Anthropogenic change is affecting mountain regions worldwide. Managing this change and advancing biodiversity information for research requires spatially detailed information on species distributions which often is incomplete. Here, we provide a model‐based approach for the integration of expert‐based elevational range information with expert range maps and point occurrences to address this need. These integrated models use expert knowledge on elevational and distributional ranges as offset in a Poisson point process species distribution model (SDM). We use this approach to model the distribution of 276 hummingbird Trochilidae species at 1 km resolution and validate model performance with extensive survey data (presence–absence inventories). Models including expert elevation information consistently outperformed those lacking this information. Improvements were greatest when the number of available occurrences was small, highlighting the added value from expert elevation information, especially for data‐poor species. Separate validation data indicated significant increases in true skill statistics, based on higher specificity and slightly improved sensitivity. SDMs that included expert range information out‐performed presence‐only models based only on occurrence data in 92.5% of cases and had higher sensitivity, lower false positive rates and smaller predicted range sizes. Generally, the integrated models removed unsuitable areas from range estimates and decreased overestimates in geographic range size (false presences) inherent in expert maps and in models lacking elevation information. By stacking SDM output we provide a first, hemispheric map of predicted hummingbird species richness modelled at 1 km resolution and identify southwest Colombia as a richness hotspot. Our study highlights the value gained from integrating multiple data types in a single framework. The presented approach improved high‐resolution range predictions for single species (reducing false presences) and aggregate biodiversity patterns (e.g. reducing species richness overestimates). The method is now being implemented and expanded in Map of Life.

中文翻译：

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从融合点记录以及横向和海拔专家信息得出的大陆规模1 km蜂鸟多样性

人为变化正在影响全世界的山区。要管理这种变化并促进生物多样性信息的研究，就需要有关物种分布的空间详细信息，而这些信息往往是不完整的。在这里，我们提供了一种基于模型的方法，用于将基于专家的海拔范围信息与专家的范围图和点出现进行集成，以解决这一需求。这些集成模型使用有关海拔和分布范围的专家知识作为Poisson点过程物种分布模型（SDM）中的偏移量。我们使用这种方法来模拟276蜂鸟Trochilidae的分布分辨率为1 km的物种，并使用广泛的调查数据（有无库存）验证模型的性能。包含专家海拔信息的模型始终优于缺少该信息的模型。当可用事件的数量很少时，改进最大，突出了专家海拔信息的增加值，尤其是对于数据贫乏的物种。单独的验证数据表明，基于更高的特异性和略微提高的灵敏度，真实技能统计数据显着增加。仅在92.5％的案例中，包含专家范围信息的SDM优于仅基于出现数据的仅存在模型，并且具有更高的敏感性，更低的误报率和较小的预测范围大小。一般来说，集成模型从距离估计中删除了不合适的区域，并减少了专家地图和缺乏海拔信息的模型固有的地理范围大小（错误存在）中的过高估计。通过堆叠SDM输出，我们提供了以1 km分辨率为模型的预测蜂鸟物种丰富度的第一个半球图，并将哥伦比亚西南部地区确定为丰富度热点。我们的研究强调了通过将多个数据类型集成到一个框架中而获得的价值。提出的方法改进了对单个物种的高分辨率范围预测（减少了错误的存在）和总体生物多样性模式（例如，降低了物种丰富度的高估）。该方法现在正在生命地图中实施和扩展。