Ecological network inference from long-term presence-absence data

Ecological communities are characterized by complex networks of trophic and nontrophic interactions, which shape the dy-namics of the community. Machine learning and correlational methods are increasingly popular for inferring networks from co-occurrence and time series data, particularly in microbial systems. In this study, we test the suitability of these methods for inferring ecological interactions by constructing networks using Dynamic Bayesian Networks, Lasso regression, and Pear-son’s correlation coefficient, then comparing the model networks to empirical trophic and nontrophic webs in two ecological systems. We find that although each model significantly replicates the structure of at least one empirical network, no model significantly predicts network structure in both systems, and no model is clearly superior to the others. We also find that networks inferred for the Tatoosh intertidal match the nontrophic network much more closely than the trophic one, possibly due to the cha...生态群落以营养级与非营养级相互作用构成的复杂网络为核心特征，此类网络主导着群落的动态变化。机器学习与相关分析方法在从共现数据及时序数据中推断生态网络的应用日益广泛，在微生物系统研究中尤为突出。本研究旨在验证此类方法用于推断生态相互作用的适用性：我们分别采用动态贝叶斯网络（Dynamic Bayesian Networks）、套索回归（Lasso regression）与皮尔逊相关系数（Pearson's correlation coefficient）构建生态网络，随后将模型生成的网络与两类生态系统中的经验营养网络及非营养网络进行对比。研究结果显示，尽管每种模型均可显著复现至少一类经验网络的结构，但没有任何一种模型能够同时准确预测两个系统的网络结构，且各模型之间未表现出显著的性能优劣差异。此外我们发现，针对塔托什潮间带（Tatoosh intertidal）推断得到的网络与非营养网络的匹配度远高于其与营养网络的匹配度，这可能源于……