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International audience; Freshwater ecosystems are among the most endangered ecosystem in the world. Understanding how human activities affect these ecosystems requires disentangling and quantifying the contribution of the factors driving community assembly. While it has been largely studied in temperate freshwaters, tropical ecosystems remain challenging to study due to the high species richness and the lack of knowledge on species distribution. Here, the use of eDNA-based fish inventories combined to a community-level modelling approach allowed depicting of assembly rules and quantifying the relative contribution of geographic, environmental and anthropic factors to fish assembly. We then used the model predictions to map spatial biodiversity and assess the representativity of sites surveyed in French Guiana within the EU Water Framework Directive (WFD) and highlighted areas that should host unique freshwater fish assemblages. We demonstrated a mismatch between the taxonomic and functional diversity. Taxonomic assemblages between but also within basins were mainly the results of dispersal limitation resulting from basin isolation and natural river barriers. Contrastingly, functional assemblages were ruled by environmental and anthropic factors. The regional mapping of fish diversity indicated that the sites surveyed within the EU WFD had a better representativity of the regional functional diversity than taxonomic diversity. Importantly, we also showed that the assemblages expected to be the most altered by anthropic factors were the most poorly represented in terms of functional diversity in the surveyed sites. The predictions of unique functional and taxonomic assemblages could, therefore, guide the establishment of new survey sites to increase fish diversity representativity and improve this monitoring program.

Nitrogen (N) availability regulates the productivity of terrestrial plants and the ecological services they provide. There is evidence for both increasing and decreasing plant N availability in different biomes, but the data are fragmentary. How plant N availability responds to climate change, N deposition and increasing atmospheric CO2 concentration remains a major uncertainty in the projection of the terrestrial carbon sink. The foliar N stable isotope ratio (δ15N) is an indicator of plant N availability but its usefulness to infer long-term global patterns has been limited by data scarcity. Combining ground-based δ15N and Landsat spectra, we derived annual global maps of Landsat-based foliar δ15N as estimates of plant N availability during 1984-2022. We found significant decreases in plant N availability for 44% and increases in 16% of vegetated Earth’s surface with large spatial heterogeneity. Plant N availability declined in woody-dominated ecosystems but increased in herbaceous-dominated ones. These δ15N trends were consistently and negatively correlated with the trends of Normalised-Difference-Vegetation-Index as they varied across ecosystems, suggesting increasing plant cover could have led to decreasing plant N availability. Our results indicate possible future reductions in plant N availability in many terrestrial ecosystems and provide a useful way to monitor those changes globally.

Landscape-scale conservation that considers metapopulation dynamics will be essential for preventing declines of species facing multiple threats to their survival. Toward this end, we developed a novel approach that combines occurrence records, spatial-environmental data, and genetic information to model habitat, connectivity, and patterns of genetic structure and link spatial attributes to underlying ecological mechanisms. Using the threatened northern quoll (Dasyurus hallucatus) as a case study, we applied this approach to address the need for conservation decision-making tools that promote resilient metapopulations of this threatened species in the Pilbara, Western Australia, a multiuse landscape that is a hotspot for biodiversity and mining. Habitat and connectivity were predicted by different landscape characteristics. Whereas habitat suitability was overwhelmingly driven by terrain ruggedness, dispersal was facilitated by proximity to watercourses. Although there is limited evidence for major physical barriers in the Pilbara, areas with high silt and clay content (i.e., alluvial and hardpan plains) showed high resistance to dispersal. Climate subtlety shaped distributions and patterns of genetic turnover, suggesting the potential for local adaptation. By understanding these spatial-environmental associations and linking them to life-history and metapopulation dynamics, we highlight opportunities to provide targeted species management. To support this, we have created habitat, connectivity, and genetic uniqueness maps for conservation decision-making in the region. These tools have the potential to provide a more holistic approach to conservation in multiuse landscapes globally.La conservación a nivel del paisaje que incluye las dinámicas metapoblacionales será esencial para prevenir la declinación de especies con múltiples amenazas a su supervivencia. Enfocados en este fin, desarrollamos una estrategia novedosa que combina los registros presenciales, datos espacio-ambientales e información genética para modelar la conectividad de hábitat y los patrones de estructura genética y conectar los atributos espaciales con los mecanismos ecológicos subyacentes. Usamos al cuol del norte (Dasyurus hallucatus) como estudio de caso para aplicar esta estrategia y abordar la necesidad de herramientas de decisión en la conservación que promuevan metapoblaciones resilientes de esta especie en la Pilbara de Australia Occidental, un paisaje multiusos que es un punto caliente para la biodiversidad y la minería. Diferentes características del paisaje pronosticaron la conectividad y el hábitat. Mientras que la escabrosidad del terreno causó enormemente la idoneidad del hábitat, la dispersión estuvo propiciada por la proximidad a los cauces. Aunque hay evidencias limitadas de barreras físicas importantes en la Pilbara, las áreas con un contenido elevado de limo y arcilla (es decir, planicies aluviales y de alio) mostraron una gran resistencia a la dispersión. La matización climática determinó la distribución y los patrones del recambio genético, lo que sugiere un potencial para la adaptación local. Si entendemos estas asociaciones espacio-ambientales y las conectamos con las dinámicas metapoblacionales y de historia de vida, podemos resaltar las oportunidades para proporcionar un manejo focalizado de la especie. Para respaldar esto hemos creado mapas de hábitat, conectividad y singularidad genética para las decisiones de conservación en la región. Estas herramientas tienen el potencial de proporcionar una estrategia más holística para la conservación en los paisajes multiusos de todo el mundo.

Understanding how biodiversity is changing over space and time is crucial for well-informed decisions that help retain Earth's biological heritage over the long term. Tracking changes in biodiversity through ecosystem accounting provides this important information in a systematic way and readily enables linking to other relevant environmental and economic data to provide an integrated perspective. We derived biodiversity accounts for the Murray-Darling Basin, Australia's largest catchment. We assessed biodiversity change from 2010 to 2015 for all vascular plants, all waterbirds, and 10 focal species. We applied a scalable habitat-based assessment approach that combined expected patterns in the distribution of biodiversity from spatial biodiversity models with a time series of spatially complete data on habitat condition derived from remote sensing. Changes in biodiversity from 2010 to 2015 varied across regions and biodiversity features. For the entire Murray-Darling Basin, the expected persistence of vascular plants increased slightly from 2010 to 2015 (from 86.8% to 87.1%), mean species richness of waterbirds decreased slightly (from 12.5 to 12.3 species), whereas for the focal species the estimated area of habitat increased for 8 species and decreased for 1 species. Regions in the north of the Murray-Darling Basin generally had decreases in biodiversity from 2010 to 2015, whereas in the south biodiversity was stable or increased. Our results demonstrate the benefits of habitat-based biodiversity assessments in providing fully scalable biodiversity accounts across different biodiversity features, consistent with the United Nations System of Environmental Economic Accounting - Ecosystem Accounting (SEEA EA) framework.Evaluación de la Biodiversidad con base en el Hábitat para la Contabilización de Ecosistemas en la Cuenca Murray-Darling Resumen El conocimiento sobre cómo está cambiando la biodiversidad en el tiempo y en el espacio es crucial para las decisiones bien informadas que ayudan a retener la herencia biológica de la Tierra a largo plazo. El seguimiento de cambios en la biodiversidad mediante la contabilidad de los ecosistemas proporciona esta información importante de manera sistémica y permite fácilmente la conexión con otros datos ambientales y económicos relevantes para proporcionar una perspectiva integrada. Derivamos la contabilidad de la biodiversidad para la Cuenca Murray-Darling, la mayor cuenca de Australia. Analizamos los cambios en la biodiversidad entre 2010 y 2015 de todas las plantas vasculares, todas las aves acuáticas y diez especies focales. Aplicamos una estrategia de evaluación basada en el hábitat que combinó los patrones esperados en la distribución de la biodiversidad a partir de modelos espaciales de la biodiversidad con una serie temporal de datos espacialmente completos derivados de la teledetección de la condición del hábitat. Los cambios en la biodiversidad entre 2010 y 2015 variaron entre las regiones y las características de la biodiversidad. Para toda la Cuenca Murray-Darling, la persistencia esperada de las plantas vasculares incrementó ligeramente durante los años de estudio (de 86.8% a 87.1%), la riqueza promedio de especies de aves acuáticas disminuyó un poco (de 12.5 a 12.3 especies), mientras que el área estimada del hábitat de las especies focales incrementó para ocho especies y disminuyó para una. Las regiones al norte de la cuenca tuvieron disminuciones generalizadas de la biodiversidad entre 2010 y 2015, mientras al sur, la biodiversidad se mantuvo estable o incrementó. Nuestros resultados demuestran los beneficios que tienen las evaluaciones de la biodiversidad basadas en el hábitat para proporcionar una contabilidad de la biodiversidad completamente escalable entre las diferentes características de la biodiversidad, acorde con la estructura del Sistema de Contabilidad Económico-Ambiental - Contabilidad de los Ecosistemas (SEEA EA) de las Naciones Unidas.了解生物多样性的时空变化对于制定帮助地球生物遗产长期续存的决策至关重要。通过生态系统核算追踪生物多样性的变化, 可以系统地提供这一重要信息, 并能随时与其他相关的环境和经济数据相联系, 提供综合性视角。本研究对澳大利亚最大的集水区--墨累达令盆地的生物多样性进行了核算, 评估了2010年到2015年所有维管植物、所有水鸟和10个重点保护物种的生物多样性变化。我们采用了一种尺度可变的基于栖息地的评估方法, 整合了空间生物多样性模型的生物多样性分布格局估计与遥感得到的栖息地状况空间完整数据的时间序列。研究发现, 2010年至2015年, 生物多样性的变化在不同地区和生物多样性特征之间有所差异。整个墨累达令流域维管植物的预期续存情况在2010年到2015年略有增加(从86.8%到87.1%), 水鸟的平均物种丰富度略有下降(从12.5种到12.3种), 而重点保护物种中8个物种的栖息地面积预计增加, 1个物种的栖息地面积下降。此外, 墨累达令盆地北部地区的生物多样性从2010年到2015年普遍下降, 而南部地区的生物多样性则保持稳定或有所增长。我们的结果表明, 基于栖息地的生物多样性评估有助于提供不同生物多样性特征全尺度的生物多样性核算, 这也符合联合国环境经济核算体系-生态系统核算(SEEA EA)框架。【翻译:胡怡思;审校:聂永刚】.

The need for reliable prediction of species distributions dependent upon traits has been hindered by a lack of model transferability testing. We tested the predictive capacity of trait-SDMs by fitting hierarchical generalised linear models with three trait and four environmental predictors for 20 eucalypt taxa in a reference region. We used these models to predict occurrence for a much larger set of taxa and target areas (82 taxa across 18 target regions) in south-eastern Australia. Median predictive performance for new species in target regions was 0.65 (area under receiver operating curve) and 1.24 times random (area under precision recall curve). Prediction in target regions did not worsen with increasing geographic, environmental or community compositional distance from the reference region, and was improved with reliable trait–environment relationships. Transfer testing also identified trait–environment relationships that did not transfer. These results give confidence that traits and transfer testing can assist in the hard problem of predicting environmental responses for new species, environmental conditions and regions.

Most future predictions of forest diversity and composition assume species will shift instantaneously. However, evidence suggests there will be considerable inertia between climate change and shifts in plant species distributions, for example due to resident communities resisting changes, the redistribution of resident species’ abundances, and dispersal limitation of species attempting to track preferred climates. To test how inertia in community responses to climate change may affect future primary productivity, and the capacity for communities to respond through local redistribution of abundances. We assessed relationships between community functional trait distributions, climate and environment with a remote-sensed measure of gross primary productivity (GPP) across 1032 woody plant communities and projected these spatially under current and future climates. Using a simulation approach, we also tested the capacity for communities to achieve future trait distributions by shifting the relative abundances of resident species. We found that climate change is likely to reduce GPP in southeast Queensland, and, contrary to expectations, there is little evidence to suggest distributional inertia will contribute substantially to this loss. We show that sites with high existing functional diversity are well-placed to achieve predicted future trait compositions in the absence of long-distance dispersal through redistribution of resident species abundances. Our results show that sites with high existing functional diversity are well placed to achieve predicted future trait compositions in the absence of long-distance dispersal through abundance redistribution of resident species, but it is unlikely this will mitigate strong climate change-driven losses of productivity.

We introduce the AusTraits database - a compilation of values of plant traits for taxa in the Australian flora (hereafter AusTraits). AusTraits synthesises data on 448 traits across 28,640 taxa from field campaigns, published literature, taxonomic monographs, and individual taxon descriptions. Traits vary in scope from physiological measures of performance (e.g. photosynthetic gas exchange, water-use efficiency) to morphological attributes (e.g. leaf area, seed mass, plant height) which link to aspects of ecological variation. AusTraits contains curated and harmonised individual- and species-level measurements coupled to, where available, contextual information on site properties and experimental conditions. This article provides information on version 3.0.2 of AusTraits which contains data for 997,808 trait-by-taxon combinations. We envision AusTraits as an ongoing collaborative initiative for easily archiving and sharing trait data, which also provides a template for other national or regional initiatives globally to fill persistent gaps in trait knowledge., Measurement(s)plant traitTechnology Type(s)digital curationSample Characteristic - OrganismViridiplantaeSample Characteristic - LocationAustralia Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14545755

Given the magnitude and rate of ongoing climate change, the physiological capacity of species to tolerate extreme conditions will play a key role in influencing outcomes for biodiversity. It is also possible that species will respond to changes in climate by shifting their physiological tolerances, through genetic adaptation. How these processes influence biodiversity outcomes will be crucial in determining the most suitable management responses to retain diversity into the future. Here we assess how accounting for physiological tolerances, genetic adaptation and community assembly processes such as species replacement, influence projected climate change outcomes for the flora of Tasmania (all 2051 plant species). We incorporate these processes into the M‐SET metacommunity model and compare four different assumptions of species niches: realized niches, broader physiological tolerances and low or high capacity for genetic adaptation. Accounting for physiological tolerances rather than realized niches had the largest impact on projected outcomes, with 358 fewer species extinctions in the hottest climate scenario (mean = 30 extinctions). In contrast, adding the capacity for species physiological tolerances to shift through genetic adaptation resulted in little additional benefits for biodiversity outcomes, even under an optimistic level of adaptive capacity. We find that this is due largely to community assembly processes such as species replacement restricting the ability of species to persist and adapt in situ, as has been suggested from theoretical metacommunity models applied in simple artificial settings. Our results highlight the importance of accounting for species physiological tolerances and community‐level processes in biodiversity projections, while the potential role for genetic adaptation may be small, requiring further exploration in alternative contexts.

Aim: Observed, realized niche space often underestimates species’ physiological tolerances due to interactions with other species, dispersal constraints, and because some combinations of influential environmental factors do not currently exist in the real world. Conversely, correlative ecological niche models rely on the assumption that the range of environmental conditions encompassed by a species’ geographic distribution accurately reflects their environmental tolerances, including community-level approaches like generalized dissimilarity modelling (GDM). We extend GDM to better understand what effect broader environmental tolerances could have on compositional turnover under climate change. Innovation: We show how GDM can be adjusted as a function of best-available estimates of the average ratio between realized and potential niche widths to modify projected temporal turnover. We demonstrate this approach by using the estimated niche ratios of Australian plant species (n = 7,184) relative to thermal extremes, and the rate at which this ratio varied with temperature. The modified GDMs showed existing thermal tolerance could reduce the turnover predicted by standard models under climate change by up to 11%. We further show how the reduction in expected turnover by 2090 will influence where a greater proportion of the current community will persist in a region. Main conclusions: We suggest that standard spatial GDMs and their modified versions represent the extremes of ecological niche perspectives (i.e., realized and potential) and the range of tolerance communities may have when responding to environmental change. GDM projections therefore identify the range of uncertainty associated with a critical model assumption, and as climate change continues, ongoing community monitoring could be used to validate the balance between the two possibilities.

Questions: The taxonomic and functional composition of plant communities captures different dimensions of diversity. Functional diversity (FD) - as calculated from species traits - typically increases with species richness in communities and is expected to be higher in less extreme environments, where a broader range of functional strategies can persist. Further, woody and herbaceous plant families may contribute disproportionately to FD in different bioregions. To build an understanding of these questions using Australia as a case study, we aimed to quantify how FD varies: (a) with species richness, (b) with climate, and (c) between major plant families representing different growth forms. Location: Australia. Methods: Data on species distribution and functional traits for 14,003 species were combined and FD approximated using hypervolumes (i.e. multidimensional species assemblage trait niche) based on three traits key to understanding plant ecological strategies: leaf size, seed mass and adult height. Plant assemblage hypervolumes were calculated including all species with suitable habitat in each 10 в 10 km grid cell across Australia, and in each of 85 bioregions. Within bioregions FD was also calculated separately for a suite of largely woody and herbaceous plant families. Relationships between FD, species richness and climate were explored. Results: As predicted, FD was positively related to species richness and annual precipitation, and negatively related to summer maximum temperature, both in analyses of 10 km в 10 km grid cells and of bioregions (all p Conclusions: Strong links between FD and climate could mean significant shifts in the FD of ecosystems with climate change. Monitoring changes in FD and associated ecosystem functions requires a detailed understanding of FD, which we begin to develop in this study.

Degradation and loss of natural habitat is the major driver of the current global biodiversity crisis. Most habitat conservation efforts to date have targeted small areas of highly threatened habitat, but emerging debate suggests retaining large intact natural systems may be just as important. We reconcile these perspectives by integrating fine-resolution global data on habitat condition and species assemblage turnover, to identify Earth’s high-value biodiversity habitat. These are areas in better condition than most other locations once supporting a similar assemblage of species, and are found within both intact regions and human dominated landscapes. However, only 18.6 % of this high-value habitat is currently protected globally. Averting permanent biodiversity loss requires clear spatially explicit targets for retaining these unprotected high-value habitats.

AIM: Spatial modelling can provide new understanding of patterns in biodiversity and help inform policy, planning and management decisions. However, for many taxa, in many regions, issues with currently available biological data curtail the application of spatial biodiversity models. Here we develop techniques to model spatial patterns of diverse subterranean invertebrate fauna (troglofauna and stygofauna) in the Pilbara region, to better inform biodiversity planning and management for these groups. In this context, the available data are highly biased in their distribution, with specimens often identified only at coarse taxonomic resolution, and individual community surveys sampling a relatively small proportion of the total assemblage at a location. LOCATION: Pilbara bioregion, NW Australia. METHODS: To overcome the challenges presented by the subterranean fauna data sets, we developed a community‐level spatial biodiversity modelling framework, and applied this to model and project patterns in species richness and compositional turnover, for both troglofauna and stygofauna, using fine‐resolution (~30 m) environmental layers. RESULTS: While there was a large amount of inherent noise in the collated data, our spatial models detected potential environmental drivers of community diversity that align with existing knowledge. For troglofauna, species richness was associated with iron‐bearing geologies, while community compositional turnover was most strongly related to geographic distance. For stygofauna, groundwater depth was the primary predictor for richness and turnover, alongside other physicochemical groundwater attributes. We identified areas of high biodiversity significance by combining richness and turnover predictions. MAIN CONCLUSIONS: The spatial projections from our biodiversity models indicate the importance of the central Hamersley Range in harbouring unique and diverse subterranean assemblages. These models and projections provide an advanced platform for various biodiversity assessments, including identifying priorities for new surveys, and assessing the likely impacts of proposed development activities on the long‐term persistence of subterranean species in the Pilbara.

Understanding influences of environmental change on biodiversity requires consideration of more than just species richness. Here we present a novel framework for understanding possible changes in species' abundance structures within communities under climate change. We demonstrate this using comprehensive survey and environmental data from 1748 woody plant communities across southeast Queensland, Australia, to model rank-abundance distributions (RADs) under current and future climates. Under current conditions, the models predicted RADs consistent with the region's dominant vegetation types. We demonstrate that under a business as usual climate scenario, total abundance and richness may decline in subtropical rainforest and shrubby heath, and increase in dry sclerophyll forests. Despite these opposing trends, we predicted evenness in the distribution of abundances between species to increase in all vegetation types. By assessing the information rich, multidimensional RAD, we show that climate-driven changes to community abundance structures will likely vary depending on the current composition and environmental context.

Background Intensification of land use threatens to increase the emergence and prevalence of zoonotic diseases, with an adverse impact on human wellbeing. Understanding how the interaction between agriculture, natural systems, climate and socioeconomic drivers influence zoonotic disease distribution is crucial to inform policy planning and management to limit the emergence of new infections. Objectives Here we assess the relative contribution of environmental, climatic and socioeconomic factors influencing reported cryptosporidiosis across Australia from 2001 to 2018. Methods We apply a Bayesian spatio-temporal analysis using Integrated Nested Laplace Approximation (INLA). Results We find that area-level risk of reported disease are associated with the proportions of the population under 5 and over 65 years of age, socioeconomic disadvantage, annual rainfall anomaly, and the proportion of natural habitat remaining. This combination of multiple factors influencing cryptosporidiosis highlights the benefits of a sophisticated spatio-temporal statistical approach. Two key findings from our model include: an estimated 4.6% increase in the risk of reported cryptosporidiosis associated with 22.8% higher percentage of postal area covered with original habitat; and an estimated 1.8% increase in disease risk associated with a 77.99 mm increase in annual rainfall anomaly at the postal area level. Discussion These results provide novel insights regarding the predictive effects of extreme rainfall and the proportion of remaining natural habitat, which add unique explanatory power to the model alongside the variance associated with other predictive variables and spatiotemporal variation in reported disease. This demonstrates the importance of including perspectives from land and water management experts for policy making and public health responses to manage environmentally mediated diseases, including cryptosporidiosis.

Aim Despite recognition that realized distributions inherently underestimate species' physiological tolerances, we are yet to identify the extent of these differences within diverse taxonomic groups. The degree to which species could tolerate environmental conditions outside their observed distributions may have a significant impact on the perceived extinction risk in ecological models. More information on this potential error is required to improve our confidence in management strategies. Location Australia. Time Period 1983–2012. Major Taxa Studied Plants. Methods To quantify the scale and spatial patterns of this disparity, we estimated the existing tolerance to thermal extremes of 7,124 Australian plants, more than one-third of the native continental flora, using data from cultivated records at 128 botanical gardens and nurseries. Hierarchical Bayesian beta regression was used to assess whether factors such as realized niches, traits or phylogeny could predict the incidence or magnitude of niche truncation (underestimation of thermal tolerances), while controlling for sources of collection bias. Results Approximately half of the cultivated species analysed could tolerate temperature extremes beyond those experienced in their native range. Niche truncation was predictable from the breadth and extremes of their realized niches and by traits such as plant growth form. Phylogenetic relationships with niche truncation were weak and appeared more suited to predicting thermal tolerances directly. Main conclusions This study highlights a widespread disparity between realized and potential thermal limits that may have significant implications for species' capacity to persist in situ with a changing climate. Identifying whether thermal niche truncation is the result of biotic interactions, dispersal constraints or other environmental factors could provide significant insight into community assembly at macroecological scales. Estimating niche truncation may help to explain why certain ecological communities are more resilient to change and may potentially improve the reliability of model projections under climate change.

Improving biodiversity predictions is essential if we are to meet the challenges posed by global change. As knowledge is key to feed models, we need to evaluate how debated theory can affect models. An important ongoing debate is whether environmental constraints limit the number of species that can coexist in a community (saturation), with recent findings suggesting that species richness in many communities might be unsaturated. Here, we propose that biodiversity models could address this issue by accounting for a duality: considering communities as unsaturated but where species composition is constrained by different scale-dependent biodiversity drivers. We identify a variety of promising advances for incorporating this duality into commonly applied biodiversity modelling approaches and improving their spatial predictions., Trends The majority of biodiversity modelling approaches do not explicitly address the question of saturation. Theoretical and methodological implications of saturation or unsaturation in biodiversity modelling. Addressing saturation or unsaturation is vital to produce more reliable conservation strategies. Integrative community modelling frameworks may be the way forward.

International audience; Aim: More than ever, ecologists seek to understand how species are distributed and have assembled into communities using the “filtering framework”. This framework is based on the hypothesis that local assemblages result from a series of abiotic and biotic filters applied to regional species pools and that these filters leave predictable signals in observed diversity patterns. In theory, statistical comparisons of expected and observed patterns enable data‐driven tests of assembly processes. However, so far this framework has fallen short in delivering generalizable conclusions, challenging whether (and how) diversity patterns can be used to characterize and understand underlying assembly processes better.Methods: By synthesizing the previously raised critiques and suggested solutions in a comprehensive way, we identify 10 pitfalls that can lead to flawed interpretations of α‐diversity patterns, summarize solutions developed to circumvent these pitfalls and provide general guidelines.Results: We find that most issues arise from an overly simplistic view of potential processes that influence diversity patterns, which is often motivated by practical constraints on study design, focal scale and methodology. We outline solutions for each pitfall, such as methods spanning over spatial, environmental or phylogenetic scales, and suggest guidelines for best scientific practices in community ecology. Among key future challenges are the integration of mechanistic modelling and multi‐trophic interactions.Main conclusions: Our conclusion is that the filtering framework still holds promise, but only if researchers successfully navigate major pitfalls, foster the integration of mechanistic modelling and multi‐trophic interactions and directly account for uncertainty in their conclusions.

We are facing a biodiversity crisis at the same time as we are acquiring an unprecedented view of the world's biodiversity. Vast new datasets (e.g., species distributions, traits, phylogenies, and interaction networks) hold knowledge to better comprehend the depths of biodiversity change, reliably anticipate these changes, and inform conservation actions. To harness this information for conservation, we need to integrate the largely independent fields of biodiversity modeling and conservation. We highlight new developments in each respective field, early examples of how they are being brought together, and ideas for a future synthesis such that conservation decisions can be made with fuller awareness of the biodiversity at stake.

Based on the sensitivity of species to ongoing climate change, and numerous challenges they face tracking suitable conditions, there is growing interest in species' capacity to adapt to climatic stress. Here, we develop and apply a new generic modelling approach (AdaptR) that incorporates adaptive capacity through physiological limits, phenotypic plasticity, evolutionary adaptation and dispersal into a species distribution modelling framework. Using AdaptR to predict change in the distribution of 17 species of Australian fruit flies (Drosophilidae), we show that accounting for adaptive capacity reduces projected range losses by up to 33% by 2105. We identify where local adaptation is likely to occur and apply sensitivity analyses to identify the critical factors of interest when parameters are uncertain. Our study suggests some species could be less vulnerable than previously thought, and indicates that spatiotemporal adaptive models could help improve management interventions that support increased species' resilience to climate change.

Aims Accurate field quantification of stem counts is time consuming and laborious, especially in ecosystems containing many small individuals where counts are immense. There are many large, long-term data sets worldwide that include various measures of woody plant abundance, such as crown cover, that are faster and easier to measure than true counts of individuals. Quantifying the number of individuals allows in-depth research into various topics including functional ecology, carbon accounting, conservation and metabolic theory. Here we test the accuracy of predicting the abundance of individual woody plants from cover and forest structure measures, and explore the implications of refined abundance measures for understanding the rarity of woody plant species across southeast Queensland. Location Southeast Queensland (SEQ), Australia. Methods To supplement an existing data set of 1251 sites with measured crown covers for woody plants in various strata, we sampled stem counts in an additional 30 sites, forming a calibration data set. We ensured that these 30 sites covered the environmental variation in the study area. Generalized additive mixed effects models were used to predict stem counts for all woody plants in the original data set from their crown cover and forest structure values and a site-level variable accounting for water balance. We used these refined abundance data to investigate relationships between local and regional abundance for different size classes. Results Stem densities increased with crown cover in a non-linear fashion; slowing once crown cover reached ca. 5% for shrubs and ca. 50% for trees, depending on their maximum potential height. Using the predicted stem count data, we found that shrubs were more locally abundant than trees, but less regionally abundant. Larger trees (>20 m maximum potential height) showed a negative relationship between local and regional abundance; there was no significant relationship between other size classes. Conclusions Our study demonstrates how crown cover can be converted to stem counts using a calibration data set and flexible mixed-effects models. This enables further ecological analysis at a fraction of the investment required to re-survey. Analyses of abundances revealed that tree species are less locally abundant than smaller woody species, likely due to space filling constraints, but more regionally abundant.

Aim Ecosystem functions such as productivity may be influenced not only by the biological diversity at each location (α-diversity) but also by the biological turnover between locations (β-diversity). We perform a continental-scale test of the strength and direction of relationships between gross primary productivity (GPP) and both α- and β-diversity. Location Continental Australia. Methods Species occurrence records were used to quantify the taxonomic α-diversity of vascular plants in approximately 11,000 1 km × 1 km grid cells across Australia, and to calculate the average β-diversity within a 10-km radius around each cell. The magnitude and variability of monthly, MODIS-derived remotely sensed GPP (2001–12) were summarized for continental Australia, as were rainfall and temperature over the same period. Generalized additive models were then used to test whether the magnitude or variability of GPP were distinctly influenced by either biodiversity measure, over and above the influence of environmental conditions. Results Precipitation and temperature explained large proportions of deviance in the magnitude (75.6%) and variability (38.3%) of GPP across the Australian continent. GPP was marginally more strongly related to species richness than it was to species turnover. However, neither diversity measure provided substantial increases in the explanatory power of GPP models over and above that of environment-only models (always

Conserving different spatial and temporal dimensions of biological diversity is considered necessary for maintaining ecosystem functions under predicted global change scenarios. Recent work has shifted the focus from spatially local (α‐diversity) to macroecological scales (β‐ and γ‐diversity), emphasizing links between macroecological biodiversity and ecosystem functions (MB–EF relationships). However, before the outcomes of MB–EF analyses can be useful to real‐world decisions, empirical modeling needs to be developed for natural ecosystems, incorporating a broader range of data inputs, environmental change scenarios, underlying mechanisms, and predictions. We outline the key conceptual and technical challenges currently faced in developing such models and in testing and calibrating the relationships assumed in these models using data from real ecosystems. These challenges are explored in relation to two potential MB–EF mechanisms: “macroecological complementarity” and “spatiotemporal compensation.” Several regions have been sufficiently well studied over space and time to robustly test these mechanisms by combining cutting‐edge spatiotemporal methods with remotely sensed data, including plant community data sets in Australia, Europe, and North America. Assessing empirical MB–EF relationships at broad spatiotemporal scales will be crucial in ensuring these macroecological processes can be adequately considered in the management of biodiversity and ecosystem functions under global change.

Global biodiversity targets have far-reaching implications for nature conservation worldwide. Scenarios and models hold unfulfilled promise for ensuring such targets are well founded and implemented; here, we review how they can and should inform the Aichi Targets of the Strategic Plan for Biodiversity and their reformulation. They offer two clear benefits: providing a scientific basis for the wording and quantitative elements of targets; and identifying synergies and trade-offs by accounting for interactions between targets and the actions needed to achieve them. The capacity of scenarios and models to address complexity makes them invaluable for developing meaningful targets and policy, and improving conservation outcomes.

As climate changes, tree decline in Mediterranean-type ecosystems is increasing worldwide, often due to decreased effective precipitation and increased drought and heat stress, and has recently been observed in coastal species of the iconic Eucalyptus (Myrtaceae) genus in the biodiversity hotspot of south-west Western Australia. To investigate how this drought-related decline is likely to continue in the future, we used species distribution modelling techniques to generate broad-scale predictions of future distribution patterns under three distinct projected climate change scenarios. In a moderate climate change scenario, suitable habitat for all species was predicted to decrease by, on average, 73% by the year 2100, with most receding into southern areas of their current distribution. Although the most severe Eucalyptus declines in south-west Western Australia have been observed in near-coastal regions, our predictions suggest that inland species are at greater risk from climate change, with six inland species predicted to lose 95% of their suitable habitat in a moderate change scenario. This is due to the shallow environmental gradients of inland regions causing larger spatial shifts of environmental envelopes, which is likely to be relevant in many regions of the world. The knowledge gained suggests that future research and conservation efforts in south-west Western Australia and elsewhere should avoid focussing disproportionately on coastal regions for reasons of convenience and proximity to population centres, and properly address the inland region where the biggest future impacts may occur.

AimBiodiversity outcomes under global change will be influenced by a range of ecological processes, and these processes are increasingly being considered in models of biodiversity change. However, the level of model complexity required to adequately account for important ecological processes often remains unclear. Here we assess how considering realistically complex frugivore-mediated seed dispersal influences the projected climate change outcomes for plant diversity in the Australian Wet Tropics (all 4313 species). LocationThe Australian Wet Tropics, Queensland, Australia. MethodsWe applied a metacommunity model (M-SET) to project biodiversity outcomes using seed dispersal models that varied in complexity, combined with alternative climate change scenarios and habitat restoration scenarios. ResultsWe found that the complexity of the dispersal model had a larger effect on projected biodiversity outcomes than did dramatically different climate change scenarios. Applying a simple dispersal model that ignored spatial, temporal and taxonomic variation due to frugivore-mediated seed dispersal underestimated the reduction in the area of occurrence of plant species under climate change and overestimated the loss of diversity in fragmented tropical forest remnants. The complexity of the dispersal model also changed the habitat restoration approach identified as the best for promoting persistence of biodiversity under climate change. Main conclusionsThe consideration of complex processes such as frugivore-mediated seed dispersal can make an important difference in how we understand and respond to the influence of climate change on biodiversity.

International audience; Aim: Modelling the response of β-diversity (i.e., the turnover in species composition among sites) to environmental variation has wide-ranging applications, including in- forming conservation planning, understanding community assembly and forecasting the impacts of climate change. However, modelling β-diversity is challenging, espe- cially for multiple diversity facets (i.e., taxonomic, functional and phylogenetic diver- sity), and current methods have important limitations. Here, we present a new approach for predicting the response of multifaceted β-diversity to the environment, called Multifaceted Biodiversity Modelling (MBM). We illustrate the approach using both a plant diversity dataset from the French Alps and a set of simulated data. We also provide an implementation via an R package.Location: French Alps.Methods: For both the French Alps and the simulated communities, we compute β- diversity indices (e.g., Sørensen dissimilarity, mean functional/phylogenetic pairwise distance) among site pairs. We then apply Gaussian process regression, a flexible nonlinear modelling technique, to predict β-diversity in response to environmental distance among site pairs. For comparison, we also perform similar analyses using Generalized Dissimilarity Modelling (GDM), a well-established method for modelling β-diversity in response to environmental distance.Results: In the Alps, we observed a general increase in taxonomic (TD) and functional (FD) β-diversity (i.e., site pairs were more different from each other) as the climatic distance between site pairs increased. GDM performed better for TD and FD when fitting to calibration data, whereas MBM performed better for both when predicting to a validation dataset. For phylogenetic β-diversity, MBM outperformed GDM in predicting the observed decrease in phylogenetic β-diversity with increasing climatic distance.Main conclusions: Multifaceted Biodiversity Modelling provides a flexible new ap- proach that expands our capacity to model multiple facets of β-diversity. Advantages of MBM over existing methods include simpler assumptions, more flexible modelling, potential to consider multiple facets of diversity across a range of diversity indices, and robust uncertainty estimation.

Metabolic scaling theory (MST) is one of ecology's most high-profile general models and can be used to link size distributions and productivity in forest systems. Much of MST's foundation is based on size distributions following a power law function with a scaling exponent of −2, a property assumed to be consistent in steady-state ecosystems. We tested the theory's generality by comparing actual size distributions with those predicted using MST parameters assumed to be general. We then used environmental variables and functional traits to explain deviation from theoretical expectations. Finally, we compared values of relative productivity predicted using MST with a remote-sensed measure of productivity. We found that fire-prone heath communities deviated from MST-predicted size distributions, whereas fire-sensitive rainforests largely agreed with the theory. Scaling exponents ranged from −1.4 to −5.3. Deviation from the power law assumption was best explained by specific leaf area, which varies along fire frequency and moisture gradients. While MST may hold in low-disturbance systems, we show that it cannot be applied under many environmental contexts. The theory should remain general, but understanding the factors driving deviation from MST and subsequent refinements is required if it is to be applied robustly across larger scales.

Aim The relationship between species richness (α-diversity) and area is well studied; however, the way in which compositional dissimilarity between pairs of sites (β-diversity) scales with area has only recently attracted research attention. The aim of this study was to improve the understanding of how both α- and β-diversity scale with area, to illuminate ecological processes structuring the distribution of biodiversity and enable prediction of α- and β-diversity for large regions from much smaller samples. Location We examined both simulated spatial community data and measurements from tropical forest tree plots in Panama. Methods We applied the simulated and measured community data to assess how both α- and β-diversity scale with area. Then we examined how accurately community α-diversity and pairwise β-diversity can be extrapolated from small sample areas of different size within each community, using the species–area power relationship. Results For both the simulated and tree plot data, pairwise β-diversity scaled with area in a corresponding manner to the much more familiar species–area relationship. By altering the attributes of the simulated communities, we found that α- and β-diversity saturated at smaller areas where abundances were more even, species distributions were less aggregated and regional richness was lower. Estimates of α- and β-diversity for a pair of communities generally increased in accuracy with the size of the local sample areas from which extrapolations were made. Main conclusions These analyses suggest that the most robust estimates of α- and β-diversity for a larger area will be obtained by local samples that are greater than 10% the size of that larger area. Our results emphasize the fundamental link in how both α- and β-diversity scale with area, and demonstrate how simple knowledge of these scaling relationships can be used to predict the diversity of larger areas from smaller samples.

Aim Species distribution models (SDMs) generally use correlative relationships between the species location and the associated environment to project the species potential distribution under climate change. While projecting a future suitable climatic space is relatively simple using SDMs, predicting a species ability to occupy that space relies on understanding dispersal capacity; a lack of knowledge about species-specific dispersal ability, varying geographical contexts and technical constraints of simple SDMs has limited the consideration of dispersal in most studies. We review the current treatment of dispersal in SDM studies addressing the effects of climate change and explore how incorporating ‘partial-dispersal’ scenarios could lead to more realistic projections of species distributions into the future. Location Global. Methods We consider the implications for projected distributions of incorporating full- and no-dispersal scenarios in SDMs and identify a range of methods and their associated information needs for implementing partial-dispersal scenarios. Results While simplistic and easy to implement, full- and no-dispersal scenarios are only realistic in a few situations. Although implementing partial-dispersal scenarios may require information that is lacking for many species, we argue that even relatively simple partial-dispersal models, with fairly basic knowledge needs, improve projections of altered distributions under climate change. More complex models, using more sophisticated modelling approaches, have been tested in a few cases and provide robust projections. Main Conclusions While climate change SDM outputs have proved useful, we highlight that careful selection of dispersal scenarios, relevant to the particular questions being addressed, is necessary for appropriate interpretation of the model outputs when projecting into novel environments (e.g. future climates). A number of methods have been developed for incorporating partial-dispersal scenarios in SDMs; however, the data and computation requirements currently limit their application to large numbers of species, highlighting the need for other techniques and generic user-friendly modelling platforms.

Aim: Refugia under past climates have been important in structuring current patterns in diversity, while refugia under anthropogenic climate change will likely be important in retaining this diversity and shaping new patterns. However, few studies have examined the congruence of past, present and future refugia, or the spatiotemporal connectivity of these refugia. Our aim was to test the extent of overlap of refugia under Last Glacial Maximum (LGM), present (2015) and likely future climates (2100), for Tasmania's palaeoendemic flora. We then aimed to identify areas of high spatiotemporal refugia connectivity, as priority areas for conservation and management. Location: Tasmania, Australia. Methods: We developed and applied a new community-level approach to identifying refugia, based on generalized dissimilarity modelling of compositional turnover and a set of reference sites with known biodiversity value. Using these projections of palaeoendemic plant refugia for past, present and future climates, we developed and applied a second approach to quantify the level of connectivity of these refugia over space and time. Results: Although there was large overlap (85%) between current and future climates in the distribution of the highest value palaeoendemic refugia, the small congruence of these areas with refugia at the LGM resulted in only a small area ( c. km 2 ) of persistent high value refugia over all three time periods. Despite this, our spatiotemporal analysis identified several areas of high connectivity in refugial environments for Tasmania's palaeoendemic flora over time. Main conclusions: The community-level approaches we demonstrate here to quantify refugia and their spatiotemporal connectivity have the potential to advance our understanding of biodiversity dynamics, particularly for taxonomic groups that are species-rich, poorly studied or comprised of many rare species, where species-level approaches are less suitable.

Estimating extinction vulnerability for a large number of species presents significant challenges for researchers, but is of high importance considering the large number of species that are currently unassessed. We present a method using a type of artificial neural network (self organizing map; SOM), which utilizes the co-occurrence patterns of species to estimate each species' vulnerability to extinction. We use this method on Australian bird assemblages and compare the SOM-generated rankings for vulnerability with assessments from the IUCN Red List for those species in which populations have actually been estimated. For species that have had their populations estimated, the SOM performed well in distinguishing those species ranked of least concern by IUCN from those species in one of the other IUCN categories. Further, 19 species that were identified as highly vulnerable by the SOM analysis have never had their populations estimated and have been ranked by the IUCN of least concern. We show how the SOM can identify spatial variation in vulnerability for a species, and identify those regions in Australia in which the resident species have the greatest levels of vulnerability (central Australia). We conclude that the SOM provides a useful tool for researchers and agencies dealing with conservation strategies focused on large numbers of species and we urge a more detailed assessment of the 19 bird species identified by this analysis as vulnerable to extinction.

Aim Spatial turnover in plant species composition reflects the interplay between species’ environmental requirements and their dispersal dynamics. However, its origins are also historical, arising from speciation, extinction and past range dynamics. Here, we test the effects of current environmental gradients and geographical distance on regional species turnover. Using a new approach, we then interpret residual turnover patterns, unexplained by these factors. Location Native vegetation in the wheatbelt of the Southwest Australian Floristic Region (SWAFR). Methods We fitted a generalized dissimilarity model of species turnover across 650 floristic inventory plots as a function of current climate, topography, soils and inter-plot distances. We then decomposed the model errors into independent axes of residual variation, representing unexplained species turnover. We mapped the spatial patterns evident on the main residual axes, and interpreted these using ancillary data on the life form and geographical ranges of associated species, the distributions of some major habitat types, and anomalies between recent and Last Glacial Maximum (LGM) climate. Results Soil and climatic variables (especially phosphorus and wet quarter precipitation) made sizeable independent contributions to explaining deviance in species turnover, whereas deviance explained by inter-plot distances and climate co-varied strongly. Total explained deviance was 34%. Analyses of residual dissimilarity highlighted unexplained floristic similarity among many southern plots. A secondary residual axis highlighted unexplained variation in a broadly westerly to easterly direction. Consistent trends in the life form and geographical ranges of associated species suggest that these patterns are climate-related. LGM climatic anomalies were correlated with patterns on the second residual axis. Main conclusions Current soil and climatic gradients are important drivers of floristic turnover in the SWAFR. However, residual analyses revealed broad-scale gradients in floristic composition unexplained by these variables. These appear to reflect imperfect modelling of the effects of current and perhaps historical climate on species turnover.

For many taxonomic groups, sparse information on the spatial distribution of biodiversity limits our capacity toanswer a variety of theoretical and applied ecological questions. Modelling community-level attributes (a- andb-diversity) over space can help overcome this shortfall in our knowledge, yet individually, predictions of a-or b-diversity have their limitations. In this study, we present a novel approach to combining models of a- andb-diversity, with sparse survey data, to predict the community composition for all sites in a region. We appliedour new approach to predict land snail community composition across New Zealand. As we demonstrate, thesepredictions of metacommunity composition have diverse potential applications, including predicting c-diversityfor any set of sites, identifying target areas for conservation reserves, locating priority areas for future ecologicalsurveys, generating realistic compositional data for metacommunity models and simultaneously predicting thedistribution of all species in a taxon consistent with known community diversity patterns.KeywordsAlpha, beta, community, dissimilarity, diversity, gamma, metacommunity, macroecology, richness, snail.Ecology Letters (2011) 14: 1043–1051