Effective use of data from monitoring programs and field studies for conservation decision making: predictions, designs and models working together

Abstract

Effective integration of information in conservation decision making requires explicit consideration of conservation goals and objectives, specification of a range of potential actions, and the willingness to use information to assist in making and improving decisions. If these conditions are met, monitoring programs would benefit from the creative interplay between predictions, designs and models. Prediction, a basic component of scientific endeavor, is also a key component of scientific decision-making. Effective application of information to decision making typically requires integration of several types of information in a common framework, including “found” data and retrospective studies, innovative sampling designs, and the use of hierarchical data structures (e.g., demographic studies nested with occupancy sampling and analysis of community structure). Sampling designs can also include quasi-experimentation, in which confounding factors are accounted for by spatial or temporal “controls” or covariates. Hierarchical and state-space modeling, often most effectively performed in a Bayesian framework, provide a unified modeling structure for such designs and data. We illustrate these ideas with the problem of investigating and mitigating the effects of climate change on terrestrial birds in North America. Ecological theory and available data provide predictions about the impacts of global, regional, and local climate changes on avian communities. We outline a hierarchical sampling design providing for assessment and monitoring of key state variables. The design integrates broad-scale metrics such as species richness and turnover from existing monitoring programs, with directed monitoring using combinations of occupancy and capture–mark–recapture sampling to address specific questions of local extinction and colonization as well as demographic rates in relation to latitudinal and altitudinal gradients. The design would incorporate temporal, spatial, and management controls (presence/absence of selective interventions) as feasible, and manipulative experiments such as supplemental feeding to test specific predictions at specific sites. Data would be incorporated into a hierarchical modeling structure directed specifically at informing and updating the predictive management model.