Using species distribution and metapopulation models to prioritize conservation actions for threatened species

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Global conservation efforts fall far short of what is required to halt the current extinction crisis. Conservation agencies must prioritize resources, usually by assigning ranks or threat categories based on the estimated probability that a species will go extinct without intervention. Such estimates depend on assessments of both threats and habitat availability. However, new research has shown that threat assessments can be biased if conducted at a single spatial or temporal scale, and that previously-assumed source habitats may actually be sinks. In addition, extinction probability may be underestimated, especially for species in isolated populations and whose decline is slow. Thus, many species may be currently neglected or mismanaged. Models predicting species distributions at multiple scales in space and time, and metapopulation models that mathematically depict interconnected populations, have the potential to greatly improve threat and habitat assessments, and estimates of extinction probability. Using such models across groups of threatened species may also allow the most urgent conservation actions to be determined, to help prevent extinctions. However, the costs of the monitoring data necessary for constructing such models must be balanced against the costs of acting on current knowledge. My proposed research will quantitatively determine the utility of multiple-scale, dynamic species distribution and metapopulation models for refining extinction risk estimates, and guiding conservation. My students and I will use uniquely detailed datasets on populations and proposed conservation actions from New Zealand and New South Wales, as well as smaller-scale datasets we collect from biodiversity hotspots in Canada, to: 1) Develop models to determine whether current extinction risk estimates are biased for certain groups of species, and whether accounting for this bias alters the threat ranking among species; 2) Determine whether using our models to prioritize actions rather than species can lead to more biodiversity conserved, and allow actions such as habitat protection and restoration to be staggered through time; and 3) Determine when monitoring data needed to derive such models are worth gathering, and which groups of species should be monitored while others are acted upon. This research will help to resolve persistent debates about extinction probabilities for threatened species, and will test the utility of an action-based prioritization approach to conservation, which is a fundamental departure from the species-based ranking approach typically used in threatened species conservation programs. It will also address the crucial question facing conservation agencies as to when to monitor species and when to act on existing information, and improve the biological basis for conservation decision-making, potentially leading to more species saved from extinction.