Generalizing game-changing species across microbial communities

Abstract

Microbes form multispecies communities that play essential roles in our environment and health. Not surprisingly, there is an increasing need for understanding if certain invader species will modify a given microbial community, producing either a desired or undesired change in the observed collection of resident species. However, the complex interactions that species can establish between each other and the diverse external factors underlying their dynamics have made constructing such understanding context-specific. Here we integrate tractable theoretical systems with tractable experimental systems to find general conditions under which non-resident species can change the collection of resident communities—game-changing species. We show that non-resident colonizers are more likely to be game-changers than transients, whereas game-changers are more likely to suppress than to promote resident species. Importantly, we find general heuristic rules for game-changers under controlled environments by integrating mutual invasibility theory with in vitro experimental systems, and general heuristic rules under changing environments by integrating structuralist theory with in vivo experimental systems. Despite the strong context-dependency of microbial communities, our work shows that under an appropriate integration of tractable theoretical and experimental systems, it is possible to unveil regularities that can then be potentially extended to understand the behavior of complex natural communities.

Towards a system-level causative knowledge of pollinator communities

“…For instance, planting field margins [8] or adding managed pollinators [9] have become, respectively, popular restoration practices in agricultural systems to increase resources for pollinators or supplement crop pollination. However, anticipating the response of these systems to interventions remains extremely challenging due to the unpredictable nature of ecological communities, whose nonlinear behaviour depends on the specific details of species interactions and the various unknown or unmeasured confounding factors [10,11]. For example, these practices often ignore side effects, such as the effects that field margins can have by altering micro-climate conditions, which in turn can change pollinators' occupancy patterns [12,13] or the co-lateral effects of introducing managed species on pollinator health.…”

“…Hence, to move towards a more general approach, we propose to follow a probabilistic systems analysis using current advancements on non-parametric causal inference [17,20]. Instead of aiming to predict exactly what would happen under an intervention—something that may be impossible to generalize [11]—we propose to focus on how much a likely cause can affect the probability that a given effect happens. In the following, we discuss a road map for how this probabilistic systems analysis can be accomplished and illustrate it with a case study.…”

Untangling the complexity of priority effects in multispecies communities

“…In invasion analysis (Grainger et al, 2019b), the invasion criteria assume only two possibilities for a community with species after invasion: Either it has n + 1 species (if the invasion was successful) or remains with species (if the invasion was unsuccessful). However, evidence indicates that the set of potential assembly dynamics in ecological communities can be larger than those considered by invasion analyses (Amor et al, 2020; Angulo et al, 2021; Barabás et al, 2018; Carlström et al, 2019; Deng et al, 2021; Saavedra et al, 2017; Warren et al, 2003). Thus, our graph‐based approach may provide a more realistic analysis of ecological dynamics than those approaches focusing on history‐independent interaction strength and invasion analysis.…”

“…In brief, one would introduce an invader species to the resident community at low abundance (relative to the abundance of the resident species) and assess whether species composition changes. Recently developed computational tools can also facilitate the inference (Deng et al, 2021;Pande et al, 2021). If we are only interested in bottom-up assembly, then many edges do not need to be mapped to construct the corresponding F I G U R E 5 Explaining the predictability of community assembly using topological features.…”

“…For instance, if in one case all different assembly histories lead to different outcomes, and if in another case only one assembly history leads to a different outcome, the latter case has a much more predictable outcome than the former (Margalef, 1958). Understanding the predictability of community assembly is not only of basic interest to ecologists but can also aid applications of community ecology for ecosystem management, including ecological restoration, biological control and the medical treatment of the gut microbiome (Deng et al, 2021; Rohr et al, 2020; Song & Saavedra, 2018; Sprockett et al, 2018). Yet, a theoretical framework is largely lacking for quantifying the predictability of community assembly in this context.…”