Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.
Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, California, USA.
Ecol Lett. 2022 Sep;25(9):1974-1985. doi: 10.1111/ele.14070. Epub 2022 Jul 13.
The potential for forecasting the dynamics of ecological systems is currently unclear, with contrasting opinions regarding its feasibility due to ecological complexity. To investigate forecast skill within and across systems, we monitored a microbial system exposed to either constant or fluctuating temperatures in a 5-month-long laboratory experiment. We tested how forecasting of species abundances depends on the number and strength of interactions and on model size (number of predictors). We also tested how greater system complexity (i.e. the fluctuating temperatures) impacted these relations. We found that the more interactions a species had, the weaker these interactions were and the better its abundance was predicted. Forecast skill increased with model size. Greater system complexity decreased forecast skill for three out of eight species. These insights into how abundance prediction depends on the connectedness of the species within the system and on overall system complexity could improve species forecasting and monitoring.
目前，由于生态系统的复杂性，预测其动态的可能性尚不清楚。对于其可行性，人们的观点存在分歧。为了研究在系统内和跨系统进行预测的能力，我们在一项为期 5 个月的实验室实验中，监测了一个暴露于恒定或波动温度下的微生物系统。我们测试了物种丰度的预测如何取决于相互作用的数量和强度以及模型大小（预测因子的数量）。我们还测试了更大的系统复杂性（即波动的温度）如何影响这些关系。我们发现，一个物种的相互作用越多，这些相互作用就越弱，其丰度的预测就越好。随着模型大小的增加，预测能力也随之提高。对于八个物种中的三个，更大的系统复杂性降低了预测能力。这些关于丰度预测如何取决于系统内物种的连接性和整体系统复杂性的见解，可以提高物种预测和监测的能力。
