Neighbourhood benthic configuration reveals hidden co-occurrence social diversity

Abstract

Ecological interactions among benthic communities are crucial for shaping marine ecosystems. Understanding these interactions is essential for predicting how ecosystems will respond to environmental changes, invasive species, and conservation management. However, determining the prevalence of species interactions at the community scale is challenging. To overcome this challenge, we employ tools from social network analysis, specifically exponential random graph modelling (ERGM). Our approach explores the relationships among animal and plant organisms within their neighbourhoods. Inspired by companion planting in agriculture, we use spatiotemporal co-occurrence as a measure of mixed species interaction. In other words, the variety of community interactions based on co-occurrence defines what we call ‘co-occurrence social diversity’. Our objective is to use ERGM to quantify the proportion of interactions at both the simple paired level and the more complex triangle level, enabling us to measure and compare co-occurrence social diversity. Applying our approach to the Spanish coastal zone across eight sites, five depths, and sunlit/shaded aspects, we discover that 80% of sessile communities, consisting of over a hundred species, exhibit co-occurrence social diversity, with 5% of species consistently forming associations with other species. These organism-level interactions probably have a significant impact on the overall character of the site. This article is part of the theme issue ‘Connected interactions: enriching food web research by spatial and social interactions’.

Connected interactions: enriching food web research by spatial and social interactions

“…There are several systems, however, in which identifying all species and their interactions to build a metaweb is unfeasible for the time and sampling effort available to a research team. Kininmonth et al [57] (P10) in this issue used spatiotemporal co-occurrence data captured by photography to infer species interactions for benthic communities of the Spanish coastal zone across eight sites, five depths and sunlit/shaded aspects, which would have been prohibitive through other methods. The authors employ Exponential Random Graph Models to infer the network of relationships from species' statistical preferences for specific neighbours, based on the sequence in which organisms of different species in the photography (i.e.…”