Modelled prey fields predict marine predator foraging success

• Published in: Ecological Indicators
• Main Authors: David B. Green, Sophie Bestley, Stuart P. Corney, Rowan Trebilco, Azwianewi B. Makhado, Patrick Lehodey, Anna Conchon, Olivier Titaud, Mark A. Hindell
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier 2023
• Subjects: Macaroni penguin; Mechanistic model; Southern Ocean; Marion Island; Body mass; Micronekton; Ecology; QH540-549.5; Eudyptes chrysolophus
• Online Access: https://doi.org/10.1016/j.ecolind.2023.109943; https://doaj.org/article/a92011c7eaf841f28e6f5b0ac87214fc

Modelling marine predator foraging habitats is a widespread research approach for projecting species responses to a rapidly changing Southern Ocean. Yet a key remaining challenge is to understand how changing prey biomass within foraging habitats could affect predator foraging success. Quantifying this using observed prey information is challenging given a paucity of synoptic data. Here, we investigated whether prey biomass from a mechanistic model, could provide useful predictions of pre-breeding arrival body mass of macaroni penguins (Eudyptes chrysolophus) from Marion Island, a standard metric of predator foraging success, measured over a 20-year period. In testing this, we used a spatially iterative correlation approach between predicted prey biomass and observed penguin arrival body mass, allowing likely foraging areas to emerge in regions most frequently associated with significant correlations. We then considered whether the distribution of these emergent foraging areas is consistent with tracking-derived foraging distributions for this species and island. Our results indicated emergent foraging areas where prey biomass was most often correlated with arrival body mass were located within expected and observed foraging ranges. Further, variability in prey biomass, within these emergent foraging areas provided reasonable predictions of annual penguin arrival body mass and outperformed metrics of primary production within these foraging areas. Our findings demonstrate that mechanistic models can provide biologically meaningful representations of difficult-to-observe prey, and can predict predator foraging success. This work could improve understanding of predator responses in a changing habitat.