Hierarchical modelling of temperature and habitat size effects on population dynamics of North Atlantic cod

• Published in: ICES Journal of Marine Science
• Main Authors: Mantzouni, Irene, Sørensen, Helle, O'Hara, Robert B., MacKenzie, Brian R.
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2010
• Subjects: ddc:570; atlantic cod; Gadus morhua; North Atlantic
• Online Access: http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/20254; https://nbn-resolving.org/urn:nbn:de:hebis:30-85141; https://doi.org/10.1093/icesjms/fsp291; http://publikationen.ub.uni-frankfurt.de/files/20254/container.zip

Understanding how temperature affects cod (Gadus morhua) ecology is important for forecasting how populations will develop as climate changes in future. The effects of spawning-season temperature and habitat size on cod recruitment dynamics have been investigated across the North Atlantic. Ricker and Beverton and Holt stock–recruitment (SR) models were extended by applying hierarchical methods, mixed-effects models, and Bayesian inference to incorporate the influence of these ecosystem factors on model parameters representing cod maximum reproductive rate and carrying capacity. We identified the pattern of temperature effects on cod productivity at the species level and estimated SR model parameters with increased precision. Temperature impacts vary geographically, being positive in areas where temperatures are <5°C, and negative for higher temperatures. Using the relationship derived, it is possible to predict expected changes in population-specific reproductive rates and carrying capacities resulting from temperature increases. Further, carrying capacity covaries with available habitat size, explaining at least half its variability across stocks. These patterns improve our understanding of environmental impacts on key population parameters, which is required for an ecosystem approach to cod management, particularly under ocean-warming scenarios. Key words: carrying capacity , cod , hierarchical models , North Atlantic , temperature , uncertainty