RESUMEN
Studying land use change in protected areas (PAs) located in tropical forests is a major conservation priority due to high conservation value (e.g., species richness and carbon storage) here, coupled with generally high deforestation rates. Land use change researchers use a variety of land cover products to track deforestation trends, including maps they produce themselves and readily available products, such as the Global Forest Change (GFC) dataset. However, all land cover maps should be critically assessed for limitations and biases to accurately communicate and interpret results. In this study, we assess deforestation in PA complexes located in agricultural frontiers in the Amazon Basin. We studied three specific sites: Amboró and Carrasco National Parks in Bolivia, Jamanxim National Forest in Brazil, and Tambopata National Reserve and Bahuaja-Sonene National Park in Peru. Within and in 20km buffer areas around each complex, we generated land cover maps using composites of Landsat imagery and supervised classification, and compared deforestation trends to data from the GFC dataset. We then performed a dissimilarity analysis to explore the discrepancies between the two remote sensing products. Both the GFC and our supervised classification showed that deforestation rates were higher in the 20km buffer than inside the PAs and that Jamanxim National Forest had the highest deforestation rate of the PAs we studied. However, GFC maps showed consistently higher rates of deforestation than our maps. Through a dissimilarity analysis, we found that many of the inconsistencies between these datasets arise from different treatment of mixed pixels or different parameters in map creation (for example, GFC does not detect reforestation after 2012). We found that our maps underestimated deforestation while GFC overestimated deforestation, and that true deforestation rates likely fall between our two estimates. We encourage users to consider limitations and biases when using or interpreting our maps, which we make publicly available, and GFC's maps.
Asunto(s)
Conservación de los Recursos Naturales , Bosques , Agricultura , Sesgo , BrasilRESUMEN
Development and implementation of effective protected area management to reduce deforestation depend in part on identifying factors contributing to forest loss and areas at risk of conversion, but standard land-use-change modeling may not fully capture contextual factors that are not easily quantified. To better understand deforestation and agricultural expansion in Amazonian protected areas, we combined quantitative land-use-change modeling with qualitative discourse analysis in a case study of Brazil's Jamanxim National Forest. We modeled land-use change from 2008 to 2018 and projected deforestation through 2028. We used variables identified in a review of studies that modeled land-use change in the Amazon (e.g., variables related to agricultural suitability and economic accessibility) and from a critical discourse analysis that examined documents produced by different actors (e.g., government agencies and conservation nonprofit organizations) at various spatial scales. As measured by analysis of variance, McFadden's adjusted pseudo R2 , and quantity and allocation disagreement, we found that including variables in the model identified as important to deforestation dynamics through the qualitative discourse analysis (e.g., the proportion of unallocated public land, distance to proposed infrastructure developments, and density of recent fires) alongside more traditional variables (e.g., elevation, distance to roads, and protection status) improved the predictive ability of these models. Models that included discourse analysis variables and traditional variables explained up to 19.3% more of the observed variation in deforestation probability than a model that included only traditional variables and 4.1% more variation than a model with only discourse analysis variables. Our approach of integrating qualitative and quantitative methods in land-use-change modeling provides a framework for future interdisciplinary work in land-use change.
El desarrollo y la implementación de la gestión efectiva de las áreas protegidas para reducir la deforestación dependen parcialmente de la identificación de los factores que contribuyen a la pérdida del bosque y de las áreas en riesgo de ser convertidas, pero el modelado estándar del cambio de uso de suelo puede no capturar completamente los factores contextuales que no se cuantifican fácilmente. Combinamos el modelado cuantitativo del cambio de uso de suelo con el análisis cualitativo del discurso en un estudio de caso del Bosque Nacional Jamanxim de Brasil para entender de mejor manera la deforestación y la expansión agrícola en las áreas protegidas del Amazonas. Modelamos el cambio de uso de suelo entre 2008 y 2018 y lo proyectamos hasta 2028. Usamos las variables identificadas en una revisión de estudios que modelaron el cambio de uso de suelo en el Amazonas (p. ej.: variables relacionadas con la idoneidad agrícola y la accesibilidad económica) y en el análisis crítico del discurso que examinó documentos producidos por diferentes actores (p. ej.: agencias gubernamentales y organizaciones sin fines de lucro para la conservación) a varias escalas espaciales. Conforme a las medidas del análisis de varianza, la pseudo-R2 ajustada de McFadden y el desacuerdo en la cantidad y la asignación, descubrimos que la inclusión dentro del modelo de las variables identificadas como importantes para las dinámicas de deforestación mediante el análisis cualitativo del discurso (p. ej.: la proporción de terrenos públicos sin asignar, la distancia hacia los desarrollos propuestos de infraestructura y la densidad de incendios recientes) junto con variables más tradicionales (p. ej.: elevación, distancia a las carreteras y estado de protección) mejoró la habilidad predictiva de dichos modelos. Los modelos que incluyeron la mezcla de variables explicaron hasta 19.3% más de la variación observada en la probabilidad de deforestación que un modelo que solamente incluyó las variables tradicionales y 4.1% más variación que un modelo con las variables del análisis del discurso. Nuestra estrategia de integrar los métodos cualitativos y cuantitativos dentro del modelado del cambio de uso de suelo proporciona un marco para futuros trabajos interdisciplinarios sobre el cambio de uso de suelo.
Asunto(s)
Conservación de los Recursos Naturales , Incendios , Bosques , Agricultura , BrasilRESUMEN
Environmental governance is more effective when the scales of ecological processes are well matched with the human institutions charged with managing human-environment interactions. The social-ecological systems (SESs) framework provides guidance on how to assess the social and ecological dimensions that contribute to sustainable resource use and management, but rarely if ever has been operationalized for multiple localities in a spatially explicit, quantitative manner. Here, we use the case of small-scale fisheries in Baja California Sur, Mexico, to identify distinct SES regions and test key aspects of coupled SESs theory. Regions that exhibit greater potential for social-ecological sustainability in one dimension do not necessarily exhibit it in others, highlighting the importance of integrative, coupled system analyses when implementing spatial planning and other ecosystem-based strategies.