Print PageBiodiversity has traditionally been described in terms of species composition and richness, but theory and growing empirical evidence indicate that the diversity of functional traits and phylogenetic relationships better predict community assembly, response to environmental change, and ecosystem functioning. Most research, however, has focused on terrestrial plants.
We utilized a high-resolution database of Chesapeake Bay, USA groundfish abundances from 10 years of bimonthly trawl surveys to investigate patterns of taxonomic (TD), functional (FD), and phylogenetic (PD) diversity across space and time. We estimated FD by assembling data for >30 functional traits representing different aspects of morphology, life history, and trophic ecology, and PD from vouchered COI gene sequences. Calculations for TD, FD, and PD were performed under the common framework provided by Rao’s quadratic entropy. The values were converted to effective numbers so that they were directly comparable.
We used this comprehensive data set to address the following questions: (1) how do TD, FD, and PD vary through space and time and relative to each other, (2) how do these indices change along major environmental gradients, and (3) more specifically, what species and traits dominated these patterns and drive turnover along these gradients?
Results/Conclusions
We used kriging interpolation to map patterns of TD, FD, and PD through space and time. All diversity components decreased with decreasing salinity along the mainstem of the Chesapeake Bay. Along this salinity gradient, TD was generally higher than FD and PD. Plotting diversity profiles showed that this was due to the low relative abundance of unique species, leading to functionally and phylogenetically homogenous communities at each sampling station. This was especially true in lower regions of the Bay, where we would expect diversity to be higher due to the ingress of marine species. Instead, we observed similar station-level values of diversity as the upper Bay but much higher turnover between stations.
Finally, we employed generalized additive models to explore non-linear relationships between diversity and temperature, salinity, and dissolved oxygen. Salinity was the predominant environmental driver of patterns in diversity. Season also strongly influenced diversity, as expected given the strong seasonal variation along the mid-Atlantic coast and the temperature-sensitivity and migratory habits of many Chesapeake Bay fishes.
This work promotes a better understanding of the relative abilities of TD, FD, and PD in describing biodiversity in open, aquatic systems, and may provide useful insights not traditionally considered in fisheries management.
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