Modeling bird species distribution change in fire prone Mediterranean landscapes: incorporating species dispersal and landscape dynamics

Species distribution models (SDMs) have traditionally been founded on the assumption that species distributions are in equilibrium with environmental conditions and that these species–environment relationships can be used to estimate species responses to environmental changes. Insight into the validity of this assumption can be obtained from comparing the performance of correlative species distribution models with more complex hybrid approaches, i.e. correlative and process‐based models that explicitly include ecological processes, thereby accounting for mismatches between habitat suitability and species occupancy patterns. Here we compared the ability of correlative SDMs and hybrid models, which can accommodate non‐equilibrium situations arising from dispersal constraints, to reproduce the distribution dynamics of the ortolan bunting Emberiza hortulana in highly dynamic, early successional, fire driven Mediterranean landscapes. Whereas, habitat availability was derived from a correlative statistical SDM, occupancy was modeled using a hybrid approach combining a grid‐based, spatially‐explicit population model that explicitly included bird dispersal with the correlative model. We compared species occupancy patterns under the equilibrium assumption and different scenarios of species dispersal capabilities. To evaluate the predictive capability of the different models, we used independent species data collected in areas affected to different degree by fires. In accordance with the view that disturbance leads to a disparity between the suitable habitat and the occupancy patterns of the ortolan bunting, our results indicated that hybrid modeling approaches were superior to correlative models in predicting species spatial dynamics. Furthermore, hybrid models that incorporated short dispersal distances were more likely to reproduce the observed changes in ortolan bunting distribution patterns, suggesting that dispersal plays a key role in limiting the colonization of recently burnt areas. We conclude that SDMs used in a dynamic context can be significantly improved by using combined hybrid modeling approaches that explicitly account for interactions between key ecological constraints such as dispersal and habitat suitability that drive species response to environmental changes.     

NicheA: creating virtual species and ecological niches in multivariate environmental scenarios

Robust methods by which to generate virtual species are needed urgently in the emerging field of distributional ecology to evaluate performance of techniques for modeling ecological niches and species distributions and to generate new questions in biogeography. Virtual species provide the opportunity to test hypotheses and methods based on known and unbiased distributions. We present Niche Analyst (NicheA), a toolkit developed to generate virtual species following the Hutchinsonian approach of an n‐multidimensional space occupied by the species. Ecological niche models are generated, analyzed, and visualized in an environmental space, and then projected to the geographic space in the form of continuous or binary species distribution models. NicheA is implemented in a stable and user‐friendly Java platform. The software, online manual, and user support are freely available at < http://nichea.sourceforge.net >.     

Disentangling scale dependencies in species environmental niches and distributions

Understanding species’ responses to environmental conditions, and how these ­species–environment associations shape spatial distributions, are longstanding goals in ecology and biogeography. However, an essential component of species–environment relationships – the spatial unit, or grain, at which they operate – remains unresolved. We identify three components of scale‐dependence in analyses of species–environment associations: 1) response grain, the grain at which species respond most strongly to their environment; 2) environment spatial structure, the pattern of spatial autocorrelation intrinsic to an environmental factor; and 3) analysis grain, the grain at which analyses are conducted and ecological inferences are made. We introduce a novel conceptual framework that defines these scale components in the context of analyzing species–environment relationships, and provide theoretical examples of their interactions for species with various ecological attributes. We then use a virtual species approach to investigate the impacts of each component on common methods of measuring and predicting species–environment relationships. We find that environment spatial structure has a substantial impact on the ability of even simple, univariate species distribution models (SDMs) to recover known species–­environment associations at coarse analysis grains. For simulated environments with ‘fine’ and ‘intermediate’ spatial structure, model explanatory power, and the frequency with which simple SDMs correctly estimated a virtual species’ response to the simulated environment, dramatically declined as analysis grain increased. Informed by these results, we use a scaling analysis to identify maximum analysis grains for individual environmental factors, and a scale optimization procedure to determine the grain of maximum predictive accuracy. Implementing these analysis grain thresholds and model performance standards in an example east African study system yields more accurate distribution predictions, compared to SDMs independently constructed at arbitrary analysis grains. Finally, we integrate our conceptual framework with virtual and empirical results to provide practical recommendations for researchers asking common questions about species–environment relationships.     

BIOMOD – a platform for ensemble forecasting of species distributions

BIOMOD is a computer platform for ensemble forecasting of species distributions, enabling the treatment of a range of methodological uncertainties in models and the examination of species-environment relationships. BIOMOD includes the ability to model species distributions with several techniques, test models with a wide range of approaches, project species distributions into different environmental conditions (e.g. climate or land use change scenarios) and dispersal functions. It allows assessing species temporal turnover, plot species response curves, and test the strength of species interactions with predictor variables. BIOMOD is implemented in R and is a freeware, open source, package.     

Detecting outliers in species distribution data: Some caveats and clarifications on a virtual species study

Liu et al. (2018) used a virtual species approach to test the effects of outliers on species distribution models. In their simulations, they applied a threshold value over the simulated suitabilities to generate the species distributions, suggesting that using a probabilistic simulation approach would have been more complex and yield the same results. Here, we argue that using a probabilistic approach is not necessarily more complex and may significantly change results. Although the threshold approach may be justified under limited circumstances, the probabilistic approach has multiple advantages. First, it is in line with ecological theory, which largely assumes non‐threshold responses. Second, it is more general, as it includes the threshold as a limiting case. Third, it allows a better separation of the relevant intervening factors that influence model performance. Therefore, we argue that the probabilistic simulation approach should be used as a general standard in virtual species studies.     

Correlation between genetic diversity and environmental suitability: taking uncertainty from ecological niche models into account

The hindcast of shifts in the geographical ranges of species as estimated by ecological niche modelling (ENM) has been coupled with phylogeographical patterns, allowing the inference of past processes that drove population differentiation and genetic variability. However, more recently, some studies have suggested that maps of environmental suitability estimated by ENM may be correlated to species' abundance, raising the possibility of using environmental suitability to infer processes related to population demographic dynamics and genetic variability. In both cases, one of the main problems is that there is a wide variation in ENM development methods and climatic models. In this study, we analyse the relationship between heterozygosity (He) and environmental suitability from multiple ENMs for 25 population estimates for Dipteryx alata, a widely distributed, endemic tree species of the Cerrado region of central Brazil. We propose a new approach for generating a statistical distribution of correlations under randomly generated ENM. The confidence intervals from these distributions indicate how model selection with different properties affects the ability to detect a correlation of interest (e.g. the correlation between He and suitability). Additionally, our approach allows us to explore which particular ensemble of ENMs produces the better result for finding an association between environmental suitability and He. Caution is necessary when choosing a method or a climatic data set for modelling geographical distributions, but the new approach proposed here provides a conservative way to evaluate the ability of ensembles to detect patterns of interest.     

Improving species distribution models using biotic interactions: a case study of parasites,pollinators and plants

Biotic interactions have been considered as an important factor to be included in species distribution modelling, but little is known about how different types of interaction or different strategies for modelling affect model performance. This study compares different methods for including interspecific interactions in distribution models for bees, their brood parasites, and the plants they pollinate. Host–parasite interactions among bumble bees (genus Bombus: generalist pollinators and brood parasites) and specialist plant–pollinator interactions between Centris bees and Krameria flowers were used as case studies. We used 7 different modelling algorithms available in the BIOMOD R package. For Bombus, the inclusion of interacting species distributions generally increased model predictive accuracy. The improvement was better when the interacting species was included with its raw distribution rather than with its modeled suitability. However, incorporating the distributions of non‐interacting species sometimes resulted in similarly increased model accuracy despite their being no significance of any interaction for the distribution. For the Centris‐Krameria system the best strategy for modelling biotic interactions was to include the interacting species model‐predicted values. However, the results were less consistent than those for Bombus species, and most models including biotic interactions showed no significant improvement over abiotic models. Our results are consistent with previous studies showing that biotic interactions can be important in structuring species distributions at regional scales. However, correlations between species distributions are not necessarily indicative of interactions. Therefore, choosing the correct biotic information, based on biological and ecological knowledge, is critical to improve the accuracy of species distribution models and forecast distribution change.     

The contribution of small collections to species distribution modelling: A case study from Fuireneae (Cyperaceae)

The recent and rapid digitization of biodiversity data from natural history collection (NHC) archives has enriched collections based data repositories; this data continues to inform studies of species' geographic distributions. Here we investigate the relative impact of plant data from small natural history collections (collections with < 100,000 specimens) on species distributional models in an effort to document the potential of data from small NHCs to contribute to and inform biodiversity research. We modelled suitable habitat of five test case species from Fuireneae (Cyperaceae) in the United States using specimen records available via the Global Biodiversity Information Facility and that of data ready to mobilize from two regional small herbaria. Data were partitioned into three datasets based on their source: 1) collections-based records from large NHCs accessed GBIF, 2) collections-based records from small NHCs accessed from GBIF, and 3) collections-based records from two small regional herbaria not yet mobilized to GBIF. We extracted and evaluated the ecological niche represented for each of the three datasets by applying dataset occurrences to 14 environmental factors, and we modelled habitat suitability using Maxent to compare the represented distribution of the environmental values among the datasets. Our analyses indicate that the data from small NHCs contributed unique information in both geographic and environmental space. When data from small collections were combined with data from large collections, species models of the ecological niche resulted in more refined predictions of habitat suitability, indicating that small collections can contribute unique occurrence data which enhance species distribution models by bridging geographic collection gaps and shifting modelled predictions of suitable habitat. Inclusion of specimen records from small collections in ongoing digitization efforts is essential for generating informed models of a species' niche and distribution.     

Biogeography of Cephalopods in the Southern Ocean Using Habitat Suitability Prediction Models

Our understanding of how environmental change in the Southern Ocean will affect marine diversity, habitats and distribution remain limited. The habitats and distributions of Southern Ocean cephalopods are generally poorly understood, and yet such knowledge is necessary for research and conservation management purposes, as well as for assessing the potential impacts of environmental change. We used net-catch data to develop habitat suitability models for 15 of the most common cephalopods in the Southern Ocean. Using modeled habitat suitability, we assessed favorable areas for each species and examined the relationships between species distribution and environmental parameters. The results compared favorably with the known ecology of these species and with spatial patterns from diet studies of squid predators. The individual habitat suitability models were overlaid to generate a “hotspot” index of species richness, which showed higher numbers of squid species associated with various fronts of the Antarctic circumpolar current. Finally, we reviewed the overall distribution of these species and their importance in the diet of Southern Ocean predators. There is a need for further studies to explore the potential impacts of future climate change on Southern Ocean squid.     

Host range and distribution of small mammal fleas in South Africa,with a focus on species of medical and veterinary importance

The host range and distribution of flea species on rodents and insectivores across multiple vegetation types in South Africa were investigated. Habitat suitability for flea species considered as important vectors of disease in humans and domestic animals was modelled. Data originated from fleas that were recovered from small mammals captured at 29 localities during 2009–2013 and published literature searched for flea records. Climate‐based predictor variables, widely used in arthropod vector distribution, were selected and habitat suitability modelled for 10 flea vector species. A total of 2469 flea individuals representing 33 species and subspecies were collected from 1185 small mammals. Ten of each of the flea and rodent species are plague vectors and reservoirs, respectively. Multiple novel flea–host associations and locality records were noted. Three vector species were recorded from insectivores. Geographic distributions of flea species ranged from broad, across‐biome distributions to narrower distributions within one or two biomes. Habitat suitability models performed excellently for the majority of flea vectors and identified regions of summer and all‐year rainfall as representing suitable habitats for most vector species. Current knowledge of vector and disease ecology can benefit from similar sampling approaches that will be important not only for South Africa, but also for the sub‐region.     

Niche and area of distribution modeling: a population ecology perspective

Statistical modeling of areas of distribution of species by correlative analysis of the environmental features of known presences has become widespread. However, to a large degree, the logic and the functioning of many of these applications remain obscure, not only due to the fact that some of the modeling methods are intrinsically complex (neural networks, genetic algorithms, generalized additive models, for example), but mainly because the role of other ecological processes affecting the species distributions sometimes is not explicitly stated. Resorting to fundamental principles of population ecology, a scheme of analysis based on separation of three factors affecting species distributions (environment, biotic interactions and movements) is used to clarify some results of niche modeling exercises. The area of distribution of a virtual species which was generated by both environmental and biotic factors serves to illustrate the possibility that, at coarse resolutions, the distribution can be approximately recovered using only information about the environmental factors and ignoring the biotic interactions. Finally, information on the distribution of a butterfly species, Baronia brevicornis , is used to illustrate the importance of interpreting the results of niche models by including hypothesis about one class of movements. The results clarify the roles of the three factors in interpreting the results of using correlative approaches to modeling species distributions or their niches.     

Can species distribution models be used to describe plant abundance patterns?

In recent years, there has been increasing interest in modelling of species abundance data in addition to presence data. In this study, we assessed the similarities and differences between presence‐absence distributions and abundance distributions along similar environmental gradients, derived, respectively, from presence‐absence and abundance data. Moreover, we examined the possibility of using presence‐absence distribution models to derive abundance distributions. For this purpose, we used Braun‐Blanquet abundance scores for 243 vascular species at 10 996 French forest sites. Species distribution models were used to analyse the link between the patterns of occurrence, low abundance and high abundance for each species with regard to mean annual temperature, June water balance, and soil pH. For each species, differences in the modelled distributions were characterised by the ecological optimum and ecological amplitude. A comparison of the presence‐absence and abundance distributions for all species revealed similar optima and different amplitudes along the three ecological factors. An abundant‐centre distribution was observed in environmental space, with species abundance being greatest at the optimal conditions and lower at less favourable conditions of the species occurrence response. Geographical habitat mapping also shows centred, high‐abundance suitability within the presence habitat of each species. We conclude that species distribution models derived from presence‐absence data provide useful information about the ecological optima of abundance distributions but overestimate the range of habitats suitable for high species abundance. This study demonstrates the utility of presence‐absence data for ecologist and conservation biologist when they are interested in the optimal conditions of high species abundance.     

Evaluating the ecological realism of plant species distribution models with ecological indicator values

Species distribution models (SDMs) are routinely applied to assess current as well as future species distributions, for example to assess impacts of future environmental change on biodiversity or to underpin conservation planning. It has been repeatedly emphasized that SDMs should be evaluated based not only on their goodness of fit to the data, but also on the realism of the modeled ecological responses. However, possibilities for the latter are hampered by limited knowledge on the true responses as well as a lack of quantitative evaluation methods. Here we compared modeled niche optima obtained from European-scale SDMs of 1476 terrestrial vascular plant species with empirical ecological indicator values indicating the preferences of plant species for key environmental conditions. For each plant species we first fitted an ensemble SDM including three modeling techniques (GLM, GAM and BRT) and extracted niche optima for climate, soil, land use and nitrogen deposition variables with a large explanatory power for the occurrence of that species. We then compared these SDM-derived niche optima with the ecological indicator values by means of bivariate correlation analysis. We found weak to moderate correlations in the expected direction between the SDM-derived niche optima and ecological indicator values. The strongest correlation occurred between the modeled optima for growing degree days and the ecological indicator values for temperature. Correlations were weaker for SDM-derived niche optima with a more distal relationship to ecological indicator values (notably precipitation and soil moisture). Further, correlations were consistently highest for BRT, followed by GLM and GAM. Our method gives insight into the ecological realism of modeled niche optima and projected core habitats and can be used to improve SDMs by making a more informed selection of environmental variables and modeling techniques.     

Integrating coalescent and ecological niche modeling in comparative phylogeography

Understanding the factors that contribute to the formation of population genetic structure is a central goal of phylogeographic research, but achieving this goal can be complicated by the stochastic variance inherent to genetic processes. Statistical approaches to testing phylogeographic hypotheses accommodate this stochasticity by evaluating competing models of putative historical population structure, often by simulating null distributions of the expected variance. The effectiveness of these tests depends on the biological realism of the models. Information from the fossil record can aid in reconstructing the historical distributions of some taxa. However, for the majority of taxa, which lack sufficient fossils, paleodistributional modeling can provide valuable spatial-geographic data concerning ancestral distributions. Paleodistributional models are generated by projecting ecological niche models, which predict the current distribution of each species, onto a model of past climatic conditions. Here, we generate paleodistributional models describing the suitable habitat during the last glacial maximum for lineages from the mesic forests of the Pacific Northwest of North America, and use these models to generate alternative phylogeographic hypotheses. Coalescent simulations are then used to test these hypotheses to improve our understanding of the historical events that promoted the formation of population genetic structure in this ecosystem. Results from Pacific Northwest mesic forest organisms demonstrate the utility of these combined approaches. Paleodistribution models and population genetic structure are congruent across three amphibian lineages, suggesting that they have responded in a concerted manner to environmental change. Two other species, a willow and a water vole, despite being currently codistributed and having similar population genetic structure, were predicted by the paleodistributional model to have had markedly different distributions during the last glacial maximum. This suggests that congruent phylogeographic patterns can arise from incongruent ancestral distributions. Paleodistributional models introduce a much-needed spatial-geographic perspective to statistical phylogeography. In conjunction with coalescent models of population genetic structure, they have the potential to improve our understanding of the factors that promote population divergence and ultimately produce regional patterns of biodiversity.     

应用Maxent生态位模型预测淡水钩虾(甲壳纲,端足目)的分布(英文)

预测物种的适生区对于物种资源的评估、保护以及生物多样性的管理非常重要。由于全球气候变化和人类的过度开发,冷水性无脊椎动物的衰减速度比在陆地和海洋生活的无脊椎动物都要高。目前关于中国淡水钩虾分布方面的研究很少,本研究基于103个地表淡水钩虾的不同分布位点和广布种湖泊钩虾Gammarus lacustris 23个不同分布位点以及32个环境因子数据,使用生态位模型(Maxent)预测了淡水钩虾和湖泊钩虾在我国的适生分布区域。结果显示淡水钩虾非常适合分布在我国的一些偏远山区,如长白山、太行山、横断山、天山、昆仑山和祁连山,而青藏高原的东部、西部边缘地区和南部分布地区、尼泊尔、不丹和朝鲜半岛也是淡水钩虾的潜在适生区域,但淡水钩虾在我国华南、华中和华北的平原地区分布却很少,其在我国的潜在分布区与-10℃和5℃1月平均气温线间的区域相似。淡水钩虾是典型的狭温性物种,在不适宜温度条件下很难存活,这可能也是限制其扩散和存活的关键性因素。     

ENVIREM: an expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling

Species distribution modeling is a valuable tool with many applications across ecology and evolutionary biology. The selection of biologically meaningful environmental variables that determine relative habitat suitability is a crucial aspect of the modeling pipeline. The 19 bioclimatic variables from WorldClim are frequently employed, primarily because they are easily accessible and available globally for past, present and future climate scenarios. Yet, the availability of relatively few other comparable environmental datasets potentially limits our ability to select appropriate variables that will most successfully characterize a species’ distribution. We identified a set of 16 climatic and two topographic variables in the literature, which we call the ENVIREM dataset, many of which are likely to have direct relevance to ecological or physiological processes determining species distributions. We generated this set of variables at the same resolutions as WorldClim, for the present, mid‐Holocene, and Last Glacial Maximum (LGM). For 20 North American vertebrate species, we then assessed whether including the ENVIREM variables led to improved species distribution models compared to models using only the existing WorldClim variables. We found that including the ENVIREM dataset in the pool of variables to select from led to substantial improvements in niche modeling performance in 13 out of 20 species. We also show that, when comparing models constructed with different environmental variables, differences in projected distributions were often greater in the LGM than in the present. These variables are worth consideration in species distribution modeling applications, especially as many of the variables have direct links to processes important for species ecology. We provide these variables for download at multiple resolutions and for several time periods at envirem.github.io. Furthermore, we have written the ‘envirem’ R package to facilitate the generation of these variables from other input datasets.     

Silvicolous on a Small Scale: Possibilities and Limitations of Habitat Suitability Models for Small,Elusive Mammals in Conservation Management and Landscape Planning

Species distribution and endangerment can be assessed by habitat-suitability modelling. This study addresses methodical aspects of habitat suitability modelling and includes an application example in actual species conservation and landscape planning. Models using species presence-absence data are preferable to presence-only models. In contrast to species presence data, absences are rarely recorded. Therefore, many studies generate pseudo-absence data for modelling. However, in this study model quality was higher with null samples collected in the field. Next to species data the choice of landscape data is crucial for suitability modelling. Landscape data with high resolution and ecological relevance for the study species improve model reliability and quality for small elusive mammals like Muscardinus avellanarius. For large scale assessment of species distribution, models with low-detailed data are sufficient. For regional site-specific conservation issues like a conflict-free site for new wind turbines, high-detailed regional models are needed. Even though the overlap with optimally suitable habitat for M. avellanarius was low, the installation of wind plants can pose a threat due to habitat loss and fragmentation. To conclude, modellers should clearly state the purpose of their models and choose the according level of detail for species and environmental data.     

Global habitat suitability of cold‐water octocorals

Aim Three‐quarters of Octocorallia species are found in deep waters. These cold‐water octocoral colonies can form a major constituent of structurally complex habitats. The global distribution and the habitat requirements of deep‐sea octocorals are poorly understood given the expense and difficulties of sampling at depth. Habitat suitability models are useful tools to extrapolate distributions and provide an understanding of ecological requirements. Here, we present global habitat suitability models and distribution maps for seven suborders of Octocorallia: Alcyoniina, Calcaxonia, Holaxonia, Scleraxonia, Sessiliflorae, Stolonifera and Subselliflorae. Location Global. Methods We use maximum entropy modelling to predict octocoral distribution using a database of 12,508 geolocated octocoral specimens and 32 environmental grids resampled to 30 arc‐second (approximately 1 km²) resolution. Additionally, a meta‐analysis determined habitat preferences and niche overlap between the different suborders of octocorals. Results Suborder Sessiliflorae had the widest potential habitat range, but all records for all suborders implied a habitat preference for continental shelves and margins, particularly the North and West Atlantic and Western Pacific Rim. Temperature, salinity, broad scale slope, productivity, oxygen and calcite saturation state were identified as important factors for determining habitat suitability. Less than 3% of octocoral records were found in waters undersaturated for calcite, but this result is affected by a shallow‐water sampling bias. Main conclusions The logistical difficulties, expense and vast areas associated with deep‐sea sampling leads to a gap in the knowledge of faunal distributions that is difficult to fill without predictive modelling. Global distribution estimates are presented, highlighting many suitable areas which have yet to be studied. We suggest that approximately 17% of oceans are suitable for at least one suborder but 3.5% may be suitable for all seven. This is the first global habitat suitability modelling study on the distribution of octocorals and forms a useful resource for researchers, managers and conservationists.