Disentangling spatiotemporal dynamics in metacommunities through a species-patch network approach

Bioclimatic zonation and spatial-scale dependence of lacustrine microbial assemblages

Bioclimatic zonation is critical for understanding how climate shapes biodiversity and biogeographic patterns. However, existing studies have primarily focused on macroorganisms, leaving microbial communities largely underexplored. This study seeks to address this gap through extensive sampling of bacterial communities from 931 sediment samples across 199 lakes in China. Based on the obtained data, we identified five distinct lacustrine microbial bioclimatic zones, each showing significant differences in multiple facets of bacterial diversity (i.e., alpha, beta, and gamma diversity) and clear bioclimatic zone-dependent microbial biogeographic patterns. Notably, the alpha and beta diversity of the bacterial communities showed opposing patterns across bioclimatic zones. Dominant environmental variables-specifically mean annual temperature, elevation, lake hydrological variables, and sediment pH-exerted contrasting effects on the alpha and beta diversity and played critical roles in shaping microbial community distribution at different spatial scales. At continental scales, predominant geographic and climatic variables dictated the patterns of bioclimatic zonation of lacustrine microbial communities. At regional scales, hydrological variables influenced the dispersal capacity of lake microbes, whereas sediment physicochemical variables were the most important selection factors shaping local microbial communities. Furthermore, our findings indicated that bioclimatic boundaries substantially enhanced the contribution of variable selection on bacterial community assembly and led to marked changes in distance-decay relationships in community dissimilarities. Overall, this study established a continental bioclimatic framework for lacustrine microbial communities, clarifying how environmental variables control microbial distributions across spatial scales, providing new insights into microbial biogeography, and advancing our knowledge about biodiversity under future climate change scenarios.

BioSense: An automated sensing node for organismal and environmental biology

Automated remote sensing has revolutionized the fields of wildlife ecology and environmental science. Yet, a cost-effective and flexible approach for large scale monitoring has not been fully developed, resulting in a limited collection of high-resolution data. Here, we describe BioSense, a low-cost and fully programmable automated sensing platform for applications in bioacoustics and environmental studies. Our design offers customization and flexibility to address a broad array of research goals and field conditions. Each BioSense is programmed through an integrated Raspberry Pi computer board and designed to collect and analyze avian vocalizations while simultaneously collecting temperature, humidity, and soil moisture data. We illustrate the different steps involved in manufacturing this sensor including hardware and software design and present the results of our laboratory and field testing in southwestern United States.

The propagation of disturbances in ecological networks

Despite the development of network science, we lack clear heuristics for how far different disturbance types propagate within and across species interaction networks. We discuss the mechanisms of disturbance propagation in ecological networks, and propose that disturbances can be categorized into structural, functional, and transmission types according to their spread and effect on network structure and functioning. We describe the properties of species and their interaction networks and metanetworks that determine the indirect, spatial, and temporal extent of propagation. We argue that the sampling scale of ecological studies may have impeded predictions regarding the rate and extent that a disturbance spreads, and discuss directions to help ecologists to move towards a predictive understanding of the propagation of impacts across interacting communities and ecosystems.