Forecasting Adoption Trends for Adaptive Management of Conservation Scaling

Abstract Achieving global climate, development, and biodiversity goals will require bringing conservation interventions to scale in suitable contexts and with appropriate timing. Practitioners and policymakers have a range of actions available to influence where, when, and by whom an initiative is adopted. Yet, to make effective management decisions, they must have a clear view of the current trajectory towards scaling goals. The non-linearity and variability of scaling processes has, however, hindered forecasting of adoption trends and environmental outcomes. Here, we adapt models of disease transmission to present a simple and flexible modeling structure for forecasting the adoption of conservation initiatives. We tested this framework on empirical adoption data from 19 distinct initiatives. Specifically, we fit the shape of the adoption timeline during the first half of each initiative, estimating the rates of independent uptake and social transmission up to that point. Forecasting the latter 50% of the timeline of cumulative adoption using just these two parameters resulted in an out-of-sample average error of 15.9% across all observations, indicating that this simple formulation captures much of the data-generating process. In one case, we included data on conservation abandonment, extending the cumulative adoption model to estimate the dropout rate and predicting net adoption with an average error of 19.6%, also indicative that this model captures the empirical process reasonably well. We caution against using such models for long-term forecasts, as they are sensitive to multiple assumptions, but instead advocate for their use in adaptive management. Iterative comparisons of forecasts allow users to retrospectively evaluate the impact of decisions and investments. When combined with estimates of environmental outcomes, our framework may provide a comprehensive modeling strategy for identifying targeted management actions and forecasting the impact of conservation projects while considering dynamic social processes.