Species distribution models and habitat suitability models (HSMs) have become a popular tool in the conservation of biodiversity. However, the ability to predict species spatial distributions at sites beyond the data source sites (i.e., spatial transferability) is critical for the applications of HSMs in the management and conservation of rare or endangered species. The main objective of our study was to assess the predictive performance and spatial transferability of expert opinion models (EOMs). To build EOMs, we identified through extensive literature reviews 17 key landscape variables to characterize habitat use by American beaver (Castor canadensis). We developed 31 pairwise opinion questions on the relative importance of the 17 selected habitat variables for an online survey in Qualtrics®. We used Saaty's analytical hierarchy process (AHP) and geospatial analysis to build EOMs for beaver. We tested the transferability of EOMs by assessing model predictive performance using the area under the curve (AUC > 0.7) in northcentral Mississippi and northern Alabama, USA. Thirty-five of 63 survey participants submitted complete, consistent surveys. Expert opinion models had fair predictive performance for beaver at the two study sites (AUC = 0.70–0.76). The fair predictive performance of EOM for the two sites, from which no opinion survey data were collected, indicated acceptable spatial transferability. The American beaver exhibits stable realized niche space throughout its geographic range, restricting habitat selection to open water bodies and associated wetlands, which may subsequently result in high transferability of HSMs.

物种分布模型和栖息地适应性模型（HSM）已成为保护生物多样性的流行工具。但是，预测HSM在稀有或濒危物种的管理和保护中的应用至关重要的是，能够预测数据源站点之外站点的物种空间分布的能力（即空间可转移性）至关重要。我们研究的主要目的是评估专家意见模型（EOM）的预测性能和空间可传递性。为了建立EOM，我们通过广泛的文献综述确定了17个关键景观变量，以描述美洲海狸（Castor canadensis）的栖息地使用特征。）。针对Qualtrics®中的在线调查，我们针对选择的17个栖息地变量的相对重要性开发了31个成对意见问题。我们使用Saaty的分析层次结构过程（AHP）和地理空间分析来构建海狸的EOM。我们通过使用密西西比州中北部和美国阿拉巴马州北部的曲线下面积（AUC> 0.7）评估模型的预测性能，测试了EOM的可传递性。63名调查参与者中有35名提交了完整，一致的调查。专家意见模型在两个研究地点对海狸具有公平的预测性能（AUC = 0.70–0.76）。没有收集任何意见调查数据的两个地点的EOM的公平预测性能表明可接受的空间转移性。美国海狸在其整个地理范围内均表现出稳定的已实现利基空间，