We evaluated a 20‐yr‐old spatially explicit model (SEM ) that predicted the spatial expansion of reintroduced Persian fallow deer in northern Israel. Using the current distribution of the deer and based on multi‐model inference we assessed the accuracy of the SEM 's prediction and what other factors affected the population's current distribution. If the SEM 's projection was still valid, the leading model in the multi‐model inference would include only the SEM 's projection as an explanatory variable with a good fit. Different leading models would reveal key variables overlooked when the SEM was constructed or changes in the landscape unforeseen at the time, thus assisting adaptive management and decision‐making. We assessed deer presence from camera trap encounter counts analyzed using N‐mixture models. Models included various combinations of seven predictors: the 20‐yr predictions of an SEM developed during the initial phases of the reintroduction, three key landscape characteristics on which the SEM was originally based but updated to reflect current conditions, distance from the release site, elevation, and the distribution of gray wolves (a predator that was absent from the area when the SEM was developed). Competing models were ranked by Akaike information criterion (AIC ). Wolf distribution was the key predictor explaining the current deer distribution, appearing in all three leading models (∆AIC < 2.0) and carrying 71% of the AIC weight (coefficient = −14.86 ± 5.6 [mean ± SE]). Of these three models, the SEM 20‐yr prediction appeared in two, but explained only a fraction of the variance (coefficient = 0.001 ± 0.08). The contribution of all other predictors was negligible. While the SEM failed to accurately predict the 20‐yr deer distribution, the divergence between its projection and reality pointed to the probable cause (wolves) of this discrepancy. The inclusion of the SEM prediction in the leading models indicates that had the wolves not spread to the study area, the predictions would still have merit suggesting that long‐term SEM s can potentially be robust. Long‐term reevaluation of SEM s can be beneficial even if model projections fail, as the process can uncover the specific factors driving this failure, supporting adaptive management procedures.

中文翻译：

---

对空间显式模型进行长期重新评估，以作为适应性野生动植物管理的一种手段。

我们评估了一个20岁的空间外显模型（SEM），该模型预测了以色列北部重新引入的波斯小鹿的空间扩展。使用鹿的当前分布并基于多模型推断，我们评估了SEM预测的准确性以及其他哪些因素影响了种群的当前分布。如果SEM的投影仍然有效，则多模型推断中的主导模型将仅包括SEM的投影作为具有良好拟合的解释变量。不同的主导模型将揭示构建SEM时忽略的关键变量或当时不可预见的景观变化，从而有助于自适应管理和决策。我们从使用N混合模型分析的相机陷阱遭遇计数中评估了鹿的存在。模型包括七个预测变量的各种组合：在重新引入的初始阶段开发的SEM的20年预测，SEM最初基于但主要反映当前条件，距释放地点的距离，海拔高度的三个主要景观特征，以及灰狼的分布情况（当开发SEM时，该地区没有捕食者）。竞争模型通过Akaike信息标准（AIC）进行排名。狼群分布是解释当前鹿群分布的主要预测因子，它出现在所有三个主要模型中（∆AIC <2.0），并承载71％的AIC重量（系数= -14.86±5.6 [平均值±SE]）。在这三个模型中，SEM 20年预测结果有两个，但仅解释了一部分差异（系数= 0.001±0.08）。所有其他预测变量的贡献可忽略不计。尽管SEM无法准确预测20年鹿的分布，但其预测与现实之间的差异指出了这种差异的可能原因（狼）。在主要模型中包含SEM预测表明，如果狼没有扩散到研究区域，则预测仍将具有优势，表明长期SEM可能具有鲁棒性。即使模型预测失败，对SEM进行长期重新评估也可能是有益的，因为该过程可以发现驱动该失败的特定因素，从而支持自适应管理程序。其预测与现实之间的差异指出了这种差异的可能原因（狼）。在主要模型中包含SEM预测表明，如果狼没有扩散到研究区域，则预测仍将具有优势，表明长期SEM可能具有鲁棒性。即使模型预测失败，对SEM进行长期重新评估也可能是有益的，因为该过程可以发现驱动该失败的特定因素，从而支持自适应管理程序。其预测与现实之间的差异指出了这种差异的可能原因（狼）。在主要模型中包含SEM预测表明，如果狼没有扩散到研究区域，则预测仍将具有优势，表明长期SEM可能具有鲁棒性。即使模型预测失败，对SEM进行长期重新评估也可能是有益的，因为该过程可以发现驱动该失败的特定因素，从而支持自适应管理程序。