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Review
. 2023 Feb;37(1):e13973.
doi: 10.1111/cobi.13973. Epub 2022 Aug 26.

Catalyzing success in community-based conservation

Brandie Fariss  1   2 , Nicole DeMello  2 , Kathryn A Powlen  3 , Christopher E Latimer  1 , Yuta Masuda  4 , Christina M Kennedy  1
Affiliations
Review

Catalyzing success in community-based conservation

Brandie Fariss et al. Conserv Biol. 2023 Feb.

Abstract
in English, Spanish, Chinese

Efforts to devolve rights and engage Indigenous Peoples and local communities in conservation have increased the demand for evidence of the efficacy of community-based conservation (CBC) and insights into what enables its success. We examined the human well-being and environmental outcomes of a diverse set of 128 CBC projects. Over 80% of CBC projects had some positive human well-being or environmental outcomes, although just 32% achieved positive outcomes for both (i.e., combined success). We coded 57 total national-, community-, and project-level variables and controls from this set, performed random forest classification to identify the variables most important to combined success, and calculated accumulated local effects to describe their individual influence on the probability of achieving it. The best predictors of combined success were 17 variables suggestive of various recommendations and opportunities for conservation practitioners related to national contexts, community characteristics, and the implementation of various strategies and interventions informed by existing CBC frameworks. Specifically, CBC projects had higher probabilities of combined success when they occurred in national contexts supportive of local governance, confronted challenges to collective action, promoted economic diversification, and invested in various capacity-building efforts. Our results provide important insights into how to encourage greater success in CBC.

Los esfuerzos por transferirle derechos e involucrar a los pueblos originarios y a las comunidades locales en la conservación han incrementado la demanda de evidencia sobre la eficiencia de la conservación basada en la comunidad (CBC) y de conocimiento sobre lo que posibilita su éxito. Analizamos los resultados ambientales y de bienestar humano en un conjunto diverso de 28 proyectos de CBC. Más del 80% de estos proyectos tuvieron resultados positivos para el ambiente o el bienestar humano, aunque sólo el 32% logró resultados positivos para ambos (es decir, éxito combinado). Codificamos en total 57 variables y controles a nivel nacional, comunitario y de proyecto en este conjunto, aplicamos una clasificación aleatoria de bosque para identificar las variables más importantes para el éxito combinado y calculamos los efectos locales acumulados para describir su influencia sobre la probabilidad de alcanzar el éxito combinado. Los mejores pronósticos del éxito combinado se obtuvieron con 17 variables sugerentes de varias políticas y oportunidades para los practicantes de la conservación relacionadas con los contextos nacionales, las características de la comunidad y la implementación de varias estrategias e intervenciones guiadas por los marcos existentes de CBC. Específicamente, los proyectos de CBC tuvieron mayor probabilidad de tener éxito combinado cuando se dieron dentro de contextos nacionales que respaldan la gobernanza local, enfrentan los retos de la acción colectiva, promueven la diversificación económica e invierten en varios esfuerzos por construir capacidades. Nuestros resultados proporcionan información importante sobre cómo alentar un mayor éxito en la CBC.

向原住民和当地社区移交权力并促进其参与保护工作的努力, 增加了对以社区为基础的保护工作(community-based conservation, CBC)有效性证据的需求和探究其成功背后深层原因的需求。本研究分析了128个不同的CBC项目的人类福祉和环境保护成效。我们发现, 超过80%的CBC项目都有积极的人类福祉或环境保护成效, 但只有32%的项目在这两方面同时取得了积极成果(即综合性成功)。我们对这些成功项目中的57个国家、社区和项目水平的变量和控制因素进行了编码, 并进行随机森林分类, 以确定影响综合性成功最重要的变量。我们还计算了累积局部效应, 以描述它们对实现综合性成功的概率的单独影响。结果表明, 有17个变量是综合性成功的最佳预测因素, 代表了保护实践者的各种政策和机会, 它们与国家背景、社区特点以及在现有的CBC框架下实施的战略和干预措施有关。具体来说, 位于支持地方治理的国家、面对着集体行动挑战、可以促进经济多样化, 以及投资于各种能力建设工作的CBC项目取得综合性成功的概率更高。我们的结果为鼓励CBC取得更大成就提供了重要的见解。【翻译:胡怡思;审校:聂永刚】.

Keywords: accumulated local effects; aprendizaje automático; clasificación aleatoria de bosque; community-based conservation; conservación basada en evidencias; conservación basada en la comunidad; conservation evaluation; conservation outcomes; efectos locales acumulados; evaluación de la conservación; evidence-based conservation; machine learning; random forest classification; resultados de la conservación; 保护结果; 保护评估; 基于社区的保护; 基于证据的保护; 机器学习; 累积局部效应; 随机森林分类.

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Figures

FIGURE 1
FIGURE 1
Analytic process applied to the sample of community‐based conservation projects examined to determine features important to combined success in human well‐being and environmental outcomes
FIGURE 2
FIGURE 2
Numbers, locations, and biomes of community‐based conservation projects represented in the analytic sample. The number of projects in each country is visualized in natural breaks on the map, a classification method that finds groupings based on maximizing the difference between classes (references in Appendix S11)
FIGURE 3
FIGURE 3
(a) Social, (b) economic, (c) environmental, and (d) combined social, economic, and environmental outcomes of community‐based conservation projects. In (a‐c) indicator‐level outcomes are summarized in the stacked bar graphs across the top (NRM, natural resource management). Indicators were scored as negative, neutral, or positive. Project‐level outcomes are in the single bar graphs across the bottom and are an aggregation of all the indicators measured for that project. Project‐level outcomes were negative if all indicators were negative, negative or neutral, or neutral only; were mixed if there were some positive indicators; and were positive if all indicators were positive. In (d), the response variable in subsequent modeling, projects were a success if human well‐being (social, economic, or both) and environmental outcomes were positive and a failure otherwise. Heat maps showing the percentage, mean, and SD for indicator and project‐level outcomes are in Appendix S12
FIGURE 4
FIGURE 4
Bootstrap estimated (B = 1000) feature importance scores for explanatory variables of the random forest classification (RFC) model of combined success for community‐based conservtion projects (Figure 3d) (IPLC, Indigenous peoples and local communities; red symbol, bootstrap median; bar, 90% confidence interval defined by the 0.05 and 0.95 quantiles of the bootstrap distribution). Feature importance scores are relative, ranked from highest to lowest feature importance, and describe which variables are important to model prediction. Definitions, hypothesized effects, and a comparison of alternate feature importance measures are provided in Table 1 and Appendix S16
FIGURE 5
FIGURE 5
Bootstrap estimated (B = 1000) accumulated local effects (ALEs) for (a) national‐, (b) community‐, and (c) project‐level variables and (d) control variables of the random forest classification (RFC) model of combined success for community‐based conservation projects. Plots are ordered from highest to lowest feature importance within each level (Fig. 4) (continuous variables: light blue line, individual bootstrap estimate; dark blue line, bootstrap median; envelope, 90% confidence interval defined by the 0.05 and 0.95 quantiles of the bootstrap distribution; dotted red line, smoothed loess curve; categorical variables: light blue barcode, individual bootstrap estimate; dark blue symbol, bootstrap median; blue error bar, 90% confidence interval defined by the 0.05 and 0.95 quantiles of the bootstrap distribution; **, significant effect; *, potential (but nonsignificant) effect). Each plot is centered and the value plotted is interpreted as a change in the probability of combined success associated with a specific value of the variable (Molnar 2019)
FIGURE 5
FIGURE 5
Bootstrap estimated (B = 1000) accumulated local effects (ALEs) for (a) national‐, (b) community‐, and (c) project‐level variables and (d) control variables of the random forest classification (RFC) model of combined success for community‐based conservation projects. Plots are ordered from highest to lowest feature importance within each level (Fig. 4) (continuous variables: light blue line, individual bootstrap estimate; dark blue line, bootstrap median; envelope, 90% confidence interval defined by the 0.05 and 0.95 quantiles of the bootstrap distribution; dotted red line, smoothed loess curve; categorical variables: light blue barcode, individual bootstrap estimate; dark blue symbol, bootstrap median; blue error bar, 90% confidence interval defined by the 0.05 and 0.95 quantiles of the bootstrap distribution; **, significant effect; *, potential (but nonsignificant) effect). Each plot is centered and the value plotted is interpreted as a change in the probability of combined success associated with a specific value of the variable (Molnar 2019)
FIGURE 5
FIGURE 5
Bootstrap estimated (B = 1000) accumulated local effects (ALEs) for (a) national‐, (b) community‐, and (c) project‐level variables and (d) control variables of the random forest classification (RFC) model of combined success for community‐based conservation projects. Plots are ordered from highest to lowest feature importance within each level (Fig. 4) (continuous variables: light blue line, individual bootstrap estimate; dark blue line, bootstrap median; envelope, 90% confidence interval defined by the 0.05 and 0.95 quantiles of the bootstrap distribution; dotted red line, smoothed loess curve; categorical variables: light blue barcode, individual bootstrap estimate; dark blue symbol, bootstrap median; blue error bar, 90% confidence interval defined by the 0.05 and 0.95 quantiles of the bootstrap distribution; **, significant effect; *, potential (but nonsignificant) effect). Each plot is centered and the value plotted is interpreted as a change in the probability of combined success associated with a specific value of the variable (Molnar 2019)
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References

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