Big-leaf mahogany (Swietenia macrophylla King) is an economically important timber species in the Neotropics. For over three centuries, it has been selectively extracted from tropical forests, threatening its populations. We investigate the actual and potential distribution of big-leaf mahogany and assess its abundance on the Yucatan Peninsula based on the National Forest and Soils Inventory database. Furthermore, we evaluate environmental factors associated with its distribution, abundance, and tree size. The actual and potential distribution models show the presence of mahogany in a wide geographic area covering the southern and eastern portions of the Yucatan Peninsula. Abundance of mahogany in the landscape varies and in general is low. The spatial potential distribution model was best explained by the environmental variables of vegetation cover (medium-and high-stature semievergreen tropical forest) and elevation (upland areas). Results also indicate that mahogany remains relatively abundant and contain larger size classes in localities where the species has been harvested and managed for decades under community forest management. Furthermore, statistical analyses show greater tree density of mahogany mostly associated with low-stature semievergreen tropical forest having deep soils (gleysols and vertisols), while larger tree size (diameter at breast height) was associated with medium-stature semievergreen tropical forests in upland areas with moderately deep or shallow soils (mostly rendzinas or leptosols). Despite deforestation, land-use change and forestry activities on the Yucatan Peninsula, particularly in the past 20 years, the distribution and abundance of mahogany do not appear to be as drastically reduced as described in other neotropical regions.

Natural forest management in the tropics is often impeded by scarcity of advanced regeneration of commercial species. To supplement natural regeneration in a forest managed by a community in the Selva Maya of Mexico, nursery-grown Swietenia macrophylla seedlings were planted in multiple-tree felling gaps, known as bosquetes. Remnant trees are often left standing in gaps for cultural and economic reasons or due to their official protected status. We focus on these purposefully retained trees and their impacts on planted seedlings. Sampled bosquetes were 400-1800 m 2 , of which remnant trees covered a mean of 29%. Seedling height growth rates over the first 18 months after out-planting more than doubled with increased canopy openness from 0.09 m year −1 under medium cover to 0.22 m year −1 in full sun. Liana infestations and shoot tip damage were most frequent on seedlings in the open, but, contrary to our expectations, height growth rates were 0.14 m year −1 faster for liana-infested seedlings than non-infested and did not differ between damaged and undamaged seedlings. Apparently the more rapid height growth of well-illuminated seedlings more than compensated for the effects of lianas or shoot tip damage. Despite the abundance of remnant trees and their negative effects on seedling growth, enrichment planting in bosquetes has potential for community-based natural forest management in the tropics in supplementing natural regeneration of commercial species. One obvious recommendation is to leave fewer remnant trees, especially those of commercial species that are non-merchantable due to stem defects and trees retained for no apparent reason, which together constituted half of the remnant crown cover in the sampled bosquetes. Finally, given the rapid growth of lianas and understory palms in large canopy gaps, at least the most vigorous of the planted seedlings should be tended for at least two years.

Detecting and monitoring forest disturbance from selective logging is necessary to develop effective strategies and polices that conserve tropical forests and mitigate climate change. We assessed the potential of using the remote sensing tool, CLASlite forest monitoring system, to detect disturbance from timber harvesting in four community forests () of the Selva Maya on the Yucatan Peninsula, Mexico. Selective logging impacts (e.g. felling gaps, skid trails, logging roads and log landings) were mapped using GPS in the 2014 annual cutting areas (ACAs) of each ejido. We processed and analyzed two pre-harvest Landsat images (2001 and 2013) and one post-harvest image (November 2014) with the CLASlite system, producing maps of degraded, deforested and unlogged areas in each ACA. Based on reference points of disturbed (felling and skidding), deforested (log landings and roads) and unlogged areas in each ACA, we applied accuracy assessments which showed very low overall accuracies (<19.1%). Selective logging impacts, mainly from log landings and new logging road construction, were detected in only one ejido which had the highest logging intensity (7 m ha).ejidos3–1

<p><strong>Background. </strong>Mapping selective logging impacts on the Yucatan Peninsula is important to pursuing carbon emissions reduction and biodiversity conservation goals. <strong>Objective.</strong> To evaluate the effectiveness of applying remote sensing techniques using LANDSAT 8 OLI imagery to detect tropical forest disturbance from timber harvesting in four communally managed forests (<em>ejidos</em>). We further assess differences among them in terms of implementing improved forest management (IFM) and reduced impact logging (RIL). <strong>Methodology. </strong>Vegetation indices were calculated, and forest cover classification was performed to map logged and unlogged forest and specific harvest disturbances (e.g. felling gaps, skid trails, logging roads and log landings) in annual cutting areas of 2014. Accuracy assessments were conducted based on validation points collected in the field after logging. <strong>Results.</strong> We found that 75% of the binary classifications (logged and unlogged forest) had mean overall accuracies greater than 60%, representing a fair (40 to 70%) accuracy, although mapping of specific harvesting disturbances had poor accuracy (&lt;40%). Vegetation indices that performed the best were normalized vegetation index (NDVI), Tasseled Cap Greenness and Tasseled Cap Wetness. Ejidos that applied IFM and RIL impacted a smaller percentage of their cutting areas and less area of forest per cubic meter of timber extracted, despite similar or higher logging intensities than ejidos without improved practices. <strong>Implication.</strong> Monitoring selective logging disturbance is important to improved forest management and certification of sustainability.  <strong>Conclusion.</strong> Mapping and monitoring impacts from selective logging by forest managers and technicians can be performed in a cost-efficient manner using LANDSAT 8 images, although accuracy could be improved with higher resolution imagery.</p>