• Energy Research

A fundamental goal of ecological research is to understand and model how processes generate patterns so that if conditions change, changes in the patterns can be predicted. Different approaches have been proposed for modelling species assemblage, but their use to predict spatial patterns of species richness and other community attributes over a range of spatial and temporal scales remains challenging. Different methods emphasize different processes of structuring communities and different goals. In this review, we focus on models that were developed for generating spatially explicit predictions of communities, with a particular focus on species richness, composition, relative abundance and related attributes. We first briefly describe the concepts and theories that span the different drivers of species assembly. A combination of abiotic processes and biotic mechanisms are thought to influence the community assembly process. In this review, we describe four categories of drivers: (i) historical and evolutionary, (ii) environmental, (iii) biotic, and (iv) stochastic. We discuss the different modelling approaches proposed or applied at the community level and examine them from different standpoints, i.e. the theoretical bases, the drivers included, the source data, and the expected outputs, with special emphasis on conservation needs under climate change. We also highlight the most promising novelties, possible shortcomings, and potential extensions of existing methods. Finally, we present new approaches to model and predict species assemblages by reviewing promising 'integrative frameworks' and views that seek to incorporate all drivers of community assembly into a unique modelling workflow. We discuss the strengths and weaknesses of these new solutions and how they may hasten progress in community-level modelling.

A fundamental goal of ecological research is to understand and model how processes generate patterns so that if conditions change, changes in the patterns can be predicted. Different approaches have been proposed for modelling species assemblage, but their use to predict spatial patterns of species richness and other community attributes over a range of spatial and temporal scales remains challenging. Different methods emphasize different processes of structuring communities and different goals. In this review, we focus on models that were developed for generating spatially explicit predictions of communities, with a particular focus on species richness, composition, relative abundance and related attributes. We first briefly describe the concepts and theories that span the different drivers of species assembly. A combination of abiotic processes and biotic mechanisms are thought to influence the community assembly process. In this review, we describe four categories of drivers: (i) historical and evolutionary, (ii) environmental, (iii) biotic, and (iv) stochastic. We discuss the different modelling approaches proposed or applied at the community level and examine them from different standpoints, i.e. the theoretical bases, the drivers included, the source data, and the expected outputs, with special emphasis on conservation needs under climate change. We also highlight the most promising novelties, possible shortcomings, and potential extensions of existing methods. Finally, we present new approaches to model and predict species assemblages by reviewing promising 'integrative frameworks' and views that seek to incorporate all drivers of community assembly into a unique modelling workflow. We discuss the strengths and weaknesses of these new solutions and how they may hasten progress in community-level modelling.

AbstractAimThe development of approaches to predict the distribution and potential expansion of invasive species is still an open challenge. Here our goal is to improve the modelling procedure for marine invaders by coupling Species Distribution Models (SDMs) with an analysis of their univariate niche dynamics. In particular, we tested for the first time whether choosing model predictors among the stable niche dimensions was effective in improving predictions of invasive species expansion.LocationMediterranean Sea.TaxonDusky spinefoot, Siganus luridus.MethodsWe analysed the univariate niche dynamics for S. luridus across its native and invaded ranges, by applying a standardized framework that allowed the identification of cases of niche stability or shift. We compared inter‐range transferability of SDMs fitted with different combinations of labile or stable predictors. Finally, we evaluated interactions in SDM settings (calibration area, model technique and predictors set) on models’ predictive ability, using independent data from the most recent phase of invasion.ResultsWe detected a pattern of niche stability for several variables, especially salinity and bathymetry, which positively influenced model inter‐ranges transferability: when the models calibrated in the native range include only stable niche axes, predictive ability is improved. We also identified a shift towards lower surface temperatures in the introduced range, which were almost never experienced by the species before invasion. The model calibrated within the combined ranges was the most ecologically congruent. Also, models calibrated in the invaded range allowed a correct prediction of range expansion, with the predicted suitable areas only slightly underestimated.Main conclusionsWe provide the first evidence that using conserved predictors in SDMs improves inter‐range projections of expanding invasive species. Variable selection, calibration area and modelling technique all matter when modelling invasive species, with important interaction effects. We provide guidelines on how to improve SDMs applications in biological invasion research.

AbstractAimThe development of approaches to predict the distribution and potential expansion of invasive species is still an open challenge. Here our goal is to improve the modelling procedure for marine invaders by coupling Species Distribution Models (SDMs) with an analysis of their univariate niche dynamics. In particular, we tested for the first time whether choosing model predictors among the stable niche dimensions was effective in improving predictions of invasive species expansion.LocationMediterranean Sea.TaxonDusky spinefoot, Siganus luridus.MethodsWe analysed the univariate niche dynamics for S. luridus across its native and invaded ranges, by applying a standardized framework that allowed the identification of cases of niche stability or shift. We compared inter‐range transferability of SDMs fitted with different combinations of labile or stable predictors. Finally, we evaluated interactions in SDM settings (calibration area, model technique and predictors set) on models’ predictive ability, using independent data from the most recent phase of invasion.ResultsWe detected a pattern of niche stability for several variables, especially salinity and bathymetry, which positively influenced model inter‐ranges transferability: when the models calibrated in the native range include only stable niche axes, predictive ability is improved. We also identified a shift towards lower surface temperatures in the introduced range, which were almost never experienced by the species before invasion. The model calibrated within the combined ranges was the most ecologically congruent. Also, models calibrated in the invaded range allowed a correct prediction of range expansion, with the predicted suitable areas only slightly underestimated.Main conclusionsWe provide the first evidence that using conserved predictors in SDMs improves inter‐range projections of expanding invasive species. Variable selection, calibration area and modelling technique all matter when modelling invasive species, with important interaction effects. We provide guidelines on how to improve SDMs applications in biological invasion research.

Amphibians are among the most endangered animals on Earth, and climatic shifts are among the hypothesized factors in their decline. We used spatial patterns of recent amphibian declines in Italy to test hypotheses pertaining to three potential, nonexclusive factors: climate change, habitat alteration, and high levels of incident solar radiation. This study was based on patterns of presence in a geographic grid for 19 species. Grid-squares in which presence had previously been documented, but was not re-confirmed after a specific threshold year, were considered to represent declines. Using a GIS-based approach, we calculated, for each cell, the mean values – or shift in mean values – of different parameters, used as proxies for the three factors. The measures of these parameters were entered as predictors in specific autocovariate models fitted on grid-square status. Our results suggest that while multiple factors have contributed to declines, climate change has been a major cause of population disappearances. We identified a common pattern of disappearances in areas that have been especially affected by climatic shifts. Our findings also strongly suggest that habitat alteration, due mainly to urban land use, has contributed to the decline of several species and that solar irradiation, though probably not a direct cause of mortality, may have been important in association with other stressors. By identifying the most threatened species, geographical hot spots of decline, and the primary causes of decline, our work provides a basis for improving management and setting conservation priorities.

Amphibians are among the most endangered animals on Earth, and climatic shifts are among the hypothesized factors in their decline. We used spatial patterns of recent amphibian declines in Italy to test hypotheses pertaining to three potential, nonexclusive factors: climate change, habitat alteration, and high levels of incident solar radiation. This study was based on patterns of presence in a geographic grid for 19 species. Grid-squares in which presence had previously been documented, but was not re-confirmed after a specific threshold year, were considered to represent declines. Using a GIS-based approach, we calculated, for each cell, the mean values – or shift in mean values – of different parameters, used as proxies for the three factors. The measures of these parameters were entered as predictors in specific autocovariate models fitted on grid-square status. Our results suggest that while multiple factors have contributed to declines, climate change has been a major cause of population disappearances. We identified a common pattern of disappearances in areas that have been especially affected by climatic shifts. Our findings also strongly suggest that habitat alteration, due mainly to urban land use, has contributed to the decline of several species and that solar irradiation, though probably not a direct cause of mortality, may have been important in association with other stressors. By identifying the most threatened species, geographical hot spots of decline, and the primary causes of decline, our work provides a basis for improving management and setting conservation priorities.