Estimating alpha, beta, and gamma diversity through deep learning

The reliable mapping of species richness is a crucial step for the identification of areas of high conservation priority, alongside other value considerations. This is commonly done by overlapping range maps of individual species, which requires dense availability of occurrence data or relies on assumptions about the presence of species in unsampled areas deemed suitable by environmental niche models. Here we present a deep learning approach that directly estimates species richness, skipping the step of estimating individual species ranges. We train a neural network model based on species lists from inventory plots, which provide ground truthing for supervised machine learning. The model learns to predict species richness based on spatially associated variables, including climatic and geographic predictors, as well as counts of available species records from online databases. We assess the empirical utility of our approach by producing independently verifiable maps of alpha, beta, and gamma plant diversity at high spatial resolutions for Australia, a continent with highly contrasting diversity patterns. Our deep learning framework provides a powerful and flexible new approach for estimating biodiversity patterns.

Disentangling the Information in Species Interaction Networks

Shannon’s entropy measure is a popular means for quantifying ecological diversity. We explore how one can use information-theoretic measures (that are often called indices in ecology) on joint ensembles to study the diversity of species interaction networks. We leverage the little-known balance equation to decompose the network information into three components describing the species abundance, specificity, and redundancy. This balance reveals that there exists a fundamental trade-off between these components. The decomposition can be straightforwardly extended to analyse networks through time as well as space, leading to the corresponding notions for alpha, beta, and gamma diversity. Our work aims to provide an accessible introduction for ecologists. To this end, we illustrate the interpretation of the components on numerous real networks. The corresponding code is made available to the community in the specialised Julia package EcologicalNetworks.jl.

Local and regional patterns of fish assemblages in coastal lagoons surrounded by mangroves, Gulf of Tehuantepec in the south Pacific of Mexico

We analyzed the local variation of fish assemblages in four coastal lagoon systems surrounded by mangroves, draining into the Gulf of Tehuantepec (Pacific versant of Mexico), and determined the spatial patterns of alpha, beta, and gamma diversity. Fish were sampled between 2004 and 2016 at 63 sites using cast nets. The collected data were supplemented with information obtained from published works for three other coastal lagoons for the regional analysis. Local richness was high (89 species in a total of 19,017 specimens in four systems). Locally, dissolved oxygen, depth, and distance to mangrove were variables that significantly affected richness and abundance of fish in one or more systems. The Chantuto-Panzacola system showed the highest richness, significantly different from the other systems, although the trophic groups were similar. Regionally, two, Istmo and Soconusco complexes were identified, whose turnover rate (0.36) and gamma diversity (176) increased from north to south. Fish species richness and abundance increased with growing mangrove area, both locally and regionally, making this a highly explanatory variable. The Gulf of Tehuantepec is an environmentally heterogeneous region, with ecological patterns defined according to the spatiotemporal scale, which should be considered in the delineation of ecoregions and coastal management planning.

Traditional Agroforestry Systems and Conservation of Native Plant Diversity of Seasonally Dry Tropical Forests

Traditional agroforestry systems (TAFS), which integrate crops with wildlife, are important reservoirs of human culture and technical experiences with a high capacity for biodiversity conservation. Our study aimed to evaluate the capacity of TAFS to conserve the floristic diversity of tropical dry forests (TDF) in the Tehuacán-Cuicatlán Valley, Mexico. We compared TAFS and TDF by measuring their forest cover, floristic composition, and structure, in addition to documenting the motivations of people to maintain native vegetation in their agricultural fields. We conducted a restricted randomized sampling of perennial plant species, including nine sites of TAFS and nine of TDF to determine the alpha, beta, and gamma diversity. Furthermore, we conducted semi-structured interviews with peasants who managed the agricultural plots we studied. We also performed workshops with people of the communities where surveys were performed. Our findings show that TAFS can maintain, on average, 68% of the species (95% of them native to the region) and 53% of the abundance of individuals occurring in the adjacent TDF. TAFS harbour 30% (39 species) of plants endemic to Mexico. Total species richness of TDF and TAFS were similar, as well as the effective number of species or communities estimated for the alpha, beta, and gamma diversity, but differed in the abundance of individuals. The high species turnover recorded in TDF (72%) and TAFS (74%) has profound implications for conservation, suggesting that it would be necessary to maintain several sites in order to conserve the regional diversity of native vegetation. Material, non-material, and regulatory contributions were reported to be the reason that peasants take into account maintaining natural vegetation. TAFS associated with TDF in the region (also called “Apancles”) contain an important richness, diversity, and endemism of components of natural ecosystems, as well as provide multiple socio-ecological contributions. These systems could represent a viable alternative to reconcile biological conservation with social well-being.

Alpha-, beta-, and gamma-diversity of bacteria varies across global habitats

Bacteria are essential parts of ecosystems and are the most abundant organisms on the planet. Yet, we still do not know which habitats support the highest diversity of bacteria across multiple scales. We analyzed alpha-, beta-, and gamma-diversity of bacterial assemblages using 11,680 samples compiled by the Earth Microbiome Project. We found that soils contained the highest bacterial richness within a single sample (alpha-diversity), but sediment assemblages were the most diverse at a global scale (gamma-diversity). Sediment, biofilms/mats, and inland water exhibited the most variation in community composition among geographic locations (beta-diversity). Within soils, agricultural lands, hot deserts, grasslands, and shrublands contained the highest richness, while forests, cold deserts, and tundra biomes consistently harbored fewer bacterial species. Surprisingly, agricultural soils encompassed similar levels of beta-diversity as other soil biomes. These patterns were robust to the alpha- and beta-diversity metrics used and the taxonomic binning approach. Overall, the results support the idea that spatial environmental heterogeneity is an important driver of bacterial diversity.

Measurement and analysis of interspecific spatial associations as a facet of biodiversity

Interspecific spatial associations (ISA), which include co-occurrences, segregations, or attractions among two or more species, can provide important insights into the spatial structuring of communities. However, ISA has primarily been examined in the context of understanding interspecific interactions, while other aspects of ISA, including its relations to other biodiversity facets and how it changes in the face of anthropogenic pressures, have been largely neglected. This is likely because it is unclear what makes ISA useful in a biodiversity context, little is known about the theoretical connections between ISA and other biodiversity facets, and there is a confusing variety of approaches to measuring ISA. Here, we first review the metrics of ISA. These include both spatially implicit and explicit indices of association for both binary and abundance data. We test and compare these approaches on empirical and simulated data, and we provide specific recommendations for how to use and interpret them in biodiversity science. We argue that measurements of ISA are more informative when they are spatially explicit (i.e. distance dependent). We then review links of ISA to other classical biodiversity facets, such as alpha, beta, and gamma diversity, and show that they mostly fail to reflect changes/variation in ISA, with the exception of average pair-wise beta diversity. This underscores the need for a specific focus on ISA in large-scale biodiversity assessments. Finally, we argue that there are important, and underappreciated, reasons to study ISA that are unrelated to its link to biotic interactions. Specifically, ISA can provide strong tests of biodiversity theories that require multiple patterns to benchmark against, and it can be explored for potentially predictive macroecological patterns.