Abstract
Several administrative polices have been implemented in order to reduce the negative impacts of fishing on natural ecosystems. Four eco-social models with different levels of complexity were constructed, which represent the seaweed harvest in central-northern Chile under two different regimes, Management and Exploitation Areas for Benthic Resources (MAEBRs) and Open Access Areas (OAAs). The dynamics of both regimes were analyzed using the following theoretical frameworks: (1) Loop Analysis, which allows the local stability or sustainability of the models and scenarios to be assessed; and (2) Hessian´s optimization procedure of a global fishery function (GFF) that represents each dynamics of each harvest. The results suggest that the current fishing dynamics in MAEBRs are not sustainable unless the market demand presents some type of control (i.e. taxes). Further, the results indicated that if the demand changes to a self-negative feedback (self-control) in MAEBRs, the stability is increased and, simultaneously, a relative maximum for the GFF is reached. Contrarily, the sustainability of the model/system representing the harvest (principally by cutting plants) in OAAs is not reached. The implementation of an “ecological” tax for intensive artisanal fisheries with low operational cost is proposed. The network analysis developed here is proposed as a general strategy for studying the effects of human interventions in marine coastal ecosystems under transient (short-term) dynamics.
Highlights
Four models and scenarios for eco-social relationships that describe the exploitation of seaweeds, keyhole limpets and crabs along the central and northern Chile were constructed, each with different levels of complexity, and the models were analyzed by Loop Analysis [1, 21, 24] and Hessian optimization analysis [25]
All four models are a partial representation of the variables and interactions underlying the ecological and social subsystem studied, it is relevant to mention that this limitation is applicable to any kind of model and is independent of its degree of complexity [27, 28]
The models described would have at least the following simplifications: (1) the ecological complexity was reduced to just three interacting variables, ignoring the other species inhabiting the benthic and pelagic environment; (2) the community was considered in a moving equilibrium; (3) only two variables from the socio-economic subsystem were included in the models; and (4) the strength of self-feedbacks was a necessary condition for estimation of maximum and minimum of global fishery function (GFF), independent of the order of variables in the Hessian matrices
Summary
Not necessarily mutually exclusive, scientific strategies can be used to analyze, evaluate and attempt to predict the changes in the natural systems as a consequence of the concurrent seaweed fisheries and interacting species-groups along the central-north Chilean coast These strategies include: (1) reducing the objects of study to their small components, assuming that the parts are more fundamental than the whole from which they are a part and supposing that the macroscopic or emergent properties of the whole ecosystems are an epiphenomenon of the parts; (2) creating a statistical “democracy” of factors, which assigns a relative weight and supposes that the factor that explains the greatest variance is the principal cause [20]; (3) quantitative simulation, supported by the capacity of computers and software to obtain numerical solutions, requiring fairly precise measurements of the variables, parameters, and exact equations; and (4) qualitative or semi-quantitative modeling, which allows integration of variables from different disciplines that do not need precise or quantitative equations [10, 21]. We use these models and scenarios to determine the local stability (as an indication of sustainability) of networks that describe MEABR and OAA subsystems using the Loop Analysis framework [21, 24], in turn, a multidimensional maximum or minimum for the harvest is estimated using the Hessian optimization analysis [25]
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