The quantity and nutritional quality of forage are key drivers for ungulate populations, including mule deer (Odocoileus hemionus) and Rocky Mountain elk (Cervus elaphus nelsoni), in the western U.S., but current vegetation maps are too coarse spatially and temporally to effectively characterize fine-scale habitat. To address some of these gaps, we tested a novel approach using existing vegetation surveys, maps, and remotely sensed data to develop fine-scale forage species distribution models (SDMs) across Idaho, USA. We modelled 20 forage species that are suitable for mule deer and Rocky Mountain elk. Climatic, topographic, soil, vegetation, and disturbance variables were attributed to approximately 44.3 million habitat patches generated using multi-scale object-oriented image analysis. Lasso logistic regression was implemented to produce predictive SDMs. We evaluated if the inclusion of distal environmental variables (i.e., indirect effects) improved model performance beyond the inclusion of proximal variables (i.e., direct physiological effect) only. Our results showed that all models provided higher predictive accuracy than chance, with an average AUC across the 20 forage species of 0.84 for distal and proximal variables and 0.81 for proximal variables only. This indicated that the addition of distal variables improved model performance. We validated the models using two independent datasets from two regions of Idaho. We found that predicted forage species occurrence was on average within 10% of observed occurrence at both sites. However, predicted occurrences had much less variability between habitat patches than the validation data, implying that the models did not fully capture fine-scale heterogeneity. We suggest that future efforts will benefit from additional fine resolution (i.e., less than 30 m) environmental predictor variables and greater accounting of environmental disturbances (i.e., wildfire, grazing) in the training data. Our approach was novel both in methodology and spatial scale (i.e., resolution and extent). Our models can inform ungulate nutrition by predicting the occurrence of forage species and aide habitat management strategies to improve nutritional quality.

牧草的数量和营养质量是有蹄类种群的主要驱动因素，其中包括m鹿（Odocoileus hemionus）和落基山麋鹿（Cervus elaphus nelsoni）），但在美国西部，但当前的植被地图在空间和时间上过于粗糙，无法有效地描述小规模的栖息地。为了解决其中的一些空白，我们使用现有的植被调查，地图和遥感数据测试了一种新颖的方法，以在美国爱达荷州建立小型饲草物种分布模型（SDM）。我们对适用于m鹿和落基山麋鹿的20种饲草物种进行了建模。气候，地形，土壤，植被和干扰变量归因于使用多尺度面向对象图像分析生成的约4,430万个栖息地斑块。套索逻辑回归被实施以产生预测性SDM。我们评估了远端环境变量（即间接影响）的引入是否比近端变量（即 直接生理作用）。我们的结果表明，所有模型都提供了比机会更高的预测准确性，在20种饲草物种中，平均AUC的平均值分别为：远端变量和近端变量为0.84，近端变量为0.81。这表明增加远端变量可改善模型性能。我们使用来自爱达荷州两个地区的两个独立数据集验证了模型。我们发现，预测的牧草物种发生率平均在两个站点的观测值发生率的10％以内。但是，与验证数据相比，预测的事件在生境斑块之间的变异性要小得多，这表明这些模型不能完全捕获精细尺度的异质性。我们建议，未来的工作将受益于其他更精细的解决方案（例如，小于30 m）的环境预测变量，并在训练数据中更多地考虑了环境干扰（如野火，放牧）。我们的方法在方法论和空间尺度（即分辨率和范围）上都是新颖的。我们的模型可以通过预测草料物种的发生和辅助栖息地管理策略来改善营养质量，从而为有蹄类动物提供营养。