Citation:

Miller, D., C. LaLone, D. Villeneuve, AND G. Ankley. Projection of Interspecific Competition (PIC) Matrices: A Conceptual Framework for Inclusion in Population Risk Assessments. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, 43(6):1406-1422, (2024). https://doi.org/10.1002/etc.5867

Impact/Purpose:

Many ecological risk assessments culminate in the analysis of population-level impacts.  While there have been many developments in data and models needed to make population-level predictions of stressor impacts, important uncertainties remain as to the ecological veracity of these predictions.  The effects of interspecific competition on population responses have been documented within numerous observational studies.  However, an understanding of the extent, importance, and dynamics of competition in stressed ecological systems is limited and remains largely unexplored.  In the present study, we propose a conceptual framework for population risk assessment that utilizes projection matrices to explicitly explore interspecific competition.   Ecosystem projection of interspecific covariance (EPIC) matrices allow for analysis of population dynamics of two or more species exposed to a given stressor(s) that compete for shared resources within a landscape.  We demonstrate the application of EPIC matrices to investigate population dynamics of two hypothetical fish species that compete with one another and have differences in net reproductive rate and intrinsic rate of population increase.  Population status predictions were made under scenarios that included exposure to a chemical stressor that reduced fecundity for one or both species.  Results of simulations demonstrated that measures obtained from the life table and Leslie matrix of an organism, including net reproductive rate and intrinsic rate of increase, can result in erroneous conclusions of population status and viability in the absence of consideration of resource limitation and interspecific competition.  This modeling approach can be used in conjunction with field monitoring efforts and/or laboratory testing to link effects due to stressors to possible outcomes within an ecosystem.  In addition, EPIC matrices could be combined with adverse outcome pathways to allow for ecosystem projection based upon taxonomic conservation of molecular targets of chemicals to predict the likelihood of relative cross-species susceptibility.  Overall, the present study shows how EPIC matrices can integrate effects across the life cycles of multiple species, provide a linkage between endpoints observed in an individual and population-level responses, and project outcomes at the community level for multiple generations for multiple species that compete for limited resources.

Description:

Accounting for intraspecific and interspecific competition when assessing the effects of chemical and nonchemical stressors is an important uncertainty in ecological risk assessments. We developed novel projection of interspecific competition (PIC) matrices that allow for analysis of population dynamics of two or more species exposed to a given stressor(s) that compete for shared resources within a landscape. We demonstrate the application of PIC matrices to investigate the population dynamics of two hypothetical fish species that compete with one another and have differences in net reproductive rate and intrinsic rate of population increase. Population status predictions were made under scenarios that included exposure to a chemical stressor that reduced fecundity for one or both species. The results of our simulations demonstrated that measures obtained from the life table and Leslie matrix of an organism, including net reproductive rate and intrinsic rate of increase, can result in erroneous conclusions of population status and viability in the absence of a consideration of resource limitation and interspecific competition. This modeling approach can be used in conjunction with field monitoring efforts and/or laboratory testing to link effects due to stressors to possible outcomes within an ecosystem. In addition, PIC matrices could be combined with adverse outcome pathways to allow for ecosystem projection based on taxonomic conservation of molecular targets of chemicals to predict the likelihood of relative cross-species susceptibility. Overall, the present study shows how PIC matrices can integrate effects across the life cycles of multiple species, provide a linkage between endpoints observed in individual and population-level responses, and project outcomes at the community level for multiple generations for multiple species that compete for limited resources.

URLs/Downloads:

Record Details: