Abstract
BackgroundAccurate network models of species interaction could be used to predict population dynamics and be applied to manage real world ecosystems. Most relevant models are nonlinear, however, and data available from real world ecosystems are too noisy and sparsely sampled for common inference approaches. Here we improved the inference of generalized Lotka–Volterra (gLV) ecological networks by using a new optimization algorithm to constrain parameter signs with prior knowledge and a perturbation-based ensemble method.ResultsWe applied the new inference to long-term species abundance data from the freshwater fish community in the Illinois River, United States. We constructed an ensemble of 668 gLV models that explained 79% of the data on average. The models indicated (at a 70% level of confidence) a strong positive interaction from emerald shiner (Notropis atherinoides) to channel catfish (Ictalurus punctatus), which we could validate using data from a nearby observation site, and predicted that the relative abundances of most fish species will continue to fluctuate temporally and concordantly in the near future. The network shows that the invasive silver carp (Hypophthalmichthys molitrix) has much stronger impacts on native predators than on prey, supporting the notion that the invader perturbs the native food chain by replacing the diets of predators.ConclusionsEnsemble approaches constrained by prior knowledge can improve inference and produce networks from noisy and sparsely sampled time series data to fill knowledge gaps on real world ecosystems. Such network models could aid efforts to conserve ecosystems such as the Illinois River, which is threatened by the invasion of the silver carp.
Highlights
Accurate network models of species interaction could be used to predict population dynamics and be applied to manage real world ecosystems
By constructing a generalized Lotka–Volterra (gLV) model, the underlying ecology is phenomenologically summarized with minimal parameterization: the biological growth is modelled by an exponential growth rate and the fitness effect of each one-way interaction is quantified by a single coefficient with magnitude and sign representing the interaction strength and type respectively
Fish community varies in space and time The Illinois River is a major tributary of the Upper Mississippi River, where the long-term monitoring efforts of the fish community spread across six field stations since 1993 (Fig. 1a)
Summary
Accurate network models of species interaction could be used to predict population dynamics and be applied to manage real world ecosystems. Computational methods that seek to infer ecological networks from laboratory or field data include parameter-free correlation-based algorithms such as Pearson’s correlation coefficients [4], parametric or non-parametric statistical and machine-learning methods such as Bayesian networks [4, 5], non-parametric approaches based on nonlinear state space reconstruction such as the convergent cross mapping [6], and nonlinear parametric models of population dynamics such as Ecopath with Ecosim [7]. Multispecies population dynamics models, the generalized Lotka–Volterra (gLV) model (Eq (1)), provide a flexible way to model and link species interactions to their temporal abundance changes. Inferring microbial ecological networks from gut microbiome time series data has revealed a native gut bacterial species that prevents invasion by a pathogenic species [17]
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