Search for: All records

ABSTRACT Identifying species with disproportionate effects on other species under press perturbations is essential, yet how species traits and community context drive their ‘keystone‐ness’ remain unclear. We quantified keystone‐ness as linearly approximated per capita net effect derived from normalised inverse community matrices and as non‐linear per capita community biomass change from simulated perturbations in food webs with varying biomass structure. In bottom‐heavy webs (negative relationship between species' body mass and their biomass within the web), larger species at higher trophic levels tended to be keystone species, whereas in top‐heavy webs (positive body mass to biomass relationship), the opposite was true and the relationships between species' energetic traits and keystone‐ness were weakened or reversed compared to bottom‐heavy webs. Linear approximations aligned well with non‐linear responses in bottom‐heavy webs, but were less consistent in top‐heavy webs. These findings highlight the importance of community context in shaping species' keystone‐ness and informing effective conservation actions. 

Ecosystem models need to capture biodiversity, because it is a fundamental determinant of food web dynamics and consequently of the cycling of energy and matter in ecosystems. In oceanic food webs, the plankton compartment encompasses by far most of the biomass and diversity. Therefore, capturing plankton diversity is paramount for marine ecosystem modelling. In recent years, many models have been developed, each representing different aspects of plankton diversity, but a systematic comparison remains lacking. Here we present established modelling approaches to study plankton ecology and diversity, discussing the limitations and strengths of each approach. We emphasize their different spatial and temporal resolutions and consider the potential of these approaches as tools to address societal challenges. Finally, we make suggestions as to how better integration of field and experimental data with modelling could advance understanding of both plankton biodiversity specifically and more broadly the response of marine ecosystems to environmental change, including climate change.