Assessing the influence of landscape conservation and protected areas on social wellbeing using random forest machine learning.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
18 May 2024
18 May 2024
Historique:
received:
08
02
2023
accepted:
10
05
2024
medline:
19
5
2024
pubmed:
19
5
2024
entrez:
18
5
2024
Statut:
epublish
Résumé
The urgency of interconnected social-ecological dilemmas such as rapid biodiversity loss, habitat loss and fragmentation, and the escalating climate crisis have led to increased calls for the protection of ecologically important areas of the planet. Protected areas (PA) are considered critical to address these dilemmas although growing divides in wellbeing can exacerbate conflict around PAs and undermine effectiveness. We investigate the influence of proximity to PAs on wellbeing outcomes. We develop a novel multi-dimensional index of wellbeing for households and across Africa and use Random Forest Machine Learning techniques to assess the importance score of households' proximity to protected areas on their wellbeing outcomes compared with the importance scores of an array of other social, environmental, and local and national governance factors. This study makes important contributions to the conservation literature, first by expanding the ways in which wellbeing is measured and operationalized, and second, by providing additional empirical support for recent evidence that proximity to PAs is an influential factor affecting observed wellbeing outcomes, albeit likely through different pathways than the current literature suggests.
Identifiants
pubmed: 38762670
doi: 10.1038/s41598-024-61924-4
pii: 10.1038/s41598-024-61924-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
11357Informations de copyright
© 2024. The Author(s).
Références
Bradshaw, C. J. A. et al. Underestimating the challenges of avoiding a ghastly future. Front. Conserv. Sci. 1, 615419. https://doi.org/10.3389/fcosc.2020.615419 (2021).
doi: 10.3389/fcosc.2020.615419
Steffen, W. et al. Trajectories of the earth system in the anthropocene. PNAS 115, 8252–8259. https://doi.org/10.1073/pnas.1810141115 (2018).
doi: 10.1073/pnas.1810141115
pubmed: 30082409
pmcid: 6099852
Dinerstein, E. et al. A Global deal for nature: guiding principles, milestones, and targets. Sci. Adv. https://doi.org/10.1126/sciadv.aaw2869 (2019).
doi: 10.1126/sciadv.aaw2869
pubmed: 31016243
pmcid: 6474764
CBD (2022) Kunming-Montreal Global Biodiversity Framework. CBD/COP/15/L.25. Convention on Biological Diversity. Available online at: https://www.cbd.int/doc/c/e6d3/cd1d/daf663719a03902a9b116c34/cop-15-l-25-en.pdf . Last accessed on 5 January 2023.
Maxwell, S. L. et al. Area-based conservation in the twenty-first century. Nature 586(7828), 217–227. https://doi.org/10.1038/s41586-020-2773-z (2020).
doi: 10.1038/s41586-020-2773-z
pubmed: 33028996
IPBES (2019). In E. S. Brondizio, J. Settele, S. Díaz, & H. T. Ngo (Eds.) Global assessment report on biodiversity and ecosystem services of the intergovernmental science-policy platform on biodiversity and ecosystem services. IPBES Secretariat.
Hanson, T. et al. Warfare in Biodiversity Hotspots. Conserv. Biol. 23(3), 578–587. https://doi.org/10.1111/j.1523-1739.2009.01166.x (2009).
doi: 10.1111/j.1523-1739.2009.01166.x
pubmed: 19236450
Fisher, J. (2022). Managing environmental conflict: an earth institute sustainability primer. New York. Columbia University Press. EISBN 978-0-231-55186-1
Daskin, J. H. & Pringle, R. M. Warfare and wildlife declines in Africa’s protected areas. Nature 553(7688), 328–332 (2018).
doi: 10.1038/nature25194
pubmed: 29320475
Golden, R. E. et al. The uncertain future of protected lands and waters. Science 364(6443), 881–886. https://doi.org/10.1126/science.aau5525 (2019).
doi: 10.1126/science.aau5525
Appleton, M. R., Courtiol, A. & Emerton, L. Protected area personnel and ranger numbers are insufficient to deliver global expectations. Nat. Sustain. 5, 1100–1110. https://doi.org/10.1038/s41893-022-00970-0 (2022).
doi: 10.1038/s41893-022-00970-0
Gatiso, T. T. et al. Sustainable protected areas: synergies between biodiversity conservation and socioeconomic development. People Nat. 4(4), 893–903. https://doi.org/10.1002/pan3.10326 (2022).
doi: 10.1002/pan3.10326
Fisher, J., Allen, S., Woomer, A. & Crawford, A. Protected area management and governance under pressure: an online survey to assess how to manage critical ecosystems for attainment of social and environmental goals and reduce stakeholder conflict. World Dev. Sustain. 3, 100084. https://doi.org/10.1016/j.wds.2023.10008 (2023).
doi: 10.1016/j.wds.2023.10008
Betley, E. C. et al. Assessing human well-being constructs with environmental and equity aspects: a review of the landscape. People Nat. 5(6), 1756–1773 (2023).
doi: 10.1002/pan3.10293
Kruczkiewicz, A. et al. Preparing for Compound risks and complex emergencies in a time of COVID. Proceed. Nat. Acad. Sci. https://doi.org/10.1073/pnas.2106795118 (2021).
doi: 10.1073/pnas.2106795118
Geldmann, J., Joppa, L. & Burgess, N. Mapping change in human pressure globally on land and within protected areas. Conserv. Biol. 28(6), 1604–1616. https://doi.org/10.1111/cobi.12332 (2014).
doi: 10.1111/cobi.12332
pubmed: 25052712
McKinnon, M. C. et al. What are the effects of nature conservation on human wellbeing? A systematic map of empirical evidence from developing countries. Environ. Evidence 5(1), 1–25 (2016).
doi: 10.1186/s13750-016-0058-7
Naidoo, R. et al. Evaluating the impacts of protected areas on human wellbeing across the developing world. Sci. Adv. 5(4), eaav3006 (2019).
doi: 10.1126/sciadv.aav3006
pubmed: 30949578
pmcid: 6447379
Ghoddousi, A., Loos, J. & Kuemmerle, T. An outcome-oriented, social-ecological framework for assessing protected area effectiveness. BioScience 72(2), 201–212 (2022).
doi: 10.1093/biosci/biab114
pubmed: 35145352
Corrigan, C. & Robinson, J. Global review of social indicators used in protected area management evaluation. Conserv. Lett. 11(2), e12397 (2018).
doi: 10.1111/conl.12397
Jones, N., McGinlay, J. & Dimitrakopoulos, P. G. Improving social impact assessment of protected areas: a review of the literature and directions for future research. Environ. Impact Assessment Rev. 64(2017), 1–7. https://doi.org/10.1016/j.eiar.2016.12.007 (2017).
doi: 10.1016/j.eiar.2016.12.007
Loveridge, R., Sallu, S., Presha, I. & Marshall, A. Measuring human wellbeing: a protocol for selecting local indicators. Environ. Sci. Policy 114, 461–469. https://doi.org/10.1016/j.envsci.2020.09.002 (2020).
doi: 10.1016/j.envsci.2020.09.002
Afrobarometer Data, [Round 6, 2015]. All available countries utilized. Available at http://www.afrobarometer.org . Last accessed 22 September 2022.
Fisher, J. et al. Four propositions on integrated sustainability: toward a theoretical framework to understand the environment, peace, and sustainability nexus. Sustain. Sci. 16(4), 1125–1145 (2021).
doi: 10.1007/s11625-021-00925-y
pubmed: 33717363
pmcid: 7943412
Breiman, L. Random forests. Machine Learn. 45, 5–32 (2001).
doi: 10.1023/A:1010933404324
Saha, S. et al. Predicting the deforestation probability using the binary logistic regression, random forest, ensemble rotational forest, REPTree: a case study at the Gumani River Basin, India. Sci. Total Environ. 15(730), 139197. https://doi.org/10.1016/j.scitotenv.2020.139197 (2020).
doi: 10.1016/j.scitotenv.2020.139197
Saha, N. & Gosh, T. GIS-based spatial prediction of recreational trail susceptibility in protected area of Sikkim Himalaya using logistic regression, decision tree and random forest model. Ecol. Inform. https://doi.org/10.1016/j.ecoinf.2021.101352 (2021).
doi: 10.1016/j.ecoinf.2021.101352
Arabameri, A., Pradhan, B. & Rezaeid, K. Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS. J. Environ. Manag. 232(2019), 928–942. https://doi.org/10.1016/j.jenvman.2018.11.110 (2019).
doi: 10.1016/j.jenvman.2018.11.110
BenYishay, A., Rotberg, R., Wells, J., Lv, Z., Goodman, S., Kovacevic, L., Runfola, D. 2017. Geocoding Afrobarometer Rounds 1–6: Methodology & Data Quality. AidData. Available online at http://docs.aiddata.org/ad4/pdfs/geocodingafrobarometer.pdf .
De Muro, P., Mazziotta, M. & Pareto, A. Composite indices of development and poverty: an application to MDGs. Social Indicators Res. 104(1), 1–18 (2011).
doi: 10.1007/s11205-010-9727-z
Dutta, I., Nogales, R. & Yalonetzky, G. Endogenous weights and multidimensional poverty: a cautionary tale. J. Dev. Econ. 151, 102649 (2021).
doi: 10.1016/j.jdeveco.2021.102649
Mazziotta, M. & Pareto, A. On a generalized non-compensatory composite index for measuring socio-economic phenomena. Social Indicators Res. 127(3), 983–1003 (2016).
doi: 10.1007/s11205-015-0998-2
UNEP-WCMC (2017). Global Database on Protected Area Management Effectiveness User Manual 1. UNEP-WCMC: Cambridge, UK. Available at: http://wcmc.io/GD-PAME_User_Manual_EN .
UNEP-WCMC, IUCN (2022). Protected Planet: The World Database on Protected Areas (WDPA). https://www.protectedplanet.net/en
Cumming, G. S. & Allen, C. R. Protected areas as social–ecological systems: perspectives from resilience and social–ecological systems theory. Ecol. Appl. 27, 1709–1717 (2017).
doi: 10.1002/eap.1584
pubmed: 28618079
Gorelick, N. et al. Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 1(202), 18–27 (2017).
doi: 10.1016/j.rse.2017.06.031
Bowler, D. E. et al. Mapping human pressures on biodiversity across the planet uncovers anthropogenic threat complexes. People Nat. 2(2), 380–394 (2020).
doi: 10.1002/pan3.10071
Hainmueller, J. Entropy balancing for causal effects: a multivariate reweighting method to produce balanced samples in observational studies. Political Anal. 20(1), 25–46 (2012).
doi: 10.1093/pan/mpr025
Breiman, L. Random forests. Machine Learn. 45, 5–32. https://doi.org/10.1023/A:1010933404324 (2001).
doi: 10.1023/A:1010933404324
Grömping, U. Variable importance assessment in regression: linear regression versus random forest. Am. Stat. 63(4), 308–319. https://doi.org/10.1198/tast.2009.08199 (2009).
doi: 10.1198/tast.2009.08199
Wright, M. N. & Ziegler, A. Ranger: a fast implementation of random forests for high dimensional data in C++ and R. J. Stat. Softw. 77(1), 1–17. https://doi.org/10.18637/jss (2017).
doi: 10.18637/jss
Powlen, K. A. et al. Identifying socioeconomic and biophysical factors driving forest loss in protected areas. Conserv. Biol. https://doi.org/10.1111/cobi.14058 (2023).
doi: 10.1111/cobi.14058
pubmed: 36661056
Galvin, K. A., Beeton, T. A. & Luizza, M. W. African community-based conservation: a systematic review of social and ecological outcomes. Ecology Society 23(3), 39 (2018).
doi: 10.5751/ES-10217-230339
Hirons, S., Matilda Collines, C. & Singh, M. Assessing variation in the effectiveness of IUCN protected area categorisation. What remotely sensed forest integrity and human modification reveals across the major tropical forest biomes. Ecol. Indicators https://doi.org/10.1016/j.ecolind.2022.109337 (2022).
doi: 10.1016/j.ecolind.2022.109337
Oparina, E., Kaiser, C., and N. Gentile. (2022). Human wellbeing and machine learning. Preprint. https://arxiv.org/pdf/2206.00574.pdf
Coleman, P. T., Liebovitch, L. S. & Fisher, J. Taking complex systems seriously: visualizing and modeling the dynamics of sustainable peace. Global Policy https://doi.org/10.1111/1758-5899.12680 (2019).
doi: 10.1111/1758-5899.12680
Elvidge, C. D., Zhizhin, M., Ghosh, T., Hsu, F. C. & Taneja, J. Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019. Remote Sens. 13(5), 922. https://doi.org/10.3390/rs13050922 (2021).
doi: 10.3390/rs13050922
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342(6160), 850–853 (2013).
doi: 10.1126/science.1244693
pubmed: 24233722
Copernicus Sentinel data [2015 - 2020]. Retrieved from ASF DAAC [Octber 1 2021], processed by ESA.
Wang, P., C. Huang, E. C. Brown de Colstoun, J. C. Tilton, and B. Tan. 2017. Global Human Built-up and Settlement Extent (HBASE) Dataset from Landsat. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4DN434S . Accessed 10/1/2021.
Center for International Earth Science Information Network - CIESIN - Columbia University, and Information Technology Outreach Services - ITOS - University of Georgia. 2013. Global Roads Open Access Data Set, Version 1 (gROADSv1). Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4VD6WCT .
UNEP-WCMC, IUCN (2022). Protected Planet: The World Database on Protected Areas (WDPA). https://www.protectedplanet.net/en .
Rosvold, E. L. & Buhaug, H. GDIS, a global dataset of geocoded disaster locations. Sci. Data 8, 61. https://doi.org/10.1038/s41597-021-00846-6 (2021).
doi: 10.1038/s41597-021-00846-6
pubmed: 33594086
pmcid: 7887188
World Bank, World Development Indicators. (2015). The world by income and region. Available at: https://datatopics.worldbank.org/world-development-indicators/the-world-by-income-and-region.html . Last accessed 29 September 2022
Didan, K. (2021). MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061 . NASA EOSDIS Land Processes Distributed Active Archive Center. Accessed 2023-09-28 from https://doi.org/10.5067/MODIS/MOD13Q1.061
Esri. "Topographic" [basemap]. Scale Not Given. "World Topographic Map". February 19, 2012. http://www.arcgis.com/home/item.html?id=30e5fe3149c34df1ba922e6f5bbf808f . (Sep 20, 2023)