatism between the two clades. We complemented this analysis with species distribution modeling to characterize current solitarious distributions and forecast potential recession range shifts under two extreme climate change scenarios at the 2050 and 2090 time horizon. Projections suggest that, at a global scale, the northern clade could contract its solitarious recession range, while the southern clade is likely to expand its recession range. However, local expansions were also predicted in the northern clade, in particular in southern and northern margins of the current geographical distribution. In conclusion, monitoring and management practices should remain in place in northern Africa, while in Southern Africa the potential for the subspecies to pose a threat in the future should be investigated more closely.
Here, we study the climatic niche and recession range of the two subspecies of desert locust, a widespread agricultural pest that is widely known for its impressive large outbreaks in northern Africa, the Middle East, and Asia, but that is also present in southern Africa. We found that, although the two subspecies occupy different climates during recession periods, their environmental niches have been conserved. However, because of the differences in climate change projections between the two regions it occupies, the northern clade is likely to contract its recession range, at least at a global scale, while the southern clade is likely to expand its recession range in the face of climate change. In conclusion, monitoring and management practices should remain in place in northern Africa, and in Southern Africa, the potential for the subspecies to pose a threat in the future should be investigated more closely.
A methodology for partitioning of biodiversity into α, β and γ components has long been debated, resulting in different mathematical frameworks. Recently, use of the Rao quadratic entropy index has ...been advocated since it allows comparison of various facets of diversity (e.g. taxonomic, phylogenetic and functional) within the same mathematical framework. However, if not well implemented, the Rao index can easily yield biologically meaningless results and lead into a mathematical labyrinth. As a practical guideline for ecologists, we present a critical synthesis of diverging implementations of the index in the recent literature and a new extension of the index for measuring β-diversity. First, we detail correct computation of the index that needs to be applied in order not to obtain negative β-diversity values, which are ecologically unacceptable, and elucidate the main approaches to calculate the Rao quadratic entropy at different spatial scales. Then, we emphasize that, similar to other entropy measures, the Rao index often produces lower-than-expected β-diversity values. To solve this, we extend a correction based on equivalent numbers, as proposed by Jost (2007), to the Rao index. We further show that this correction can be applied to additive partitioning of diversity and not only its multiplicative form. These developments around the Rao index open up an exciting avenue to develop an estimator of turnover diversity across different environmental and temporal scales, allowing meaningful comparisons of partitioning across species, phylogenetic and functional diversities within the same mathematical framework. We also propose a set of R functions, based on existing developments, which perform different key computations to apply this framework in biodiversity science.
Many species are shifting their distributions due to climate change and to increasing international trade that allows dispersal of individuals across the globe. In the case of agricultural pests, ...such range shifts may heavily impact agriculture. Species distribution modelling may help to predict potential changes in pest distributions. However, these modelling strategies are subject to large uncertainties coming from different sources. Here we used the case of the tomato red spider mite (Tetranychus evansi), an invasive pest that affects some of the most important agricultural crops worldwide, to show how uncertainty may affect forecasts of the potential range of the species. We explored three aspects of uncertainty: (1) species prevalence; (2) modelling method; and (3) variability in environmental responses between mites belonging to two invasive clades of T. evansi. Consensus techniques were used to forecast the potential range of the species under current and two different climate change scenarios for 2080, and variance between model projections were mapped to identify regions of high uncertainty. We revealed large predictive variations linked to all factors, although prevalence had a greater influence than the statistical model once the best modelling strategies were selected. The major areas threatened under current conditions include tropical countries in South America and Africa, and temperate regions in North America, the Mediterranean basin and Australia. Under future scenarios, the threat shifts towards northern Europe and some other temperate regions in the Americas, whereas tropical regions in Africa present a reduced risk. Analysis of niche overlap suggests that the current differential distribution of mites of the two clades of T. evansi can be partially attributed to environmental niche differentiation. Overall this study shows how consensus strategies and analysis of niche overlap can be used jointly to draw conclusions on invasive threat considering different sources of uncertainty in species distribution modelling.
The Mediterranean Sea (0.82% of the global oceanic surface) holds 4%–18% of all known marine species (∼17,000), with a high proportion of endemism 1, 2. This exceptional biodiversity is under severe ...threats 1 but benefits from a system of 100 marine protected areas (MPAs). Surprisingly, the spatial congruence of fish biodiversity hot spots with this MPA system and the areas of high fishing pressure has not been assessed. Moreover, evolutionary and functional breadth of species assemblages 3 has been largely overlooked in marine systems. Here we adopted a multifaceted approach to biodiversity by considering the species richness of total, endemic, and threatened coastal fish assemblages as well as their functional and phylogenetic diversity. We show that these fish biodiversity components are spatially mismatched. The MPA system covers a small surface of the Mediterranean (0.4%) and is spatially congruent with the hot spots of all taxonomic components of fish diversity. However, it misses hot spots of functional and phylogenetic diversity. In addition, hot spots of endemic species richness and phylogenetic diversity are spatially congruent with hot spots of fishery impact. Our results highlight that future conservation strategies and assessment efficiency of current reserve systems will need to be revisited after deconstructing the different components of biodiversity.
► The Mediterranean Sea is a marine biodiversity hot spot under severe threats ► The system of marine protected areas covers 0.4% of the Mediterranean Sea surface ► This system is spatially congruent with hot spots of fish taxonomic diversity ► This system misses hot spots of fish functional and phylogenetic diversity
Aim To test statistical models used to predict species distributions under different shapes of occurrence-environment relationship. We addressed three questions: (1) Is there a statistical technique ...that has a consistently higher predictive ability than others for all kinds of relationships? (2) How does species prevalence influence the relative performance of models? (3) When an automated stepwise selection procedure is used, does it improve predictive modelling, and are the relevant variables being selected? Location We used environmental data from a real landscape, the state of California, and simulated species distributions within this landscape. Methods Eighteen artificial species were generated, which varied in their occurrence response to the environmental gradients considered (random, linear, Gaussian, threshold or mixed), in the interaction of those factors (no interaction vs. multiplicative), and on their prevalence (50% vs. 5%). The landscape was then randomly sampled with a large (n = 2000) or small (n = 150) sample size, and the predictive ability of each statistical approach was assessed by comparing the true and predicted distributions using five different indexes of performance (area under the receiver-operator characteristic curve, Kappa, correlation between true and predictive probability of occurrence, sensitivity and specificity). We compared generalized additive models (GAM) with and without flexible degrees of freedom, logistic regressions (general linear models, GLM) with and without variable selection, classification trees, and the genetic algorithm for rule-set production (GARP). Results Species with threshold and mixed responses, additive environmental effects, and high prevalence generated better predictions than did other species for all statistical models. In general, GAM outperforms all other strategies, although differences with GLM are usually not significant. The two variable-selection strategies presented here did not discriminate successfully between truly causal factors and correlated environmental variables. Main conclusions Based on our analyses, we recommend the use of GAM or GLM over classification trees or GARP, and the specification of any suspected interaction terms between predictors. An expert-based variable selection procedure was preferable to the automated procedures used here. Finally, for low-prevalence species, variability in model performance is both very high and sample-dependent. This suggests that distribution models for species with low prevalence can be improved through targeted sampling.
AIM: We assessed the temporal trends of taxonomic, functional and phylogenetic diversities in the French avifauna over the last two decades. Additionally, we investigated whether and how this ...multifaceted approach to biodiversity dynamics can reveal an increasing similarity of local assemblages in terms of species, traits and/or lineages. LOCATION: France. METHODS: We analysed a large‐scale dataset that recorded annual changes in the abundance of 116 breeding birds in France between 1989 and 2012. We decomposed and analysed the spatio‐temporal dynamics of taxonomic, phylogenetic and functional diversities and each of their α‐, β‐ and γ‐components. We also calculated the trend in the mean specialization of bird communities to track the relative success of specialist versus generalist species within communities during the same period. RESULTS: We found large variation within and among the temporal trends of each biodiversity facet. On average, we found a marked increase in species and phylogenetic diversity over the period considered, but no particular trend was found for functional diversity. Conversely, changes in β‐diversities for the three facets were characterized by independent and nonlinear trends. We also found a general increase in the local occurrence and abundance of generalist species within local communities. MAIN CONCLUSIONS: These results highlight a relative asynchrony of the different biodiversity facets occurring at large spatial scales. We show why a multifaceted approach to biodiversity dynamics is needed to better describe and understand changes in community composition in macroecology and conservation biogeography.
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