Natural population variability may be masking the more-individuals hypothesis

Community-wide correlations between species richness, abundance and population genomic diversity in a freshwater biodiversity hotspot

Understanding patterns of diversity across macro (e.g. species-level) and micro (e.g. molecular-level) scales can shed light on community function and stability by elucidating the abiotic and biotic drivers of diversity within ecological communities. We examined the relationships among taxonomic and genetic metrics of diversity in freshwater mussels (Bivalvia: Unionidae), an ecologically important and species-rich group in the southeastern United States. Using quantitative community surveys and reduced-representation genome sequencing across 22 sites in seven rivers and two river basins, we surveyed 68 mussel species and sequenced 23 of these species to characterize intrapopulation genetic variation. We tested for the presence of species diversity-abundance correlations (i.e. the more-individuals hypothesis, MIH), species-genetic diversity correlations (SGDCs) and abundance-genetic diversity correlations (AGDCs) across all sites to evaluate relationships between different metrics of diversity. Sites with greater cumulative multispecies density (a standardized metric of abundance) had a greater number of species, consistent with the MIH hypothesis. Intrapopulation genetic diversity was strongly associated with the density of most species, indicating the presence of AGDCs. However, there was no consistent evidence for SGDCs. Although sites with greater overall densities of mussels had greater species richness, sites with higher genetic diversity did not always exhibit positive correlations with species richness, suggesting that there are spatial and evolutionary scales at which the processes influencing community-level diversity and intraspecific diversity differ. Our work reveals the importance of local abundance as indicator (and possibly a driver) of intrapopulation genetic diversity.

Moth Diversity Increases along a Continent-Wide Gradient of Environmental Productivity in South African Savannahs

Environmental productivity, i.e., the amount of biomass produced by primary producers, belongs among the key factors for the biodiversity patterns. Although the relationship of diversity to environmental productivity differs among studied taxa, detailed data are largely missing for most groups, including insects. Here, we present a study of moth diversity patterns at local and regional scales along a continent-wide gradient of environmental productivity in southern African savannah ecosystems. We sampled diversity of moths (Lepidoptera: Heterocera) at 120 local plots along a gradient of normalized difference vegetation index (NDVI) from the Namib Desert to woodland savannahs along the Zambezi River. By standardized light trapping, we collected 12,372 specimens belonging to 487 moth species. The relationship between species richness for most analyzed moth groups and environmental productivity was significantly positively linear at the local and regional scales. The absence of a significant relationship of most moth groups' abundance to environmental productivity did not support the role of the number of individuals in the diversity-productivity relationship for south African moths. We hypothesize the effects of water availability, habitat complexity, and plant diversity drive the observed moth diversity patterns.

A modern method of multiple working hypotheses to improve inference in ecology

Science provides a method to learn about the relationships between observed patterns and the processes that generate them. However, inference can be confounded when an observed pattern cannot be clearly and wholly attributed to a hypothesized process. Over-reliance on traditional single-hypothesis methods (i.e. null hypothesis significance testing) has resulted in replication crises in several disciplines, and ecology exhibits features common to these fields (e.g. low-power study designs, questionable research practices, etc.). Considering multiple working hypotheses in combination with pre-data collection modelling can be an effective means to mitigate many of these problems. We present a framework for explicitly modelling systems in which relevant processes are commonly omitted, overlooked or not considered and provide a formal workflow for a pre-data collection analysis of multiple candidate hypotheses. We advocate for and suggest ways that pre-data collection modelling can be combined with consideration of multiple working hypotheses to improve the efficiency and accuracy of research in ecology.