A Framework for Simulating Ecosystem Effects in Data-Poor Small-Scale Fisheries Using Science-Based and Local Ecological Knowledge-Based Models

The constant demand for seafood products and the undeniable effects of fishing on marine ecosystems make it urgent to implement an ecosystem approach, even in data-poor scenarios such as small-scale fisheries. Understanding the impacts of fishing is essential for promoting management strategies that prevent irreversible damage to marine ecosystems. Thus, ecosystem quantitative science-based models have been frequently used to evaluate the effects of fishing, although fishers’ local ecological knowledge (LEK) can aid the implementation of qualitative models, particularly in data-poor conditions. Here, we present a framework for simulating and assessing the effects of fishing following two strategies: (1) for both types of models, we simulated species removal scenarios, and (2) for quantitative science-based models, we fitted time series to dynamically assessed impacts. The impacts were analyzed through ecological indicators commonly used for quantitative models, and because these indicators cannot be easily estimated for qualitative models, we propose the use of topological indicators in both types of models. The approach was applied to three case studies of small-scale finfish fisheries in northwestern Mexico. We found that the ecosystem response to species removal was different in each case study and that the target species can play an important role in ecosystems, but their removal does not generate abrupt changes in the ecosystem structure. The quantitative science-based models were able to reproduce the historical catch trends, which allowed us to reveal that changes in ecosystems are indeed influenced by fishing effort but also by underlying primary productivity. Furthermore, topological and ecological indicators showed similar trends in the quantitative models, which suggests that the former could be useful when data-poor conditions allow only qualitative models. This result confirms the relevance of the participation of fishers in generating qualitative models and their decisive role in the discussion of co-management strategies and risk scenarios in a better-informed manner.

Disentangling direct from indirect relationships in association networks

Networks are vital tools for understanding and modeling interactions in complex systems in science and engineering, and direct and indirect interactions are pervasive in all types of networks. However, quantitatively disentangling direct and indirect relationships in networks remains a formidable task. Here, we present a framework, called iDIRECT (Inference of Direct and Indirect Relationships with Effective Copula-based Transitivity), for quantitatively inferring direct dependencies in association networks. Using copula-based transitivity, iDIRECT eliminates/ameliorates several challenging mathematical problems, including ill-conditioning, self-looping, and interaction strength overflow. With simulation data as benchmark examples, iDIRECT showed high prediction accuracies. Application of iDIRECT to reconstruct gene regulatory networks in Escherichia coli also revealed considerably higher prediction power than the best-performing approaches in the DREAM5 (Dialogue on Reverse Engineering Assessment and Methods project, #5) Network Inference Challenge. In addition, applying iDIRECT to highly diverse grassland soil microbial communities in response to climate warming showed that the iDIRECT-processed networks were significantly different from the original networks, with considerably fewer nodes, links, and connectivity, but higher relative modularity. Further analysis revealed that the iDIRECT-processed network was more complex under warming than the control and more robust to both random and target species removal (P < 0.001). As a general approach, iDIRECT has great advantages for network inference, and it should be widely applicable to infer direct relationships in association networks across diverse disciplines in science and engineering.

Comparative Study between Imprinted Polymer Technology and Economic Adsorption Methodologies for the Removal of Arsenic Species from Water

Among the various arsenic sources in the environment, water may pose the greatest threat to human health. Arsenic and its compounds are known to have adverse health effects on humans, including skin cancer, bladder cancer, kidney cancer, and lung cancer, as well as vascular diseases of the legs and feet. There are a few separation methods that have been studied to remove arsenic species from water. Methods to remove arsenic species such as adsorption and ion exchange, coagulation and flocculation and membrane filtration have been developed to remove arsenic species from water. However, certain separation methods require a sophisticated equipment and are too expensive. From the different possible methods, this review is based in adsorption studies using imprinted polymer technology and economic sorbents as a media to remove arsenic from water. The details of adsorption processes for imprinted polymer technology have been discussed briefly and the comparative properties for arsenic species removal using different types of sorbents has been addressed significantly for being a user-friendly, highly extended and inexpensive methodology. However, a few drawbacks for each sorbent have been determined and the details was included in this review.

Stage-specific overcompensation, the hydra effect, and the failure to eradicate an invasive predator

As biological invasions continue to increase globally, eradication programs have been undertaken at significant cost, often without consideration of relevant ecological theory. Theoretical fisheries models have shown that harvest can actually increase the equilibrium size of a population, and uncontrolled studies and anecdotal reports have documented population increases in response to invasive species removal (akin to fisheries harvest). Both findings may be driven by high levels of juvenile survival associated with low adult abundance, often referred to as overcompensation. Here we show that in a coastal marine ecosystem, an eradication program resulted in stage-specific overcompensation and a 30-fold, single-year increase in the population of an introduced predator. Data collected concurrently from four adjacent regional bays without eradication efforts showed no similar population increase, indicating a local and not a regional increase. Specifically, the eradication program had inadvertently reduced the control of recruitment by adults via cannibalism, thereby facilitating the population explosion. Mesocosm experiments confirmed that adult cannibalism of recruits was size-dependent and could control recruitment. Genomic data show substantial isolation of this population and implicate internal population dynamics for the increase, rather than recruitment from other locations. More broadly, this controlled experimental demonstration of stage-specific overcompensation in an aquatic system provides an important cautionary message for eradication efforts of species with limited connectivity and similar life histories.