In a macroecological approach, we have used the data abundant species or archetypal cohorts as proxies for the data deficient species, to model their distributions. Upon successful modelling, we assessed climate change impacts on their distribution in the Himalayan arc extending from the Indian borders in the west to the hills in Myanmar. Out of 34 Galliformes species occurring in the Himalayan arc, 21 species were retained in this study, rest were dropped due to very low occurrences. Best performing variables from the set of environmental variables (n = 36) consisting of topography, vegetation, soil, anthropogenic indices and bioclimatic factors were tested for collinearity. Ordination (PCA and NMDS) and clustering (hierarchical clustering, agnes, partitioning around medoids and k–means clustering) and Species Archetype Modelling (SAM) methods were performed for finding the archetypal cohorts among the species. The clusters were used for two different modelling frameworks- Species Distribution Models (SDMs) with a combination of biophysical and topographical parameters; and Bioclimatic Envelope Models (BEMs) with only bioclimatic variables. Predicted climate-driven changes in species ranges (year 2070, RCP 4.5 and 8.5) were assessed. The 21 species were clustered in four groups. Precipitation emerged as the overall significant driving factor for all the three clusters. Random Forest was the highest performing model across the clusters. Two cluster restricted to the eastern Himalayas were found to be the most affected in a climate change scenario. Cluster belonging to the western Himalayas was predicted to lose about 70% of its bioclimatic habitats in both the scenarios. In a first attempt, this study presents a novel approach towards distribution and climate change modelling for the rare Galliformes, using abundant Galliformes over a pan Himalayan scale.

中文翻译：

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利用丰富的原型数据群体进行适应规划，对稀有喜马拉雅鸡形目物种进行分布建模和气候变化风险评估策略


在宏观生态学方法中，我们使用数据丰富的物种或原型群体作为数据缺乏的物种的代理，以对其分布进行建模。成功建模后，我们评估了气候变化对其在从西部印度边境延伸到缅甸山区的喜马拉雅山弧内分布的影响。在喜马拉雅弧区出现的 34 种鸡形目物种中，本研究保留了 21 种，其余物种由于出现率极低而被放弃。对由地形、植被、土壤、人为指数和生物气候因素组成的环境变量组 (n = 36) 中表现最佳的变量进行共线性测试。排序（PCA 和 NMDS）和聚类（层次聚类、agnes、围绕中心点的划分和 k 均值聚类）和物种原型建模 (SAM) 方法用于查找物种中的原型队列。这些簇用于两种不同的建模框架——结合了生物物理和地形参数的物种分布模型（SDM）；和仅包含生物气候变量的生物气候包络模型（BEM）。对预测的气候驱动的物种范围变化（2070 年，RCP 4.5 和 8.5）进行了评估。这 21 个物种分为 4 个组。降水成为所有三个集群的总体重要驱动因素。随机森林是集群中性能最高的模型。研究发现，喜马拉雅山脉东部的两个集群在气候变化情景中受影响最严重。在这两种情况下，喜马拉雅山西部的集群预计将失去约 70% 的生物气候栖息地。 在第一次尝试中，这项研究提出了一种针对稀有鸡形目动物的分布和气候变化建模的新方法，使用了泛喜马拉雅范围内丰富的鸡形目动物。