In Central America, drug traffickers are deforesting the region's remaining forests and protected areas through a process known as narco-ganader ıa, narco-cattle ranching. Drawing on the case study of Laguna del Tigre National Park, this article argues that narco-cattle ranching is a key driver of deforestation in Guatemala's Maya Biosphere Reserve. Using ethnographic and remote-sensing methods, we describe narco-cattle ranching's money-laundering practices, its territorial dynamics, and its environmental impacts. We draw on theorisations of "political forests" to explain how drug trafficking organisations transform land use in the reserve, and along the way, remake its ecology, territories and subjects. Our work illustrates that drug policy is inextricably linked to conservation policy in the Americas. More specifically, we argue that community-based resource management improves forest and protected area residents' abilities to resist drug-trafficking related land use change by strengthening local governance and land tenure regimes.Resumen: En Am erica Central, los traficantes de drogas est an deforestando los bosques y areas protegidas restantes de la regi on a trav es de un proceso localmente conocido como narco-ganader ıa. Con base en el estudio de caso del Parque Nacional Laguna del Tigre, este art ıculo argumenta que no es la agricultura de subsistencia practicada por campesinos sin tierras la que est a causando deforestaci on en la Reserva de la Bi osfera Maya de Guatemala, sino la narco-ganader ıa. Por medio de m etodos etnogr aficos y de percepci on remota, describimos las pr acticas de lavado de dinero de la narco-ganader ıa, sus din amicas territoriales y sus impactos ambientales. A partir de teorizaciones de "bosques pol ıticos", explicamos c omo las organizaciones de narcotr afico transforman el uso del suelo en la reserva y, como consecuencia, reconfiguran su ecolog ıa, territorios y sujetos. Nuestro trabajo ilustra que la pol ıtica en materia de drogas est a ıntimamente relacionada con la pol ıtica de conservaci on en las Am ericas. M as espec ıficamente, sostenemos que el manejo de recursos de manera comunitaria mejora las habilidades de los residentes de areas protegidas para resistir cambios de uso del suelo relacionados con el tr afico de drogas a trav es del fortalecimiento de la gobernanza local y de los reg ımenes de tenencia de la tierra.

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Groundwater depletion is an important problem driven by population growth, land use and land cover (LULC) change, climate change, and other factors. Groundwater depletion generates water stress and encourages unstainable resource use. The aim of this study is to determine how population growth, LULC change, and climate change relate to groundwater depletion in the Alto Atoyac sub-basin, Oaxaca, Mexico. Twenty-five years of dry season water table data from 1984 to 2009 are analyzed to examine annual groundwater depletion. Kriging is used to interpolate the region’s groundwater levels in a geographic information system (GIS) from mapped point measurements. An analysis of remotely sensed data revealed patterns of LULC change during a 34-year (1986–2018) period, using a supervised, machine-learning classification algorithm to calculate the changes in LULC. This analysis is shown to have an 85% accuracy. A global circulation model (GFDL-CM3) and the RCP4.5 and RCP8.5 scenarios were used to estimate the effects of climate change on the region’s groundwater. Estimates of evapotranspiration (using HELP3.5 code) and runoff (USDA-SCS-CN), were calculated. Since 1984, the region’s mean annual temperature has increased 1.79 °C and urban areas have increased at a rate of 2.3 km2/year. Population growth has increased water consumption by 97.93 × 106 m3/year. The volume of groundwater is shrinking at a rate of 284.34 × 106 m3/year, reflecting the extreme pressure on groundwater supply in the region. This research reveals the nature of the direct impacts that climate change, changing LULCs, and population growth have in the process of groundwater depletion.

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Assessing forest degradation has been a challenging task due to the generally slow-changing nature of the process, which demands long periods of observation and high frequency of records. This research contributes to efforts aimed at detecting forest degradation by analyzing the trend component of the time series of Leaf Area Index (LAI) collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) over Central Mexico from 2002 to 2017. The analysis of the trend component is proposed to overcome the challenge of identifying very subtle and gradual changes that can be undetected if only the raw time series is examined. Additionally, the use of LAI as an alternative to other widely used indexes (e.g., Normalize Difference Vegetation Index and Enhanced Vegetation Index) facilitates consideration of the structural changes evident from degradation though not necessarily observable with spectral indices. Overall, results indicate that 52% of the study area has experienced positive trends of vegetation change (i.e., increasing LAI), 37% has remained unchanged, and 11% exhibits some level of forest degradation. Particularly, the algorithm estimated that 0.6% (385 km 2 ) is highly degraded, 5.3% (3406 km 2 ) moderately degraded, and 5.1% (3245 km 2 ) slightly degraded. Most of the moderate and highly degraded areas are distributed over the east side of the study area and evergreen broadleaf appears to be the most affected forest type. Model validation resulted an accuracy of 63%. Some actions to improve this accuracy are suggested, but also a different approach to validate this type of study is suggested as an area of opportunity for future research.