Abstract
Food-web theory can be a powerful guide to the management of complex ecosystems. However, we show that indices of species importance common in food-web and network theory can be a poor guide to ecosystem management, resulting in significantly more extinctions than necessary. We use Bayesian Networks and Constrained Combinatorial Optimization to find optimal management strategies for a wide range of real and hypothetical food webs. This Artificial Intelligence approach provides the ability to test the performance of any index for prioritizing species management in a network. While no single network theory index provides an appropriate guide to management for all food webs, a modified version of the Google PageRank algorithm reliably minimizes the chance and severity of negative outcomes. Our analysis shows that by prioritizing ecosystem management based on the network-wide impact of species protection rather than species loss, we can substantially improve conservation outcomes.
Highlights
Food-web theory can be a powerful guide to the management of complex ecosystems
Across the food web indices tested here, the maximum potential departure from the optimal management performance ranged from 8.6% fewer species surviving when using the modified Google PageRank index, through to 60.8% fewer species when managing based on Return-On-Investment
We discovered that common indices of species importance in food webs do not reveal the best species to manage to conserve the maximum number of species in those webs
Summary
Food-web theory can be a powerful guide to the management of complex ecosystems. we show that indices of species importance common in food-web and network theory can be a poor guide to ecosystem management, resulting in significantly more extinctions than necessary. We use Bayesian Networks and Constrained Combinatorial Optimization to find optimal management strategies for a wide range of real and hypothetical food webs This Artificial Intelligence approach provides the ability to test the performance of any index for prioritizing species management in a network. The potential of food webs to guide ecosystem management and conservation is widely recognized[14,15,16,17], and ecosystem models (akin to food webs) are frequently used to simulate the ecosystem impact of alternative fisheries management actions (for example, refs 18,19). Food-web studies have conventionally treated species persistence either through a ‘topological approach’ where extinctions are determined solely by web structure (for example, (refs 4,20,21)) or by a dynamic approach where species interactions are represented by energy flow models We use a BBN framework to model food webs but extend it by adding management actions so as to investigate the propagation of management benefits through these webs
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