Modelled prey fields predict marine predator foraging success
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 t...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X23000857 |
| _version_ | 1797895565570211840 |
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| author | David B. Green Sophie Bestley Stuart P. Corney Rowan Trebilco Azwianewi B. Makhado Patrick Lehodey Anna Conchon Olivier Titaud Mark A. Hindell |
| author_facet | David B. Green Sophie Bestley Stuart P. Corney Rowan Trebilco Azwianewi B. Makhado Patrick Lehodey Anna Conchon Olivier Titaud Mark A. Hindell |
| author_sort | David B. Green |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T07:29:04Z |
| format | Article |
| id | doaj.art-a92011c7eaf841f28e6f5b0ac87214fc |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-04-10T07:29:04Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-a92011c7eaf841f28e6f5b0ac87214fc2023-02-24T04:29:50ZengElsevierEcological Indicators1470-160X2023-03-01147109943Modelled prey fields predict marine predator foraging successDavid B. Green0Sophie Bestley1Stuart P. Corney2Rowan Trebilco3Azwianewi B. Makhado4Patrick Lehodey5Anna Conchon6Olivier Titaud7Mark A. Hindell8Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, TAS, Australia; Australian Centre for Excellence in Antarctic Science (ACEAS), University of Tasmania, Hobart, TAS, Australia; CSIRO Environment, Hobart, TAS, Australia; Corresponding author.Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, TAS, Australia; Australian Antarctic Program Partnership, University of Tasmania, Hobart, TAS, AustraliaInstitute for Marine and Antarctic Studies, University of Tasmania, Hobart, TAS, Australia; Australian Antarctic Program Partnership, University of Tasmania, Hobart, TAS, AustraliaCSIRO Environment, Hobart, TAS, Australia; Centre for Marine Socioecology, University of Tasmania, Hobart, TAS, AustraliaDepartment of Forestry, Fisheries and the Environment (DFFE), Cape Town, South Africa; FitzPatrick Institute of African Ornithology, DSI-NRF Centre of Excellence, University of Cape Town, Cape Town, South AfricaOceanic Fisheries Programme, Pacific Community, Noumea, New CaledoniaCollecte Localisation Satellites, Ramonville St Agne, FranceCollecte Localisation Satellites, Ramonville St Agne, FranceInstitute for Marine and Antarctic Studies, University of Tasmania, Hobart, TAS, AustraliaModelling 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.http://www.sciencedirect.com/science/article/pii/S1470160X23000857Macaroni penguinMechanistic modelSouthern OceanMarion IslandBody massMicronekton |
| spellingShingle | David B. Green Sophie Bestley Stuart P. Corney Rowan Trebilco Azwianewi B. Makhado Patrick Lehodey Anna Conchon Olivier Titaud Mark A. Hindell Modelled prey fields predict marine predator foraging success Ecological Indicators Macaroni penguin Mechanistic model Southern Ocean Marion Island Body mass Micronekton |
| title | Modelled prey fields predict marine predator foraging success |
| title_full | Modelled prey fields predict marine predator foraging success |
| title_fullStr | Modelled prey fields predict marine predator foraging success |
| title_full_unstemmed | Modelled prey fields predict marine predator foraging success |
| title_short | Modelled prey fields predict marine predator foraging success |
| title_sort | modelled prey fields predict marine predator foraging success |
| topic | Macaroni penguin Mechanistic model Southern Ocean Marion Island Body mass Micronekton |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X23000857 |
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