Strong self‐regulation and widespread facilitative interactions in phytoplankton communities

Abstract

1. The persistence of phytoplanktonic diversity in spite of competition for basic resources has long been a source of wonder and inspiration to ecologists. To sort out, among the many coexistence mechanisms suggested by theory and experiments, which ones actually maintain diversity in natural ecosystems, long-term field studies are paramount. 2. We analysed a large dataset of phytoplankton abundance time series using dynamic, multivariate autoregressive (MAR) models. Phytoplankton was counted and identified down to the genus level, every 2 weeks over 20 years, at 10 sites along the French coastline. MAR models allowed to estimate biotic interaction networks while also accounting for abiotic variables that may drive part of the phytoplankton fluctuations. We then analysed the ratio of intra to intertaxa interactions (a measure of niche differentiation), the frequency of negative versus positive interactions, and how stability metrics (both at the network and genus level) relate to network complexity and genus self-regulation or abundance. 3. We showed that a strong self-regulation, with competition strength within a taxon (genus) an order of magnitude higher than between taxa, was present in all phytoplanktonic interaction networks. This much stronger intragenus competition suggests that niche differentiation-rather than neutrality-is commonplace in phytoplankton. Furthermore, interaction networks were dominated by positive net effects between phytoplanktonic taxa (on average, more than 50% of interactions were positive). While network stability (sensu resilience) was unrelated to complexity measures, we unveiled links between self-regulation, intergenera interaction strengths and abundance. The less common taxa tend to be more strongly self-regulated and can therefore maintain in spite of competition with more abundant ones. 4. Synthesis. We demonstrate that strong niche differentiation, widespread facilitation between phytoplanktonic taxa and stabilizing covariances between interaction strengths should be common features of coexisting phytoplankton communities in the field. These are structural properties that we can expect to emerge from plausible mechanistic models of phytoplankton communities. We discuss mechanisms, such as predation or restricted microscale movement, that are consistent with these findings, which paves the way for further research.

Fluctuation spectra of large random dynamical systems reveal hidden structure in ecological networks

“…We found that the empirical data is indicative of an ecology dominated by competitive and mutalistic interactions, with far fewer predator-prey interactions. This insight is consistent with recent results that suggest that self-regulation (competition) and facilitation (mutualism) are widespread in phytoplankton communities 41 .…”

Niche differences, not fitness differences, explain coexistence across ecological groups

“…through self-limitation and net positive interactions . These results consolidate and expand on findings from primary studies on annual plants (Levine and HilleRisLambers, 2009), perennial plants (Adler, Ellner, et al, 2010;Usinowicz et al, 2012), and phytoplankton Picoche and Barraquand, 2020). Our results show that niche di erences are the main determinant for coexistence across ecological groups, highlighting the importance to sustain mechanisms that promote niche di erences.…”

“…Thus, findings that positive interactions can be abundant in nature (Choler et al, 2001;Martorell and Freckleton, 2014;Soliveres et al, 2015;Wainwright et al, 2016;Picoche and Barraquand, 2020) do not contradict our result that net facilitation was only frequently observed in perennial plants and phytoplankton.…”

“…Apart from predicting coexistence, niche and fitness di erences also tightly link to the three processes driven by species interactions (positive and negative frequency dependence and facilitation) but also predict when exclusion and priority e ects occur Picoche and Barraquand, 2020;Spaak, Godoy, et al, n.d.;Ko el et al, 2021). A second question that one could in principle address is therefore if communities belonging to the same ecological group are driven by the same processes and experience the same outcome of species interactions.…”

Disentangling key species interactions in diverse and heterogeneous communities: A Bayesian sparse modeling approach

“…Further, co-occurrence patterns are scale dependent and regional analyses are not suited to assessing local-scale species interactions (König et al, 2021). Recognizing a need to directly estimate species interaction coefficients, recent work has expanded multivariate autoregressive models for use in more diverse communities (Picoche & Barraquand, 2020), including examining which linear combinations of species abundances best predict future growth rates (Ovaskainen et al, 2017a). This approach is effective for binning species based on their competitive effects, but does not account for variation in the environment.…”