Abstract
Short-distance dispersal enables introduced alien species to colonise and invade local habitats following their initial introduction, but is often poorly understood for many freshwater taxa. Knowledge gaps in range expansion of alien species can be overcome using predictive approaches such as individual based models (IBMs), especially if predictions can be improved through fitting to empirical data, but this can be challenging for models having multiple parameters. We therefore estimated the parameters of a model implemented in the RangeShifter IBM platform by approximate Bayesian computation (ABC) in order to predict the further invasion of a lowland river (Great Ouse, England) by a small-bodied invasive fish (bitterling Rhodeus sericeus). Prior estimates for parameters were obtained from the literature and expert opinion. Model fitting was conducted using a time-series (1983 to 2018) of sampling data at fixed locations and revealed that for 5 of 11 model parameters, the posterior distributions differed markedly from prior assumptions. In particular, sub-adult maximum emigration probability was substantially higher in the posteriors than priors. Simulations of bitterling range expansion predicted that following detection in 1984, their early expansion involved a relatively high population growth rate that stabilised after 5 years. The pattern of bitterling patch occupancy was sigmoidal, with 20% of the catchment occupied after 20 years, increasing to 80% after 30 years. Predictions were then for 95% occupancy after 69 years. The development of this IBM thus successfully simulated the range expansion dynamics of this small-bodied invasive fish, with ABC improving the simulation precision. This combined methodology also highlighted that sub-adult dispersal was more likely to contribute to the rapid colonisation rate than expert opinion suggested. These results emphasise the importance of time-series data for refining IBM parameters generally and increasing our understanding of dispersal behaviour and range expansion dynamics specifically.
Highlights
Biological invasions are a major aspect of global environmental change, responsible for pervasive changes to native biota and ecosystems (Simberloff et al 2013; Gamez-Virues et al 2015)
The posterior distributions for six of the parameters were similar to their priors, i.e. the empirical data provided little additional information upon which to reduce parameter uncertainty
For sub-adult maximum emigration probability, there was a substantial difference between the relatively high maximum emigration probability posterior estimates and the prior assumption of extremely low probability. These five posterior distributions showed some tendency to be inter-correlated, especially sub-adult maximum emigration probability, for which low values tended to be associated with high values of 1/b and low values of per-step mortality probability (Table 4)
Summary
Biological invasions are a major aspect of global environmental change, responsible for pervasive changes to native biota and ecosystems (Simberloff et al 2013; Gamez-Virues et al 2015). Following the introduction of an alien species into a new range, its invasion success depends, at least in part, on its dispersal dynamics (Byers and Pringle 2006; Havel et al 2015). The extent of many invasions has proved difficult to predict due to a general lack of knowledge on dispersal dynamics and their relationship with population parameters, especially where the invader lacks data from their native range (Karakus et al 2018), so here we demonstrate an analytical method that potentially overcomes these issues. A range of modelling approaches exist for predicting the dynamics of range expansions, including analytical methods such as integro-difference modelling (Gilbert et al 2014, 2017) and stochastic simulations, including individual-based models (IBMs). Improvements in computer performance in recent years have helped overcome some of these challenges, resulting in IBMs being increasingly applied to ecological issues (e.g. Hedger et al 2013a, b; DeAngelis and Grimm 2014; Boyd et al 2018), with approaches for fitting these models to empirical data emerging (van der Vaart et al 2018)
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