Accounting for a nonlinear functional response when estimating prey dynamics using predator diet data

• Published in: Methods in Ecology and Evolution
• Main Authors: Robertson, Matthew D., Koen‐Alonso, Mariano, Regular, Paul M., Cadigan, Noel, Zhang, Fan
• Other Authors: Ocean Frontier Institute, Natural Sciences and Engineering Research Council of Canada
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2022
• Subjects: Newfoundland
• Online Access: http://dx.doi.org/10.1111/2041-210x.13795; https://onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13795; https://onlinelibrary.wiley.com/doi/full-xml/10.1111/2041-210X.13795; https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13795

Abstract Forage fish species are key in the transfer of energy from lower to upper trophic levels in marine ecosystems. Therefore, understanding their population dynamics, including population levels, is crucial for understanding productivity and the regulation of marine food webs. However, many forage fishes are poorly sampled by bottom trawl surveys, leading to poor estimates of their abundance. These estimates can be improved by using predator stomach contents as an additional sampling strategy; however, nonlinear relationships between prey abundance and predator consumption (i.e. the functional response) may bias stomach data as well. Using predator stomach contents and bottom trawl survey data, this study aimed to minimize this bias by developing a model to estimate prey dynamics and account for the predator functional response. This model was tested using a series of simulations and applied to a case study of northern sand lance Ammodytes dubius on the Grand Bank, Newfoundland, Canada. The simulations revealed that when predators consumed prey following a nonlinear functional response, our model outperformed a classical model (the model adopted by most studies) that assumed a linear functional response. In the case study, we estimated the relative abundance of sand lance from 1995 to 2018, which exhibited oscillatory dynamics with a period of approximately 7 years. Our results demonstrate that our model is capable of more accurately estimating the abundance of data‐limited prey populations, which contributes to a better understanding of food web dynamics.