Department of Biological Sciences, University of Alabama, Tuscaloosa, Alabama, 35487, USA.
Aquatic Ecology Laboratory, Department of Evolution, Ecology, and Organismal Biology, Translational Data Analytics Institute, The Ohio State University, Columbus, Ohio, 43212, USA.
Ecology. 2021 Oct;102(10):e03467. doi: 10.1002/ecy.3467. Epub 2021 Aug 8.
Understanding the observed temperature dependence of decomposition (i.e., its "apparent" activation energy) requires separation of direct effects of temperature on consumer metabolism (i.e., the "inherent" activation energy) from those driven by indirect seasonal patterns in phenology and biomass, and by longer-term, climate-driven shifts in acclimation, adaptation, and community assembly. Such parsing is important because studies that relate temperature to decomposition usually involve multi-season data and/or spatial proxies for long-term shifts, and so incorporate these indirect factors. The various effects of such factors can obscure the inherent temperature dependence of detrital processing. Separating the inherent temperature dependence of decomposition from other drivers is important for accurate prediction of the contribution of detritus-sourced greenhouse gases to climate warming and requires novel approaches to data collection and analysis. Here, we present breakdown rates of red maple litter incubated in coarse- and fine-mesh litterbags (the latter excluding macroinvertebrates) for serial approximately one-month increments over one year in nine streams along a natural temperature gradient (mean annual: 12.8°-16.4°C) from north Georgia to central Alabama, USA. We analyzed these data using distance-based redundancy analysis and generalized additive mixed models to parse the dependence of decomposition rates on temperature, seasonality, and shredding macroinvertebrate biomass. Microbial decomposition in fine-mesh bags was significantly influenced by both temperature and seasonality. Accounting for seasonality corrected the temperature dependence of decomposition rate from 0.25 to 0.08 eV. Shredder assemblage structure in coarse-mesh bags was related to temperature across both sites and seasons, shifting from "cold" stonefly-dominated communities to "warm" communities dominated by snails or crayfish. Shredder biomass was not a significant predictor of either coarse-mesh or macroinvertebrate-mediated (i.e., coarse- minus fine-mesh) breakdown rates, which were also jointly influenced by temperature and seasonality. Unlike fine-mesh bags, however, temperature dependence of litter breakdown did not differ between models with and without seasonality for either coarse-mesh (0.36 eV) or macroinvertebrate-mediated (0.13 eV) rates. We conclude that indirect (non-thermal) seasonal and site-level effects play a variable and potentially strong role in shaping the apparent temperature dependence of detrital breakdown. Such effects should be incorporated into studies designed to estimate inherent temperature dependence of slow ecological processes.
了解分解的观察到的温度依赖性（即其“表观”活化能）需要将温度对消费者代谢的直接影响（即“固有”活化能）与季节模式和生物量以及长期气候驱动的驯化、适应和群落组装中的间接影响分开。这种解析很重要，因为将温度与分解相关联的研究通常涉及多季节数据和/或长期变化的空间代理，因此包含了这些间接因素。这些因素的各种影响可能会掩盖碎屑处理的固有温度依赖性。将分解的固有温度依赖性与其他驱动因素分开对于准确预测碎屑源温室气体对气候变暖的贡献至关重要，这需要采用新的数据收集和分析方法。在这里，我们展示了在美国佐治亚州北部到阿拉巴马州中部的九个溪流中，粗网和细网袋（后者不包括大型无脊椎动物）中红枫落叶的分解率，这些落叶在一年中进行了大约一个月的连续递增孵化。我们使用基于距离的冗余分析和广义加性混合模型分析了这些数据，以解析分解速率对温度、季节性和撕碎大型无脊椎动物生物量的依赖关系。细网袋中的微生物分解受到温度和季节性的显著影响。考虑到季节性，分解率对温度的依赖性从 0.25 变为 0.08 eV。粗网袋中的撕碎者群落结构与两个地点和季节的温度有关，从以石蝇为主导的“寒冷”群落转变为以蜗牛或小龙虾为主导的“温暖”群落。撕碎者生物量不是粗网或大型无脊椎动物介导（即粗网减去细网）分解率的重要预测因子，这两个分解率也受到温度和季节性的共同影响。然而，与细网袋不同，粗网或大型无脊椎动物介导的分解率（0.36 eV 和 0.13 eV）的模型中，季节性对分解率的温度依赖性没有差异。我们得出的结论是，间接（非温度）季节性和地点水平的影响在塑造碎屑分解的表观温度依赖性方面起着可变且潜在强大的作用。在设计旨在估计缓慢生态过程固有温度依赖性的研究中，应考虑这些影响。
