One of the most reliable features of natural systems is that they change through time. Theory predicts that temporally fluctuating conditions shape community composition, species distribution patterns, and life history variation, yet features of temporal variability are rarely incorporated into studies of species–environment associations. In this study, we evaluated how two components of temporal environmental variation—variability and predictability—impact plant community composition and species distribution patterns in the alpine tundra of the Southern Rocky Mountains in Colorado (USA). Using the Sensor Network Array at the Niwot Ridge Long‐Term Ecological Research site, we used in situ, high‐resolution temporal measurements of soil moisture and temperature from 13 locations (“nodes”) distributed throughout an alpine catchment to characterize the annual mean, variability, and predictability in these variables in each of four consecutive years. We combined these data with annual vegetation surveys at each node to evaluate whether variability over short (within‐day) and seasonal (2‐ to 4‐month) timescales could predict patterns in plant community composition, species distributions, and species abundances better than models that considered average annual conditions alone. We found that metrics for variability and predictability in soil moisture and soil temperature, at both daily and seasonal timescales, improved our ability to explain spatial variation in alpine plant community composition. Daily variability in soil moisture and temperature, along with seasonal predictability in soil moisture, was particularly important in predicting community composition and species occurrences. These results indicate that the magnitude and patterns of fluctuations in soil moisture and temperature are important predictors of community composition and plant distribution patterns in alpine plant communities. More broadly, these results highlight that components of temporal change provide important niche axes that can partition species with different growth and life history strategies along environmental gradients in heterogeneous landscapes.

中文翻译：

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时间变异性和可预测性可预测高山植物群落组成和分布模式


自然系统最可靠的特征之一是它们会随着时间的推移而变化。理论预测，时间波动的条件塑造了群落组成、物种分布模式和生活史变化，但时间变化的特征很少被纳入物种-环境关联的研究中。在这项研究中，我们评估了时间环境变化的两个组成部分——可变性和可预测性——如何影响美国科罗拉多州南部落基山脉高山苔原的植物群落组成和物种分布模式。使用 Niwot Ridge 长期生态研究站点的传感器网络阵列，我们对分布在整个高山集水区的 13 个位置（“节点”）的土壤湿度和温度进行了原位、高分辨率的时间测量，以表征这些变量的年度平均值、变异性和可预测性连续四年中的每一年。我们将这些数据与每个节点的年度植被调查相结合，以评估短期（一天内）和季节性（2 至 4 个月）时间尺度的变化是否能比单独考虑平均年条件的模型更好地预测植物群落组成、物种分布和物种丰度的模式。我们发现，在每日和季节性时间尺度上，土壤水分和土壤温度的变异性和可预测性指标提高了我们解释高山植物群落组成的空间变化的能力。土壤湿度和温度的每日变化以及土壤湿度的季节性可预测性，对于预测群落组成和物种出现尤为重要。 这些结果表明，土壤水分和温度波动的大小和模式是高寒植物群落群落组成和植物分布模式的重要预测指标。更广泛地说，这些结果强调，时间变化的组成部分提供了重要的生态位轴，这些轴可以沿着异质景观中的环境梯度划分具有不同生长和生活史策略的物种。