Abstract Species’ range maps based on expert opinion are a critical resource for conservation planning. Expert maps are usually accompanied by species descriptions that specify sources of internal range heterogeneity, such as habitat associations, but these are rarely considered when using expert maps for analyses. We developed a quantitative metric (expert score) to evaluate the agreement between an expert map and a habitat probability surface obtained from a species distribution model. This method rewards both the avoidance of unsuitable sites and the inclusion of suitable sites in the expert map. We obtained expert maps of 330 butterfly species from each of 2 widely used North American sources (Glassberg [1999, 2001] and Scott [1986]) and computed species‐wise expert scores for each. Overall, the Glassberg maps secured higher expert scores than Scott (0.61 and 0.41, respectively) due to the specific rules (e.g., Glassberg only included regions where the species was known to reproduce whereas Scott included all areas a species expanded to each year) they used to include or exclude areas from ranges. The predictive performance of expert maps was almost always hampered by the inclusion of unsuitable sites, rather than by exclusion of suitable sites (deviance outside of expert maps was extremely low). Map topology was the primary predictor of expert performance rather than any factor related to species characteristics such as mobility. Given the heterogeneity and discontinuity of suitable landscapes, expert maps drawn with more detail are more likely to agree with species distribution models and thus minimize both commission and omission errors.

中文翻译：

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将专家范围图与物种分布模型预测相匹配

摘要 基于专家意见的物种分布图是保护规划的重要资源。专家地图通常伴随着物种描述，指定内部范围异质性的来源，例如栖息地关联，但在使用专家地图进行分析时很少考虑这些。我们开发了一个定量指标（专家评分）来评估专家地图和从物种分布模型获得的栖息地概率表面之间的一致性。这种方法奖励避免不合适的站点和在专家地图中包含合适的站点。我们从 2 个广泛使用的北美来源（Glassberg [1999, 2001] 和 Scott [1986]）中的每一个中获得了 330 种蝴蝶的专家地图，并计算了每个物种的专家分数。全面的，由于特定的规则（例如，Glassberg 只包括已知物种繁殖的区域，而 Scott 包括一个物种每年扩展到的所有区域），Glassberg 地图获得了比 Scott 更高的专家分数（分别为 0.61 和 0.41）。包括或排除范围内的区域。专家地图的预测性能几乎总是受到包含不合适站点的阻碍，而不是由于排除合适站点（专家地图之外的偏差极低）。地图拓扑是专家表现的主要预测因素，而不是与物种特征（如流动性）相关的任何因素。鉴于适宜景观的异质性和不连续性，