Approaches integrating geospatial “big data” and machine learning will likely be increasingly used to predict conservation-related human behavior, such as patterns of local engagement, in socioecological systems. Yet, few studies evaluate both the technical and ethical aspects of such applications. Here, we provide a nation-scale worked example that combines machine learning and publicly available data to predict spatial patterns of Community Forestry establishment among 539,221 settlements across Zambia. Our model accurately predicted out-of-sample spatial establishment patterns three-quarters of the time (balanced accuracy = 76.5%, sensitivity = 64.0%, specificity = 89.1%), though it had a high false positive rate (precision = 24.3%). Accurately forecasting conservation establishment patterns for effective resource allocation requires better data on local preferences and programmatic decision-making, among other factors. Furthermore, such artificial intelligence applications risk making decision-making more technocratic, top-down, and opaque; therefore, they should only inform deliberation over possible future scenarios within wider, multistakeholder governance processes.
This work is licensed under CC-BY 4.0
This work is licensed under CC-BY 4.0
DOI:
https://doi.org/10.1111/con4.70022Altmetric score:
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Publication year
2026
ISSN
1755-263X
Authors
Pienkowski, T.; Mills, M.; Clark, M.; Moombe, K.; Chilufya, H.; Sfyridis, A.; Sze, J.S.; Olsson, E.; Jørgensen, A.C.S.
Language
English
Keywords
artificial intelligence, community forestry, conservation, forest management, geospatial data, machine learning, participation, spatial analysis
Source
Conservation Letters. 19(2): e70022
Geographic
Zambia
