Large-sample-size assessment of socioeconomic predictors of community-level resource management occurrence

Tom David Brewer, Neil Andrew, Bernd Gruber, Johnathan Kool

Research output: Contribution to journalReview articlepeer-review

1 Citation (Scopus)
4 Downloads (Pure)


Community-level resource management efforts are cornerstones in ensuring sustainable use of natural resources. Yet, understanding how community characteristics influence management practices remains contested. With a sample size of ≥725 communities, we assessed the effects of key community (i.e., socioeconomic) characteristics (human population size and density, market integration, and modernization) on the probability of occurrence of fisheries management practices, including gear, species, and spatial restrictions. The study was based in Solomon Islands, a Pacific Island country with a population that is highly dependent on coastal fisheries. People primarily dwell in small communities adjacent to the coastline dispersed across 6 island provinces and numerous smaller islands. We used nationally collected data in binomial logistic regression models to examine the likelihood of management occurrence, given socioeconomic context of communities. In contrast to prevailing views, we identified a positive and statistically significant association between both human population size and market integration and all 3 management practices. Human population density, however, had a statistically significant negative association and modernization a varied and limited association with occurrence of all management practices. Our method offers a way to remotely predict the occurrence of resource management practices based on key socioeconomic characteristics. It could be used to improve understanding of why some communities conduct natural resource management activities when statistical patterns suggest they are not likely to and thus improve understanding of how some communities of people beat the odds despite limited market access and high population density.

Original languageEnglish
Article numbere13800
Pages (from-to)1-11
Number of pages11
JournalConservation Biology
Issue number2
Early online date23 Jun 2021
Publication statusPublished - Apr 2022


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