Biotic interactions and environmental modifications determine symbiotic microbial diversity and stability.

Taking amphibians as island models, we examined the effects of interspecific interaction on the diversity and stability of microbial ecological. As skin area increased, the diversity and stability of skin microbes decreased, but the strength of negative interactions increased significantly. In contrast, as gut area increased, the diversity and stability of gut microbes increased, but the strength of interactions remained constant. These results indicate that microbial interactions are affected by habitat properties. When living in fluctuating environments without strong filtering, microorganisms can enhance their negative interactions with other taxa by changing the pH of their surroundings. In contrast, the pH of the gut is relatively stable, and colonized microorganisms cannot alter the gut pH and inhibit other colonizers. This study demonstrates that in the field of microbiology, diversity and stability are predominantly influenced by the intensity of interspecies interactions. The findings in this study deepen our understanding of microbial diversity and stability and provide a mechanistic link between species interactions, biodiversity, and stability in microbial ecosystems.

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.

Diversity-scaling analysis of human breast milk microbiomes from population perspective.

Quantitative measuring the population-level diversity-scaling of human microbiomes is different from conventional approach to traditional individual-level diversity analysis, and it is of obvious significance. For example, it is well known that individuals are of significant heterogeneity with their microbiome diversities, and the population-level analysis can effectively capture such kind of individual differences. Here we reanalyze a dozen datasets of 2,115 human breast milk microbiome (BMM) samples with diversity-area relationship (DAR) to tackle the previous questions. Our focus on BMM is aimed to offer insights for supplementing the gut microbiome research from nutritional perspective. DAR is an extension to classic species-area relationship, which was discovered in the 19th century and established as one of a handful fundamental laws in community ecology. Our DAR modeling revealed the following numbers, all approximately: (i) The population-level potential diversity of BMM is 1,108 in terms of species richness (number of total species), and 67 in terms of typical species. (ii) On average, an individual carry 17% of population-level diversity in terms of species richness, and 61% in terms of typical species. (iii) The similarity (overlap) between individuals according to pair-wise diversity overlap (PDO) should be approximately 76% in terms of total species, and 92% in terms of typical species, which symbolizes the inter-individual heterogeneity. (iv) The average individual (alpha-) diversity of BMM is approximately 188 (total-species) and 37 (typical-species). (v) To deal with the potential difference among 12 BMM datasets, we conducted DAR modeling separately for each dataset, and then performed permutation tests for DAR parameters. It was found that the DAR scaling parameter that measures inter-individual heterogeneity in diversity is invariant (constant), but the population potential diversity is different among 30% of the pair-wise comparison between 12 BMM datasets. These results offer comprehensive biodiversity analyses of the BMM from host individual, inter-individual, and population level perspectives.

Host-Population Microbial Diversity Scaling of Chinese Gut Microbiomes in Gout Patients.

Gout is a prevalent chronic inflammatory disease that affects the life of tens of millions of people worldwide, and it typically presents as gout arthritis, gout stone, or even kidney damage. Research has revealed its connection with the gut microbiome, although exact mechanism is still unclear. Studies have shown the decline of microbiome diversity in gout patients and change of microbiome compositions between the gout patients and healthy controls. Nevertheless, how diversity changes across host individuals at a cohort (population) level has not been investigated to the best of our knowledge. Here we apply the diversity-area relationship (DAR), which is an extension to the classic SAR (species-area relationship) and establishes the power-function model between microbiome diversity and the number of individuals within cohort, to comparatively investigate diversity scaling (changes) of gut microbiome in gout patients and healthy controls. The DAR modeling with a study involving 83 subjects (41 gout patients) revealed that the potential number of microbial species in gout patients is only 70% of that in the healthy control (2790 vs 3900) although the difference may not be statistically significant. The other DAR parameters including diversity scaling and similarity parameters did not show statistically significant differences. We postulate that the high resilience of gut microbiome may explain the lack of significant gout-disease effects on gut microbial diversity at the population level. The lack of statistically significant difference between the gout patients and healthy controls at host population (cohort) level is different from the previous findings at individual level in the existing literature.

Diversity Scaling Analysis of Chinese Gut Microbiomes Across Ethnicities and Lifestyles.

Diversity scaling (changes) of human gut microbiome is important because it measures the inter-individual heterogeneity of diversity and other important parameters of population-level diversity. Understanding the heterogeneity of microbial diversity can be used as a reference for the personalized medicine of microbiome-associated diseases. Similar to diversity per se, diversity scaling may also be influenced by host factors, especially lifestyles and ethnicities. Nevertheless, this important topic regarding Chinese populations has not been addressed, to our best knowledge. Here, we fill the gap by applying a recent extension to the classic species-area relationship (SAR), i.e., diversity-area relationship (DAR), to reanalyze a large dataset of Chinese gut microbiomes covering the seven biggest Chinese ethnic groups (covering > 95% Chinese) living rural and urban lifestyles. Four DAR profiles were constructed to investigate the diversity scaling, diversity overlap, potential maximal diversity, and the ratio of local to global diversity of Chinese gut microbiomes. We discovered the following: (i) The diversity scaling parameters (z) at various taxon levels are little affected by either ethnicity or lifestyles, as exhibited by less than 0.5% differences in pairwise comparisons. (ii) The maximal accrual diversity (potential diversity) exhibited difference in only about 5% of pairwise comparisons, and all of the differences occurred in ethnicity comparisons (i.e., lifestyles had no effects). (iii) Ethnicity seems to have stronger effects than lifestyles across all taxon levels, and this may reflect the reality that China has been experiencing rapid urbanization in the last few decades, while the ethnic-related genetic background may change relatively little during the same period.

Diversity Scaling of Human Digestive Tract (DT) Microbiomes: The Intra-DT and Inter-individual Patterns.

The human gut microbiome has been extensively studied, but its diversity scaling (changes or heterogeneities) along the digestive tract (DT) as well as their inter-individual heterogeneities have not been adequately addressed to the best of our knowledge. Here we fill the gap by applying the diversity-area relationship (DAR), a recent extension to the classic species-area relationship (SAR) in biogeography, by reanalyzing a dataset of over 2000 16s-rRNA microbiome samples obtained from 10 DT sites of over 200 individuals. We sketched out the biogeography "maps" for each of the 10 DT sites by cross-individual DAR analysis, and the intra-DT distribution pattern by cross-DT-site DAR analysis. Regarding the inter-individual biogeography, it was found that all DT sites have the invariant (constant) scaling parameter-all sites possessing the same diversity change rate across individuals, but most sites have different potential diversities, which include the portions of diversity that may be absent locally but present regionally. In the case of this study, the potential diversity of each DT site covers the total diversity of the respective site from all individuals in the cohort. In terms of the genus richness, an average individual hosts approximately 20% of the population-level genus richness (total bacterial genus of a human population). In contrast, in terms of community biodiversity, the percentages of individual over population may exceed 90%. This suggests that the differences between individuals in their DT microbiomes are predominantly in the composition of bacterial species, rather than how their abundances are distributed (i.e., biodiversity). Regarding the intra-DT patterns, the scaling parameter (z) is larger-suggesting that the intra-DT biodiversity changes are larger than inter-individual changes. The higher intra-DT heterogeneity of bacteria diversity, as suggested by larger intra-DT z than the inter-individual heterogeneity, should be expected since the intra-DT heterogeneity reflects the functional differentiations of the DT tract, while the inter-individual heterogeneity (z) reflects the difference of the same DT site across individuals. On average, each DT site contains 21-36% of the genus diversity of the whole DT, and the percentages are even higher in terms of higher taxon levels.

Population-level diversity-disease relationship (p-DDR) in the human microbiome associated diseases.

Diversity-disease relationship (DDR) is a de facto standard analysis in the studies of human microbiome associated diseases (MADs). For example, the species richness or Shannon entropy are routinely compared between the healthy and diseased groups. Nevertheless, the basic scale of the standard diversity analysis is individual subject rather than a cohort or population because the diversity is computed for individual samples, not for the group. Here we aim to expand the current DDR study from individual focus to population level, which can offer important insights for understanding the epidemiology of MADs. We analyzed the diversity-disease relationship at cohort scale based on a collection of 23 datasets covering the major human MADs. Methodologically, we harness the power of a recent extension to the classic species-area relationship (SAR), i.e., the diversity-area relationship (DAR), to achieve the expansion from individual DDR to inter-subject diversity scaling analysis. Specifically, we apply the DAR analysis to estimate and compare the potentially maximal accrual diversities of the healthy and diseases groups, as well as the inter-subject diversity scaling parameters and the individual-to-population diversity ratios. It was shown that, except for the potential diversity (D max) at the cohort level in approximately 5.4% cases of MADs, DAR parameters displayed no significant differences between healthy and diseased treatments. That is, the DAR parameters are rather resilient against MADs, except for the potential diversity in some diseases. We compared our population-level DDR with the existing individual-level DDR patterns and proposed a hypothesis to interpret their differences.

Inter-Individual Diversity Scaling Analysis of the Human Virome With Classic Diversity-Area Relationship (DAR) Modeling.

The human virome is a critical component of the human microbiome, and it is believed to hold the richest diversity within human microbiomes. Yet, the inter-individual scaling (changes) of the human virome has not been formally investigated to the best of our knowledge. Here we fill the gap by applying diversity-area relationship (DAR) modeling (a recent extension to the classic species-area law in biodiversity and biogeography research) for analyzing four large datasets of the human virome with three DAR profiles: DAR scaling (z)-measuring the inter-individual heterogeneity in virome diversity, MAD (maximal accrual diversity: D max ) and LGD ratio (ratio of local diversity to global diversity)-measuring the percentage of individual to population level diversity. Our analyses suggest: (i) The diversity scaling parameter (z) is rather resilient against the diseases as indicated by the lack of significant differences between the healthy and diseased treatments. (ii) The potential maximal accrual diversity (D max ) is less resilient and may vary between the healthy and diseased groups or between different body sites. (iii) The LGD ratio of bacterial communities is much smaller than for viral communities, and relates to the comparatively greater heterogeneity between local vs. global diversity levels found for bacterial-biomes.

Diversity scaling of human vaginal microbial communities.

The composition and diversity of the human vaginal microbial community have been investigated intensively due to the diversity-stability relationship (DSR)-based hypothesis for bacterial vaginosis (BV) etiology, which was first proposed in the 1990s and has received renewed interest in recent years. Nevertheless, diversity changes (scaling) across individuals in a cohort or population have not yet been addressed, which is significant both theoretically and practically. Theoretically, biodiversity scaling is the core of biogeography, and practically, inter-subject heterogeneity is critical for understanding the etiology and epidemiology of human microbiome-associated diseases such as BV. Here we applied the diversity-area relationship (DAR), a recent extension to the classic species-area relationship (SAR), to study diversity scaling of the vaginal microbiome by reanalyzing reported data collected from 1 107 postpartum women. The model used here characterized the power-law (or its extension) relationships between accrued diversity and areas (numbers of individuals), upon which four biogeographic profiles were thus defined. Specifically, we established the DAR profile (relationship between diversity scaling parameter and so-termed diversity order (q)), similarly pair-wise diversity overlap (PDO) profile, maximal accrual diversity (MAD) profile, and ratio of individual-level to population-level diversity (RIP) profile. These four profiles offer valuable tools to assess and predict diversity scaling (changes) in the human vaginal microbiome across individuals, as well as to understand the dynamics of vaginal microbiomes in healthy women.

A test of trophic and functional island biogeography theory with the avifauna of a continental archipelago.

The classical MacArthur-Wilson theory of island biogeography (TIB) emphasizes the role of island area and isolation in determining island biotas, but is neutral with respect to species differences that could affect community assembly and persistence. Recent extensions of island biogeography theory address how functional differences among species may lead to non-random community assembly processes and different diversity-area scaling patterns. First, the trophic TIB considers how diversity scaling varies across trophic position in a community, with species at higher trophic levels being most strongly influenced by island area. Second, further extensions have predicted how trait distributions, and hence functional diversity, should scale with area. Trait-based theory predicts richness-corrected functional diversity should be low on small islands but converge to null on larger islands. Conversely, competitive assembly predicts high diversity on small islands converging to null with increasing size. However, despite mounting interest in diversity-area relationships across different dimensions of diversity, these predictions derived from theory have not been extensively tested across taxa and island systems. Here, we develop and test predictions of the trophic TIB and extensions to functional traits, by examining the diversity-area relationship across multiple trophic ranks and dimensions of avian biodiversity in the Ryukyu archipelago of Japan. We find evidence for a positive species- and phylogenetic diversity-area relationship, but functional diversity was not strongly affected by island area. Counter to the trophic TIB, we found no differences in the slopes of species-area relationships among trophic ranks, although slopes varied among trophic guilds at the same rank. We revealed differential assembly of trophic ranks, with evidence of trait-based assembly of intermediate predators but otherwise neutral community assembly. Our results suggest that niche space differs among trophic guilds of birds, but that differences are mostly not predicted by current extensions of island biogeography theory. While predicted patterns do not fit the empirical data well in this case, the development of such theory provides a useful framework to analyse island patterns from new perspectives. The application of empirical datasets such as ours should help provide a basis for developing further iterations of island biogeography theory.

Global Microbiome Diversity Scaling in Hot Springs With DAR (Diversity-Area Relationship) Profiles.

The spatial distribution of biodiversity (i.e., the biogeography) of the hot-spring microbiome is critical for understanding the microbial ecosystems in hot springs. We investigated the microbiome diversity scaling (changes) over space by analyzing the diversity-area relationship (DAR), which is an extension to classic SAR (species-area relationship) law in biogeography. We built DAR models for archaea and bacteria with 16S-rRNA sequencing datasets from 165 hot springs globally. From the DAR models, we sketch out the biogeographic maps of hot-spring microbiomes by constructing: (i) DAR profile-measuring the archaea or bacteria diversity scaling over space (areas); (ii) PDO (pair-wise diversity overlap or similarity) profile-estimating the PDO between two hot springs; (iii) MAD (maximal accrual diversity) profile-predicting the global MAD; (iv) LRD/LGD (ratio of local diversity to regional or global diversity) profile. We further investigated the differences between archaea and bacteria in their biogeographic maps. For example, the comparison of DAR-profile maps revealed that the archaea diversity is more heterogeneous (i.e., more diverse) or scaling faster than the bacterial diversity does in terms of species numbers (species richness), but is less heterogeneous (i.e., less diverse) or scaling slower than bacteria when the diversity (Hill numbers) were weighted in favor of more abundant dominant species. When the diversity is weighted equally in terms of species abundances, archaea, and bacteria are equally heterogeneous over space or scaling at the same rate. Finally, unified DAR models (maps) were built with the combined datasets of archaea and bacteria.

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.

DAR (diversity-area relationship): Extending classic SAR (species-area relationship) for biodiversity and biogeography analyses.

I extend the classic SAR, which has achieved status of ecological law and plays a critical role in global biodiversity and biogeography analyses, to general DAR (diversity-area relationship). The extension was aimed to remedy a serious application limitation of the traditional SAR that only addressed one aspect of biodiversity scaling-species richness scaling over space, but ignoring species abundance information. The extension was further inspired by a recent consensus that Hill numbers offer the most appropriate measures for alpha-diversity and multiplicative beta-diversity. In particular, Hill numbers are essentially a series of Renyi's entropy values weighted differently along the rare-common-dominant spectrum of species abundance distribution and are in the units of effective number of species (or species equivalents such as OTUs). I therefore postulate that Hill numbers should follow the same or similar law of the traditional SAR. I test the postulation with the American gut microbiome project (AGP) dataset of 1,473 healthy North American individuals. I further propose three new concepts and develop their statistical estimation formulae based on the new DAR extension, including: (i) DAR profile-z-q relationship (DAR scaling parameter z at different diversity order q), (ii) PDO (pair-wise diversity overlap) profile-g-q relationship (PDO parameter g at order q, and (iii) MAD (maximal accrual diversity: D max) profile-D max-q. While the classic SAR is a special case of our new DAR profile, the PDO and MAD profiles offer novel tools for analyzing biodiversity (including alpha-diversity and beta-diversity) and biogeography over space.

Assessing and Interpreting the Within-Body Biogeography of Human Microbiome Diversity.

A human body hosts a relatively independent microbiome including five major regional biomes (i.e., airway, oral, gut, skin, and urogenital). Each of them may possess different regional characteristics with important implications to our health and diseases (i.e., so-termed microbiome associated diseases). Nevertheless, these regional microbiomes are connected with each other through diffusions and migrations. Here, we investigate the within-body (intra-individual) distribution feature of microbiome diversity via diversity area relationship (DAR) modeling, which, to the best of our knowledge, has not been systematically studied previously. We utilized the Hill numbers for measuring alpha and beta-diversities and built 1,200 within-body DAR models with to date the most comprehensive human microbiome datasets of 18 sites from the human microbiome project (HMP) cohort. We established the intra-DAR profile (z-q pattern: the diversity scaling parameter z of the power law (PL) at diversity order q = 0-3), intra-PDO (pair-wise diversity overlap) profile (g-q), and intra-MAD (maximal accrual diversity) profile (D max-q) for the within-body biogeography of the human microbiome. These profiles constitute the "maps" of the within-body biogeography, and offer important insights on the within-body distribution of the human microbiome. Furthermore, we investigated the heterogeneity among individuals in their biogeography parameters and found that there is not an "average Joe" that can represent majority of individuals in a cohort or population. For example, we found that most individuals in the HMP cohort have relatively lower maximal accrual diversity (MAD) or in the "long tail" of the so-termed power law distribution. In the meantime, there are a small number of individuals in the cohort who possess disproportionally higher MAD values. These findings may have important implications for personalized medicine of the human microbiome associated diseases in practice, besides their theoretical significance in microbiome research such as establishing the baseline for the conservation of human microbiome.

Effects of patch size and basal area on avian taxonomic and functional diversity in pine forests: Implication for the influence of habitat quality on the species-area relationship.

Relationships between avian diversity and habitat area are assumed to be positive; however, often little attention has given to how these relationships can be influenced by the habitat structure or quality. In addition, other components of biodiversity, such as functional diversity, are often overlooked in assessing habitat patch value. In the Sandhills Ecoregion of Georgia, USA, we investigated the relationship between avian species richness and functional diversity, forest basal area, and patch size in pine forests using basal area as a surrogate for overstory structure which in turn impacts vegetation structure and determines habitat quality within a patch. We conducted bird surveys in planted mature pine stands, during breeding season of 2011. We used three classes of stand basal area (BA): OS, overstocked (BA ≥ 23 m2/ha); FS, fully/densely stocked (13.8 m2/ha ≤ BA < 23 m2/ha); and MS, moderately stocked (2.3 m2/ha ≤ BA < 13.8 m2/ha). MS patches showed more structural diversity due to higher herbaceous vegetation cover than other two pine stocking classes of patches. Total species richness and functional richness increased with the size of MS patches, whereas functional divergence decreased with the size of OS patches (p < 0.05). Functional richness tended to be lower than expected as the size of OS patches increased. Greater richness of pine-grassland species was also found at MS patches. Percent cover of MS patches within a landscape influenced positively the richness of pine-grassland species (p < 0.05). Our results suggest that (a) avian species-habitat area relationship can be affected by habitat quality (structural diversity) and varies depending on diversity indices considered, and (b) it is important to maintain moderate or low levels of pine basal area and to preserve large-sized patches of the level of basal area to enhance both taxonomic and functional diversity in managed pine forests.

Semen Microbiome Biogeography: An Analysis Based on a Chinese Population Study.

Investigating inter-subject heterogeneity (or spatial distribution) of human semen microbiome diversity is of important significance. Theoretically, the spatial distribution of biodiversity constitutes the core of microbiome biogeography. Practically, the inter-subject heterogeneity is crucial for understanding the normal (healthy) flora of semen microbiotas as well as their possible changes associated with abnormal fertility. In this article, we analyze the scaling (changes) of semen microbiome diversity across individuals with DAR (diversity-area relationship) analysis, a recent extension to classic SAR (species-area relationship) law in biogeography and ecology. Specifically, the unit of "area" is individual subject, and the microbial diversity in seminal fluid of an individual (area) is assessed via metagenomic DNA sequencing technique and measured in the Hill numbers. The DAR models were then fitted to the accrued diversity across different number of individuals (area size). We further tested the difference in DAR parameters among the healthy, subnormal, and abnormal microbiome samples in terms of their fertility status based on a cross-sectional study of a Chinese cohort. Given that no statistically significant differences in the DAR parameters were detected among the three groups, we built unified DAR models for semen microbiome by combining the healthy, subnormal, and abnormal groups. The model parameters were used to (i) estimate the microbiome diversity scaling in a population (cohort), and construct the so-termed DAR profile; (ii) predict/construct the maximal accrual diversity (MAD) profile in a population; (iii) estimate the pair-wise diversity overlap (PDO) between two individuals and construct the PDO profile; (iv) assess the ratio of individual diversity to population (RIP) accrual diversity. The last item (RIP) is a new concept we propose in this study, which is essentially a ratio of local diversity to regional or global diversity (LRD/LGD), applicable to general biodiversity investigation beyond human microbiome.

Spectral diversity area relationships for assessing biodiversity in a wildland-agriculture matrix.

Species-area relationships have long been used to assess patterns of species diversity across scales. Here, this concept is extended to spectral diversity using hyperspectral data collected by NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) over western Michigan. This mixture of mesic forest and agricultural lands offers two end-points on the local-scale diversity continuum; one set of well-mixed forest patches and one set of highly homogeneous agricultural patches. Using the sum of the first three principal component values and the principal components' convex hull volume, spectral diversity was compared within and among these plots and to null expectations for perfectly random and perfectly patchy landscapes. Overall, the spectral diversity-area relationship confirms the patterns that would be expected for this landscape, but this application suggests that this approach could be extended to less well-understood landscapes and could reveal key insights about the relative importance of different drivers of community assembly, even in the absence of additional data about plant functional traits or species' identities.

Selective extinction drives taxonomic and functional alpha and beta diversities in island bird assemblages.

Taxonomic diversity considers all species being equally different from each other and thus disregards species' different ecological functions. Exploring taxonomic and functional aspects of biodiversity simultaneously can better understand the processes of community assembly. We analysed taxonomic and functional alpha and beta diversities of breeding bird assemblages on land-bridge islands in the Thousand Island Lake, China. Given the high dispersal ability of most birds at this spatial scale (several kilometres), we predicted (i) selective extinction driving alpha and beta diversities after the creation of land-bridge islands of varying area and (ii) low taxonomic and functional beta diversities that were not correlated to spatial distance. Breeding birds were surveyed on 37 islands annually from 2007 to 2014. We decomposed beta diversity of breeding birds into spatial turnover and nestedness-resultant components, and related taxonomic and functional diversities to island area and isolation using power regression models (for alpha diversity) and multiple regression models on distance matrices (for beta diversity). We then ran simulations to assess the strength of the correlations between taxonomic and functional diversities. Results revealed that both taxonomic and functional alpha diversities increased with island area. The taxonomic nestedness-resultant and turnover components increased and decreased with difference in area, respectively, but functional counterparts did not. Isolation played a minor role in explaining alpha- and beta-diversity patterns. By partitioning beta diversity, we found low levels of overall taxonomic and functional beta diversities. The functional nestedness-resultant component dominated overall functional beta diversity, whereas taxonomic turnover was the dominant component for taxonomic beta diversity. The simulation showed that functional alpha and beta diversities were significantly correlated with taxonomic diversities, and the observed values of correlations were significantly different from null expectations of random extinction. Our assessment of island bird assemblages validated the predictions of no distance effects and low beta diversity due to pervasive dispersal events among islands and also suggested that selective extinction drives taxonomic and functional alpha and beta diversities. The contrasting turnover and nestedness-resultant components of taxonomic and functional beta diversities demonstrate the importance of considering the multifaceted nature of biodiversity when examining community assembly.

Multifaceted diversity-area relationships reveal global hotspots of mammalian species, trait and lineage diversity.

AIM: To define biome-scale hotspots of phylogenetic and functional mammalian biodiversity (PD and FD, respectively) and compare them to 'classical' hotspots based on species richness (SR) only. LOCATION: Global. METHODS: SR, PD & FD were computed for 782 terrestrial ecoregions using distribution ranges of 4616 mammalian species. We used a set of comprehensive diversity indices unified by a recent framework that incorporates the species relative coverage in each ecoregion. We build large-scale multifaceted diversity-area relationships to rank ecoregions according to their levels of biodiversity while accounting for the effect of area on each diversity facet. Finally we defined hotspots as the top-ranked ecoregions. RESULTS: While ignoring species relative coverage led to a relative good congruence between biome top ranked SR, PD and FD hotspots, ecoregions harboring a rich and abundantly represented evolutionary history and functional diversity did not match with top ranked ecoregions defined by species richness. More importantly PD and FD hotspots showed important spatial mismatches. We also found that FD and PD generally reached their maximum values faster than species richness as a function of area. MAIN CONCLUSIONS: The fact that PD/FD reach faster their maximal value than SR may suggest that the two former facets might be less vulnerable to habitat loss than the latter. While this point is expected, it is the first time that it is quantified at global scale and should have important consequences in conservation. Incorporating species relative coverage into the delineation of multifaceted hotspots of diversity lead to weak congruence between SR, PD and FD hotspots. This means that maximizing species number may fail at preserving those nodes (in the phylogenetic or functional tree) that are relatively abundant in the ecoregion. As a consequence it may be of prime importance to adopt a multifaceted biodiversity perspective to inform conservation strategies at global scale.