Decision‐centered systems for the future of wildlife conservation

Abstract

The future of wildlife conservation will be determined by how well we decide, not simply by how much we know. Advances in quantitative ecology, sensor networks, and artificial intelligence have enhanced the field's ability to estimate population states and predict ecological dynamics. Yet, these technological and analytical advancements have not translated into proportional improvements in wildlife conservation decision making. The prevailing science‐centered paradigm, which prioritizes research production as the primary route to better management, was appropriate when limited ecological understanding was the primary constraint on decision making. Today, wildlife conservation challenges are more often restricted by inadequate decision‐making processes rather than by insufficient information. This paper defines a decision‐centered system as an institutional and organizational arrangement in which the generation, integration, and application of scientific knowledge are explicitly structured around the decisions required for wildlife conservation. Decision systems evaluate success not only through scientific rigor, but through the quality of decision‐making processes and outcomes they support. Artificial intelligence (AI) is likely to deepen this mismatch by rapidly expanding analytical capacity while leaving the underlying challenges of decision making unresolved. As AI data synthesis, modeling, and estimation become increasingly automated, the comparative advantage of conservation professionals is likely to shift toward tasks requiring human judgment (e.g., eliciting stakeholder values, weighting competing management objectives). The conceptual basis for this shift already exists in structured decision making, adaptive management, and similar frameworks. The remaining barriers to implementation are both institutional and organizational. The rules, norms, and incentive structures governing what counts as good conservation work, including what gets funded, published, and rewarded, will benefit from shifting alongside the agencies, universities, and professional societies that train and employ wildlife professionals. Conditions for this transition include redesigning incentives, training, and governance so that organizations are prepared not only to generate knowledge, but to consistently translate it into effective action.