Environmental vulnerability of the global ocean plankton community interactome

Abstract

Marine plankton form complex communities of interacting organisms at the base of the food web, which sustain oceanic biogeochemical cycles, and help regulate climate. Though global surveys are starting to reveal ecological drivers underlying planktonic community structure, and predicted climate change responses, it is unclear how community-scale species interactions will be affected by climate change. Here we leveraged Tara Oceans sampling to infer a global ocean cross-domain plankton co-occurrence network – the community interactome – and used niche modeling to assess its vulnerabilities to environmental change. Globally, this revealed a plankton interactome self-organized latitudinally into marine biomes (Trades, Westerlies, Polar), and more connected poleward. Integrated niche modeling revealed biome-specific community interactome responses to environmental change, and forecasted most affected lineages for each community. These results provide baseline approaches to assess community structure and organismal interactions under climate scenarios, while identifying plausible plankton bioindicators for ocean monitoring of climate change.

Ocean-wide comparisons of mesopelagic planktonic community structures

“…competition, symbiosis) rather than taxa avoidance or exclusion. This dominance of positive relations was also reported for epipelagic plankton communities [27, 78]. The global network had a modularity value greater than 0.4 (Table 3), indicating that the network has a modular structure [74].…”

Disentangling temporal associations in marine microbial networks

“…Seasonal bacterial freshwater networks (Zhao et al ., 2016) also showed higher clustering in fall and winter than spring and summer, but in contrast to our work, networks were biggest in summer and smallest in winter. In agreement with our results, Chaffron et al reported higher association strength, edge density, and transitivity in polar regions (colder) compared to other regions (warmer) of the global ocean (Chaffron et al ., 2020). Colder waters in the Mediterranean Sea are milder than polar waters, but together, these results suggest that either microorganisms interact more in colder environments, or that their recurrence is higher due to higher environmental selection exerted by low temperatures and therefore, they tend to co-occur.…”

“…Similar to previous studies (Giner et al ., 2019; Lambert et al ., 2019; Auladell et al ., 2020; Krabberød et al ., 2021), our temporal network displayed seasonality with annual periodicity for most global network metrics. In general, our measured global network metrics are within previous work range (Steele et al ., 2011; Chow et al ., 2013, 2014; Cram et al ., 2015; Lima-Mendez et al ., 2015; Zhao et al ., 2016; Chaffron et al ., 2020) (Table 2 for edge density, transitivity, and average path length). Contrary to early works reporting biological networks generally being disassortative (negative assortativity based on degree) (Newman, 2002), our single static network and monthly subnetworks were assortative.…”

Disentangling temporal associations in marine microbial networks

“…Seasonal bacterial freshwater networks 36 also showed higher clustering in fall and winter than spring and summer, but in contrast to our work, networks were biggest in summer and smallest in winter. In agreement with our results, Chaffron et al reported higher association strength, edge density, and transitivity in polar regions (colder) compared to other regions (warmer) of the global ocean 37 . Colder waters in the Mediterranean Sea are milder than polar waters, but together, these results suggest that either microorganisms interact more in colder environments, or that their recurrence is higher due to higher environmental selection exerted by low temperatures and therefore, they tend to co-occur.…”

“…Similar to previous studies 13,14,28,34 , our temporal network displayed seasonality with annual periodicity for most global network metrics. In general, our measured global network metrics are within previous work range [22][23][24][25][35][36][37] (Table 2 for edge density, transitivity, and average path length). Contrary to early works reporting biological networks generally being disassortative (negative assortativity based on degree) 38 , our single static network and monthly subnetworks were assortative.…”