Exploration of Trends in Interspecific Abundance-Occupancy Relationships Using Empirically Derived Simulated Communities

• Published in: PLOS ONE
• Main Authors: Martinez, Christopher M., Duplisea, Daniel E., Cerrato, Robert M., Frisk, Michael G.
• Format: Text
• Language: English
• Published: Public Library of Science 2017
• Subjects: Research Article; Northwest Atlantic
• Online Access: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5268422/; http://www.ncbi.nlm.nih.gov/pubmed/28125670; https://doi.org/10.1371/journal.pone.0170816

The interspecific abundance-occupancy relationship (AOR) is a widely used tool that describes patterns of habitat utilization and, when evaluated over time, may be used to identify large-scale changes in community structure. Our primary goal for this research was to validate the utility of AORs as temporal indicators of community state. We used long-term survey data in four regions of the northwest Atlantic coastal shelf (NWACS) to estimate the diversity of spatial behaviors in each community, which we modeled with negative binomial (NB) distributions. NB parameters were used to generate time series data for simulated communities, from which AORs were then estimated and evaluated for temporal trends. We found that AORs from simulated communities were similar in year-to-year variation to empirical relationships. In order to further understand the role of spatial diversity in the generation of AOR trends, we did additional simulations where NB parameters were manually manipulated. In one instance, we ran simulations while holding species’ parameters constant over time. This treatment effectively removed trends, suggesting that temporal change in community relationships was the result of genuine variation in intraspecific spatial use. In another set of simulations, we conducted a case study to evaluate the impact of a select group of schooling and spatially aggregating species on an especially rapid shift in AORs in the Gulf of Maine from 1973 to 1983. Removals of these species reduced the magnitudes of most trends, demonstrating their importance to observed community changes. This research directly links variation in AORs to distribution and density-related processes and provides a potentially powerful framework to identify community-level change and to test ecological and mechanistic hypotheses.