Meta-analysis of the Microbial Diversity Cultured in Bioreactors Simulating the Gut Microbiome

Understanding the intricate ecological interactions within the gut microbiome and unravelling its impact on human health is a challenging task. Bioreactors are valuable tools that have contributed to our understanding of gut microbial ecology. However, there is a lack of studies describing and comparing the microbial diversity cultivated in these models. This knowledge is crucial for refining current models to reflect the gastrointestinal microbiome accurately. In this study, we analysed the microbial diversity of 1512 samples from 18 studies available in public repositories that employed cultures performed in batches and various bioreactor models to cultivate faecal microbiota. Community structure comparison between samples using t-distributed stochastic neighbour embedding and the Hellinger distance revealed a high variation between projects. The main driver of these differences was the inter-individual variation between the donor faecal inocula. Moreover, there was no overlap in the structure of the microbial communities between studies using the same bioreactor platform. In addition, α-diversity analysis using Hill numbers showed that highly complex bioreactors did not exhibit higher diversities than simpler designs. However, analyses of five projects in which the samples from the faecal inoculum were also provided revealed an amplicon sequence variants enrichment in bioreactors compared to the inoculum. Finally, a comparative analysis of the taxonomy of the families detected in the projects and the GMRepo database revealed bacterial families exclusively found in the bioreactor models. These findings highlight the potential of bioreactors to enrich low-abundance microorganisms from faecal samples, contributing to uncovering the gut microbial "dark matter".

Mathematical constraints on a family of biodiversity measures via connections with Rényi entropy

The Hill numbers are statistics for biodiversity measurement in ecological studies, closely related to the Rényi and Shannon entropies from information theory. Recent developments in the mathematics of diversity in the setting of population genetics have produced mathematical constraints that characterize how standard measures depend on the highest-frequency class in a discrete probability distribution. Here, we apply these constraints to diversity statistics in ecology, focusing on the Hill numbers and the Rényi and Shannon entropies. The mathematical bounds can shift perspectives on the diversities of communities, in that when upper and lower bounds on Hill numbers are evaluated in a classic butterfly example, Hill numbers that are initially larger in one community switch positions-so that associated normalized Hill numbers are instead smaller than those of the other community. The new bounds hence add to the tools available for interpreting a commonly used family of statistics for ecological data.

Fewer bowl traps and more hand netting can increase effective number of bee species and reduce excessive captures

Reports increasingly point to substantial declines in wild bee abundance and diversity, yet there is uncertainty about how best to measure these attributes in wild bee populations. Two commonly used methods are passive trapping with bee bowls or active netting of bees on flowers, but each of these has drawbacks. Comparing the outcomes of the two methods is complicated by their uncomparable units of effort. The abundance distribution of bee species is also typically highly skewed, making it difficult to accurately assess diversity when rarer species are unlikely to be caught. The effective number of species, or Hill numbers, provides a way forward by basing the response metric on the number of equally abundant species. Our goal is to compare the effective number of bee species captured between hand netting and bowl trapping in wheatgrass prairie in South Dakota and tallgrass prairie in Minnesota, USA. Species overlap between the two methods ranged from ~40% to ~60%. Emphasis placed on rare species was important, so that 95% confidence limits overlapped between the two methods for species richness but netting exceeded trapping for Shannon's and Simpson's diversities. Netting always captured more bee species with fewer bee individuals than trapping. In most cases, the number of bees captured in bowl traps indicated substantial over-sampling, with little increase in bee species detected. Correlations between bee and floral abundance, richness, and diversity differed between netted and trapped samples. We conclude that netting and trapping together produce a more complete account of species richness, but shifting sampling emphasis from trapping to netting will result in fewer bees, but more bee species captured. Due to the different relationships between bee and floral diversities that depended on sampling method, it is unwise to compare habitat associations determined by netting with those determined by trapping.

A comprehensive diversity analysis on the gut microbiomes of ASD patients: from alpha, beta to gamma diversities

Autism spectrum disorder (ASD) is estimated to influence as many as 1% children worldwide, but its etiology is still unclear. It has been suggested that gut microbiomes play an important role in regulating abnormal behaviors associated with ASD. A de facto standard analysis on the microbiome-associated diseases has been diversity analysis, and nevertheless, existing studies on ASD-microbiome relationship have not produced a consensus. Here, we perform a comprehensive analysis of the diversity changes associated with ASD involving alpha-, beta-, and gamma-diversity metrics, based on 8 published data sets consisting of 898 ASD samples and 467 healthy controls (HC) from 16S-rRNA sequencing. Our findings include: (i) In terms of alpha-diversity, in approximately 1/3 of the studies cases, ASD patients exhibited significantly higher alpha-diversity than the HC, which seems to be consistent with the "1/3 conjecture" of diversity-disease relationship (DDR). (ii) In terms of beta-diversity, the AKP (Anna Karenina principle) that predict all healthy microbiomes should be similar, and every diseased microbiome should be dissimilar in its own way seems to be true in approximately 1/2 to 3/4 studies cases. (iii) In terms of gamma-diversity, the DAR (diversity-area relationship) modeling suggests that ASD patients seem to have large diversity-area scaling parameter than the HC, which is consistent with the AKP results. However, the MAD (maximum accrual diversity) and RIP (ratio of individual to population diversity) parameters did not suggest significant differences between ASD patients and HC. Throughout the study, we adopted Hill numbers to measure diversity, which stratified the diversity measures in terms of the rarity-commonness-dominance spectrum. It appears that the differences between ASD patients and HC are more propounding on rare-species side than on dominant-species side. Finally, we discuss the apparent inconsistent diversity-ASD relationships among different case studies and postulate that the relationships are not monotonic.

Hill-Chao numbers allow decomposing gamma multifunctionality into alpha and beta components

Biodiversity-ecosystem functioning (BEF) research has provided strong evidence and mechanistic underpinnings to support positive effects of biodiversity on ecosystem functioning, from single to multiple functions. This research has provided knowledge gained mainly at the local alpha scale (i.e. within ecosystems), but the increasing homogenization of landscapes in the Anthropocene has raised the potential that declining biodiversity at the beta (across ecosystems) and gamma scales is likely to also impact ecosystem functioning. Drawing on biodiversity theory, we propose a new statistical framework based on Hill-Chao numbers. The framework allows decomposition of multifunctionality at gamma scales into alpha and beta components, a critical but hitherto missing tool in BEF research; it also allows weighting of individual ecosystem functions. Through the proposed decomposition, new BEF results for beta and gamma scales are discovered. Our novel approach is applicable across ecosystems and connects local- and landscape-scale BEF assessments from experiments to natural settings.

Quantifying mitochondrial heteroplasmy diversity: A computational approach

Biodiversity plays a pivotal role in sustaining ecosystem processes, encompassing diverse biological species, genetic types and the intricacies of ecosystem composition. However, the precise definition of biodiversity at the individual level remains a challenging endeavour. Hill numbers, derived from Rényi's entropy, have emerged as a popular measure of diversity, with a recent unified framework extending their application across various levels, from genetics to ecosystems. In this study, we employ a computational approach to exploring the diversity of mitochondrial heteroplasmy using real-world data. By adopting Hill numbers with q = 2, we demonstrate the feasibility of quantifying mitochondrial heteroplasmy diversity within and between individuals and populations. Furthermore, we investigate the alpha diversity of mitochondrial heteroplasmy among different species, revealing heterogeneity at multiple levels, including mitogenome components and protein-coding genes (PCGs). Our analysis explores large-scale mitochondrial heteroplasmy data in humans, examining the relationship between alpha diversity at the mitogenome components and PCGs level. Notably, we do not find a significant correlation between these two levels. Additionally, we observe significant correlations in alpha diversity between mothers and children in blood samples, exceeding the reported R2 value for allele frequency correlations. Moreover, our investigation of beta diversity and local overlay similarity demonstrates that heteroplasmy variant distributions in different tissues of children more closely resemble those of their mothers. Through systematic quantification and analysis of mitochondrial heteroplasmy diversity, this study enhances our understanding of heterogeneity at multiple levels, from individuals to populations, providing new insights into this fundamental dimension of biodiversity.

Metabarcoding data reveal vertical multitaxa variation in topsoil communities during the colonization of deglaciated forelands

Ice-free areas are expanding worldwide due to dramatic glacier shrinkage and are undergoing rapid colonization by multiple lifeforms, thus representing key environments to study ecosystem development. It has been proposed that the colonization dynamics of deglaciated terrains is different between surface and deep soils but that the heterogeneity between communities inhabiting surface and deep soils decreases through time. Nevertheless, tests of this hypothesis remain scarce, and it is unclear whether patterns are consistent among different taxonomic groups. Here, we used environmental DNA metabarcoding to test whether community diversity and composition of six groups (Eukaryota, Bacteria, Mycota, Collembola, Insecta, and Oligochaeta) differ between the surface (0-5 cm) and deeper (7.5-20 cm) soil at different stages of development and across five Alpine glaciers. Taxonomic diversity increased with time since glacier retreat and with soil evolution. The pattern was consistent across groups and soil depths. For Eukaryota and Mycota, alpha-diversity was highest at the surface. Time since glacier retreat explained more variation of community composition than depth. Beta-diversity between surface and deep layers decreased with time since glacier retreat, supporting the hypothesis that the first 20 cm of soil tends to homogenize through time. Several molecular operational taxonomic units of bacteria and fungi were significant indicators of specific depths and/or soil development stages, confirming the strong functional variation of microbial communities through time and depth. The complexity of community patterns highlights the importance of integrating information from multiple taxonomic groups to unravel community variation in response to ongoing global changes.

Multi-taxon biodiversity responses to the 2019-2020 Australian megafires

Conditions conducive to fires are becoming increasingly common and widespread under climate change. Recent fire events across the globe have occurred over unprecedented scales, affecting a diverse array of species and habitats. Understanding biodiversity responses to such fires is critical for conservation. Quantifying post-fire recovery is problematic across taxa, from insects to plants to vertebrates, especially at large geographic scales. Novel datasets can address this challenge. We use presence-only citizen science data from iNaturalist, collected before and after the 2019-2020 megafires in burnt and unburnt regions of eastern Australia, to quantify the effect of post-fire diversity responses, up to 18 months post-fire. The geographic, temporal, and taxonomic sampling of this dataset was large, but sampling effort and species discoverability were unevenly spread. We used rarefaction and prediction (iNEXT) with which we controlled sampling completeness among treatments, to estimate diversity indices (Hill numbers: q = 0-2) among nine broad taxon groupings and seven habitats, including 3885 species. We estimated an increase in species diversity up to 18 months after the 2019-2020 Australian megafires in regions which were burnt, compared to before the fires in burnt and unburnt regions. Diversity estimates in dry sclerophyll forest matched and likely drove this overall increase post-fire, while no taxon groupings showed clear increases inconsistent with both control treatments post-fire. Compared to unburnt regions, overall diversity across all taxon groupings and habitats greatly decreased in areas exposed to extreme fire severity. Post-fire life histories are complex and species detectability is an important consideration in all post-fire sampling. We demonstrate how fire characteristics, distinct taxa, and habitat influence biodiversity, as seen in local-scale datasets. Further integration of large-scale datasets with small-scale studies will lead to a more robust understanding of fire recovery.

The effects of anthropogenic disturbance and seasonality on the ant communities of Lang Tengah Island

Anthropogenic disturbances and seasonal changes significantly impact diversity and community composition of ants, but their effects are often intertwined. We investigated these drivers on Lang Tengah Island, a location with a pronounced monsoon season and three resorts that close during this period. We surveyed four sites, two disturbed and two undisturbed, before and after the monsoon season, using pitfall traps to sample epigaeic ant communities. Undisturbed habitats had higher species diversity, but both habitats (undisturbed and disturbed sites) have a high proportion of ants with characteristics of being encroached by generalist and invasive/tramp ant species. Post-monsoon sampling yielded an increase in species richness and diversity. Seasonal changes, such as monsoonal rains, can temporarily alter ant interactions and resource distribution, potentially maintaining diversity. Future studies should validate these findings for ant communities under similar pressures, using ant composition and functional roles for conservation and management purposes.

Quantifying and estimating ecological network diversity based on incomplete sampling data

An ecological network refers to the ecological interactions among sets of species. Quantification of ecological network diversity and related sampling/estimation challenges have explicit analogues in species diversity research. A unified framework based on Hill numbers and their generalizations was developed to quantify taxonomic, phylogenetic and functional diversity. Drawing on this unified framework, we propose three dimensions of network diversity that incorporate the frequency (or strength) of interactions, species phylogenies and traits. As with surveys in species inventories, nearly all network studies are based on sampling data and thus also suffer from under-sampling effects. Adapting the sampling/estimation theory and the iNEXT (interpolation/extrapolation) standardization developed for species diversity research, we propose the iNEXT.link method to analyse network sampling data. The proposed method integrates the following four inference procedures: (i) assessment of sample completeness of networks; (ii) asymptotic analysis via estimating the true network diversity; (iii) non-asymptotic analysis based on standardizing sample completeness via rarefaction and extrapolation with network diversity; and (iv) estimation of the degree of unevenness or specialization in networks based on standardized diversity. Interaction data between European trees and saproxylic beetles are used to illustrate the proposed procedures. The software iNEXT.link has been developed to facilitate all computations and graphics. This article is part of the theme issue 'Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'.

How does variation in total and relative abundance contribute to gradients of species diversity?

Patterns of biodiversity provide insights into the processes that shape biological communities around the world. Variation in species diversity along biogeographical or ecological gradients, such as latitude or precipitation, can be attributed to variation in different components of biodiversity: changes in the total abundance (i.e., more-individual effects) and changes in the regional species abundance distribution (SAD). Rarefaction curves can provide a tool to partition these sources of variation on diversity, but first must be converted to a common unit of measurement. Here, we partition species diversity gradients into components of the SAD and abundance using the effective number of species (ENS) transformation of the individual-based rarefaction curve. Because the ENS curve is unconstrained by sample size, it can act as a standardized unit of measurement when comparing effect sizes among different components of biodiversity change. We illustrate the utility of the approach using two data sets spanning latitudinal diversity gradients in trees and marine reef fish and find contrasting results. Whereas the diversity gradient of fish was mostly associated with variation in abundance (86%), the tree diversity gradient was mostly associated with variation in the SAD (59%). These results suggest that local fish diversity may be limited by energy through the more-individuals effect, while species pool effects are the larger determinant of tree diversity. We suggest that the framework of the ENS-curve has the potential to quantify the underlying factors influencing most aspects of diversity change.

Determining macrophyte species richness and dark diversity sources - A novel approach to improve the biodiversity estimation based on species traits

Biodiversity measures deliver valuable ecological information by reflecting a range of ecosystem processes. However, the accuracy of environmental assessment based on species patterns may often be affected by insufficient survey details. The comprehensive evaluation of plant taxa richness in rivers requires an extensive sampling effort. The use of Hill numbers and Chao estimators improves species diversity assessment based on a feasible number of samples. The aim of this work was to identify macrophyte groups, associated with various species traits, which are rich in species, as well as those whose detection is particularly difficult as it requires an exceptional sampling effort (sources of dark diversity). Analyses were performed with the use of Hill numbers and Chao estimators. It was shown that the field identification of all estimated macrophytes is particularly difficult for low trophy indicators and generally submerged plants, as well as for small-leaved species. A field survey encompassing the full (expected) macrophyte diversity encountered within a river is easiest to perform in the case of free-floating plants and large-leaved macrophytes, as well as for species with high trophic tolerance. The study proved that ecological assessment of rivers based on a small number of sampling units may lead to incorrect diversity estimates. Conversely, the estimation of diversity patterns at the level of the Shannon and Simpson indices does not require extensive sampling, and the extrapolation approach is not needed. The effectiveness of diversity assessment in fluvial ecosystems can be increased by extrapolation of gray diversity which can be considered in planning of monitoring programs. Moreover even estimated dark diversity bight be already efficient to identify ecological pattern and when comparing biodiversity across regions and ecosystems.

The precautionary principle and dietary DNA metabarcoding: Commonly used abundance thresholds change ecological interpretation

Dietary DNA metabarcoding enables researchers to identify and characterize trophic interactions with a high degree of taxonomic precision. It is also sensitive to sources of bias and contamination in the field and laboratory. One of the earliest and most common strategies for dealing with such sensitivities has been to remove all low-abundance sequences and conduct ecological analyses based on the presence or absence of food taxa. Although this step is now often perceived to be necessary, evidence of its sufficiency is lacking and more attention to the risk of introducing other errors is needed. Using computer simulations, we demonstrate that common strategies to remove low-abundance sequences can erroneously eliminate true dietary sequences in ways that impact downstream inferences. Using real data from well-studied wildlife populations in Yellowstone National Park, we further show how these strategies can markedly alter the composition of dietary profiles in ways that scale-up to obscure ecological interpretations about dietary generalism, specialism, and composition. Although the practice of removing low-abundance sequences may continue to be a useful strategy to address research questions that focus on a subset of relatively abundant foods, its continued widespread use risks generating misleading perceptions about the structure of trophic networks. Researchers working with dietary DNA metabarcoding data-or similar data such as environmental DNA, microbiomes, or pathobiomes-should be aware of drawbacks and consider alternative bioinformatic, experimental, and statistical solutions.

Improved quantitative microbiome profiling for environmental antibiotic resistance surveillance

BACKGROUND

Understanding environmental microbiomes and antibiotic resistance (AR) is hindered by over reliance on relative abundance data from next-generation sequencing. Relative data limits our ability to quantify changes in microbiomes and resistomes over space and time because sequencing depth is not considered and makes data less suitable for Quantitative Microbial Risk Assessments (QMRA), critical in quantifying environmental AR exposure and transmission risks.

RESULTS

Here we combine quantitative microbiome profiling (QMP; parallelization of amplicon sequencing and 16S rRNA qPCR to estimate cell counts) and absolute resistome profiling (based on high-throughput qPCR) to quantify AR along an anthropogenically impacted river. We show QMP overcomes biases caused by relative taxa abundance data and show the benefits of using unified Hill number diversities to describe environmental microbial communities. Our approach overcomes weaknesses in previous methods and shows Hill numbers are better for QMP in diversity characterisation.

CONCLUSIONS

Methods here can be adapted for any microbiome and resistome research question, but especially providing more quantitative data for QMRA and other environmental applications.

Land use, but not distance, drives fungal beta diversity

Fungi are one of the most diverse taxonomic groups on the planet, but much of their diversity and community organization remains unknown, especially at local scales. Indeed, a consensus on how fungal communities change across spatial or temporal gradients-beta diversity-remains nascent. Here, we use a data set of plant-associated fungal communities (leaf, root, and soil) across multiple land uses from a New Zealand-wide study to look at fungal community turnover at small spatial scales (<1 km). Using hierarchical Bayesian beta regressions and Hill-number-based diversity profiles, we show that fungal communities are often markedly dissimilar at even small distances, regardless of land use. Moreover, diversity profile plots indicate that leaf, root, and soil-associated communities show different patterns in the dominance or rarity of dissimilar species. Leaf-associated communities differed from site to site in their low-abundance species, whereas root-associated communities differed between sites in the dominant species; soil-associated communities were intermediate. Land-use differences were largely driven by the lower turnover between high-productivity grassland sites. Further, we discuss the implications and benefits of using diversity profile plots of turnover to draw inferences into the mechanisms of how communities are structured across spatial gradients.

Response of benthic invertebrate assemblages to seasonal and habitat condition in the Wewe River, Ashanti region (Ghana)

Aquatic macro-invertebrates play a vital role in the food chain of river ecosystem at several trophic guilds and consumer levels, and are used as biomonitoring tools for aquatic ecosystem health. However, hydrologic conditions of these ecosystems have been severely altered because of the increase in urban development and agricultural expansion. This study examined benthic invertebrate response to processes that structure their community in the Wewe River, segmented into intact, medium, and severe condition zones. We sampled in 100 stations in a period of 4 months in the wet (June–September, 2019) and 3 months in the dry (January–March, 2020) seasons. Geometric series, rarefaction, and Hill numbers models were used to quantify invertebrate assemblages, while ordination technique, canonical correspondence analysis, was used to evaluate the influence of predictive factors on their assemblages. A total of 2,075 individuals belonging to 20 family taxa were registered. There was no significant difference in benthic assemblages between the dry and wet seasons. Predictive factors accounted for 47.04 and 50.84% variances, respectively. Taxa distribution patterns differed significantly only in the severely disturbed zone during the wet season. Neptidae, Libellulidae, and Chironomidae were the most abundant taxa, indicating their broad range habitat preference and their ability to adapt to seasonal changes. Asellidae and Perlidae were the least detected, suggesting their sensitivity to elevated levels of some water quality parameters. The findings highlight the threats to the benthic community and overall functional state of the Wewe River, with the need to consider the proposed conservation interventions indicated in this study.

Bacterial communities of the Salvia lyrata rhizosphere explained by spatial structure and sampling grain

Advancements in molecular technology have reduced the constraints that the grain of observation, or the spatial resolution and volume of the sampling unit, has on the characterization of plant-associated microbiomes. With discrete ecological sampling and massive parallel sequencing, we can more precisely portray microbiome community assembly and microbial recruitment to host tissue over space and time. Here, we differentiate rarefied community richness and relative abundance in bacterial microbiomes of Salvia lyrata dependent on three spatial depths, which are discrete physical distances from the soil surface within the rhizosphere microhabitat as a proxy for the root system zones. To assess the impact of sampling grain on rarefied community richness and relative abundance, we evaluated the variation of these metrics between samples pooled prior to DNA extraction and samples pooled after sequencing. A distance-based redundancy analysis with the quantitative Jaccard distance revealed that rhizosphere microbiomes vary in richness between rhizosphere soil depths. At all orders of diversity, rarefied microbial richness was consistently lowest at the deepest samples taken (approximately 4 cm from soil surface) in comparison with other rhizosphere soil depths. We additionally show that finer grain sampling (i.e., three samples of equal volume pooled after sequencing) recovers greater microbial richness when using 16S rRNA gene sequencing to describe microbial communities found within the rhizosphere system. In summary, to further elucidate the extent host-specific microbiomes assemble within the rhizosphere, the grain at which bacterial communities are sampled should reflect and encompass fine-scale heterogeneity of the system.

Raman Spectroscopy-Based Measurements of Single-Cell Phenotypic Diversity in Microbial Populations

Microbial cells that live in the same community can exist in different physiological and morphological states that change as a function of spatiotemporal variations in environmental conditions. This phenomenon is commonly known as phenotypic heterogeneity and/or diversity. Measuring this plethora of cellular expressions is needed to better understand and manage microbial processes. However, most tools to study phenotypic diversity only average the behavior of the sampled community. In this work, we present a way to quantify the phenotypic diversity of microbial samples by inferring the (bio)molecular profile of its constituent cells using Raman spectroscopy. We demonstrate how this tool can be used to quantify the phenotypic diversity that arises after the exposure of microbes to stress. Raman spectroscopy holds potential for the detection of stressed cells in bioproduction.

Microbial cells experience physiological changes due to environmental change, such as pH and temperature, the release of bactericidal agents, or nutrient limitation. This has been shown to affect community assembly and physiological processes (e.g., stress tolerance, virulence, or cellular metabolic activity). Metabolic stress is typically quantified by measuring community phenotypic properties such as biomass growth, reactive oxygen species, or cell permeability. However, bulk community measurements do not take into account single-cell phenotypic diversity, which is important for a better understanding and the subsequent management of microbial populations. Raman spectroscopy is a nondestructive alternative that provides detailed information on the biochemical makeup of each individual cell. Here, we introduce a method for describing single-cell phenotypic diversity using the Hill diversity framework of Raman spectra. Using the biomolecular profile of individual cells, we obtained a metric to compare cellular states and used it to study stress-induced changes. First, in two Escherichia coli populations either treated with ethanol or nontreated and then in two Saccharomyces cerevisiae subpopulations with either high or low expression of a stress reporter. In both cases, we were able to quantify single-cell phenotypic diversity and to discriminate metabolically stressed cells using a clustering algorithm. We also described how the lipid, protein, and nucleic acid compositions changed after the exposure to the stressor using information from the Raman spectra. Our results show that Raman spectroscopy delivers the necessary resolution to quantify phenotypic diversity within individual cells and that this information can be used to study stress-driven metabolic diversity in microbial populations.

IMPORTANCE Microbial cells that live in the same community can exist in different physiological and morphological states that change as a function of spatiotemporal variations in environmental conditions. This phenomenon is commonly known as phenotypic heterogeneity and/or diversity. Measuring this plethora of cellular expressions is needed to better understand and manage microbial processes. However, most tools to study phenotypic diversity only average the behavior of the sampled community. In this work, we present a way to quantify the phenotypic diversity of microbial samples by inferring the (bio)molecular profile of its constituent cells using Raman spectroscopy. We demonstrate how this tool can be used to quantify the phenotypic diversity that arises after the exposure of microbes to stress. Raman spectroscopy holds potential for the detection of stressed cells in bioproduction.

Coprophagous Hydrophilid Beetles (Coleoptera, Hydrophilidae, Sphaeridiinae) Distribution in the Polish Carpathians

Research on coprophagous beetles of the Hydrophilidae family in the Polish Carpathians was conducted from 2011 to 2013. The beetles were caught using baited traps. The research sites were selected to take into account both the horizontal diversity of habitat conditions and the vertical diversity associated with elevation above sea level. During the study, 9589 coprophagous hydrophilid individuals were collected, representing 17 species and five genera. Two species that were new to Poland were found: Cercyon tatricus and Pachysternum capense. The vertical ranges of the individual species of coprophagous hydrophilid beetles within the Polish Carpathians were determined as well as the elevations above sea level, with the highest and lowest species richness of this group of insects. The capture of Pachysternum capense in the Tatra Mountains may indicate the existence of an unrecognized path of migration of small insects from Southern to Northern Europe. The route and mechanisms of their migration are discussed.

Representational Rényi Heterogeneity

A discrete system’s heterogeneity is measured by the Rényi heterogeneity family of indices (also known as Hill numbers or Hannah–Kay indices), whose units are the numbers equivalent. Unfortunately, numbers equivalent heterogeneity measures for non-categorical data require a priori (A) categorical partitioning and (B) pairwise distance measurement on the observable data space, thereby precluding application to problems with ill-defined categories or where semantically relevant features must be learned as abstractions from some data. We thus introduce representational Rényi heterogeneity (RRH), which transforms an observable domain onto a latent space upon which the Rényi heterogeneity is both tractable and semantically relevant. This method requires neither a priori binning nor definition of a distance function on the observable space. We show that RRH can generalize existing biodiversity and economic equality indices. Compared with existing indices on a beta-mixture distribution, we show that RRH responds more appropriately to changes in mixture component separation and weighting. Finally, we demonstrate the measurement of RRH in a set of natural images, with respect to abstract representations learned by a deep neural network. The RRH approach will further enable heterogeneity measurement in disciplines whose data do not easily conform to the assumptions of existing indices.

Rare species, functional groups, and evolutionary lineages drive successional trajectories in disturbed forests

Following natural disturbances, additional anthropogenic disturbance may alter community recovery by affecting the occurrences of species, functional groups, and evolutionary lineages. However, our understanding of whether rare, common, or dominant species, functional groups, or evolutionary lineages are most strongly affected by an additional disturbance, particularly across multiple taxa, is limited. Here, we used a generalized diversity concept based on Hill numbers to quantify the community differences of vascular plants, bryophytes, lichens, wood-inhabiting fungi, saproxylic beetles, and birds in a storm-disturbed, experimentally salvage logged forest. Communities of all investigated species groups showed dissimilarities between logged and unlogged plots. Most species groups showed no significant changes in dissimilarities between logged and unlogged plots over the first seven years of succession, indicating a lack of community recovery. In general, the dissimilarities of communities were mainly driven by rare species. Convergence of dissimilarities occurred more often than divergence during the early stages of succession for rare species, indicating a major role in driving decreasing taxonomic dissimilarities between logged and unlogged plots over time. Trends in species dissimilarities only partially match the trends in dissimilarities of functional groups and evolutionary lineages, with little significant changes in successional trajectories. Nevertheless, common and dominant species contributed to a convergence of dissimilarities over time in the case of the functional dissimilarities of wood-inhabiting fungi. Our study shows that salvage logging following disturbances can alter successional trajectories in early stages of forest succession following natural disturbances. However, community changes over time may differ remarkably in different taxonomic groups and are best detected based on taxonomic, rather than functional or phylogenetic dissimilarities.

Epiedaphic Ground Beetle (Carabidae) Diversity in Ecosystems Transformed by Plantations of Eucalyptus pellita in the Orinoco Region of Colombia

Patterns of land use are changing dramatically in the Orinoco region of Colombia, including extensive commercial forestation of Pinus caribaea, Acacia mangium, and Eucalyptus pellita that are replacing savannas, with unknown consequences for biodiversity. We studied the effects of E. pellita plantations on the diversity of epiedaphic carabid beetles (Carabidae) sampled with pitfall traps at El Vita (Vichada) and Villanueva (Casanare). Furthermore, we assessed stand structure data (basal area, and canopy cover), and soil physical and chemical properties to explain differences in ground beetle composition using redundancy analysis (RDA). We compared diversity and species turnover using Hill numbers and Bray-Curtis dissimilarity, respectively. Low differences in richness were observed between savannas and plantations (at El Vita) and between pastures and plantations (at Villanueva). In general, carabid richness was significantly (not overlap in 95% confidence intervals) higher during the rainy season, and in young plantations than in other habitats. Variation in carabid species composition was mainly explained by a gradient of volumetric humidity, number of trees, basal area at El Vita and pH, nitrogen content of the soil, number of trees, soil clay content, and area of exposed ground at Villanueva. Thirteen carabid (which eight are commons in natural forests) species were identified as indicators of 3- and 14-year-old E. pellita plantations and pastures. Results suggest a strong response of ground beetles (Carabidae) to changes in land use, seasonality, and plantation age. Further research is needed to better understand how landscape heterogeneity, and distance to contiguous of natural habitats, influences biodiversity.

Proportional mixture of two rarefaction/extrapolation curves to forecast biodiversity changes under landscape transformation

Progressive habitat transformation causes global changes in landscape biodiversity patterns, but can be hard to quantify. Rarefaction/extrapolation approaches can quantify within-habitat biodiversity, but may not be useful for cases in which one habitat type is progressively transformed into another habitat type. To quantify biodiversity patterns in such transformed landscapes, we use Hill numbers to analyse individual-based species abundance data or replicated, sample-based incidence data. Given biodiversity data from two distinct habitat types, when a specified proportion of original habitat is transformed, our approach utilises a proportional mixture of two within-habitat rarefaction/extrapolation curves to analytically predict biodiversity changes, with bootstrap confidence intervals to assess sampling uncertainty. We also derive analytic formulas for assessing species composition (i.e. the numbers of shared and unique species) for any mixture of the two habitat types. Our analytical and numerical analyses revealed that species unique to each habitat type are the most important determinants of landscape biodiversity patterns.

Diversity and its decomposition into variety, balance and disparity

Diversity is a central concept in many fields. Despite its importance, there is no unified methodological framework to measure diversity and its three components of variety, balance and disparity. Current approaches take into account disparity of the types by considering their pairwise similarities. Pairwise similarities between types may not adequately capture total disparity, since they do not take into account in which way pairs are similar. Hence, pairwise similarities do not discriminate between similarities of types in terms of the same feature and similarities in which all pairs share different features. This paper presents an alternative approach which is based on the overlap of features over the whole set of types. This results in a measure of diversity that takes into account the aspects of variety, balance and disparity. Based on this measure, the 'ABC decomposition' is introduced, which provides separate measures for the variety, balance and disparity, allowing them to enter analysis separately. The method is illustrated by analysing the industrial diversity from 1850 to present while taking into account the overlap in occupations they employ. Finally, the framework is extended to take into account disparity considering multiple features, providing a helpful tool in analysis of high-dimensional data.

Drivers of tropical rainforest composition and alpha diversity patterns over a 2,520 m altitudinal gradient

AIM

We sought to determine the relationship of forest composition and alpha diversity (the species diversity of a local assemblage) to altitude, soil, and spatial factors over a 440-2,950 m a.s.l gradient.

LOCATION

Altitudinal gradient on the Caribbean slope of the Talamanca Cordillera, Costa Rica.

TAXON

Angiosperm and gymnosperm trees, palms, and tree ferns.

METHODS

We measured and identified all stems ≥10 cm dbh in 32 0.25-ha undisturbed rain forest plots over the gradient. We determined compositional patterns using Non-Metric Multidimensional Scaling (NMS) ordination, and used linear regressions to explore the relationship between four alpha diversity metrics and altitude. With variation partitioning (VARPART), we determined the compositional variation explained by altitude, soil, and spatial variables quantified using Principle Components of Neighbor matrices.

RESULTS

We identified 425 species. NMS axis 1 separated a lowland zone (440-1,120 m asl) from a transitional one dominated by holarctic Oreomunnea mexicana (1,400-1,600 m asl) and Quercus-dominated forests at altitudes >2,100 m asl. The lowland zone was separated into two clusters of plots on NMS axis 2, the first in the 430-620 m asl range and the second at 1,000-1,120 masl. Regressions showed that all alpha diversity metrics were strongly negatively related to altitude (R 2 > 0.78). Overall, adjusted R 2 from VARPART was 0.43, with 0.30, 0.21, and 0.17 for altitude, soil, and space respectively. The respective adjusted R 2 of individual matrices, on controlling for the other two, was 0.06, 0.05 and 0.09 (p < 0.001).

MAIN CONCLUSIONS

There are two well-defined forest compositional zones on this gradient-lowlands 430-1,120 m asl and montane forests >2,150 m asl-with a transitional zone at 1,400-1,600 m asl, where lowland tropical and montane holarctic species are found together. Montane forests are very distinct in their composition and low alpha diversity. Vegetation and soil respond to altitude, and therefore temperature, as an integrated system, a model that goes beyond niche assembly as shown by the significant effect of space in the VARPART.

Populations and assemblages living on the edge: dung beetles responses to forests-pasture ecotones

Edge effects alter insect biodiversity in several ways. However, we still have a limited understanding on simultaneous responses of ecological populations and assemblages to ecotones, especially in human modified landscapes. We analyze edge effects on dung beetle populations and assemblages between livestock pastures and native temperate forests (Juniperus and pine-oak forests (POFs)) to describe how species abundances and assemblage parameters respond to edge effects through gradients in forest-pasture ecotones. In Juniperus forest 13 species avoided the ecotones: six species showed greater abundance in forest interior and seven in pasturelands, while the other two species had a neutral response to the edge. In a different way, in POF we found five species avoiding the edge (four with greater abundance in pastures and only one in forest), two species had a neutral response, and two showed a unimodal pattern of abundance near to the edge. At the assemblage level edge effects are masked, as species richness, diversity, functional richness, functional evenness, and compositional incidence dissimilarity did not vary along forest-pasture ecotones. However, total abundance and functional divergence showed higher values in pastures in one of the two sampling localities. Also, assemblage similarity based on species’ abundance showed a peak near to the edge in POF. We propose that conservation efforts in human-managed landscapes should focus on mitigating current and delayed edge effects. Ecotone management will be crucial in livestock dominated landscapes to conserve regional biodiversity and the environmental services carried out by dung beetles.

Diversity from genes to ecosystems: A unifying framework to study variation across biological metrics and scales

Biological diversity is a key concept in the life sciences and plays a fundamental role in many ecological and evolutionary processes. Although biodiversity is inherently a hierarchical concept covering different levels of organization (genes, population, species, ecological communities and ecosystems), a diversity index that behaves consistently across these different levels has so far been lacking, hindering the development of truly integrative biodiversity studies. To fill this important knowledge gap, we present a unifying framework for the measurement of biodiversity across hierarchical levels of organization. Our weighted, information-based decomposition framework is based on a Hill number of order q = 1, which weights all elements in proportion to their frequency and leads to diversity measures based on Shannon's entropy. We investigated the numerical behaviour of our approach with simulations and showed that it can accurately describe complex spatial hierarchical structures. To demonstrate the intuitive and straightforward interpretation of our diversity measures in terms of effective number of components (alleles, species, etc.), we applied the framework to a real data set on coral reef biodiversity. We expect our framework will have multiple applications covering the fields of conservation biology, community genetics and eco-evolutionary dynamics.

Measuring metagenome diversity and similarity with Hill numbers

The first step of any metagenome sequencing project is to get the inventory of OTU abundances (operational taxonomic units) and/or metagenomic gene abundances. The former is generated with 16S-rRNA-tagged amplicon sequencing technology, and the latter can be generated from either gene-targeted or whole-sample shotgun metagenomics technologies. With 16S-rRNA data sets, measuring community diversity with diversity indexes such as species richness and Shannon's index has been a de facto standard analysis; nevertheless, similarly comprehensive approaches to metagenomic gene abundances are still largely missing, despite that both OTU and gene abundances are DNA reads. Here, we adapt the Hill numbers, which were reintroduced to macrocommunity ecology recently and are now widely regarded as a most appropriate measure system for ecological diversity, for measuring metagenome alpha-, beta- and gamma-diversities, and similarity. Our proposal includes the following: (a) Metagenomic gene (MG) diversity measures the single-gene-level metagenome diversity; (b) Type-I metagenome functional gene cluster (MFGC) diversity measures the diversity of functional gene clusters but ignoring within-cluster gene abundance information; (c) Type-II MFGC diversity considers within-cluster gene abundances information and integrates gene-cluster-level metagenome diversity and functional gene redundancy information; and (d) Four classes of Hill-numbers-based similarity metrics, including local gene overlap, regional gene overlap, gene homogeneity measure and gene turnover complement, were introduced in terms of MG and MFGC, respectively. We demonstrate the proposal with the gut metagenomes from healthy and IBD (inflammatory bowel disease) cohorts. The Hill numbers offer a unified approach to cohesively and comprehensively measuring the ecological and metagenome diversities of microbiomes.

Epidemiology and spa-type diversity of meticillin-resistant Staphylococcus aureus in community and healthcare settings in Norway

BACKGROUND

There has been a marked increase in the incidence of meticillin-resistant Staphylococcus aureus (MRSA) during the past decade in Norway; a country with one of the lowest prevalence rates and an active 'search-and-destroy' policy applied to hospital settings.

AIM

To characterize the trends of notification rates of community-associated (CA) and healthcare-associated (HA) MRSA in Norway, and explore the diversity and circulation of MRSA spa types within and outside healthcare settings.

METHODS

A registry-based study on notified MRSA infections and colonizations was conducted in Norway between 2006 and 2015. The diversity and abundance of CA- and HA-MRSA spa types were compared using novel ecological diversity measures (Hill numbers).

FINDINGS

During the study period, the monthly notification rate increased 6.9-fold and 1.8-fold among CA- and HA-MRSA, respectively; the increase was steeper among colonizations than infections. In both settings, the distribution of spa types was uneven, with a few dominant spa types and many singletons. The spa-type diversity of CA-MRSA was higher than HA-MRSA in terms of different types (685 vs 481), and increased during the study period. However, the diversity associated with the dominant spa types was similar and remained stable. A high overlap of spa types was estimated between the settings; spa-t002, t019 and t008 were the most common.

CONCLUSION

The present findings suggest a strong connection between CA- and HA-MRSA epidemiology in Norway. If the fast-growing trend of CA-MRSA continues in the years to come, it may challenge current guidelines and infection control of MRSA in healthcare environments.

Community assessment techniques and the implications for rarefaction and extrapolation with Hill numbers

Diversity estimates play a key role in ecological assessments. Species richness and abundance are commonly used to generate complex diversity indices that are dependent on the quality of these estimates. As such, there is a long-standing interest in the development of monitoring techniques, their ability to adequately assess species diversity, and the implications for generated indices. To determine the ability of substratum community assessment methods to capture species diversity, we evaluated four methods: photo quadrat, point intercept, random subsampling, and full quadrat assessments. Species density, abundance, richness, Shannon diversity, and Simpson diversity were then calculated for each method. We then conducted a method validation at a subset of locations to serve as an indication for how well each method captured the totality of the diversity present. Density, richness, Shannon diversity, and Simpson diversity estimates varied between methods, despite assessments occurring at the same locations, with photo quadrats detecting the lowest estimates and full quadrat assessments the highest. Abundance estimates were consistent among methods. Sample-based rarefaction and extrapolation curves indicated that differences between Hill numbers (richness, Shannon diversity, and Simpson diversity) were significant in the majority of cases, and coverage-based rarefaction and extrapolation curves confirmed that these dissimilarities were due to differences between the methods, not the sample completeness. Method validation highlighted the inability of the tested methods to capture the totality of the diversity present, while further supporting the notion of extrapolating abundances. Our results highlight the need for consistency across research methods, the advantages of utilizing multiple diversity indices, and potential concerns and considerations when comparing data from multiple sources.

Nectar-living yeasts of a tropical host plant community: diversity and effects on community-wide floral nectar traits

We characterize the diversity of nectar-living yeasts of a tropical host plant community at different hierarchical sampling levels, measure the associations between yeasts and nectariferous plants, and measure the effect of yeasts on nectar traits. Using a series of hierarchically nested sampling units, we extracted nectar from an assemblage of host plants that were representative of the diversity of life forms, flower shapes, and pollinator types in the tropical area of Yucatan, Mexico. Yeasts were isolated from single nectar samples; their DNA was identified, the yeast cell density was estimated, and the sugar composition and concentration of nectar were quantified using HPLC. In contrast to previous studies from temperate regions, the diversity of nectar-living yeasts in the plant community was characterized by a relatively high number of equally common species with low dominance. Analyses predict highly diverse nectar yeast communities in a relatively narrow range of tropical vegetation, suggesting that the diversity of yeasts will increase as the number of sampling units increases at the level of the species, genera, and botanical families of the hosts. Significant associations between specific yeast species and host plants were also detected; the interaction between yeasts and host plants impacted the effect of yeast cell density on nectar sugars. This study provides an overall picture of the diversity of nectar-living yeasts in tropical host plants and suggests that the key factor that affects the community-wide patterns of nectar traits is not nectar chemistry, but rather the type of yeasts interacting with host plants.

Effect of wetland management: are lentic wetlands refuges of plant-species diversity in the Andean-Orinoco Piedmont of Colombia?

Accelerated degradation of the wetlands and fragmentation of surrounding vegetation in the Andean–Orinoco Piedmont are the main threats to diversity and ecological integrity of these ecosystems; however, information on this topic is of limited availability. In this region, we evaluated the value of 37 lentic wetlands as reservoirs of woody and aquatic plants and analyzed diversity and changes in species composition within and among groups defined according to management given by: (1) type (swamps, heronries, rice fields, semi-natural lakes, constructed lakes and fish farms) and (2) origins (natural, mixed and artificial). A total of 506 plant species were recorded: 80% woody and 20% aquatic. Of these, 411 species (81%) were considered species typical of the area (Meta Piedmont distribution). Diversity patterns seem to be driven by high landscape heterogeneity and wetland management. The fish farms presented the highest diversity of woody plants, while swamps ranked highest for aquatic plant diversity. Regarding wetland origin, the artificial systems were the most diverse, but natural wetlands presented the highest diversity of typical species and can therefore be considered representative ecosystems at the regional scale. Our results suggest that lentic wetlands act as refuges for native vegetation of Meta Piedmont forest, hosting 55% of the woody of Piedmont species and 29% of the aquatic species of Orinoco basin. The wetlands showed a high species turnover and the results indicated that small wetlands (mean ± SD: size = 11 ± 18.7 ha), with a small area of surrounding forest (10 ± 8.6 ha) supported high local and regional plant diversity. To ensure long-term conservation of lentic wetlands, it is necessary to develop management and conservation strategies that take both natural and created wetlands into account.

Taxonomic and functional divergence of tree assemblages in a fragmented tropical forest

Tropical forests are being exposed to increasing levels of habitat loss and fragmentation, threatening the maintenance of global biodiversity. However, the effect that land-use change may have on the spatial dissimilarities in taxonomic and functional composition of remaining assemblages (i.e., taxonomic/functional β-diversity) remains poorly understood. We examined a large vegetation database from an old and severely fragmented Atlantic forest landscape to test two alternative hypotheses: (1) tree assemblages experience a taxonomic and functional homogenization (reduced β-diversity) between forest fragments and along forest edges, or alternatively, (2) these edge-affected forests show increased taxonomic and functional differentiation (increased β-diversity) when compared to forest interior (reference) stands. Taxonomic and functional β-diversity were examined via novel abundance-based metrics and considering functional traits related to plant dispersion, recruitment, and growth. Overall, taxonomic β-diversity among fragments was significantly higher than among edge and reference plots. Edge plots also showed higher β-diversity than reference plots, but only when considering dominant species. In functional terms, β-diversity among reference plots was also lower than among forest fragments and among edge plots. These patterns support the landscape-divergence hypothesis, which postulates that variable human disturbances among forest fragments and along forest edges can lead to contrasting trajectories of vegetation changes, thus increasing the compositional and functional differentiation of tree communities in these emerging environments. Our results also show that such differentiation can preserve landscape-wide biodiversity, thus overriding negative effects of habitat fragmentation on local (α) diversity. Therefore, our findings demonstrate that forest fragments and forest edges can be more valuable for maintaining species diversity and ecosystem function in fragmented tropical landscapes than previously thought.

Viral quasispecies complexity measures

Mutant spectrum dynamics (changes in the related mutants that compose viral populations) has a decisive impact on virus behavior. The several platforms of next generation sequencing (NGS) to study viral quasispecies offer a magnifying glass to study viral quasispecies complexity. Several parameters are available to quantify the complexity of mutant spectra, but they have limitations. Here we critically evaluate the information provided by several population diversity indices, and we propose the introduction of some new ones used in ecology. In particular we make a distinction between incidence, abundance and function measures of viral quasispecies composition. We suggest a multidimensional approach (complementary information contributed by adequately chosen indices), propose some guidelines, and illustrate the use of indices with a simple example. We apply the indices to three clinical samples of hepatitis C virus that display different population heterogeneity. Areas of virus biology in which population complexity plays a role are discussed.

Assessing the Ecological Response of Dung Beetles in an Agricultural Landscape Using Number of Individuals and Biomass in Diversity Measures

The global increase in demand for productive land requires us to increase our knowledge of the value of agricultural landscapes for the management and conservation of biodiversity, particularly in tropical regions. Thus, comparative studies of how different community attributes respond to changes in land use under different levels of deforestation intensity would be useful. We analyzed patterns of dung beetle diversity in an Andean region dominated by sun-grown coffee. Diversity was estimated using two measures of species abundance (the number of individuals and biomass) and was compared among four types of vegetation cover (forest, riparian forest, sun-grown coffee, and pastures) in three landscape plots with different degrees of deforestation intensity (low, intermediate, and high). We found that dung beetle diversity patterns differed between types of vegetation cover and degree of deforestation, depending on whether the number of individuals or biomass was used. Based on biomass, inequality in the dung beetle community was lowest in the forest, and increased in the sun-grown coffee and pastures across all levels of deforestation, particularly for the increasing dominance of large species. The number of beetles and biomass indicate that the spatial dominance of sun-grown coffee does not necessarily imply the drastic impoverishment of dung beetle diversity. In fact, for these beetles, it would seem that the landscape studied has not yet crossed "a point of no return." This system offers a starting point for exploring biodiversity management and conservation options in the sun-grown coffee landscapes of the Colombian Andes.

Estimating and comparing microbial diversity in the presence of sequencing errors

Estimating and comparing microbial diversity are statistically challenging due to limited sampling and possible sequencing errors for low-frequency counts, producing spurious singletons. The inflated singleton count seriously affects statistical analysis and inferences about microbial diversity. Previous statistical approaches to tackle the sequencing errors generally require different parametric assumptions about the sampling model or about the functional form of frequency counts. Different parametric assumptions may lead to drastically different diversity estimates. We focus on nonparametric methods which are universally valid for all parametric assumptions and can be used to compare diversity across communities. We develop here a nonparametric estimator of the true singleton count to replace the spurious singleton count in all methods/approaches. Our estimator of the true singleton count is in terms of the frequency counts of doubletons, tripletons and quadrupletons, provided these three frequency counts are reliable. To quantify microbial alpha diversity for an individual community, we adopt the measure of Hill numbers (effective number of taxa) under a nonparametric framework. Hill numbers, parameterized by an order q that determines the measures' emphasis on rare or common species, include taxa richness (q = 0), Shannon diversity (q = 1, the exponential of Shannon entropy), and Simpson diversity (q = 2, the inverse of Simpson index). A diversity profile which depicts the Hill number as a function of order q conveys all information contained in a taxa abundance distribution. Based on the estimated singleton count and the original non-singleton frequency counts, two statistical approaches (non-asymptotic and asymptotic) are developed to compare microbial diversity for multiple communities. (1) A non-asymptotic approach refers to the comparison of estimated diversities of standardized samples with a common finite sample size or sample completeness. This approach aims to compare diversity estimates for equally-large or equally-complete samples; it is based on the seamless rarefaction and extrapolation sampling curves of Hill numbers, specifically for q = 0, 1 and 2. (2) An asymptotic approach refers to the comparison of the estimated asymptotic diversity profiles. That is, this approach compares the estimated profiles for complete samples or samples whose size tends to be sufficiently large. It is based on statistical estimation of the true Hill number of any order q ≥ 0. In the two approaches, replacing the spurious singleton count by our estimated count, we can greatly remove the positive biases associated with diversity estimates due to spurious singletons and also make fair comparisons across microbial communities, as illustrated in our simulation results and in applying our method to analyze sequencing data from viral metagenomes.

The effects of forest conversion to oil palm on ground-foraging ant communities depend on beta diversity and sampling grain

Beta diversity – the variation in species composition among spatially discrete communities – and sampling grain – the size of samples being compared – may alter our perspectives of diversity within and between landscapes before and after agricultural conversion. Such assumptions are usually based on point comparisons, which do not accurately capture actual differences in total diversity. Beta diversity is often not rigorously examined. We investigated the beta diversity of ground-foraging ant communities in fragmented oil palm and forest landscapes in Sabah, Malaysia, using diversity metrics transformed from Hill number equivalents to remove dependences on alpha diversity. We compared the beta diversities of oil palm and forest, across three hierarchically nested sampling grains. We found that oil palm and forest communities had a greater percentage of total shared species when larger samples were compared. Across all grains and disregarding relative abundances, there was higher beta diversity of all species among forest communities. However, there were higher beta diversities of common and very abundant (dominant) species in oil palm as compared to forests. Differences in beta diversities between oil palm and forest were greatest at the largest sampling grain. Larger sampling grains in oil palm may generate bigger species pools, increasing the probability of shared species with forest samples. Greater beta diversity of all species in forest may be attributed to rare species. Oil palm communities may be more heterogeneous in common and dominant species because of variable community assembly events. Rare and also common species are better captured at larger grains, boosting differences in beta diversity between larger samples of forest and oil palm communities. Although agricultural landscapes support a lower total diversity than natural forests, diversity especially of abundant species is still important for maintaining ecosystem stability. Diversity in agricultural landscapes may be greater than expected when beta diversity is accounted for at large spatial scales.

Decomposing changes in phylogenetic and functional diversity over space and time

The α, β, γ diversity decomposition methodology is commonly used to investigate changes in diversity over space or time but rarely conjointly. However, with the ever-increasing availability of large-scale biodiversity monitoring data, there is a need for a sound methodology capable of simultaneously accounting for spatial and temporal changes in diversity.Using the properties of Chao's index, we adapted Rao's framework of diversity decomposition between orthogonal dimensions to a multiplicative α, β, γ decomposition of functional or phylogenetic diversity over space and time, thereby combining their respective properties. We also developed guidelines for interpreting both temporal and spatial β-diversities and their interaction.We characterised the range of β-diversity estimates and their relationship to the nested decomposition of diversity. Using simulations, we empirically demonstrated that temporal and spatial β-diversities are independent from each other and from α and γ-diversities when the study design is balanced, but not otherwise. Furthermore, we showed that the interaction term between the temporal and the spatial β-diversities lacked such properties.We illustrated our methodology with a case study of the spatio-temporal dynamics of functional diversity in bird assemblages in four regions of France. Based on these data, our method makes it possible to discriminate between regions experiencing different diversity changes in time. Our methodology may therefore be valuable for comparing diversity changes over space and time using large-scale datasets of repeated surveys.