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

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 F. Fit of the Gaussian distribution to daily displacement distance measured for Strongylocentrotus droebachiensis.

Fit of the Gaussian distribution to daily displacement distance measured for Strongylocentrotus droebachiensis

Appendix A. Photographs of feeding fronts of Oreaster reticulatus and Strongylocentrotus droebachiensis.

Photographs of feeding fronts of Oreaster reticulatus and Strongylocentrotus droebachiensis

Relative Importance of Biotic and Abiotic Forces on the Composition and Dynamics of a Soft-Sediment Intertidal Community

<div><p>Top-down, bottom-up, middle-out and abiotic factors are usually viewed as main forces structuring biological communities, although assessment of their relative importance, in a single study, is rarely done. We quantified, using multivariate methods, associations between abiotic and biotic (top-down, bottom-up and middle-out) variables and infaunal population/community variation on intertidal mudflats in the Bay of Fundy, Canada, over two years. Our analysis indicated that spatial structural factors like site and plot accounted for most of the community and population variation. Although we observed a significant relationship between the community/populations and the biotic and abiotic variables, most were of minor importance relative to the structural factors. We suggest that community and population structure were relatively uncoupled from the structuring influences of biotic and abiotic factors in this system because of high concentrations of resources that sustain high densities of infauna and limit exploitative competition. Furthermore, we hypothesize that the infaunal community primarily reflects stochastic spatial events, namely a “first come, first served” process.</p></div

Appendix E. Details of the procedures used in uncertainty and elasticity analyses.

Details of the procedures used in uncertainty and elasticity analyses

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)

Stochastic dispersal increases the rate of upstream spread: A case study with green crabs on the northwest Atlantic coast

<div><p>Dispersal heterogeneity is an important process that can compensate for downstream advection, enabling aquatic organisms to persist or spread upstream. Our main focus was the effect of year-to-year variation in larval dispersal on invasion spread rate. We used the green crab, <i>Carcinus maenas</i>, as a case study. This species was first introduced over 200 years ago to the east coast of North America, and once established has maintained a relatively consistent spread rate against the dominant current. We used a stage-structured, integro-difference equation model that couples a demographic matrix for population growth and dispersal kernels for spread of individuals within a season. The kernel describing larval dispersal, the main dispersive stage, was mechanistically modeled to include both drift and settlement rate components. It was parameterized using a 3-dimensional hydrodynamic model of the Gulf of St Lawrence, which enabled us to incorporate larval behavior, namely vertical swimming. Dispersal heterogeneity was modeled at two temporal scales: within the larval period (months) and over the adult lifespan (years). The kernel models variation within the larval period. To model the variation among years, we allowed the kernel parameters to vary by year. Results indicated that when dispersal parameters vary with time, knowledge of the time-averaged dispersal process is insufficient for determining the upstream spread rate of the population. Rather upstream spread is possible over a number of years when incorporating the yearly variation, even when there are only a few “good years” featured by some upstream dispersal among many “bad years” featured by only downstream dispersal. Accounting for annual variations in dispersal in population models is important to enhance understanding of spatial dynamics and population spread rates. Our developed model also provides a good platform to link the modeling of larval behavior and demography to large-scale hydrodynamic models.</p></div

Representations of the larval dispersal kernel <i>k</i>_<i>13</i> (Eqs 3–5) with different values for the net rate of displacement <i>v</i> (km d^-1) and diffusion coefficient <i>D</i> (km² d^-1).

<p>For this graph, <i>T</i>₁ = 50 d and <i>T</i>₂ = 90 d. Note how the kernel shifts, flattens, and appears to approach uniform distribution as the magnitude of <i>v</i> increases.</p