Distance-Decay Relationship for Biological Wastewater Treatment Plants

Abstract

Patterns in the spatial distribution of organisms provide important information about mechanisms underlying biodiversity and the complexity of ecosystems. One of the most well-documented spatial patterns is the distance-decay relationship, which is a universal biogeographic pattern observed repeatedly for plant and animal communities, particularly for microorganisms in natural ecosystems such as soil, ocean, and salt marsh sediment. However, it is uncertain whether the microorganisms exhibit a distance-decay pattern in engineered ecosystems. Therefore, we measured the distance-decay relationship across various microbial functional and phylogenetic groups in 26 biological wastewater treatment plants (WWTPs) in China using a functional gene array (GeoChip 4.2). We found that microbial communities of activated sludge in WWTPs exhibited a significant but very weak distance-decay relationship. The taxon-area z values for different functional and phylogenetic groups were <0.0065, which is about 1 to 2 orders of magnitude lower than those observed in microbial communities elsewhere. Variation-partitioning analysis (VPA) showed that the relationships were driven by both environmental heterogeneity and geographic distance. Collectively, these results provided new insights into the spatial scaling of microbial communities in engineering ecosystems and highlighted the importance of environmental heterogeneity and geographic distance in shaping biogeographic patterns.

Importance: Determining the distance-decay relationship of microbial biodiversity is important but challenging in microbial ecology. All studies to date are based on natural environments; thus, it remains unclear whether there is such a relationship in an engineered ecosystem. The present study shows that there is a very weak distance-decay relationship in an engineered ecosystem (WWTPs) at the regional-to-continental scale. This study makes fundamental contributions to a mechanistic, predictive understanding of microbial biogeography.