Half of global islands have reached critical area thresholds for undergoing rapid increases in biological invasions.

Biological invasions are among the threats to global biodiversity and social sustainability, especially on islands. Identifying the threshold of area at which non-native species begin to increase abruptly is crucial for early prevention strategies. The small-island effect (SIE) was proposed to quantify the nonlinear relationship between native species richness and area but has not yet been applied to non-native species and thus to predict the key breakpoints at which established non-native species start to increase rapidly. Based on an extensive global dataset, including 769 species of non-native birds, mammals, amphibians and reptiles established on 4277 islands across 54 archipelagos, we detected a high prevalence of SIEs across 66.7% of archipelagos. Approximately 50% of islands have reached the threshold area and thus may be undergoing a rapid increase in biological invasions. SIEs were more likely to occur in those archipelagos with more non-native species introduction events, more established historical non-native species, lower habitat diversity and larger archipelago area range. Our findings may have important implications not only for targeted surveillance of biological invasions on global islands but also for predicting the responses of both non-native and native species to ongoing habitat fragmentation under sustained land-use modification and climate change.

Phenotypic limits of crop diversity: a data exploration of functional trait space.

Relationships between crop genetic and functional diversity are key to addressing contemporary agricultural challenges. Yet, there are few approaches for quantifying the relationship between genetic diversity and crop functional trait expression. Here, we introduce 'functional space accumulation curves' to analyze how trait space increases with the number of crop genotypes within a species. We explore the potential for functional space accumulating curves to quantify genotype-trait space relationships in four common annual crop species: barley (Hordeum vulgare), rice (Oryza sativa), soybean (Glycine max), and durum wheat (Triticum durum). We also employ these curves to describe genotype-trait space relationships in the wild annual Arabidopsis thaliana, which has not been subjected to artificial selection. All five species exhibited asymptotic functional space accumulation curves, suggesting a limit to intraspecific functional crop diversity, likely due to: dominant phenotypes represented by several genotypes; or functional redundancy that might exist among genotypes. Our findings indicate that there is a diminishing return of functional diversity with increasing number of genotypes. Our analysis demonstrates the efficacy of functional space accumulation curves in quantifying trait space occupancy of crops, with implications for managing crop diversity in agroecosystems, and genetic diversity in crop breeding programs.

A global analysis of avian island diversity-area relationships in the Anthropocene.

Research on island species-area relationships (ISAR) has expanded to incorporate functional (IFDAR) and phylogenetic (IPDAR) diversity. However, relative to the ISAR, we know little about IFDARs and IPDARs, and lack synthetic global analyses of variation in form of these three categories of island diversity-area relationship (IDAR). Here, we undertake the first comparative evaluation of IDARs at the global scale using 51 avian archipelagic data sets representing true and habitat islands. Using null models, we explore how richness-corrected functional and phylogenetic diversity scale with island area. We also provide the largest global assessment of the impacts of species introductions and extinctions on the IDAR. Results show that increasing richness with area is the primary driver of the (non-richness corrected) IPDAR and IFDAR for many data sets. However, for several archipelagos, richness-corrected functional and phylogenetic diversity changes linearly with island area, suggesting that the dominant community assembly processes shift along the island area gradient. We also find that archipelagos with the steepest ISARs exhibit the biggest differences in slope between IDARs, indicating increased functional and phylogenetic redundancy on larger islands in these archipelagos. In several cases introduced species seem to have 're-calibrated' the IDARs such that they resemble the historic period prior to recent extinctions.

The role of continental shelf bathymetry in shaping marine range shifts in the face of climate change.

As a consequence of anthropogenic climate change, marine species on continental shelves around the world are rapidly shifting deeper and poleward. However, whether these shifts deeper and poleward will allow species to access more, less, or equivalent amounts of continental shelf area and associated critical habitats remains unclear. By examining the proportion of seabed area at a range of depths for each large marine ecosystem (LME), we found that shelf area declined monotonically for 19% of LMEs examined. However, the majority exhibited a greater proportion of shelf area in mid-depths or across several depth ranges. By comparing continental shelf area across 2° latitudinal bands, we found that all coastlines exhibit multiple instances of shelf area expansion and contraction, which have the potential to promote or restrict poleward movement of marine species. Along most coastlines, overall shelf habitat increases or exhibits no significant change moving towards the poles. The exception is the Southern West Pacific, which experiences an overall loss of area with increasing latitude. Changes in continental shelf area availability across latitudes and depths are likely to affect the number of species local ecosystems can support. These geometric analyses help identify regions of conservation priority and ecological communities most likely to face attrition or expansion due to variations in available area.

Species accumulation in small-large vs large-small order: more species but not all species?

Although groups of small habitat patches often support more species than large patches of equal total area, their biodiversity value remains controversial. An important line of evidence in this debate compares species accumulation curves, where patches are ordered from small-large and large-small (aka 'SLOSS analysis'). However, this method counts species equally and is unable to distinguish patch size dependence in species' occupancies. Moreover, because of the species-area relationship, richness differences typically only contribute to accumulation in small-large order, maximizing the probability of adding species in this direction. Using a null model to control for this, I tested 202 published datasets from archipelagos, habitat islands and fragments for patch size dependence in species accumulation and compared conclusions regarding relative species accumulation with SLOSS analysis. Relative to null model expectations, species accumulation was on average 2.7% higher in large-small than small-large order. The effect was strongest in archipelagos (5%), intermediate for fragments (1.5%) and smallest for habitat islands (1.1%). There was no difference in effect size among taxonomic groups, but each shared this same trend. Results suggest most meta-communities include species that either prefer, or depend upon, larger habitat patches. Relative to SLOSS analysis, null models found lower frequency of greater small-patch importance for species representation (e.g., for fragments: 69 vs 16% respectively) and increased frequency for large patches (fragments: 3 vs 25%). I suggest SLOSS analysis provides unreliable inference on species accumulation and the outcome largely depends on island species-area relationships, not the relative diversity value of small vs large patches.

A global model of island species-area relationships.

The increase in species richness with island area (ISAR) is a well-established global pattern, commonly described by the power model, the parameters of which are hypothesized to vary with system isolation and to be indicative of ecological process regimes. We tested a structural equation model of ISAR parameter variation as a function of taxon, isolation, and archipelago configuration, using a globally distributed dataset of 151 ISARs encompassing a range of taxa and archipelago types. The resulting models revealed a negative relationship between ISAR intercept and slope as a function of archipelago species richness, in turn shaped by taxon differences and by the amount and disposition of archipelago area. These results suggest that local-scale (intra-archipelago) processes have a substantial role in determining ISAR form, obscuring the diversity patterns predicted by island theory as a function of archipelago isolation. These findings have implications for the use and interpretation of ISARs as a tool within biogeography, ecology, and conservation.

Area, isolation and climate explain the diversity of mammals on islands worldwide.

Insular biodiversity is expected to be regulated differently than continental biota, but their determinants remain to be quantified at a global scale. We evaluated the importance of physical, environmental and historical factors on mammal richness and endemism across 5592 islands worldwide. We fitted generalized linear and mixed models to accommodate variation among biogeographic realms and performed analyses separately for bats and non-volants. Richness on islands ranged from one to 234 species, with up to 177 single island endemics. Diversity patterns were most consistently influenced by the islands' physical characteristics. Area positively affected mammal diversity, in particular the number of non-volant endemics. Island isolation, both current and past, was associated with lower richness but greater endemism. Flight capacity modified the relative importance of past versus current isolation, with bats responding more strongly to current and non-volant mammals to past isolation. Biodiversity relationships with environmental factors were idiosyncratic, with a tendency for greater effects sizes with endemism than richness. The historical climatic change was positively associated with endemism. In line with theory, we found that area and isolation were among the strongest drivers of mammalian biodiversity. Our results support the importance of past conditions on current patterns, particularly of non-volant species.

The spatial scaling of food web structure across European biogeographical regions.

The species-area relationship (SAR) is one of the most well-established scaling patterns in ecology. Its implications for understanding how communities change across spatial gradients are numerous, including the effects of habitat loss on biodiversity. However, ecological communities are not mere collections of species. They are the result of interactions between these species forming complex networks that tie them together. Should we aim to grasp the spatial scaling of biodiversity as a whole, it is fundamental to understand the changes in the structure of interaction networks with area. In spite of a few empirical and theoretical studies that address this challenge, we still do not know much about how network structure changes with area, or what are the main environmental drivers of these changes. Here, using the meta-network of potential interactions between all terrestrial vertebrates in Europe (1140 species and 67 201 feeding interactions), we analysed network-area relationships (NARs) that summarize how network properties scale with area. We do this across ten biogeographical regions, which differ in environmental characteristics. We found that the spatial scaling of network complexity strongly varied across biogeographical regions. However, once the variation in SARs was accounted for, differences in the shape of NARs vanished. On the other hand, the proportion of species across trophic levels remained remarkably constant across biogeographical regions and spatial scales, despite the great variation in species richness. Spatial variation in mean annual temperature and habitat clustering were the main environmental determinants of the shape of both SARs and NARs across Europe. Our results suggest new avenues in the exploration of the effects of environmental factors on the spatial scaling of biodiversity. We argue that NARs can provide new insights to analyse and understand ecological communities.

Estimating non-native plant richness with a species-accumulation model along roads.

Monitoring non-native plant richness is important for biodiversity conservation and scientific research. The species-area model (SA model) has been used frequently to estimate the total species richness within a region. However, the conventional SA model may not provide robust estimations of non-native plant richness because the ecological processes associated with the accumulation of exotic and native plants may differ. Because roads strongly dictate the distributions of exotic plants, we propose a species-accumulation model along roads (SR model), rather than an SA model, to estimate the non-native plant richness within a region. Using 270 simulated data sets, we compared the differences in performance between the SR and SA models. A decision tree based on prediction accuracy was created to guide model application, which was validated using field data from 3 national nature reserves in 3 different provinces in China. The SR model significantly outperformed the SA model when non-native species were restricted to the roadsides and the proportion of uncommon exotic species was small. More importantly, the SR model accurately estimated the non-native plant richness in all field sites with an error of <1 species per site. We believe our new model meets the practical need to efficiently and robustly estimate non-native plant richness, which may facilitate effective biodiversity conservations and promote research on non-native plant invasion and vegetation dynamics.

Estimación de la Riqueza de Plantas No Nativas Mediante un Modelo de Acumulación de Especies a lo Largo de las Carreteras Resumen El monitoreo de la riqueza de especies no nativas es importante para la conservación de la biodiversidad y para la investigación científica. El modelo de especie-área (modelo EA) se ha utilizado frecuentemente para estimar la riqueza total de especies dentro de una región. Sin embargo, el modelo EA convencional puede no proporcionar estimaciones sólidas de la riqueza de plantas no nativas porque pueden diferir los procesos ecológicos asociados con la acumulación de plantas exóticas y nativas. Ya que las carreteras dictan con mucha fuerza la distribución de las plantas exóticas, proponemos un modelo de acumulación de especies a lo largo de las carreteras (modelo RE) en lugar de un modelo EA para estimar la riqueza de plantas no nativas dentro de una región. Usamos 270 conjuntos de datos simulados para comparar las diferencias en el desempeño entre los modelos RE y EA. Creamos un árbol de decisión con base en la precisión para guiar la aplicación del modelo, lo cual después se validó con datos de campo de tres reservas naturales en tres provincias diferentes de China. El modelo RE tuvo un desempeño considerablemente mejor que el modelo EA cuando las especies no nativas estuvieron restringidas a las orillas de la carretera y la proporción de las especies exóticas poco comunes fue pequeña. Más importante todavía, el modelo SR estimó con exactitud la riqueza de plantas no nativas en todos los sitios de campo con un error de <1 especie por sitio. Creemos que nuestro nuevo modelo cumple con la necesidad práctica para estimar eficiente y sólidamente la riqueza de plantas no nativas, lo que puede facilitar la conservación efectiva de la biodiversidad y promover la investigación sobre la invasión de plantas no nativas y las dinámicas de la vegetación.

Extinction rate of discovered and undiscovered plants in Singapore.

Extinction is a key issue in the assessment of global biodiversity. However, many extinction rate measures do not account for species that went extinct before they could be discovered. The highly developed island city-state of Singapore has one of the best-documented tropical floras in the world. This allowed us to estimate the total rate of floristic extinctions in Singapore since 1822 after accounting for sampling effort and crypto extinctions by collating herbaria records. Our database comprised 34,224 specimens from 2076 native species, of which 464 species (22%) were considered nationally extinct. We assumed that undiscovered species had the same annual per-species extinction rates as discovered species and that no undiscovered species remained extant. With classical and Bayesian algorithms, we estimated that 304 (95% confidence interval, 213-414) and 412 (95% credible interval, 313-534) additional species went extinct before they could be discovered, respectively; corresponding total extinction rate estimates were 32% and 35% (range 30-38%). We detected violations of our 2 assumptions that could cause our extinction estimates, particularly the absolute numbers, to be biased downward. Thus, our estimates should be treated as lower bounds. Our results illustrate the possible magnitudes of plant extirpations that can be expected in the tropics as development continues.

Tasa de Extinción de Plantas Descubiertas y No Descubiertas en Singapur Resumen La extinción es un tema importante para la valoración de la biodiversidad global. Sin embargo, muchas medidas de la tasa de extinción no consideran a las especies que se extinguieron antes de que pudieran ser descubiertas. Singapur, la ciudad-estado isleña altamente desarrollada, tiene una de las floras mejor documentadas del mundo. Esto nos permitió estimar la tasa total de las extinciones florísticas en Singapur desde 1822 después de considerar el esfuerzo de muestreo y las criptoextinciones cuando recopilamos los registros de herbarios. Nuestra base de datos incluyó 34,224 especímenes de unas 2,076 especies nativas, de las cuales 464 especies (22%) estaban consideradas como extintas a nivel nacional. Asumimos que las especies no descubiertas tuvieron la misma tasa anual de extinción por especie que las especies descubiertas y que ninguna especie no descubierta permanecía en existencia. Con algoritmos clásicos y bayesianos, respectivamente, estimamos que 304 (95% IC 213-414) y 412 (95% IC 313-534) especies adicionales se extinguieron antes de que fueran descubiertas; las estimaciones correspondientes de la tasa de extinción total fueron 32% y 35% (rango de 30-38%). Detectamos violaciones en nuestras dos suposiciones que podrían causar que nuestras estimaciones de extinción, particularmente los números absolutos, tuvieran un sesgo hacia abajo. Por lo tanto, nuestras estimaciones deberían ser tratadas como límites inferiores. Nuestros resultados ilustran las magnitudes posibles de las extirpaciones de plantas que pueden esperarse en los trópicos conforme el desarrollo continúa.

Larger Area Facilitates Richness-Function Effects in Experimental Microcosms.

Species-area relationships (SAR) and biodiversity-ecosystem function (BEF) relationships are central patterns in community ecology. Although research on both patterns often invokes mechanisms of community assembly, both SARs and BEFs are generally treated as separate phenomena. Here we link the two by creating an experimental SAR in microcosm communities and show that greater species richness in larger areas is accompanied by greater ecosystem function. We then explore mechanisms of community assembly by determining whether rare, large, or high-biomass species are more likely to persist in the larger microcosms. Our results indicate that larger areas harbor more rare species of a wider range of body sizes and have higher functional diversity, implying that the addition of niche space that supports rare species underlies the effect of area on species richness and function. Our results suggest that the preservation of large areas is a potentially useful way of maximizing the provisioning of ecosystem services through the maintenance of biodiversity.

Genetic Diversity-Area Relationships across Bird Species.

The genetic diversity-area relationship (GAR), compared with the extensively explored species-area relationship (SAR), remains poorly recognized despite the importance of understanding it for the development and application of biodiversity theory. It has been hypothesized that maintaining genetic diversity within a population is mechanistically similar to maintaining species diversity within a community, implying that GAR trajectories should behave mathematically as SAR ones. Here we test this prediction by fitting microsatellite heterozygosity and allelic richness in relation to distribution range size across bird species against eight well-known SAR models. The Monod model best described the data on resident and migratory species combined and especially the data on resident species only, showing that with increasing range size, genetic diversity across species rapidly increased up to a certain level and then tended toward an asymptote. None of the candidate models provided an adequate fit for the data on migratory species, likely because their breeding range size mostly is large in that a GAR curve has become flat. Our work takes the first step toward formulating GARs and applying them to predicting the effect of habitat fragmentation on genetic diversity.

A geometric approach to scaling individual distributions to macroecological patterns.

Understanding macroecological patterns across scales is a central goal of ecology and a key need for conservation biology. Much research has focused on quantifying and understanding macroecological patterns such as the species-area relationship (SAR), the endemic-area relationship (EAR) and relative species abundance curve (RSA). Understanding how these aggregate patterns emerge from underlying spatial pattern at individual level, and how they relate to each other, has both basic and applied relevance. To address this challenge, we develop a novel spatially explicit geometric framework to understand multiple macroecological patterns, including the SAR, EAR, RSA, and their relationships. First, we provide a general theory that can be used to derive the asymptotic slopes of the SAR and EAR, and demonstrates the dependency of RSAs on the shape of the sampling region. Second, assuming specific shapes of the sampling region, species geographic ranges, and individual distribution patterns therein based on theory of stochastic point processes, we demonstrate various well-documented macroecological patterns can be recovered, including the tri-phasic SAR and various RSAs (e.g., Fisher's logseries and the Poisson lognormal distribution). We also demonstrate that a single equation unifies RSAs across scales, and provide a new prediction of the EAR. Finally, to demonstrate the applicability of the proposed model to ecological questions, we provide how beta diversity changes with spatial extent and its grain over multiple scales. Emergent macroecological patterns are often attributed to ecological and evolutionary mechanisms, but our geometric approach still can recover many previously observed patterns based on simple assumptions about species geographic ranges and the spatial distribution of individuals, emphasizing the importance of geometric considerations in macroecological studies.

Sketching the Human Microbiome Biogeography with DAR (Diversity-Area Relationship) Profiles.

SAR (species area relationship) is a classic ecological theory that has been extensively investigated and applied in the studies of global biogeography and biodiversity conservation in macro-ecology. It has also found important applications in microbial ecology in recent years thanks to the breakthroughs in metagenomic sequencing technology. Nevertheless, SAR has a serious limitation for practical applications-ignoring the species abundance and treating all species as equally abundant. This study aims to explore the biogeography discoveries of human microbiome over 18 sites of 5 major microbiome habitats, establish the baseline DAR (diversity-area scaling relationship) parameters, and perform comparisons with the classic SAR. The extension from SAR to DAR by adopting the Hill numbers as diversity measures not only overcomes the previously mentioned flaw of SAR but also allows for obtaining a series of important findings on the human microbiome biodiversity and biogeography. Specifically, two types of DAR models were built, the traditional power law (PL) and power law with exponential cutoff (PLEC), using comprehensive datasets from the HMP (human microbiome project). Furthermore, the biogeography "maps" for 18 human microbiome sites using their DAR profiles for assessing and predicting the diversity scaling across individuals, PDO profiles (pair-wise diversity overlap) for measuring diversity overlap (similarity), and MAD profile (for predicting the maximal accrual diversity in a population) were sketched out. The baseline biogeography maps for the healthy human microbiome diversity can offer guidelines for conserving human microbiome diversity and investigating the health implications of the human microbiome diversity and heterogeneity.

Quantifying and sustaining biodiversity in tropical agricultural landscapes.

Decision-makers increasingly seek scientific guidance on investing in nature, but biodiversity remains difficult to estimate across diverse landscapes. Here, we develop empirically based models for quantifying biodiversity across space. We focus on agricultural lands in the tropical forest biome, wherein lies the greatest potential to conserve or lose biodiversity. We explore two questions, drawing from empirical research oriented toward pioneering policies in Costa Rica. First, can remotely sensed tree cover serve as a reliable basis for improved estimation of biodiversity, from plots to regions? Second, how does tropical biodiversity change across the land-use gradient from native forest to deforested cropland and pasture? We report on understory plants, nonflying mammals, bats, birds, reptiles, and amphibians. Using data from 67,737 observations of 908 species, we test how tree cover influences biodiversity across space. First, we find that fine-scale mapping of tree cover predicts biodiversity within a taxon-specific radius (of 30-70 m) about a point in the landscape. Second, nearly 50% of the tree cover in our study region is embedded in countryside forest elements, small (typically 0.05-100 ha) clusters or strips of trees on private property. Third, most species use multiple habitat types, including crop fields and pastures (to which 15% of species are restricted), although some taxa depend on forest (57% of species are restricted to forest elements). Our findings are supported by comparisons of 90 studies across Latin America. They provide a basis for a planning tool that guides investments in tropical forest biodiversity similar to those for securing ecosystem services.

Derivations of the Core Functions of the Maximum Entropy Theory of Ecology.

The Maximum Entropy Theory of Ecology (METE), is a theoretical framework of macroecology that makes a variety of realistic ecological predictions about how species richness, abundance of species, metabolic rate distributions, and spatial aggregation of species interrelate in a given region. In the METE framework, "ecological state variables" (representing total area, total species richness, total abundance, and total metabolic energy) describe macroecological properties of an ecosystem. METE incorporates these state variables into constraints on underlying probability distributions. The method of Lagrange multipliers and maximization of information entropy (MaxEnt) lead to predicted functional forms of distributions of interest. We demonstrate how information entropy is maximized for the general case of a distribution, which has empirical information that provides constraints on the overall predictions. We then show how METE's two core functions are derived. These functions, called the "Spatial Structure Function" and the "Ecosystem Structure Function" are the core pieces of the theory, from which all the predictions of METE follow (including the Species Area Relationship, the Species Abundance Distribution, and various metabolic distributions). Primarily, we consider the discrete distributions predicted by METE. We also explore the parameter space defined by the METE's state variables and Lagrange multipliers. We aim to provide a comprehensive resource for ecologists who want to understand the derivations and assumptions of the basic mathematical structure of METE.

Embracing scale-dependence to achieve a deeper understanding of biodiversity and its change across communities.

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.

A global synthesis of the small-island effect in habitat islands.

Habitat loss and fragmentation are considered to be the leading drivers of biodiversity loss. The small-island effect (SIE) can be used to predict species extinctions resulting from habitat loss and has important implications for species conservation. However, to date, no study has explicitly evaluated the prevalence of SIEs in habitat islands. Here, we compiled 90 global datasets to systematically investigate the prevalence and underlying factors determining the ubiquity of SIEs in habitat island systems. Among the 90 datasets, SIEs were unambiguously detected in 36 cases. We found significant effects of habitat island types and taxon groups on the threshold area of SIEs. The number of islands, area range, species range, island type and taxon group were key variables that determined the prevalence of SIEs. Our study demonstrates that SIEs occur in 40% of cases and thus are common in habitat islands. We conclude that conservation biologists and applied ecologists should consider the prevalence of SIEs when making management strategies in fragmented landscapes.

Loss of only the smallest patches will reduce species diversity in most discrete habitat networks.

Under many global-change scenarios, small habitat patches are the most vulnerable to destruction. For example, smaller ponds are at greater risk in a drying climate and their loss would remove any obligate aquatic individuals present. We asked what proportional loss of species diversity from metacommunities comprised of discrete habitat patches should be expected from attrition (complete loss) of only the smallest patches under such a premise. We analyzed 175 published datasets for different taxonomic groups (vertebrates, invertebrates, and plants) and habitat types (islands, habitat islands, and fragments). We simulated the destruction of only the smallest patches to an approximate 20% of total area (range: 15.2%-24.2%) and analyzed species loss. Mean [± 95% CI] species loss was 12.7% [10.8, 14.6], although 18.3% of datasets lost no species. Four broad patterns of species loss were evident, reflecting underlying differences in minimum area requirements and the degree of species turnover among patches. Regression modeling showed species loss increased with greater species turnover among patches (ßSIM ) and decreased with greater area scaling of diversity (i.e., larger power-law island species-area relationship exponents). Losses also increased with greater numbers of single-patch endemics and with increasing proportions of patches destroyed. After accounting for these predictors, neither taxonomic group nor habitat type increased explained variation in species loss. Attrition of the smallest patches removed species in >80% of metacommunities, despite all larger patches and >75% of total area remaining intact. At both 10% and 20% area reduction, median species loss across all datasets was around 50% higher than predicted from methods based on the species-area relationship. We conclude that any mechanism of global change that selectively destroys small habitat patches will lead to imminent extinctions in most discrete metacommunities.

The relationship between the spatial scaling of biodiversity and ecosystem stability.

AIM: Ecosystem stability and its link with biodiversity have mainly been studied at the local scale. Here we present a simple theoretical model to address the joint dependence of diversity and stability on spatial scale, from local to continental. METHODS: The notion of stability we use is based on the temporal variability of an ecosystem-level property, such as primary productivity. In this way, our model integrates the well-known species-area relationship (SAR) with a recent proposal to quantify the spatial scaling of stability, called the invariability-area relationship (IAR). RESULTS: We show that the link between the two relationships strongly depends on whether the temporal fluctuations of the ecosystem property of interest are more correlated within than between species. If fluctuations are correlated within species but not between them, then the IAR is strongly constrained by the SAR. If instead individual fluctuations are only correlated by spatial proximity, then the IAR is unrelated to the SAR. We apply these two correlation assumptions to explore the effects of species loss and habitat destruction on stability, and find a rich variety of multi-scale spatial dependencies, with marked differences between the two assumptions. MAIN CONCLUSIONS: The dependence of ecosystem stability on biodiversity across spatial scales is governed by the spatial decay of correlations within and between species. Our work provides a point of reference for mechanistic models and data analyses. More generally, it illustrates the relevance of macroecology for ecosystem functioning and stability.