Modelling the biological invasion of Carcinus maenas (the European green crab)

Effects of climate variability on an estuarine green crab Carcinus maenas population

The increase in frequency and intensity of extreme climate events over the last few decades has been leading to profound changes in estuarine and marine ecosystems worldwide, with strong implications for the species inhabiting these ecosystems as well as for the services provided by them. In this study, we analysed the effects of climate variability on the temporal and spatial variations in population dynamics of the green crab Carcinus maenas in the Mondego estuary (Portugal), between 2003 and 2018. In this 15-year period, a greater recruitment of C. maenas was observed during drought periods, periods which was matched by an increase in secondary production. Ontogenic stage segregation was also observed, with juveniles being found mainly in the further upriver areas of the estuary. The estuarine population was mainly composed of the green morphotype, with the orange and red morphotypes present in more downstream areas of the estuary. Redundancy analysis (RDA) showed high spatial and temporal variability of C. maenas in the estuary which was related with environmental changes over the 15-year period. A correlation between C. maenas biological features and several local-scale (salinity and river runoff) and large-scale (North Atlantic Oscillation index and Eastern Atlantic pattern) environmental variables was identified through cumulative sums analysis (CUSUM), indicating a strong environmental control on C. maenas population dynamics. This paper shows the importance of relatively long-term datasets to unravel the effects of extreme weather events due to climate change on key epibenthic estuarine species, and also how they might cope with a changing marine environment.

Stochastic optimal switching model for migrating population dynamics

An optimal switching control formalism combined with the stochastic dynamic programming is, for the first time, applied to modelling life cycle of migrating population dynamics with non-overlapping generations. The migration behaviour between habitats is efficiently described as impulsive switching based on stochastic differential equations, which is a new standpoint for modelling the biological phenomenon. The population dynamics is assumed to occur so that the reproductive success is maximized under an expectation. Finding the optimal migration strategy ultimately reduces to solving an optimality equation of the quasi-variational type. We show an effective linkage between our optimality equation and the basic reproduction number. Our model is applied to numerical computation of optimal migration strategy and basic reproduction number of an amphidromous fish Plecoglossus altivelis altivelis in Japan as a target species.

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

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, Carcinus maenas, 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.