Garcia Francisca C, Warfield Ruth, Yvon-Durocher Gabriel
Environment and Sustainability Institute, University of Exeter, Penryn, Cornwall, United Kingdom.
Red Sea Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Front Microbiol. 2022 Aug 17;13:906252. doi: 10.3389/fmicb.2022.906252. eCollection 2022.
Understanding the ecological processes that underpin the dynamics of community turnover in response to environmental change is critical to predicting how warming will influence ecosystem functioning. Here, we quantify the effect of changing temperature on community composition and ecosystem functioning the action of ecological selection on population-level thermal traits. To achieve this, we use microbes isolated from a network of geothermal streams in Iceland where temperatures span 8-38°C within a single catchment. We first quantified variability in thermal tolerance between taxa, and then assembled synthetic communities along a broad thermal gradient to explore how temperature-driven selection on thermal tolerance traits shaped the emergent community structures and functions. We found marked changes in community structure and composition with temperature, such that communities exposed to extreme temperatures (10, 35°C) had highly asymmetric biomass distributions and low taxonomic richness. Thermal optima were a good predictor of the presence and relative abundance of taxa in the high-temperature treatments. We also found that the evenness of the abundance distribution was related to ecosystem production, such that communities with more equitable abundance distribution were also the most productive. Our results highlight the utility of using a multi-level approach that links population-level traits with community structure and ecosystem functioning to better understand how ecological communities will respond to global warming.
了解支撑群落更替动态以应对环境变化的生态过程对于预测气候变暖将如何影响生态系统功能至关重要。在此,我们量化了温度变化对群落组成和生态系统功能的影响,即生态选择对种群水平热性状的作用。为实现这一目标,我们使用了从冰岛地热溪流网络中分离出的微生物,在单个集水区内,这些溪流的温度范围为8 - 38°C。我们首先量化了不同分类群之间耐热性的变异性,然后沿着广泛的温度梯度构建合成群落,以探究温度驱动的耐热性状选择如何塑造了群落的结构和功能。我们发现群落结构和组成随温度发生显著变化,以至于暴露于极端温度(10、35°C)下的群落具有高度不对称的生物量分布和较低的分类丰富度。最适温度是高温处理中分类群存在和相对丰度的良好预测指标。我们还发现丰度分布的均匀度与生态系统生产力相关,即丰度分布更均衡的群落也是生产力最高的群落。我们的结果强调了采用多层次方法的实用性,该方法将种群水平性状与群落结构和生态系统功能联系起来,以更好地理解生态群落将如何应对全球变暖。