Evans Luke C, Sibly Richard M, Thorbek Pernille, Sims Ian, Oliver Tom H, Walters Richard J
1School of Biological Sciences, University of Reading, Whiteknights, PO Box 217, Berkshire, Reading RG6 6AH UK.
2Syngenta, Jealott's Hill International Research Centre, Bracknell, Berkshire, RG42 6EY UK.
Mov Ecol. 2019 Sep 2;7:24. doi: 10.1186/s40462-019-0171-7. eCollection 2019.
Understanding the factors influencing movement is essential to forecasting species persistence in a changing environment. Movement is often studied using mechanistic models, extrapolating short-term observations of individuals to longer-term predictions, but the role of weather variables such as air temperature and solar radiation, key determinants of ectotherm activity, are generally neglected. We aim to show how the effects of weather can be incorporated into individual-based models of butterfly movement thus allowing analysis of their effects.
We constructed a mechanistic movement model and calibrated it with high precision movement data on a widely studied species of butterfly, the meadow brown (), collected over a 21-week period at four sites in southern England. Day time temperatures during the study ranged from 14.5 to 31.5 °C and solar radiation from heavy cloud to bright sunshine. The effects of weather are integrated into the individual-based model through weather-dependent scaling of parametric distributions representing key behaviours: the durations of flight and periods of inactivity.
Flight speed was unaffected by weather, time between successive flights increased as solar radiation decreased, and flight duration showed a unimodal response to air temperature that peaked between approximately 23 °C and 26 °C. After validation, the model demonstrated that weather alone can produce a more than two-fold difference in predicted weekly displacement.
Individual Based models provide a useful framework for integrating the effect of weather into movement models. By including weather effects we are able to explain a two-fold difference in movement rate of consistent with inter-annual variation in dispersal measured in population studies. Climate change for the studied populations is expected to decrease activity and dispersal rates since these butterflies already operate close to their thermal optimum.
了解影响物种迁移的因素对于预测其在不断变化的环境中的存续至关重要。人们常使用机械模型来研究迁移，即将个体的短期观测结果外推至长期预测，但诸如气温和太阳辐射等天气变量（变温动物活动的关键决定因素）的作用通常被忽视。我们旨在展示如何将天气影响纳入基于个体的蝴蝶迁移模型，从而能够分析其影响。
我们构建了一个机械迁移模型，并用在英国南部四个地点为期21周收集的、关于一种广泛研究的蝴蝶——草地褐蝶（）的高精度迁移数据对其进行校准。研究期间白天温度范围为14.5至31.5摄氏度，太阳辐射从浓云到充足日照都有。天气影响通过对代表关键行为（飞行持续时间和静止期）的参数分布进行与天气相关的缩放，被整合到基于个体的模型中。
飞行速度不受天气影响，连续飞行之间的时间间隔随着太阳辐射的减少而增加，飞行持续时间对气温呈现单峰响应，在约23摄氏度至26摄氏度之间达到峰值。经过验证后，该模型表明仅天气因素就能使预测的每周位移产生两倍以上的差异。
基于个体的模型为将天气影响纳入迁移模型提供了一个有用的框架。通过纳入天气影响，我们能够解释迁移速率两倍的差异，这与种群研究中测量的扩散年际变化一致。由于这些蝴蝶已经接近其热最优状态，预计研究种群的气候变化将降低其活动和扩散速率。
