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The Red-cockaded Woodpecker (Dryobates borealis, RCW) was listed under the U.S. Endangered Species Act in 1973 due to significant population declines resulting from habitat loss and fragmentation, and the species has been intensively managed since then. We reviewed management strategies commonly used to conserve the RCW, emphasizing studies conducted after publication of the most recent Recovery Plan in 2003, to evaluate the efficacy of each strategy across the RCW’s range and identify demographic and environmental factors that influence the success of each strategy. Of the management strategies reviewed, outcomes from prescribed fire vary the most across the RCW’s range, because prescribed fire is influenced by the site’s vegetation, abiotic conditions, and land use history. The abundance of cavity kleptoparasites varies across sites, but kleptoparasite control is only a high priority in small RCW populations. The long-term effectiveness of artificial cavities and translocations, which are highly effective across the RCW’s range in the short-term, requires suitable habitat, which is strongly influenced by prescribed fire. Regional variation in RCW management may be needed, because RCW populations that are not in archetypical suitable habitat (sensu Recovery Plan Standards) may benefit from management methods that are not suitable for large RCW populations in archetypical habitats (e.g., installing many cavity restrictor plates and cavity inserts). RCW management strategies have been studied most in the South Central Plains and Southeastern Plains ecoregions, and more research in other ecoregions would be valuable. We encourage consideration of how management varies according to population demographics and site characteristics as opposed to a “one-size fits all” management approach for the RCW, which inhabits broad geographic ranges and sites of varying productivity and will continue to rely on management efforts after downlisting or delisting from the Endangered Species Act.
The Red-cockaded Woodpecker (Dryobates borealis, RCW) was listed under the U.S. Endangered Species Act in 1973 due to significant population declines resulting from habitat loss and fragmentation, and the species has been intensively managed since then. We reviewed management strategies commonly used to conserve the RCW, emphasizing studies conducted after publication of the most recent Recovery Plan in 2003, to evaluate the efficacy of each strategy across the RCW’s range and identify demographic and environmental factors that influence the success of each strategy. Of the management strategies reviewed, outcomes from prescribed fire vary the most across the RCW’s range, because prescribed fire is influenced by the site’s vegetation, abiotic conditions, and land use history. The abundance of cavity kleptoparasites varies across sites, but kleptoparasite control is only a high priority in small RCW populations. The long-term effectiveness of artificial cavities and translocations, which are highly effective across the RCW’s range in the short-term, requires suitable habitat, which is strongly influenced by prescribed fire. Regional variation in RCW management may be needed, because RCW populations that are not in archetypical suitable habitat (sensu Recovery Plan Standards) may benefit from management methods that are not suitable for large RCW populations in archetypical habitats (e.g., installing many cavity restrictor plates and cavity inserts). RCW management strategies have been studied most in the South Central Plains and Southeastern Plains ecoregions, and more research in other ecoregions would be valuable. We encourage consideration of how management varies according to population demographics and site characteristics as opposed to a “one-size fits all” management approach for the RCW, which inhabits broad geographic ranges and sites of varying productivity and will continue to rely on management efforts after downlisting or delisting from the Endangered Species Act.
Conservation Canopy Height Humid Tropics Point Cloud ModelAgrosystems have different canopy strata due to shade trees that serve as available habitats for endangered species such as birds, reptiles, and mammals. LiDAR is a technology used to assess habitat quality as a support for designing conservation strategies. The objective of this research was to develop a model with data derived from LiDAR to obtain the height of the shade canopy in cocoa agrosystems, as a habitat available for wildlife species. Through the data of the height of the vegetation taken in the field and the data obtained from a LiDAR point cloud, the Canopy Height Model was generated. The data from the mapping of the canopy height model of the agrosystems taken as study sites were validated using the coefficient of determination (R 2 ), mean absolute error (MAE), and the RMSE. The mean canopy height at the study sites was 14.63, 13.84, and 13.95 m, and the results of the validation using the model predicted canopy height shows good agreement with the actual value with an R 2 of 0.86, and very low values of MAE=1.88, MSE=5.64, and RMSE=2.37, which indicates that they have an acceptable degree regarding the canopy height model between the LiDAR data and the data taken in the field. Research using LiDAR provides useful information to determine the height of the canopy, in the cocoa agrosystems up to 3 strata are found, this is due to the diversity of tree species used as shade, ranging from timber, fruit, ornamental, which are used as feeding, nesting, and resting of wildlife, in the study area populations of howler monkey species that are listed as endangered by the International Union for Conservation of Nature (IUCN), in addition to other species such as bats and birds, with the presence of these species indicate that the cocoa agrosystems, serve as a habitat for a diversity of species, which is why it is important to conserve these agrosystems in the humid tropics.
Airborne Laser Scanners (ALS) and Terrestrial Laser Scanners (TLS) are two lidar systems frequently used for remote sensing forested ecosystems. The aim of this study was to compare crown metrics derived from TLS, ALS, and a combination of both for describing the crown structure and fuel attributes of longleaf pine (Pinus palustris Mill.) dominated forest located at Eglin Air Force Base (AFB), Florida, USA. The study landscape was characterized by an ALS and TLS data collection along with field measurements within three large (1963 m2 each) plots in total, each one representing a distinct stand condition at Eglin AFB. Tree-level measurements included bole diameter at breast height (DBH), total height (HT), crown base height (CBH), and crown width (CW). In addition, the crown structure and fuel metrics foliage biomass (FB), stem branches biomass (SB), crown biomass (CB), and crown bulk density (CBD) were calculated using allometric equations. Canopy Height Models (CHM) were created from ALS and TLS point clouds separately and by combining them (ALS + TLS). Individual trees were extracted, and crown-level metrics were computed from the three lidar-derived datasets and used to train random forest (RF) models. The results of the individual tree detection showed successful estimation of tree count from all lidar-derived datasets, with marginal errors ranging from −4 to 3%. For all three lidar-derived datasets, the RF models accurately predicted all tree-level attributes. Overall, we found strong positive correlations between model predictions and observed values (R2 between 0.80 and 0.98), low to moderate errors (RMSE% between 4.56 and 50.99%), and low biases (between 0.03% and −2.86%). The highest R2 using ALS data was achieved predicting CBH (R2 = 0.98), while for TLS and ALS + TLS, the highest R2 was observed predicting HT, CW, and CBD (R2 = 0.94) and HT (R2 = 0.98), respectively. Relative RMSE was lowest for HT using three lidar datasets (ALS = 4.83%, TLS = 7.22%, and ALS + TLS = 4.56%). All models and datasets had similar accuracies in terms of bias (<2.0%), except for CB in ALS (−2.53%) and ALS + TLS (−2.86%), and SB in ALS + TLS data (−2.22%). These results demonstrate the usefulness of all three lidar-related methodologies and lidar modeling overall, along with lidar applicability in the estimation of crown structure and fuel attributes of longleaf pine forest ecosystems. Given that TLS measurements are less practical and more expensive, our comparison suggests that ALS measurements are still reasonable for many applications, and its usefulness is justified. This novel tree-level analysis and its respective results contribute to lidar-based planning of forest structure and fuel management.
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