Mitrani Department of Desert Ecology, the Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, Midreshet, 84990, Israel.
Smithsonian Conservation Biology Institute, 1500 Remount Rd, Front Royal, Virginia, 22630, USA.
Ecol Appl. 2020 Jun;30(4):e02088. doi: 10.1002/eap.2088. Epub 2020 Mar 11.
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 SEMs can potentially be robust. Long-term reevaluation of SEMs can be beneficial even if model projections fail, as the process can uncover the specific factors driving this failure, supporting adaptive management procedures.
我们评估了一个 20 年的空间显式模型（SEM），该模型预测了在以色列北部重新引入的波斯赤鹿的空间扩张。使用鹿的当前分布，并基于多模型推断，我们评估了 SEM 预测的准确性以及影响种群当前分布的其他因素。如果 SEM 的预测仍然有效，那么多模型推断中的主导模型将只包括 SEM 的预测作为解释变量，并且拟合良好。不同的主导模型将揭示 SEM 构建时忽略的关键变量或当时无法预见的景观变化，从而有助于适应性管理和决策。我们使用 N-混合模型分析相机陷阱遭遇计数来评估鹿的存在情况。模型包括七个预测因子的各种组合：在重新引入的初始阶段开发的 SEM 的 20 年预测，SEM 最初基于的三个关键景观特征，但已更新以反映当前条件，距释放地点的距离，海拔和灰狼（当 SEM 开发时，该地区没有这种捕食者）的分布。竞争模型按 Akaike 信息准则（AIC）进行排名。狼的分布是解释当前鹿分布的关键预测因子，出现在所有三个主导模型中（∆AIC<2.0），并携带 71％的 AIC 权重（系数=-14.86±5.6[平均值±SE]）。在这三个模型中，SEM 20 年预测出现在两个模型中，但仅解释了方差的一部分（系数=0.001±0.08）。所有其他预测因子的贡献微不足道。尽管 SEM 未能准确预测 20 年鹿的分布，但它的预测与现实之间的差异指出了这种差异的可能原因（狼）。SEM 预测的纳入表明，如果狼没有传播到研究区域，那么这些预测仍然有价值，这表明长期 SEM 可能具有稳健性。即使模型预测失败，长期重新评估 SEM 也可能是有益的，因为该过程可以揭示导致这种失败的具体因素，支持适应性管理程序。
