Global variation in zooplankton niche divergence across ocean basins

Modelling responses to climate change assume zooplankton populations remain similar over time with little adaptation (niche conservatism). Oceanic barriers, genetic, phenotypic variation and species interactions in cosmopolitan species could drive niche divergence within species. We assess niche divergence among 224 globally distributed species across the seven main ocean basins. There were 357 diverged niches out of 828 ocean basin comparisons. The proportion of diverged niches varied both across and within phyla. Copepoda (156 of 223 species) were used to test for niche divergence between same species populations across different environmental gradients. Global niche divergence was found to be more likely for species in colder temperatures and near shore environments. Opposing temperature responses were found for four comparisons which may relate to the different connectivity patterns between them. This study demonstrates adaptive potential across environmental-niche gradients, which must be considered when modelling population responses to climate change.

Methods

Both biological and environmental data are freely available for download.

The zooplankton data are available from the 'Zoobase' dataset https://zenodo.org/records/5101349. This is a collated reseource of all observation records for the main groups of meso-zooplankton 

The environmental data were extracted as monthly climatologies from the World Ocean Atlas (2022). Bathymetric depth were extracted from GEBCO’s current gridded bathymetric data set (2024). Monthly climatologies of chlorophyll-a were extracted from the globcolour database (http://www.globcolour.info/).

The repository contains code to each of the four main steps of the manuscript. Here we provide a detailed description to replicate the results in the manuscript. We 1) First extract the presences from the 'Zoobase' database and retain species present when n > 50 in two ocean basins, 2) Perform an ensemble model on each species for the global presence distribution in teh dataset, 3) Extract observations of a species from paired ocean basin areas to examine niche divergence, 4) Use the results of the global ensemble model and the binary niche divergence/ non-divergence classifier to perform a heriarchical generalised additive model.