Dominant species influence the composition and abundance of other species present in ecosystems. ... more Dominant species influence the composition and abundance of other species present in ecosystems. However, forecasts of distributional change under future climates have predominantly focused on changes in species distribution and ignored possible changes in spatial and temporal patterns of dominance. We develop forecasts of spatial changes for the distribution of species dominance, defined in terms of basal area, and for species occurrence, in response to sea level rise for three tree taxa within an extensive mangrove ecosystem in northern Australia. Three new metrics are provided, indicating the area expected to be suitable under future conditions (Eoccupied ), the instability of suitable area (Einstability ) and the overlap between the current and future spatial distribution (Eoverlap ).The current dominance and occurrence were modeled in relation to a set of environmental variables using Boosted Regression Tree (BRT) models, under two scenarios of seedling establishment; unrestric...
Species distribution models (SDMs) are widely used to forecast changes in the spatial distributio... more Species distribution models (SDMs) are widely used to forecast changes in the spatial distributions of species and communities in response to climate change. However, spatial autocorrelation (SA) is rarely accounted for in these models, despite its ubiquity in broad-scale ecological data. While spatial autocorrelation in model residuals is known to result in biased parameter estimates and the inflation of type I errors, the influence of unmodeled SA on species' range forecasts is poorly understood. Here we quantify how accounting for SA in SDMs influences the magnitude of range shift forecasts produced by SDMs for multiple climate change scenarios. SDMs were fitted to simulated data with a known autocorrelation structure, and to field observations of three mangrove communities from northern Australia displaying strong spatial autocorrelation. Three modeling approaches were implemented: environment-only models (most frequently applied in species' range forecasts), and two app...
Coextinction (loss of dependent species with their host or partner species) presents a threat to ... more Coextinction (loss of dependent species with their host or partner species) presents a threat to untold numbers of organisms. Climate change may act synergistically to accelerate rates of coextinction. In this review, we present the first synthesis of the available literature and propose a novel schematic diagram that can be used when assessing the potential risk climate change represents for dependent species. We highlight traits that may increase the susceptibility of insect species to coextinction induced by climate change, suggest the most influential host characteristics, and identify regions where climate change may have the greatest impact on dependent species. The aim of this review was to provide a platform for future research, directing efforts toward taxa and habitats at greatest risk of species loss through coextinction accelerated by climate change.
... One of the critical challenges presented by these multivariate problems is that observa-tiona... more ... One of the critical challenges presented by these multivariate problems is that observa-tional studies ... Responses of plant populations and communities to environmental changes of the late Quaternary. ... invest-ment and expected lifespan of leaves and stems: leaf mass per area ...
Elevated global temperatures are expected to alter vegetation dynamics by interacting with physio... more Elevated global temperatures are expected to alter vegetation dynamics by interacting with physiological processes, biotic relationships and disturbance regimes. However, few studies have explicitly modeled the effects of these interactions on rates of vegetation change, despite such information being critical to forecasting temporal patterns in vegetation dynamics. In this study, we build and parameterize rate-change models for three dominant alpine life forms using data from a 7-year warming experiment. These models allowed us to examine how the interactions between experimental warming, the abundance of bare ground (a measure of past disturbance) and neighboring life forms (a measure of life form interaction) affect rates of cover change in alpine shrubs, graminoids and forbs. We show that experimental warming altered rates of life form cover change by reducing the negative effects of neighboring life forms and positive effects of bare ground. Furthermore, we show that our models...
ABSTRACT AimMany mangrove communities form bands parallel to the shoreline with each community do... more ABSTRACT AimMany mangrove communities form bands parallel to the shoreline with each community dominated by a single species. However, the key determinants of mangrove species distribution across the intertidal zone are not well understood. We aimed to quantify the relationship between species' dominance and the hydroperiod (defined as the duration of inundation in a year), soil salinity and the salinity of inundating water for three dominant species, Sonneratia alba, Rhizophora stylosa and Ceriops tagal. LocationAn extensive (20,000 ha), largely intact mangrove forest in northern Australia, of some note as mangrove forests are threatened globally. Methods We related species dominance to the explanatory variables by applying two statistical modelling approaches: generalized linear models (GLMs), where a set of competing models were evaluated; and boosted regression tree models (BRTs), an approach that automatically captures interactions and nonlinear relationships between variables. ResultsBoth GLM and BRT models achieved strong predictive performance for all species based on cross‐validation, with receiver operating characteristics above 0.85 for all species, and 88% of deviance explained for S. alba, 42% for R. stylosa and 35% for C. tagal. All models indicated that the hydroperiod was the key variable influencing distribution, followed by soil salinity. The salinity of inundating water was the least informative variable in the models. Ecological space, determined by gradients in hydroperiod and soil salinity, was partitioned between the three species with little overlap. Main conclusionsAs anticipated changes in sea level will alter the hydroperiod, our findings are critical for global forecasting of future distributions of mangrove communities, and for the design of mitigation and adaptation measures.
Dominant species influence the composition and abundance of other species present in ecosystems. ... more Dominant species influence the composition and abundance of other species present in ecosystems. However, forecasts of distributional change under future climates have predominantly focused on changes in species distribution and ignored possible changes in spatial and temporal patterns of dominance. We develop forecasts of spatial changes for the distribution of species dominance, defined in terms of basal area, and for species occurrence, in response to sea level rise for three tree taxa within an extensive mangrove ecosystem in northern Australia. Three new metrics are provided, indicating the area expected to be suitable under future conditions (Eoccupied ), the instability of suitable area (Einstability ) and the overlap between the current and future spatial distribution (Eoverlap ).The current dominance and occurrence were modeled in relation to a set of environmental variables using Boosted Regression Tree (BRT) models, under two scenarios of seedling establishment; unrestric...
Species distribution models (SDMs) are widely used to forecast changes in the spatial distributio... more Species distribution models (SDMs) are widely used to forecast changes in the spatial distributions of species and communities in response to climate change. However, spatial autocorrelation (SA) is rarely accounted for in these models, despite its ubiquity in broad-scale ecological data. While spatial autocorrelation in model residuals is known to result in biased parameter estimates and the inflation of type I errors, the influence of unmodeled SA on species' range forecasts is poorly understood. Here we quantify how accounting for SA in SDMs influences the magnitude of range shift forecasts produced by SDMs for multiple climate change scenarios. SDMs were fitted to simulated data with a known autocorrelation structure, and to field observations of three mangrove communities from northern Australia displaying strong spatial autocorrelation. Three modeling approaches were implemented: environment-only models (most frequently applied in species' range forecasts), and two app...
Coextinction (loss of dependent species with their host or partner species) presents a threat to ... more Coextinction (loss of dependent species with their host or partner species) presents a threat to untold numbers of organisms. Climate change may act synergistically to accelerate rates of coextinction. In this review, we present the first synthesis of the available literature and propose a novel schematic diagram that can be used when assessing the potential risk climate change represents for dependent species. We highlight traits that may increase the susceptibility of insect species to coextinction induced by climate change, suggest the most influential host characteristics, and identify regions where climate change may have the greatest impact on dependent species. The aim of this review was to provide a platform for future research, directing efforts toward taxa and habitats at greatest risk of species loss through coextinction accelerated by climate change.
... One of the critical challenges presented by these multivariate problems is that observa-tiona... more ... One of the critical challenges presented by these multivariate problems is that observa-tional studies ... Responses of plant populations and communities to environmental changes of the late Quaternary. ... invest-ment and expected lifespan of leaves and stems: leaf mass per area ...
Elevated global temperatures are expected to alter vegetation dynamics by interacting with physio... more Elevated global temperatures are expected to alter vegetation dynamics by interacting with physiological processes, biotic relationships and disturbance regimes. However, few studies have explicitly modeled the effects of these interactions on rates of vegetation change, despite such information being critical to forecasting temporal patterns in vegetation dynamics. In this study, we build and parameterize rate-change models for three dominant alpine life forms using data from a 7-year warming experiment. These models allowed us to examine how the interactions between experimental warming, the abundance of bare ground (a measure of past disturbance) and neighboring life forms (a measure of life form interaction) affect rates of cover change in alpine shrubs, graminoids and forbs. We show that experimental warming altered rates of life form cover change by reducing the negative effects of neighboring life forms and positive effects of bare ground. Furthermore, we show that our models...
ABSTRACT AimMany mangrove communities form bands parallel to the shoreline with each community do... more ABSTRACT AimMany mangrove communities form bands parallel to the shoreline with each community dominated by a single species. However, the key determinants of mangrove species distribution across the intertidal zone are not well understood. We aimed to quantify the relationship between species' dominance and the hydroperiod (defined as the duration of inundation in a year), soil salinity and the salinity of inundating water for three dominant species, Sonneratia alba, Rhizophora stylosa and Ceriops tagal. LocationAn extensive (20,000 ha), largely intact mangrove forest in northern Australia, of some note as mangrove forests are threatened globally. Methods We related species dominance to the explanatory variables by applying two statistical modelling approaches: generalized linear models (GLMs), where a set of competing models were evaluated; and boosted regression tree models (BRTs), an approach that automatically captures interactions and nonlinear relationships between variables. ResultsBoth GLM and BRT models achieved strong predictive performance for all species based on cross‐validation, with receiver operating characteristics above 0.85 for all species, and 88% of deviance explained for S. alba, 42% for R. stylosa and 35% for C. tagal. All models indicated that the hydroperiod was the key variable influencing distribution, followed by soil salinity. The salinity of inundating water was the least informative variable in the models. Ecological space, determined by gradients in hydroperiod and soil salinity, was partitioned between the three species with little overlap. Main conclusionsAs anticipated changes in sea level will alter the hydroperiod, our findings are critical for global forecasting of future distributions of mangrove communities, and for the design of mitigation and adaptation measures.
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Papers by Peter Vesk