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It is commonly thought that the biodiversity crisis includes widespread declines in the spatial variation of species composition, called biotic homogenization. Using a typology relating homogenization and differentiation to local and regional diversity changes, we synthesize patterns across 461 metacommunities surveyed for 10 to 91 years, and 64 species checklists (13 to 500+ years). Across all datasets, we found that no change was the most common outcome, but with many instances of homogenization and differentiation. A weak homogenizing trend of a 0.3% increase in species shared among communities/year on average was driven by increased numbers of widespread (high occupancy) species and strongly associated with checklist data that have longer durations and large spatial scales. At smaller spatial and temporal scales, we show that homogenization and differentiation can be driven by changes in the number and spatial distributions of both rare and common species. The multiscale perspective introduced here can help identify scale-dependent drivers underpinning biotic differentiation and homogenization.

It is commonly thought that the biodiversity crisis includes widespread declines in the spatial variation of species composition, called biotic homogenization. Using a typology relating homogenization and differentiation to local and regional diversity changes, we synthesize patterns across 461 metacommunities surveyed for 10 to 91 years, and 64 species checklists (13 to 500+ years). Across all datasets, we found that no change was the most common outcome, but with many instances of homogenization and differentiation. A weak homogenizing trend of a 0.3% increase in species shared among communities/year on average was driven by increased numbers of widespread (high occupancy) species and strongly associated with checklist data that have longer durations and large spatial scales. At smaller spatial and temporal scales, we show that homogenization and differentiation can be driven by changes in the number and spatial distributions of both rare and common species. The multiscale perspective introduced here can help identify scale-dependent drivers underpinning biotic differentiation and homogenization.

BackgroundDisturbances alter the diversity and composition of microbial communities. Yet a generalized empirical assessment of microbiome responses to disturbance across different environments is needed to understand the factors driving microbiome recovery, and the role of the environment in driving these patterns.ResultsTo this end, we combined null models with Bayesian generalized linear models to examine 86 time series of disturbed mammalian, aquatic, and soil microbiomes up to 50 days following disturbance. Overall, disturbances had the strongest effect on mammalian microbiomes, which lost taxa and later recovered their richness, but not their composition. In contrast, following disturbance, aquatic microbiomes tended away from their pre-disturbance composition over time. Surprisingly, across all environments, we found no evidence of increased compositional dispersion (i.e., variance) following disturbance, in contrast to the expectations of the Anna Karenina Principle.ConclusionsThis is the first study to systematically compare secondary successional dynamics across disturbed microbiomes, using a consistent temporal scale and modeling approach. Our findings show that the recovery of microbiomes is environment-specific, and helps to reconcile existing, environment-specific research into a unified perspective.

It is commonly thought that the biodiversity crisis includes widespread decreases in the uniqueness of different sites in a landscape (biotic homogenization). Using a typology relating homogenization and differentiation to local and regional diversity changes, we synthesize patterns across 283 metacommunities surveyed for 10-91 years, and 54 species checklists (13-500+ years). On average, there is a 0.2% increase in species shared among communities/year (i.e., weak homogenization), but across data sets, differentiation frequently occurs, with no statistically significant change being most common. Local (not regional) diversity frequently underlies composition change, and homogenization is strongly associated with checklist data that have longer durations and large spatial scales. Conservation and management can benefit from the multiscale perspective used here as it disentangles the implications of both the differentiation and homogenization currently unfolding.

Estimating biodiversity change across the planet in the context of widespread human modification is a critical challenge. Here, we review how biodiversity has changed in recent decades across scales and taxonomic groups, focusing on four diversity metrics: species richness, temporal turnover, spatial beta-diversity and abundance. At local scales, change across all metrics includes many examples of both increases and declines and tends to be centred around zero, but with higher prevalence of declining trends in beta-diversity (increasing similarity in composition across space or biotic homogenization) and abundance. The exception to this pattern is temporal turnover, with changes in species composition through time observed in most local assemblages. Less is known about change at regional scales, although several studies suggest that increases in richness are more prevalent than declines. Change at the global scale is the hardest to estimate accurately, but most studies suggest extinction rates are probably outpacing speciation rates, although both are elevated. Recognizing this variability is essential to accurately portray how biodiversity change is unfolding, and highlights how much remains unknown about the magnitude and direction of multiple biodiversity metrics at different scales. Reducing these blind spots is essential to allow appropriate management actions to be deployed.

Biodiversity metrics often integrate data on the presence and abundance of multiple species. Yet our understanding of covariation between changes to the numbers of individuals, the evenness of species relative abundances, and the total number of species remains limited. Using individual-based rarefaction curves, we introduce a conceptual framework to understand how expected positive relationships among changes in abundance, evenness and richness arise, and how they can break down. We then examined interdependencies between changes in abundance, evenness and richness in more than 1100 assemblages sampled either through time or across space. As predicted, richness changes were greatest when abundance and evenness changed in the same direction, and countervailing changes in abundance and evenness acted to constrain the magnitude of changes in species richness. Site-to-site differences in abundance, evenness, and richness were often decoupled, and pairwise relationships between these components across assemblages were weak. In contrast, changes in species richness and relative abundance were strongly correlated for assemblages varying through time. Temporal changes in local biodiversity showed greater inertia and stronger relationships between the component changes when compared to site-to-site variation. Overall, local variation in assemblage diversity was rarely due to repeated passive samples from an approximately static species abundance distribution. Instead, changing species relative abundances often dominated local variation in diversity. Moreover, how changing relative abundances combined with changes to total abundance frequently determined the magnitude of richness changes. Embracing the interdependencies between changing abundance, evenness and richness can provide new information for better understanding biodiversity change in the Anthropocene.

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Ecological thresholds comprise relatively fast changes in ecological conditions, with respect to time or external drivers, and are an attractive concept in both scientific and policy arenas. However, there is considerable debate concerning the existence, underlying mechanisms, and generalizability of ecological thresholds across a range of ecological subdisciplines. Here, we use the general concept of scale as a unifying framework with which to systematically navigate the variability within ecological threshold research. We review the literature to show how the observational scale adopted in any one study, defined by its organizational level, spatiotemporal grain and extent, and analytical method, can influence threshold detection and magnitude. We highlight a need for nuance in synthetic studies of thresholds, which could improve our predictive understanding of thresholds. Nuance is also needed when translating threshold concepts into policies, including threshold contingencies and uncertainties.

Global change drivers, such as anthropogenic nutrient inputs, are increasing globally. Nutrient deposition simultaneously alters plant biodiversity, species composition and ecosystem processes like aboveground biomass production. These changes are underpinned by species extinction, colonisation and shifting relative abundance. Here, we use the Price equation to quantify and link the contributions of species that are lost, gained or that persist to change in aboveground biomass in 59 experimental grassland sites. Under ambient (control) conditions, compositional and biomass turnover was high, and losses (i.e. local extinctions) were balanced by gains (i.e. colonisation). Under fertilisation, the decline in species richness resulted from increased species loss and decreases in species gained. Biomass increase under fertilisation resulted mostly from species that persist and to a lesser extent from species gained. Drivers of ecological change can interact relatively independently with diversity, composition and ecosystem processes and functions such as aboveground biomass due to the individual contributions of species lost, gained or persisting.

Broad-scale biodiversity monitoring relies, at least in part, on the efforts of citizen, or community, scientists. To ensure robust inferences from citizen science data, it is important to understand the spatial pattern of sampling effort by citizen scientists and how it deviates from an optimal pattern. Here, we develop a generalized workflow to estimate the optimal distribution of sampling effort for inference of species diversity (e.g., species richness, Shannon diversity, and Simpson's diversity) patterns using the relationship between species diversity and land cover. We used data from the eBird citizen science project that was collected across heterogeneous landscapes in Florida (USA) to illustrate this workflow across different grain sizes. We found that a relatively small number of samples are needed to meet 95% sampling completeness when diversity estimation is focused on dominant species: 43, 64, 96, 123, 172, and 176 for 5 × 5, 10 × 10, 15 × 15, 20 × 20, 25 × 25, and 30 × 30-km2 grain sizes, respectively. In contrast, three to five times more samples are necessary to infer species diversity when estimation is focused on rare species. However, in both cases, the optimal distribution of effort was spatially heterogeneous, with more effort needed in regions of higher diversity. Our results highlight the potential of citizen science data to make informed comparisons of species diversity in space and time, as well as how sampling effort inherently depends on monitoring goals, such as whether dominant or rare species are targeted. Our general workflow allows for the quantification of sampling effort needed to estimate species diversity with citizen science data and can guide future adaptive sampling by citizen science participants.

Seed dispersal limitation, which can be exacerbated by a number of anthropogenic causes, can result in local communities having fewer species than they might potentially support, representing a potential diversity deficit. The link between processes that shape natural variation in diversity, such as dispersal limitation, and the consequent effects on productivity is less well known. Here, we synthesised data from 12 seed addition experiments in grassland communities to examine the influence of reducing seed dispersal limitation (from 1 to 60 species added across experiments) on species richness and productivity. For every 10 species of seed added, we found that species richness increased by about two species. However, the increase in species richness by overcoming seed limitation did not lead to a concomitant increase in above‐ground biomass production. This highlights the need to consider the relationship between biodiversity and ecosystem functioning in a pluralistic way that considers both the processes that shape diversity and productivity simultaneously in naturally assembled communities.

Climate change and other anthropogenic drivers of biodiversity change are unequally distributed across the world. Overlap in the distributions of different drivers have important implications for biodiversity change attribution and the potential for interactive effects. However, the spatial relationships among different drivers and whether they differ between the terrestrial and marine realm has yet to be examined.We compiled global gridded datasets on climate change, land‐use, resource exploitation, pollution, alien species potential and human population density. We used multivariate statistics to examine the spatial relationships among the drivers and to characterize the typical combinations of drivers experienced by different regions of the world.We found stronger positive correlations among drivers in the terrestrial than in the marine realm, leading to areas with high intensities of multiple drivers on land. Climate change tended to be negatively correlated with other drivers in the terrestrial realm (e.g. in the tundra and boreal forest with high climate change but low human use and pollution), whereas the opposite was true in the marine realm (e.g. in the Indo‐Pacific with high climate change and high fishing).We show that different regions of the world can be defined by Anthropogenic Threat Complexes (ATCs), distinguished by different sets of drivers with varying intensities. We identify 11 ATCs that can be used to test hypotheses about patterns of biodiversity and ecosystem change, especially about the joint effects of multiple drivers.Our global analysis highlights the broad conservation priorities needed to mitigate the impacts of anthropogenic change, with different priorities emerging on land and in the ocean, and in different parts of the world.

Although habitat loss is the predominant factor leading to biodiversity loss in the Anthropocene(1,2), exactly how this loss manifests-and at which scales-remains a central debate(3-6). The 'passive sampling' hypothesis suggests that species are lost in proportion to their abundance and distribution in the natural habitat(7,8), whereas the 'ecosystem decay' hypothesis suggests that ecological processes change in smaller and more-isolated habitats such that more species are lost than would have been expected simply through loss of habitat alone(9,10). Generalizable tests of these hypotheses have been limited by heterogeneous sampling designs and a narrow focus on estimates of species richness that are strongly dependent on scale. Here we analyse 123 studies of assemblage-level abundances of focal taxa taken from multiple habitat fragments of varying size to evaluate the influence of passive sampling and ecosystem decay on biodiversity loss. We found overall support for the ecosystem decay hypothesis. Across all studies, ecosystems and taxa, biodiversity estimates from smaller habitat fragments-when controlled for sampling effort-contain fewer individuals, fewer species and less-even communities than expected from a sample of larger fragments. However, the diversity loss due to ecosystem decay in some studies (for example, those in which habitat loss took place more than 100 years ago) was less than expected from the overall pattern, as a result of compositional turnover by species that were not originally present in the intact habitats. We conclude that the incorporation of non-passive effects of habitat loss on biodiversity change will improve biodiversity scenarios under future land use, and planning for habitat protection and restoration.

Disentangling the drivers of diversity gradients can be challenging. The Measurement of Biodiversity (MoB) framework decomposes scale‐dependent changes in species diversity into three components of community structure: the species abundance distribution (SAD), the total community abundance, and the within‐species spatial aggregation. Here we extend MoB from categorical treatment comparisons to quantify variation along continuous geographic or environmental gradients. Our approach requires sites along a gradient, each consisting of georeferenced plots of abundance‐based species composition data. We demonstrate our method using a case study of ants sampled along an elevational gradient of 28 sites in a mixed deciduous forest of the Great Smoky Mountains National Park, USA. MoB analysis revealed that decreases in ant species richness along the elevational gradient were associated with decreasing evenness and total number of species which counteracted the modest increase in richness associated with decreasing spatial aggregation along the gradient. Total community abundance had a negligible effect on richness at all but the finest spatial grains, SAD effects increased in importance with sampling effort, while the aggregation effect had the strongest effect at coarser spatial grains. These results do not support the more‐individuals hypothesis, but they are consistent with a hypothesis of stronger environmental filtering at coarser spatial grains. Our extension of MoB has the potential to elucidate how components of community structure contribute to changes in diversity along environmental gradients and should be useful for a variety of assemblage‐level data collected along gradients.

1.Little consensus has emerged regarding how proximate and ultimate drivers such as productivity, disturbance, and temperature may affect species richness and other aspects of biodiversity. Part of the confusion is that most studies examine species richness at a single spatial scale and ignore how the underlying components of species richness can vary with spatial scale.2.We provide an approach for the measurement of biodiversity (MoB) that decomposes changes in species rarefaction curves into proximate components attributed to: 1) the species abundance distribution, 2) density of individuals, and 3) the spatial arrangement of individuals. We decompose species richness by comparing spatial and nonspatial sample‐ and individual‐based species rarefaction curves that differentially capture the influence of these components to estimate the relative importance of each in driving patterns of species richness change.3.We tested the validity of our method on simulated data, and we demonstrate it on empirical data on plant species richness in invaded and uninvaded woodlands. We integrated these methods into a new R package (mobr).4.The metrics that mobr provides will allow ecologists to move beyond comparisons of species richness in response to ecological drivers at a single spatial scale towards a dissection of the proximate components that determine species richness across scales.

Because biodiversity is multidimensional and scale‐dependent, it is challenging to estimate its change. However, it is unclear (1) how much scale‐dependence matters for empirical studies, and (2) if it does matter, how exactly we should quantify biodiversity change. To address the first question, we analysed studies with comparisons among multiple assemblages, and found that rarefaction curves frequently crossed, implying reversals in the ranking of species richness across spatial scales. Moreover, the most frequently measured aspect of diversity – species richness – was poorly correlated with other measures of diversity. Second, we collated studies that included spatial scale in their estimates of biodiversity change in response to ecological drivers and found frequent and strong scale‐dependence, including nearly 10% of studies which showed that biodiversity changes switched directions across scales. Having established the complexity of empirical biodiversity comparisons, we describe a synthesis of methods based on rarefaction curves that allow more explicit analyses of spatial and sampling effects on biodiversity comparisons. We use a case study of nutrient additions in experimental ponds to illustrate how this multi‐dimensional and multi‐scale perspective informs the responses of biodiversity to ecological drivers.