Many countries have committed to protecting at least 30% of their lands and waters by 2030 under Target 3 of the Kunming-Montreal Global Biodiversity Framework. While expanding protected areas is an essential step toward meeting this goal, where new protected areas are established matters just as much as how much land is protected.
Networks of isolated reserves often fail to support wildlife movement, gene flow, and ecological processes that allow species to adapt to habitat fragmentation and climate change. Integrating connectivity into conservation planning can help ensure that new protected areas strengthen existing conservation networks rather than creating disconnected patches on the landscape.
O’Brien, P., Carr, N. and Bowman, J. 2026. A predictive approach to integrating connectivity into landscape scale protected areas planning. PloS ONE 21(4): e0346336. DOI: 10.1371/journal.pone.0346336
A recent study presents a practical approach for incorporating connectivity into protected area planning at regional scales. Rather than evaluating each potential conservation area individually, researchers developed a predictive framework that estimates how much different landscapes are likely to improve connectivity if they are protected.
The goal was to provide decision makers with a tool that can quickly identify areas where conservation investments would have the greatest benefit for building connected protected area networks, an increasingly important consideration as governments work toward their 30×30 commitments.
The researchers tested their approach across Ontario, Canada, where large areas remain available for future conservation. They first calculated connectivity values for numerous candidate protected areas using circuit theory, which models how wildlife may move across landscapes containing varying levels of habitat and human disturbance.
They then used those results to build statistical models capable of predicting connectivity benefits from readily available landscape characteristics. This allowed them to estimate the connectivity value of many additional areas without performing computationally intensive connectivity analyses for every possible conservation scenario.
The predictive models performed well, identifying landscape features that consistently contributed to stronger connectivity. Areas located near existing protected lands, those containing relatively intact natural habitats, and landscapes positioned to bridge gaps between protected areas often provided the greatest improvements to the overall conservation network. Because the models rely on landscape characteristics that can be measured across broad regions, they offer a scalable alternative to repeatedly running complex connectivity simulations.
The framework is designed to complement, rather than replace, detailed connectivity analyses. By rapidly screening candidate areas, it can help conservation planners prioritize locations for further evaluation and make more informed decisions when conservation funding or opportunities are limited.
As countries continue working toward 30×30, approaches like this could help ensure that expanding protected areas also strengthens ecological connectivity. Although the study focused on Ontario, the overall framework is broadly transferable to other regions where land managers have access to habitat and land cover data. Adapting the method to different landscapes could help governments, conservation organizations, and Indigenous partners identify opportunities to build more resilient, connected protected area networks that better support biodiversity in a changing world.
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