Evaluating alternative rodenticides for island conservation: roof rat eradication from the San Jorge Islands, Mexico

Introduced commensal rats (Rattus spp.) are a major contributor to the extinction and endangerment of island plants and animals. The use of the toxin brodifacoum to completely eradicate rats from islands is a powerful conservation tool. However, brodifacoum is toxic to animals other than rats and on some islands its use may not be feasible without prohibitively expensive mitigation. As part of a regional conservation program, we experimentally tested brodifacoum and two less toxic rodenticides, diphacinone and cholecalciferol, in eradicating Rattus rattus from three small islands in the northern Gulf of California, Mexico. All three rodenticides were successful in eradicating rats, suggesting that the less toxic diphacinone and cholecalciferol may be useful alternatives to brodifacoum for some island eradication programs. However, the choice of rodenticide must be balanced between efficacy and the risks to non-target species. Applied field research is needed on less toxic rodenticides, as well as improving palatability of baits. This may prove invaluable in preventing extinctions and in restoring larger and more diverse island ecosystems.     

Managing and learning with multiple models: Objectives and optimization algorithms

The quality of environmental decisions should be gauged according to managers’ objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimization algorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives – a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimization algorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved.