Abstract
The sustainability of area‐based conservation initiatives depends on their value to society. Conservation often focuses inside protected areas, but the impacts of protected areas beyond their boundaries (spillovers) are critical for their social license. Despite their importance, spillovers remain poorly documented and difficult to characterize. To understand the current state of the field and identify avenues for improved generalization, we reviewed 51 spillover analyses of terrestrial protected areas published between 2000 and 2024. We identified several pervasive problems, including widespread use of unvalidated or proportionally scaled sampling zones, biased and incomplete coverage of different regions and ecosystem types, insufficient attention to spatial autocorrelation, statistical inadequacies, and a general failure to conceptualize spillover analysis as a hypothesis‐testing inquiry into scale and protected area effect size. We argue that the lack of standardized methodological and reporting frameworks for spillover analysis has led to inconsistent sampling designs and unmatched spatial and temporal scales of analysis. Few studies consider spatial patterns of nonecological spillovers and their interactions with ecosystems, and limited integration of legal and management frameworks further constrains the capacity of spillover analysis to inform and be integrated with global conservation efforts and targets (e.g., the 30×30 goal of the Global Biodiversity Framework). We propose a set of methodological recommendations and best practices for reporting, sampling design, and analytical decisions. Improved rigor and comparability between studies are needed so that meta‐analyses can be conducted to support global conservation policy for spillover governance. We suggest that future research and conservation practice focus on improving sampling designs and guiding analytical decisions for spillover analyses that explicitly define appropriate sampling distances and spatial granularity, incorporate spatial autocorrelation, and more deliberately consider ecosystem heterogeneity.