Spatial Variation in Population Structure and Its Relation to Movement and the Potential for Dispersal in a Model Intertidal Invertebrate

Dispersal, the movement of an individual away from its natal or breeding ground, has been studied extensively in birds and mammals to understand the costs and benefits of movement behavior. Whether or not invertebrates disperse in response to such attributes as habitat quality or density of conspecifics remains uncertain, due in part to the difficulties in marking and recapturing invertebrates. In the upper Bay of Fundy, Canada, the intertidal amphipod Corophium volutator swims at night around the new or full moon. Furthermore, this species is regionally widespread across a large spatial scale with site-to-site variation in population structure. Such variation provides a backdrop against which biological determinants of dispersal can be investigated. We conducted a large-scale study at nine mudflats, and used swimmer density, sampled using stationary plankton nets, as a proxy for dispersing individuals. We also sampled mud residents using sediment cores over 3 sampling rounds (20–28 June, 10–17 July, 2–11 August 2010). Density of swimmers was most variable at the largest spatial scales, indicating important population-level variation. The smallest juveniles and large juveniles or small adults (particularly females) were consistently overrepresented as swimmers. Small juveniles swam at most times and locations, whereas swimming of young females decreased with increasing mud presence of young males, and swimming of large juveniles decreased with increasing mud presence of adults. Swimming in most stages increased with density of mud residents; however, proportionally less swimming occurred as total mud resident density increased. We suggest small juveniles move in search of C. volutator aggregations which possibly act as a proxy for better habitat. We also suggest large juveniles and small adults move if potential mates are limiting. Future studies can use sampling designs over large spatial scales with varying population structure to help understand the behavioral ecology of movement, and dispersal in invertebrate taxa

Geographic Variation in Salt Marsh Structure and Function for Nekton: a Guide to Finding Commonality Across Multiple Scales

Coastal salt marshes are distributed widely across the globe and are considered essential habitat for many fish and crustacean species. Yet, the literature on fishery support by salt marshes has largely been based on a few geographically distinct model systems, and as a result, inadequately captures the hierarchical nature of salt marsh pattern, process, and variation across space and time. A better understanding of geographic variation and drivers of commonalities and differences across salt marsh systems is essential to informing future management practices. Here, we address the key drivers of geographic variation in salt marshes: hydroperiod, seascape configuration, geomorphology, climatic region, sediment supply and riverine input, salinity, vegetation composition, and human activities. Future efforts to manage, conserve, and restore these habitats will require consideration of how environmental drivers within marshes affect the overall structure and subsequent function for fisheries species. We propose a future research agenda that provides both the consistent collection and reporting of sources of variation in small-scale studies and collaborative networks running parallel studies across large scales and geographically distinct locations to provide analogous information for data poor locations. These comparisons are needed to identify and prioritize restoration or conservation efforts, identify sources of variation among regions, and best manage fisheries and food resources across the globe

Climate change implications for tidal marshes and food web linkages to estuarine and coastal nekton

Climate change is altering naturally fluctuating environmental conditions in coastal and estuarine ecosystems across the globe. Departures from long-term averages and ranges of environmental variables are increasingly being observed as directional changes [e.g., rising sea levels, sea surface temperatures (SST)] and less predictable periodic cycles (e.g., Atlantic or Pacific decadal oscillations) and extremes (e.g., coastal flooding, marine heatwaves). Quantifying the short- and long-term impacts of climate change on tidal marsh seascape structure and function for nekton is a critical step toward fisheries conservation and management. The multiple stressor framework provides a promising approach for advancing integrative, cross-disciplinary research on tidal marshes and food web dynamics. It can be used to quantify climate change effects on and interactions between coastal oceans (e.g., SST, ocean currents, waves) and watersheds (e.g., precipitation, river flows), tidal marsh geomorphology (e.g., vegetation structure, elevation capital, sedimentation), and estuarine and coastal nekton (e.g., species distributions, life history adaptations, predator-prey dynamics). However, disentangling the cumulative impacts of multiple interacting stressors on tidal marshes, whether the effects are additive, synergistic, or antagonistic, and the time scales at which they occur, poses a significant research challenge. This perspective highlights the key physical and ecological processes affecting tidal marshes, with an emphasis on the trophic linkages between marsh production and estuarine and coastal nekton, recommended for consideration in future climate change studies. Such studies are urgently needed to understand climate change effects on tidal marshes now and into the future

Behavioural mechanisms underlying functional response of sea stars Asterias vulgaris preying on juvenile sea scallops Placopecten magellanicus

The functional response characterises the relationship between prey density and the consumption rate of individual predators. Typically, it is studied by fitting a model to observations of predation rate at different prey densities. The behavioural mechanisms underlying a functional response are not well understood, and estimates of model parameters seldom conform to observations of behaviour. We have developed a mechanistic approach that directly incorporates behavioural observations into characterisation of the functional response. Laboratory experiments were used to record predation rates and observe foraging behaviour of sea stars Asterias vulgaris preying on juvenile sea scallops Placopecten magellanicus at different densities. Experiments were conducted in tanks with no sediment and tanks with sediment. Behavioural data from the experiments were used to calculate parameters of functional response models Th (handling time per prey) and a (rate of successful search). On both substrates, Th remained constant across prey density, while a was density-dependent. An inverse quadratic was used to describe a and was incorporated into a functional response model. Estimates of a were also obtained by fitting the functional response model to the predation rate data using regression analysis. These estimates of a were highly consistent with the estimates calculated from behavioural data. On both substrates, sea stars preying on scallops had a Type III sigmoid-shaped functional response; on sediment, predation rate decreased at high prey densities. Sea star ability to capture attacked prey was probably the mechanism underlying the observed responses. In general, behavioural information can lead to better understanding of observed functional responses. © Inter-Research 2006

Appendix B. Details of the von Mises distribution of turning angles for Oreaster reticulatus.

Details of the von Mises distribution of turning angles for Oreaster reticulatus

Appendix D. Simulations of Model 1 (for Oreaster reticulatus) and Model 2 (for Strongylocentrotus droebachiensis) with different gradients in food distribution.

Simulations of Model 1 (for Oreaster reticulatus) and Model 2 (for Strongylocentrotus droebachiensis) with different gradients in food distribution

Appendix C. Initial conditions of Model 1 (for Oreaster reticulatus) and Model 2 (for Strongylocentrotus droebachiensis).

Initial conditions of Model 1 (for Oreaster reticulatus) and Model 2 (for Strongylocentrotus droebachiensis)

Appendix G. List of parameter values used in Model 2 for sea urchins Strongylocentrotus droebachiensis.

List of parameter values used in Model 2 for sea urchins Strongylocentrotus droebachiensis