Data from: The importance of individual heterogeneity for interpreting faecal glucocorticoid metabolite levels in wildlife studies

1. Being a non-invasive and inexpensive method, the analysis of faecal corticosteroid metabolites (FCM) is increasingly being applied in wildlife research. Various environmental factors have been shown to influence FCM levels, but most studies did not account for inter-individual variance, which we hypothesized may substantially affect the results. 2. We combined FCM analysis with genetic analysis to identify the sex and individual of samples collected in three consecutive winters, with repeated samples per individual, across the entire range of an endangered population of capercaillie (Tetrao urogallus) in Southwestern Germany. Using generalized additive mixed models, we modelled FCM levels as a function of sex, season and environmental covariates at two spatial scales (sampling location and home range scales). We compared two models: one including information on the individual animal, and the other excluding this information (i.e. naïve model) to assess the influence of individual effects on the results obtained. 3. Most of the variance (44.0% and 45.1% at the sampling and home range scale, respectively) was explained by the inter-individual differences, and only very little (4.0% and 5.1%, respectively) by the environmental predictors. When ignoring individual effects, the model results changed considerably, with other, previously non-informative predictors, becoming significant. 4. In the full models, accounting for inter-individual variance, no effect was found of weather conditions, at either scale. FCM levels were negatively correlated with habitat quality, at the sampling location, whereas human recreation at the home range scale led to elevated FCM levels. In the naïve models, two additional predictors appeared significant: one weather variable at local scales and two at home range scale. In all models, seasonal FCM patterns differed significantly between males and females. 5. Synthesis and applications. Our results highlight the importance of considering the effects of individual heterogeneity when studying FCM in wildlife research, as ignoring information on the individual might lead to erroneous conclusions. Combining FCM analyses with genetic analyses can be an efficient approach to adequately address this issue.21-Feb-2018

1. 粪便糖皮质激素代谢物（faecal corticosteroid metabolites, FCM）分析作为一种非侵入性且成本低廉的检测手段，目前在野生动物研究领域的应用愈发广泛。已有多项研究证实，多种环境因素可对FCM水平产生影响，但绝大多数相关研究未考虑个体间变异，我们推测该变异可能对研究结果造成显著影响。
2. 我们结合FCM分析与遗传学分析，对德国西南部濒危松鸡（Tetrao urogallus）种群全分布范围内连续三个冬季采集的样本进行了个体性别与身份鉴定，每个个体均有重复采样样本。本研究采用广义加性混合模型（generalized additive mixed models），以两个空间尺度（采样点位尺度与家域尺度）下的性别、季节及环境协变量为自变量，对FCM水平进行建模。我们对比了两类模型：一类纳入个体动物信息，另一类则未纳入该信息（即朴素模型），以此评估个体效应对所得研究结果的影响。
3. 在采样点位尺度与家域尺度下，分别有44.0%与45.1%的变异可由个体间差异解释，而环境预测因子仅能解释极少部分变异（分别为4.0%与5.1%）。若忽略个体效应，模型结果会发生显著变化，原本无显著关联的预测因子会变为显著相关。
4. 在纳入个体变异的完整模型中，无论在哪个空间尺度下，天气条件均未对FCM水平产生显著影响。在采样点位尺度上，FCM水平与生境质量呈负相关；而在家域尺度上，人类休闲活动会导致FCM水平升高。在朴素模型中，额外出现了两个显著的预测因子：局部尺度下的一个天气变量，以及家域尺度下的两个天气变量。在所有模型中，雄性与雌性的FCM季节变化模式均存在显著差异。
5. 总结与应用。本研究结果强调，在野生动物FCM相关研究中考虑个体异质性效应的重要性——若忽略个体信息，可能会得出错误的研究结论。将FCM分析与遗传学分析相结合，可成为有效解决该问题的可靠手段。
2018年2月21日