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Citizen monitoring promotes informed and inclusive forest governance in Liberia.

ABSTRACT: Global forest loss depends on decisions made in the rural, often poor communities living beside the Earth's remaining forests. Governance problems in these forest-edge communities contribute to rapid deforestation and household vulnerability. In coordination with experimental studies in 5 other countries, we evaluate a program that recruits, trains, and deploys citizens to monitor communal forestland in 60 communities in rural Liberia. The year-long intervention is designed to promote more informed and inclusive resource governance, so that that citizens' preferences (and not just leaders' interests) are reflected in forest management. In our control communities, households are uninformed and disengaged; leaders' authority is unchecked. The program both engages and mobilizes community members: households are better informed and participate more in the design and enforcement of rules around forest use. They also report receiving more material benefits from outside investors' activities in their community forests. The chiefs who lead these communities attest to strengthened accountability. Using both on-the-ground environmental assessments and remotely sensed data, we find no effects on forest use or deforestation. Households do not favor more conservation, and, thus, more inclusive management does not reduce forest use. Conservation likely requires compensating community members for foregoing forest use; citizen monitoring, we argue, could ensure that such schemes enjoy popular support and do not just benefit local elites.

SUBMITTER: Christensen D

PROVIDER: S-EPMC8307445 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Objective: Given that positive psychological capital motivates citizens to actively participate in social affairs, this study aims to provide insight into food safety risk management in China by empirically determining which individual characteristics are associated with positive psychological capital for actively participating in social co-governance. Methods: A questionnaire-based survey was undertaken between December 5 and 10, 2020. The study participants were residents of Wuxi in China over the age of 18 years. A validated and pretested questionnaires was used to elicit responses with the participants. Student's t-test and one-way analysis of variance were performed to determine which individual characteristics are significantly correlated with the psychological capital of citizens who participate in co-governance. Post-hoc multiple comparisons were performed for each individual characteristic with a significant correlation to determine which categories of these characteristics yielded the significant differences. Study data were analyzed using IBM SPSS Statistics 24.0. Results: A total of 752 completed responses were received. Most respondents were females (52.39%), aged 26-45 (66.09%), married (70.48%), company employees (44.28%), and in good health (89.76%). Most had a household size of 3 (55.98%), a bachelor's degree (40.96%), a personal annual income of more than 100,000 yuan (26.46%), and no children aged under 18 (50.27%) or pregnant women (93.22%) in their households. Data analysis indicated that education, income, and health status significantly associate with the psychological capital of citizens to participate in co-governance. Citizens with high education, high income, and good health status have higher psychological capital to participate in co-governance. Conclusion: The present study suggested citizens are likely to actively participate in food safety social co-governance only when they have at least one of the following three characteristics: (1) higher than average income in their city of residence; (2) a bachelor's degree or higher education; or (3) good health. Therefore, motivating citizens to participate in co-governance is a long-term process in China. The fundamental strategy is to increase the income of citizens, especially among low-income groups, promote education to improve the food safety literacy of the public, and improve sanitation and public health.

Project description:The extensive spatial and temporal coverage of many citizen science datasets (CSD) makes them appealing for use in species distribution modeling and forecasting. However, a frequent limitation is the inability to validate results. Here, we aim to assess the reliability of CSD for forecasting species occurrence in response to national forest management projections (representing 160,366 km2) by comparison against forecasts from a model based on systematically collected colonization-extinction data. We fitted species distribution models using citizen science observations of an old-forest indicator fungus Phellinus ferrugineofuscus. We applied five modeling approaches (generalized linear model, Poisson process model, Bayesian occupancy model, and two MaxEnt models). Models were used to forecast changes in occurrence in response to national forest management for 2020-2110. Forecasts of species occurrence from models based on CSD were congruent with forecasts made using the colonization-extinction model based on systematically collected data, although different modeling methods indicated different levels of change. All models projected increased occurrence in set-aside forest from 2020 to 2110: the projected increase varied between 125% and 195% among models based on CSD, in comparison with an increase of 129% according to the colonization-extinction model. All but one model based on CSD projected a decline in production forest, which varied between 11% and 49%, compared to a decline of 41% using the colonization-extinction model. All models thus highlighted the importance of protected old forest for P. ferrugineofuscus persistence. We conclude that models based on CSD can reproduce forecasts from models based on systematically collected colonization-extinction data and so lead to the same forest management conclusions. Our results show that the use of a suite of models allows CSD to be reliably applied to land management and conservation decision making, demonstrating that widely available CSD can be a valuable forecasting resource.

Project description:Citizen science approaches are of great interest for their potential to efficiently and sustainably monitor wildlife populations on both public and private lands. Here we present two studies that worked with volunteers to set camera traps for ecological surveys. The photographs recorded by these citizen scientists were archived and verified using the eMammal software platform, providing a professional grade, vouchered database of biodiversity records. Motivated by managers' concern with perceived high bear activity, our first example enlisted the help of homeowners in a short-term study to compare black bear activity inside a National Historic Site with surrounding private land. We found similar levels of bear activity inside and outside the NHS, and regional comparisons suggest the bear population is typical. Participants benefited from knowing their local bear population was normal and managers refocused bear management given this new information. Our second example is a continuous survey of wildlife using the grounds of a nature education center that actively manages habitat to maintain a grassland prairie. Center staff incorporated the camera traps into educational programs, involving visitors with camera setup and picture review. Over two years and 5,968 camera-nights this survey has collected 41,393 detections of 14 wildlife species. Detection rates and occupancy were higher in open habitats compared to forest, suggesting that the maintenance of prairie habitat is beneficial to some species. Over 500 volunteers of all ages participated in this project over two years. Some of the greatest benefits have been to high school students, exemplified by a student with autism who increased his communication and comfort level with others through field work with the cameras. These examples show how, with the right tools, training and survey design protocols, citizen science can be used to answer a variety of applied management questions while connecting participants with their secretive mammal neighbors.

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Citizen monitoring promotes informed and inclusive forest governance in Liberia.

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