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.

中文翻译：

---

将基于个人的模型与近似贝叶斯计算相结合以预测淡水鱼的入侵，可以深入了解扩散和范围扩展动态

短距离扩散使引入的外来物种在最初引入后就能够定居并入侵当地的生境，但是对于许多淡水类群来说，了解却很少。可以使用诸如基于个体的模型（IBMs）之类的预测方法来克服外来物种范围扩展中的知识缺口，尤其是如果可以通过拟合经验数据来改善预测的情况下，但这对于具有多个参数的模型可能是具有挑战性的。因此，我们通过近似贝叶斯计算（ABC）估计了RangeShifter IBM平台中实现的模型的参数，以便预测小体侵入性鱼类（苦涩的Rhodeus sericeus）对低地河（Great Ouse，英格兰）的进一步入侵）。参数的先前估计是从文献和专家意见中获得的。使用固定位置的采样数据的时间序列（1983年至2018年）进行模型拟合，结果表明，对于11个模型参数中的5个，后验分布与先前的假设显着不同。特别是，后成年人中次成年人的最大移民几率大大高于先人。苦味范围扩大的模拟预测，在1984年被发现后，它们的早期扩大涉及相对较高的人口增长率，并在5年后稳定下来。苦味斑块的占用呈乙状结肠，20年后流域面积占20％，30年后增至80％。当时的预测是69年后入住率达到95％。因此，该IBM的开发成功地模拟了这种小身侵入性鱼类的射程扩展动态，而ABC则提高了模拟精度。这种综合的方法还强调，与专家意见所建议的相比，亚成人传播更可能有助于快速定居。这些结果强调了时序数据对于总体上细化IBM参数以及特别是增进我们对分散行为和范围扩展动态的理解的重要性。