Integrating fisheries approaches and household utility models for improved resource management

Abstract

Natural resource management is littered with cases of overexploitation and ineffectual management, leading to loss of both biodiversity and human welfare. Disciplinary boundaries stifle the search for solutions to these issues. Here, I combine the approach of management strategy evaluation, widely applied in fisheries, with household utility models from the conservation and development literature, to produce an integrated framework for evaluating the effectiveness of competing management strategies for harvested resources against a range of performance metrics. I demonstrate the strengths of this approach with a simple model, and use it to examine the effect of manager ignorance of household decisions on resource management effectiveness, and an allocation tradeoff between monitoring resource stocks to reduce observation uncertainty and monitoring users to improve compliance. I show that this integrated framework enables management assessments to consider household utility as a direct metric for system performance, and that although utility and resource stock conservation metrics are well aligned, harvest yield is a poor proxy for both, because it is a product of household allocation decisions between alternate livelihood options, rather than an end in itself. This approach has potential far beyond single-species harvesting in situations where managers are in full control; I show that the integrated approach enables a range of management intervention options to be evaluated within the same framework.hunting | bushmeat M anagement Strategy Evaluation (MSE) is fast becoming the dominant framework for the development and assessment of management procedures for commercial fisheries (1, 2). This powerful approach uses simulation in a virtual environment to test the robustness of potential management strategies to a range of uncertainties. Unlike traditional approaches, MSE explicitly models the whole management system; not just the resource stock and its reaction to different harvest rules, but the gathering of data, the conversion of those data into a harvest rule, and the implementation of that rule (3). This approach then allows fisheries scientists to evaluate the effects of a lack of knowledge or understanding on the performance of a range of harvest rules. The management advice that comes from MSEs is nonprescriptive and probabilistic, enabling stakeholders to evaluate the tradeoffs inherent in choosing one or other management procedure. Indeed, one of the strengths of MSE is that it can encourage the participation of stakeholders, both in defining the metrics against which the performance of harvest rules can be evaluated, and in the generation of scenarios for testing

Resilience and risk: a demographic model to inform conservation planning for polar bears

“…The model extends previous work for polar bears (Hunter and others, 2010) by (1) including density-dependent functions to link vital rates (e.g., survival and recruitment) to changes in population size and environmental carrying capacity; (2) including sex-and age-specific harvest vulnerabilities; and (3) linking the demographic model to simulated population assessments, so that management strategies can be evaluated in light of uncertain information and time lags (e.g., Milner-Gulland, 2011).…”

Managing wildlife for ecological, socioeconomic, and evolutionary sustainability

“…natural system; an observation model, incorporating uncertainties from the monitoring process; and an assessment model reflecting the management decision-making process based on the monitoring. Recent developments have also included an additional decision-making model for the resource user based on their economic incentives (18). In most applications to date, the biology of the harvested species has generally been represented in MSE by matrix population models (19).…”

Advancing Empirical Approaches to the Concept of Resilience: A Critical Examination of Panarchy, Ecological Information, and Statistical Evidence

“…While these models have decision-making potential on optimal managerial problems, e.g., decisions relevant to resource harvesting vs. regenerating strategies, they do not fully capture the feedback of changing environmental dynamics on human decision-making and management. Specifically, the response and adaptability of decisions [28,29] to environmental feedback needs to be better reflected. More recent models attempt to address adaptive human behavior to environmental change using agent-based approaches to represent social changes [30][31][32][33].…”