Australian Rivers Institute, School of Environment, Griffith University, Gold Coast, Qld, 4222, Australia.
Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, Blindern, 0316, Oslo, Norway.
Sci Rep. 2017 Oct 31;7(1):14424. doi: 10.1038/s41598-017-14774-2.
All communities may re-assemble after disturbance. Predictions for re-assembly outcomes are, however, rare. Here we model how fish communities in an extremely variable Australian desert river re-assemble following episodic floods and drying. We apply information entropy to quantify variability in re-assembly and the dichotomy between stochastic and deterministic community states. Species traits were the prime driver of community state: poor oxygen tolerance, low dispersal ability, and high fecundity constrain variation in re-assembly, shifting assemblages towards more stochastic states. In contrast, greater connectivity, while less influential than the measured traits, results in more deterministic states. Ecology has long recognised both the stochastic nature of some re-assembly trajectories and the role of evolutionary and bio-geographic processes. Our models explicitly test the addition of species traits and landscape linkages to improve predictions of community re-assembly, and will be useful in a range of different ecosystems.
所有社区在受到干扰后都可能重新组合。然而，对于重新组合的结果预测却很少。在这里，我们模拟了澳大利亚极不稳定的沙漠河流中的鱼类社区在间歇性洪水和干涸后是如何重新组合的。我们应用信息熵来量化重新组合的可变性以及随机和确定性社区状态之间的二分法。物种特征是社区状态的主要驱动因素：低氧气耐受性、低扩散能力和高繁殖力限制了重新组合的变化，使组合向更随机的状态转移。相比之下，更大的连通性虽然不如测量的特征有影响力，但会导致更确定的状态。生态学长期以来一直认识到一些重新组合轨迹的随机性以及进化和生物地理过程的作用。我们的模型明确测试了增加物种特征和景观联系以提高社区重新组合预测的效果，并且在一系列不同的生态系统中都将非常有用。
