ALBERT

Description: Sea level rise is mixing formerly isolated freshwater communities with saltwater communities. The structure of these new aquatic communities is jointly controlled by pre- and post-colonization processes. Similarly, since salinity is a strong abiotic determinant of post-colonization survival in coastal systems, changes in salinity will likely impact community composition. In this study, we examine how a strong abiotic gradient affects the diversity and structure of bacterial and zooplankton communities and associated ecosystem functions (decomposition and carbon mineralization). We ran a six week dispersal experiment using mesocosm ponds with four distinct salinity profiles (0, 5, 9, and 13 psu). We find that salinity is the primary driver of both bacterial and zooplankton community composition. We find evidence that as bacterial richness increases so does the amount of decomposition. A phenomenological model suggests carbon mineralization may decrease at mid-salinities; this warrants future work into possible mechanisms for this apparent loss of function. Understanding how salinization changes community structure and ecosystem function may be paramount for managing and conserving coastal plain ecosystems where salinity is increasing due to sea level rise, saltwater intrusion, storm surges, and drought.

Description: We investigated how recent changes in the distribution and abundance of a fouling organism affected the strength of interactions between a commercially important foundation species and a common predator. Increases in the abundance of boring sponges that bioerode the calcified shells of oysters and other shelled organisms have been attributed to increased salinization of estuarine ecosystems. We tested the hypothesis that fouling by boring sponges will change the interaction strength between oysters and a common predator (stone crabs). We generated five oyster density treatments crossed with two sponge treatments (sponge and no sponge). We contrasted the interaction strength between stone crabs and fouled and non-fouled oysters by comparing the parameters of fitted functional response curves based on Rogers random predation model. We found that fouled oysters suffered higher predation from stone crabs, and that the increased predation risk stemmed from a reduction in the handling time needed to consume the fouled oysters. These findings highlight the importance of understanding the effects of abiotic changes on both the composition of ecological communities, and on the strengths of direct and indirect interactions among species. Global climate change is altering local ecosystems in complex ways, and the success of restoration, management, and mitigation strategies for important species requires a better appreciation for how these effects cascade through ecosystems.

Description: In ecology and evolution generalized linear mixed models (GLMMs) are becoming increasingly used to test for differences in variation by treatment at multiple hierarchical levels. Yet, the specific sampling schemes that optimize the power of an experiment to detect differences in random effects by treatment/group remain unknown. In this paper we develop a blueprint for conducting power analyses for GLMMs focusing on detecting differences in variance by treatment. We present parameterization and power analyses for random-intercepts and random-slopes GLMMs because of their generality as focal parameters for most applications and because of their immediate applicability to emerging questions in the field of behavioral ecology. We focus on the extreme case of hierarchically structured binomial data, though the framework presented here generalizes easily to any error distribution model. First, we determine the optimal ratio of individuals to repeated measures within individuals that maximizes power to detect differences by treatment in among-individual variation in intercept, among-individual variation in slope, and within-individual variation in intercept. Second, we explore how power to detect differences in target variance parameters is affected by total variation. Our results indicate heterogeneity in power across ratios of individuals to repeated measures with an optimal ratio determined by both the target variance parameter and total sample size. Additionally, power to detect each variance parameter was low overall (in most cases 〉1,000 total observations per treatment needed to achieve 80% power) and decreased with increasing variance in non-target random effects. With growing interest in variance as the parameter of inquiry, these power analyses provide a crucial component for designing experiments focused on detecting differences in variance. We hope to inspire novel experimental designs in ecology and evolution investigating the causes and implications of individual-level phenotypic variance, such as the adaptive significance of within-individual variation.