University of Chicago, Department of Ecology and Evolution, Chicago, 60637, USA.
University of Chicago, Computation Institute, Chicago, 60637, USA.
Sci Rep. 2017 Aug 2;7(1):7154. doi: 10.1038/s41598-017-07009-x.
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 challenges of identifying trophic interactions from presence-absence data. Our findings suggest that although these methods hold some promise for ecological network inference, presence-absence data does not provide enough signal for models to consistently identify interactions, and networks inferred from these data should be interpreted with caution.
生态群落的特征是复杂的营养和非营养相互作用网络，这些网络塑造了群落的动态。机器学习和相关方法越来越受欢迎，用于从共现和时间序列数据中推断网络，特别是在微生物系统中。在这项研究中，我们通过使用动态贝叶斯网络、Lasso 回归和 Pearson 相关系数构建网络，来测试这些方法用于推断生态相互作用的适用性，然后将模型网络与两个生态系统中的经验营养和非营养网络进行比较。我们发现，虽然每个模型都显著复制了至少一个经验网络的结构，但没有一个模型能够显著预测两个系统中的网络结构，而且没有一个模型明显优于其他模型。我们还发现，潮汐间带的 Tatoosh 推断网络与非营养网络的匹配程度远高于营养网络，这可能是由于从存在-缺失数据中识别营养相互作用存在挑战。我们的研究结果表明，尽管这些方法在生态网络推断方面具有一定的前景，但存在-缺失数据并不能为模型提供足够的信号来一致地识别相互作用，并且应该谨慎解释从这些数据推断出的网络。
