The European rabbit (Oryctolagus cuniculus ) is a notorious economic and environmental pest species in its invasive range. To better understand the population and range dynamics of this species, 41 yr of abundance data have been collected from 116 unique sites across a broad range of climatic and environmental conditions in Australia. We analyzed this time series of abundance data to determine whether interannual variation in climatic conditions can be used to map historic, contemporary, and potential future fluctuations in rabbit abundance from regional to continental scales. We constructed a hierarchical Bayesian regression model of relative abundance that corrected for observation error and seasonal biases. The corrected abundances were regressed against environmental and disease variables in order to project high spatiotemporal resolution, continent‐wide rabbit abundances. We show that rabbit abundance in Australia is highly variable in space and time, being driven primarily by internnual variation in temperature and precipitation in concert with the prevalence of a non‐pathogenic virus. Moreover, we show that internnual variation in local spatial abundances can be mapped effectively at a continental scale using highly resolved spatiotemporal predictors, allowing “hot spots” of persistently high rabbit abundance to be identified. Importantly, cross‐validated model performance was fair to excellent within and across distinct climate zones. Long‐term monitoring data for invasive species can be used to map fine‐scale spatiotemporal fluctuations in abundance patterns when accurately accounting for inherent sampling biases. Our analysis provides ecologists and pest managers with a clearer understanding of the determinants of rabbit abundance in Australia, offering an important new approach for predicting spatial abundance patterns of invasive species at the near‐term temporal scales that are directly relevant to resource management.

中文翻译：

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兔子数量的时空变化模型揭示了入侵物种的管理热点。

欧洲兔（穴兔）是其入侵范围内的一个臭名昭著的经济和环境害虫物种。为了更好地了解该物种的种群和范围动态，已经从澳大利亚广泛的气候和环境条件下的116个独特站点收集了41年的丰度数据。我们分析了这个时间序列的丰度数据，以确定是否可以使用气候条件的年际变化来绘制兔子丰度从区域到大陆尺度的历史，当代和潜在的未来波动的地图。我们构建了相对丰度的分层贝叶斯回归模型，该模型对观测误差和季节偏差进行了校正。校正后的丰度针对环境和疾病变量进行回归，以预测高时空分辨率，整个大陆的兔子丰度。我们显示，澳大利亚的兔子丰度在空间和时间上变化很大，主要是由温度和降水的年际变化与非致病性病毒的流行共同驱动的。此外，我们表明，可以使用高度解析的时空预测因子在大陆范围内有效地绘制局部空间丰度的年际变化，从而可以确定持续高兔子丰度的“热点”。重要的是，交叉验证的模型性能在不同气候区域内和不同气候区域之间都相当出色。在准确考虑固有采样偏差时，可使用入侵物种的长期监测数据来绘制丰度模式中的精细时空波动图。