Spatially Explicit Model to Disentangle Effects of Environment on Annual Fish Reproduction

Abstract

Population growth models are essential tools for natural resources management and conservation since they provide understanding on factors affecting renewal of natural animal populations. However, we still do not properly understand how the processes underlying reproduction of natural animal populations are affected by the environment at the spatial scale at which reproduction actually happens. A particular challenge for analyzing these processes is that observations from different life cycle stages are often collected at different spatial scales, and there is a lack of statistical methods to link local and spatially aggregated information. We address this challenge by developing spatially explicit population growth models for annually reproducing fish. Our approach integrates mechanistic Ricker and Beverton–Holt population growth models with a zero-inflated species distribution model and utilizes the hierarchical Bayesian approach to estimate the model parameters from data with varying spatial support: local scale count data on offspring and environment, and areal data from commercial fisheries informing about a spawning stock size. We show, both theoretically and empirically, that our models are identifiable and have good inferential performance. As a proof of concept application, we used the proposed models to analyze the drivers of whitefish Coregonus laveratus (L.) s.l.) reproduction along the Finnish coast of the Gulf of Bothnia in the Baltic Sea. The results show that the proposed model provides novel understanding beyond what would be attainable with earlier methods. The distributions of the reproduction areas, spawner density, and maximum proliferation rate were strongly dependent on local environmental conditions, but the effects and the relative importance of the covariates varied between these processes. The proposed models can be extended to other systems and organisms and enable ecologists to extract a better understanding of processes driving animal reproduction.