Predicting the impacts of anthropogenic disturbances on marine populations

• Main Authors: Nabe-Nielsen, Jacob, van Beest, Floris, Grimm, Volker, Sibly, Richard, Teilmann, Jonas, Thompson, Paul M
• Format: Conference Object
• Language: English
• Published: 2018
• Subjects: Agent based modeling; Harbour porpoise; Individual-based model; Management models
• Online Access: https://pure.au.dk/portal/en/publications/45ed8d19-f1a8-43d5-9271-ad51925562f8; https://doi.org/10.7490/f1000research.1115899.1

Marine ecosystems are increasingly exposed to anthropogenic disturbances that cause animals to change behavior and move away from potential foraging grounds. Here we present a process-based modeling framework for assessing population consequences of such sub-lethal behavioral effects. It builds on how disturbances influence animal movements, and how this in turn affect their foraging and energetics. The animals’ tendency to move away from disturbances is directly related to the experienced noise level. The reduced foraging in noisy areas affects the animals’ energy budget, fitness, and subsequently the population size, which is an emergent property of the model. Due to the generality of these processes the framework is applicable to a wide range of species, but here we demonstrate its use by assessing the impact of wind farm construction noise on the North Sea harbor porpoise population. Although realistic wind farm construction scenarios did not influence the harbor porpoise population size, scenarios with increased noise levels influenced the population differently depending on the order of the pilings. Therefore, our case study demonstrates that detailed spatial planning of offshore constructions can be crucial for protecting marine populations. Mechanistic models like the one we present here can be used to pinpoint the processes that populations are particularly sensitive to, and that should therefore be the focus for further research. Such models, that build on fundamental processes that determine animal fitness, are expected to have high predictive power in novel environments, making them ideal tools for marine management.