Liang Xiaoping, Wei Kaili, Li Qinfang, Gooley Aaron, Zhang Minghai, Yu Jingjing, Wang Zhongbin, Yin Changxiao, Zhang Weiqi
College of Wildlife and Protected Area, Northeast Forestry University, Harbin, Heilongjiang Province, China.
Langfang Bureau of Natural Resources and Planning, Langfang, Hebei Province, China.
PeerJ. 2024 Nov 27;12:e18614. doi: 10.7717/peerj.18614. eCollection 2024.
Tibetan red deer () in the high-altitude environment of the Qinghai-Tibet Plateau could face seasonal challenges from food shortages and nutritional deficiencies but the nutritional requirements are complex. Analyzing diet composition pattern(s) is the first step to disentangle this complexity. From a systematic perspective, we hypothesize that: (A) diet composition pattern or patterns exist within the population and (B) a portion of the diet beyond characterized diet combinations will consist of random combinations. In this study, we investigated diet composition patterns of a Tibetan red deer population distributed in the Sangri Red Deer Reserve, Tibet Autonomous Region, during the harsh lean season. In March 2021 and 2022, we searched for Tibetan red deer in the reserve and collected freshly defecated samples. Diet composition at the individual level was determined using micro-histological analysis, followed by k-means clustering and co-occurrence network analysis to reveal population level diet composition patterns. Diet composition of Tibetan red deer included 14 and 19 plant species (or genera) in 2021 and 2022, respectively. K-means clustering indicated two distinct diet patterns within the population across both sampling periods. In 2021, diet composition of both clusters was dominated by spp. (58.49% and 33.67%). In 2022, had the highest ranking and occupied 34.83% of diet composition in the first cluster while spp. (39.39%) was the most consumed food in the second cluster. Results of co-occurrence networks showed positively associated food combinations of less dominant food items, with a staple food occurring in all food item pairs in both years. However, randomness accounted for 95.83% and 93% of all food item pairs in 2021 and 2022, respectively, which implies a stable dietary complex system. The 2022 co-occurrence network displayed complex associations, while the 2021 network exhibited limited and simple associations. Our results suggest that Tibetan red deer fulfill their nutritional requirements by consuming high quantities of several food items or a balanced combination of foods with complex co-occurrence associations to overcome potential food shortages, but multilayer networks containing nutritional values and food availabilities are necessary to entangle the complexity of the dietary system.
青藏高原高海拔环境中的藏马鹿可能面临食物短缺和营养缺乏带来的季节性挑战，但其营养需求较为复杂。分析饮食组成模式是理清这种复杂性的第一步。从系统的角度来看，我们假设：（A）种群中存在一种或多种饮食组成模式，以及（B）除了已确定的饮食组合之外，一部分饮食将由随机组合构成。在本研究中，我们调查了分布于西藏自治区桑日马鹿自然保护区的一个藏马鹿种群在严酷的食物匮乏季节的饮食组成模式。在2021年3月和2022年3月，我们在自然保护区内搜寻藏马鹿并收集新鲜粪便样本。通过微观组织学分析确定个体水平的饮食组成，随后进行k均值聚类和共现网络分析以揭示种群水平的饮食组成模式。2021年和2022年藏马鹿的饮食组成分别包括14种和19种植物物种（或属）。k均值聚类表明在两个采样期内种群中存在两种不同的饮食模式。2021年，两个聚类的饮食组成均以 spp. 为主（分别占58.49%和33.67%）。2022年， 在第一个聚类中排名最高，占饮食组成的34.83%，而 spp.（39.39%）是第二个聚类中消耗最多的食物。共现网络的结果显示，不太占主导地位的食物之间存在正相关的食物组合，且两年中所有食物对中均有主食出现。然而，2021年和2022年所有食物对中随机组合分别占95.83%和93%，这意味着存在一个稳定的饮食复杂系统。2022年的共现网络显示出复杂的关联，而2021年的网络关联有限且简单。我们的结果表明，藏马鹿通过大量食用几种食物或食用具有复杂共现关联的食物的平衡组合来满足其营养需求，以克服潜在的食物短缺，但需要包含营养价值和食物可获得性的多层网络来理清饮食系统的复杂性。
