Assessing impacts of bycatch policies and fishers' heterogeneous information on food webs and fishery sustainability.

Ecosystem-based fisheries management (EBFM) has emerged as a promising framework for understanding and managing the long-term interactions between fisheries and the larger marine ecosystems in which they are nested. However, successful implementation of EBFM has been elusive because we still lack a comprehensive understanding of the network of interacting species in marine ecosystems (the food web) and the dynamic relationship between the food web and the humans who harvest those ecosystems. Here, we advance such understanding by developing a network framework that integrates the complexity of food webs with the economic dynamics of different management policies. Specifically, we generate hundreds of different food web models with 20-30 species, each harvested by five different fishers extracting the biomass of a target and a bycatch species, subject to two different management scenarios and exhibiting different information in terms of avoiding bycatch when harvesting the target species. We assess the different ecological and economic consequences of these policy alternatives as species extinctions and profit from sustaining the fishery. We present the results of different policies relative to a benchmark open access scenario where there are no management policies in place. The framework of our network model would allow policymakers to evaluate different management approaches without compromising on the ecological complexities of a fishery.This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Effect of migrations on synchrony in host-parasitoid system.

Insect outbreaks can cause large scale defoliation of forest trees or destruction of crops, leading to ecosystem degradation and economic losses. Some outbreaks occur simultaneously across large geographic scales and some outbreaks occur periodically every few years across space. Parasitoids are a natural enemy of these defoliators and could help mitigate these pest outbreaks. A holistic understanding of the host-parasitoid interactions in a spatial context would thus enhance our ability to understand, predict and prevent these outbreaks. We use a discrete time deterministic model of the host parasitoid system with populations migrating between 2 patches to elucidate features of spatial host outbreaks. We show that whenever populations persist indefinitely, host outbreaks in both patches can occur alternatively (out of phase) at low migration between patches whereas host outbreaks always occur simultaneously (in phase) in both patches at high migration between patches. We show that our results are robust across a large range of parameters across different modelling approaches used typically to model intraspecific competition among hosts and parasitism, in the host-parasitoid literature. We give an analytical expression for the period of oscillations when the migration is low i.e., when host outbreaks in both patches are out of phase, show it is in agreement with numerical results. We end our paper by showing that we get the same results whether we include the biologically rooted formulations from May et al. (1981) or a general cellular automata model with qualitative rules.

When big data fails: Adaptive agents using coarse-grained information have competitive advantage.

The recent trend for acquiring big data assumes that possessing quantitatively more and qualitatively finer data necessarily provides an advantage that may be critical in competitive situations. Using a model complex adaptive system where agents compete for a limited resource using information coarse grained to different levels, we show that agents having access to more and better data perform worse than others in certain situations. The relation between information asymmetry and individual payoffs is seen to be complex, depending on the composition of the population of competing agents.