Integrating conflict, lobbying, and compliance to predict the sustainability of natural resource use

Abstract

Predictive models are sorely needed to guide the management of harvested natural resources worldwide, yet existing frameworks fail to integrate the dynamic and interacting governance processes driving unsustainable use. We developed a new framework in which the conflicting interests of three key stakeholders are modeled: managers seeking sustainability, users seeking increases in harvest quota, and conservationists seeking harvest restrictions. Our model allows stakeholder groups to influence management decisions and illegal harvest through flexible functions that reflect widespread lobbying and noncompliance processes. Decision making is modeled through the use of a genetic algorithm, which allows stakeholders to respond to a dynamic social-ecological environment to satisfy their goals. To provide the critical link between conceptual and empirical approaches, we compare predictions from our model against data on 206 harvested terrestrial species from the IUCN Red List. We show that, although lobbying for a ban on resource use can offset low levels of noncompliance, such bias leads to an increased risk of extinction when noncompliance (and therefore illegal harvesting) is high. Management decisions unaffected by lobbying, combined with high rule compliance, resulted in more sustainable resource use. Model predictions were strongly reflected in our analysis of harvested IUCN species, with 81% of those classified under regulated harvest and high compliance showing stable or increasing population trends. Our results highlight the fine balance between maintaining compliance and biasing decisions in the face of lobbying. They also emphasize the urgent need to quantify lobbying and compliance processes across a range of natural resources. Overall, our work provides a holistic and versatile approach to addressing complex social processes underlying the mismanagement of natural resources.

Predictive models are sorely needed to guide the management of harvested natural resources worldwide, yet existing frameworks fail to integrate the dynamic and interacting governance processes driving unsustainable use. We developed a new framework in which the conflicting interests of three key stakeholders are modeled: managers seeking sustainability, users seeking increases in harvest quota, and conservationists seeking harvest restrictions. Our model allows stakeholder groups to influence management decisions and illegal harvest through flexible functions that reflect widespread lobbying and noncompliance processes. Decision making is modeled through the use of a genetic algorithm, which allows stakeholders to respond to a dynamic social-ecological environment to satisfy their goals. To provide the critical link between conceptual and empirical approaches, we compare predictions from our model against data on 206 harvested terrestrial species from the IUCN Red List. We show that, although lobbying for a ban on resource use can offset low levels of noncompliance, such bias leads to an increased risk of extinction when noncompliance (and therefore illegal harvesting) is high. Management decisions unaffected by lobbying, combined with high rule compliance, resulted in more sustainable resource use. Model predictions were strongly reflected in our analysis of harvested IUCN species, with 81% of those classified under regulated harvest and high compliance showing stable or increasing population trends. Our results highlight the fine balance between maintaining compliance and biasing decisions in the face of lobbying. They also emphasize the urgent need to quantify lobbying and compliance processes across a range of natural resources. Overall, our work provides a holistic and versatile approach to addressing complex social processes underlying the mismanagement of natural resources.