Modelling species across vast distributions in remote, high mountain regions like the Himalayas remains a challenging task. Challenges include, first and foremost, large-scale sampling of species occurrences and acquisition of sufficient high quality, fine-scale environmental parameters. We compiled a review of 157 Himalayan studies published between 2010 and 2021, aiming at identifying their main modelling objective in relation to the conceptualization of their methodological framework, evaluating origin of species occurrence data, taxonomic groups, spatial and temporal scale, selection of predictor variables and applied modelling algorithms. The majority of the analysed studies (40%) attempted to answer questions about potential range changes under future or past climatic conditions. The most studied organisms were trees (27%), followed by mammals (22%), herbaceous plants (20%), and birds (9%).

For almost all studies we noted that a critical investigation and evaluation of input parameters and their ability to account for the species ecological requirements were neglected. Over 87% of all studies used Worldclim climate data as predictor variables, while around 50% of these studies solely relied on Worldclim climate data. Climate data from other sources were incorporated in only 7% and an additional 6% solely used remotely sensed predictors. Only around 2% of all studies attempted to compare the influence of different climate data sources on model performance. By far, Maxent was the most used modelling algorithm with 66%, followed by ensemble approaches (16%), whereas statistical modelling techniques lagged far behind (9%). Surprisingly, we found in 37% of the studies no interpretation on the relationship between the species and the predictor variables, while 27% of all studies included brief information, and 36% provided an elaborate, detailed interpretation on species ecological needs reflected in the final model.

With this review we highlight the necessity to identify and reduce biases and uncertainty associated with species’ occurrence records and environmental data a priori. Since flawed input parameters produce misleading models without ecological causality, their implementation may have detrimental consequences when the best possible adaptation to future climatic conditions is at stake.

对喜马拉雅山等偏远高山地区广泛分布的物种进行建模仍然是一项具有挑战性的任务。挑战首先包括对物种发生的大规模采样和获得足够高质量、精细尺度的环境参数。我们对 2010 年至 2021 年间发表的 157 项喜马拉雅研究进行了综述，旨在确定与方法框架概念化相关的主要建模目标、评估物种发生数据的来源、分类群、空间和时间尺度、预测变量的选择和应用建模算法。大多数分析的研究 (40%) 试图回答有关未来或过去气候条件下潜在范围变化的问题。研究最多的生物是树木（27%），

对于几乎所有的研究，我们都注意到对输入参数及其解释物种生态需求的能力的批判性调查和评估被忽略了。超过 87% 的研究使用Worldclim气候数据作为预测变量，而这些研究中约有 50% 仅依赖于Worldclim气候数据。来自其他来源的气候数据仅包含在 7% 中，另外 6% 单独使用了遥感预测器。只有大约 2% 的研究试图比较不同气候数据源对模型性能的影响。到目前为止，Maxent 是最常用的建模算法，占 66%，其次是集成方法 (16%)，而统计建模技术远远落后 (9%)。令人惊讶的是，我们发现 37% 的研究没有解释物种与预测变量之间的关系，而所有研究中有 27% 包含简要信息，36% 对最终反映的物种生态需求提供了详尽、详细的解释。模型。

通过这次审查，我们强调了识别和减少与物种发生记录和先验环境数据相关的偏见和不确定性的必要性。由于有缺陷的输入参数会产生没有生态因果关系的误导性模型，因此当对未来气候条件的最佳适应受到威胁时，它们的实施可能会产生不利后果。