Incorporation of feeding functional group information informs explanatory patterns of long-term population changes in fish assemblages

PeerJ

article

Jason C. Doll¹  Stephen J. Jacquemin² 

[1] Freshwater Ecology Center, Department of Biology, Francis Marion University;Agriculture and Water Quality Educational Center, Wright State University—Lake Campus

关键词: Southern Lake Michigan;    Feeding guilds;    Bayesian analysis;    Hierarchical model;   

DOI  :  10.7717/peerj.11032

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

The objective of this study was to evaluate long term trends of fish taxa in southern Lake Michigan while incorporating their functional roles to improve our understanding of ecosystem level changes that have occurred in the system over time. The approach used here highlighted the ease of incorporating ecological mechanisms into population models so researchers can take full advantage of available long-term ecosystem information. Long term studies of fish assemblages can be used to inform changes in community structure resulting from perturbations to aquatic systems and understanding these changes in fish assemblages can be better contextualized by grouping species according to functional groups that are grounded in niche theory. We hypothesized that describing the biological process based on partial pooling of information across functional groups would identify shifts in fish assemblages that coincide with major changes in the ecosystem (e.g., for this study, shifts in zooplankton abundance over time). Herein, we analyzed a long-term Lake Michigan fisheries dataset using a multi-species state space modeling approach within a Bayesian framework. Our results suggested the population growth rates of planktivores and benthic invertivores have been more variable than general invertivores over time and that trends in planktivores can be partially explained by ecosystem changes in zooplankton abundance. Additional work incorporating more ecosystem parameters (e.g., primary production, etc.) should be incorporated into future iterations of this novel modeling concept.

【 授权许可】

CC BY