Search Results (35)

In the forest ecosystems of Central European Russia, the influence of forest edges on the spatial distribution of Drosophilidae was studied for the first time. The research was conducted during the period of 2021–2022 in the Republic of Mordovia. Beer traps baited with fermented beer and sugar were used to collect Drosophilidae. Two study plots were selected, differing in their forest edges, tree stands, and adjacent open ecosystems. In both cases, the forest directly bordered an open ecosystem. Edges serve as transitional biotopes, where both forest and meadow (open area) faunas coexist. Knowing that many drosophilid species prefer forest habitats, we designated forest interior sites as control points. Traps were set at heights of 1.5 m (lower) and 7.5 m (upper) on trees. A total of 936 specimens representing 27 species were collected. Nine species were common across all traps, while ten species were recorded only once. At the forest edges, 23 species were captured across both heights, compared to 19 species in the forest interiors. However, the total abundance at the forest edges was 370 specimens, while it was 1.5 times higher in the forest interiors. Both abundance and species richness varied between plots. Margalef’s index was higher at the forest edges than in the forest interiors, particularly at 1.5 m height at the edge and at 7.5 m height in the forest interior. Shannon and Simpson indices showed minimal variation across traps at different horizontal and vertical positions. The highest species diversity was observed among xylosaprobionts (9 species) and mycetophages (8 species). All ecological groups were represented at the forest edges, whereas only four groups were recorded in the forest interiors, with the phytosaprophagous species Scaptomyza pallida being absent. In general, both species richness and drosophilid abundance increased in the lower strata, both at the forest edge and within the interior. Using the R package Indicspecies, we identified Gitona distigma as an indicator species for the forest edge and Scaptodrosophila rufifrons as an indicator for the forest interior in the lower tier for both plots. In addition, Drosophila testacea, D. phalerata, and Phortica semivirgo were found to be indicator species for the lower tier in both plots, while Leucophenga quinquemaculata was identified as an indicator species for the upper tier at the second plot. Full article

Tropical forests face increasing threats and are often replaced by secondary forests that regenerate after disturbances. In the Atlantic Forest, this creates fragments of different successional stages. The aim of this study is to understand how soil nutrients and light availability gradients influence the species composition and structure of trees and regenerating strata in remnants of lowland rainforest. We sampled 15 plots for the tree stratum (DBH ≥ 5 cm) and 45 units for the regenerating stratum (height ≥ 50 cm, DBH < 5 cm), obtaining phytosociological, entropy and equitability data for both strata. Canopy openness was assessed with hemispherical photos and soil samples were homogenized. To analyze the interactions between the vegetation of the tree layer and the environmental variables, we carried out three principal component analyses and two redundancy analyses and applied a linear model. The young fragments showed good recovery, significant species diversity, and positive successional changes, while the older ones had higher species richness and were in an advanced stage of succession. In addition, younger forests are associated with sandy, nutrient-poor soils and greater exposure to light, while mature forests have more fertile soils, display a greater diversity of dispersal strategies, are rich in soil clay, and have less light availability. Mature forests support biodiversity and regeneration better than secondary forests, highlighting the importance of preserving mature fragments and monitoring secondary ones to sustain tropical biodiversity. Full article

Habitat degradation poses a critical threat to the Malabar slender loris (Loris lydekkerianus malabaricus), yet little is known about its microhabitat requirements in intact forest. In Aralam Wildlife Sanctuary, we combined nocturnal trail surveys (337 loris sightings) with plotless sampling of 2830 trees (86 species from 35 families) to characterize both vegetation structure and loris presence. Our results show that lorises occur almost exclusively in mildly degraded wet evergreen and secondary moist deciduous subcanopies, where understory trees and climber networks provide continuous pathways. Individuals are most often encountered at heights of 5–15 m—ascending into higher strata as the night progresses—reflecting a balance between foraging access and predator avoidance. Substrate analysis revealed strong preferences for twigs ≤ 1 cm (36.98%) and small branches 2–5 cm in diameter, oriented obliquely to minimize energetic costs and maintain stability during slow, deliberate arboreal locomotion. Day-sleeping sites were overwhelmingly located within dense tangles of lianas on large-girth trees, where intertwined stems and thorny undergrowth offer concealment from both mammalian and avian predators. Vegetation surveys documented a near-equal mix of evergreen (50.6%) and deciduous (49.4%) species—including 26 endemics (18 restricted to the Western Ghats)—with Aporosa cardiosperma emerging as the most abundant riparian pioneer, suggesting both ecological resilience and potential simplification in fragmented patches. Complementing field observations, our recent habitat-suitability modeling in Aralam indicates that broad-scale climatic and anthropogenic factors—precipitation patterns, elevation, and proximity to roads—are the strongest predictors of loris occupancy, underscoring the interplay between landscape-level processes and microhabitat structure. Together, these findings highlight the imperative of multi-strata forest restoration—planting insect-hosting native trees, maintaining continuous canopy and climber networks, and integrating small “mini-forest” modules—to recreate the structural complexity vital for slender loris conservation and the broader resilience of Western Ghats biodiversity. Full article

The assessment of fire effects in Aleppo pine forests is crucial for guiding the recovery of burnt areas. This study presents a methodology using UAV-LiDAR data to quantify malleability and elasticity in four burnt areas (1970, 1995, 2008 and 2015) through the statistical analysis of different metrics related to height structure and diversity (Height mean, 99th percentile and Coefficient of Variation), coverage, relative shape and distribution strata (Canopy Cover, Canopy Relief Ratio and Strata Percent Coverage), and canopy complexity (Profile Area and Profile Area Change). In general terms, malleability decreases over time in forest ecosystems that have been affected by wildfires, whereas elasticity is higher than what has been determined in previous studies. However, a particular specificity has been detected from the 1995 fire, so we can assume that there are other situational factors that may be affecting ecosystem resilience. LiDAR metrics and uni-temporal sampling between burnt sectors and control aids are used to understand community resilience and to identify the different recovery stages in P. halepensis forests. Full article

Culicoides biting midges are small dipterous insects known as biological vectors of arboviruses, protozoa, and filaria parasites worldwide. Many studies on Culicoides focus on trapping them at ground level, without considering the best trap heights for different vector species. This implies that we might overlook insects positioned higher in the canopy. From June to August, we used UV traps to catch Culicoides biting midges at three different heights in three temperate mature forest areas in east Lithuania, Baltic region of Europe. We conducted this study to test the differences in midge numbers, male and female proportions, and female parity at each height. We caught the majority of biting midges (80.6%) at the mid-canopy and high-canopy. A higher number of female Culicoides midges than males was caught, with the proportion of males varying based on height and reaching its lowest point at ground level. No significant difference between the proportion of nulliparous and parous females caught at different height was detected. Culicoides pictipennis and C. festivipennis were the most common species of biting midge we found. They were found in the mid-canopy (86.8%) and the high-canopy (50.0%), respectively. Culicoides kibunensis was next, found at ground level (66.2%), and C. punctatus was found at the high canopy strata (63.0%). Each species’ abundance was seasonal dependent. Information on the vertical distribution of vector species in the temperate forest ecosystem is an important step in understanding patterns of vector borne disease transmission in wildlife. Full article

The feeding posture of a group of François’ langurs in Fusui County, Guangxi, was studied using instantaneous scan sampling from January to December 2016 to explore how the species adapts to karst limestone forests by collecting data on feeding posture, forest strata height, and substrate use. The results showed that leaves were the main food type of the François’ langurs, with young leaves accounting for 64.97% ± 19.08% of the food composition, mature leaves accounting for 11.88% ± 12.09%, fruits accounting for 12.96% ± 12.89%, flowers accounting for 4.16% ± 4.06%, and other food types, including stems, petioles, and other unknown parts of the tree, accounting for a total of 6.03% ± 9.09%. The François’ langurs had four main postures during feeding, of which sitting and bipedal standing feeding accounted for the largest proportions, at 85.99% ± 5.97% and 12.33% ± 6.08% of the total records, respectively. Quadrupedal standing and suspending were rarely observed and only appeared occasionally during feeding activities at the peak resting period, the two postures together accounting for 1.39% ± 1.59% of the total records. The feeding postures of the langurs had marked seasonal variation, as evidenced by the fact that seated feeding accounted for a significantly higher proportion of the total behavioral records in the rainy season than in the dry season, whereas feeding while standing bipedally was significantly more frequent during the dry season. Correlation analyses showed that feeding posture was correlated with food composition, showing a positive correlation between the proportion of bipedal standing feeding and mature leaf consumption. François’ langurs preferred to forage in the lower and middle forest layers, with the lower forest layer accounting for 55.93% ± 16.50% of the total number of recordings and the middle forest layer accounting for 33.63% ± 18.33%. Langurs were less likely to forage on the ground (rocks), accounting for only 6.79% ± 4.78% of the records. The frequency of langurs feeding in the upper part of the forest layer was the lowest at 3.65% ± 2.73%. Additionally, in the dry season, langurs utilized the lower forest layer more but used the middle forest layer less than in the rainy season. This study demonstrates that the spatial distribution of foods in the limestone forest has an important effect on the feeding posture of François’ langurs and their forest layer utilization. Full article

Tropical coastal freshwater forested wetlands in coastal regions are rapidly disappearing as a result of various disturbance agents, mainly wildfires caused by high accumulations of forest fuels. The objective of this study was to characterize the structure and composition of fuel beds in tropical coastal freshwater forested wetlands with three levels of disturbance at El Castaño, La Encrucijada Biosphere Reserve. Seventeen sampling units were used to describe the structure of the forest’s fuel beds (canopy, sub-canopy, and understory). Fallen woody material and litter (surface and fermented) were characterized using the planar intersection technique. Diversity comprised eight species of trees, two shrubs, five lianas, and two herbaceous species. The vertical strata were dominated by trees between 2 and 22 m in height. The horizontal structure had a higher percentage of trees with normal diameter between 2.5 and 7.5 cm (61.4%) of the total. Sites with low disturbance had the highest arboreal density (2686 ind. ha⁻¹). Diversity of species showed that the Fisher, Margalef, Shannon, and Simpson α indices were higher in the low disturbance sites. The Berger–Parker index exhibited greater dominance in the sites with high disturbance. Pachira aquatica Aubl. Showed the highest importance value index and was the largest contributor to fuel beds. Sites with the highest disturbance had the highest dead fuel load (222.18 ± 33.62 Mg ha⁻¹), with woody fuels of classes 1, 10, and 1000 h (rotten) being the most representative. This study contributes to defining areas prone to fire in these ecosystems and designing prevention strategies. Full article

Wildfire hazard is a prominent issue in subtropical forests as climate change and extreme drought events increase in frequency. Stand-level fuel load and forest structure are determinants of forest fire occurrence and spread. However, current fuel management often lacks detailed vertical fuel distribution, limiting accurate fire risk assessment and effective fuel policy implementation. In this study, backpack laser scanning (BLS) is used to estimate several 3D structural parameters, including canopy height, crown base height, canopy volume, stand density, vegetation area index (VAI), and vegetation coverage, to characterize the fuel structure characteristics and vertical density distribution variation in different stands of subtropical forests in China. Through standard measurement using BLS point cloud data, we found that canopy height, crown base height, stand density, and VAI in the lower and middle-height strata differed significantly among stand types. Compared to vegetation coverage, the LiDAR-derived VAI can better show significant stratified changes in fuel density in the vertical direction among stand types. Among stand types, conifer-broadleaf mixed forest and C. lanceolata had a higher VAI in surface strata than other stand types, while P. massoniana and conifer-broadleaf mixed forests were particularly unique in having a higher VAI in the lower and middle-height strata, corresponding to the higher surface fuel and ladder fuel in the stand, respectively. To provide more informative support for forest fuel management, BLS LiDAR data combined with other remote sensing data were advocated to facilitate the visualization of fuel density distribution and the development of fire risk assessment. Full article

Due to climate change, treelines are moving to higher elevations and latitudes. The estimation of biomass of trees and shrubs advancing into alpine areas is necessary for carbon reporting. Remotely sensed (RS) data have previously been utilised extensively for the estimation of forest variables such as tree height, volume, basal area, and aboveground biomass (AGB) in various forest types. Model-based inference is found to be efficient for the estimation of forest attributes using auxiliary RS data, and this study focused on testing model-based estimations of AGB in the treeline ecotone using an area-based approach. Shrubs (Salix spp., Betula nana) and trees (Betula pubescens ssp. czerepanovii, Sorbus aucuparia, Populus tremula, Pinus sylvestris, Picea abies) with heights up to about five meters constituted the AGB components. The study was carried out in a treeline ecotone in Hol, southern Norway, using field plots and point cloud data obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP). The field data were acquired for two different strata: tall and short vegetation. Two separate models for predicting the AGB were constructed for each stratum based on metrics calculated from ALS and DAP point clouds, respectively. From the stratified predictions, mean AGB was estimated for the entire study area. Despite the prediction models showing a weak fit, as indicated by their R²-values, the 95% CIs were relatively narrow, indicating adequate precision of the AGB estimates. No significant difference was found between the mean AGB estimates for the ALS and DAP models for either of the strata. Our results imply that RS data from ALS and DAP can be used for the estimation of AGB in treeline ecotones. Full article

Culicid species, which include potential vectors of yellow fever, are diverse and abundant, with species commonly co-occurring in certain sites. Studying these species can provide important insights into their vector potential and, consequently, epizootic cycles of arboviruses carried about by these vectors. Here, we evaluated the vertical distribution and temporal segregation of mosquito oviposition with emphasis on arbovirus vectors in a fragment of the Atlantic Forest in Casimiro de Abreu, Rio de Janeiro, Brazil. Two sampling points were selected: Fazenda Três Montes and the Reserva Natural de Propriedade Privada Morro Grande. Collections were carried out at two sites using 10 ovitraps installed on the vegetation cover at different heights (0, 2, 4, 6, and 8 m above ground level) and monitored monthly from July 2018 to December 2020. The hypotheses of temporal and vertical stratification were tested through a PERMANOVA, and the relationship of each species with the vertical distribution was evaluated individually through a correlation analysis. We collected a total of 3075 eggs, including four species of medical importance: Haemagogus leucocelaenus (n = 1513), Haemagogus janthinomys (n = 16), Aedes albopictus (n = 1097), and Aedes terrens (n = 449). We found that Hg. leucocelaenus had a positive relationship with height, exhibiting behavior that appears to benefit from higher heights. The abundance of Ae. terrens seemed to follow Hg. leucocelaenus, although we did not find a relationship with height for the former species. On the other hand, Ae. albopictus exhibited a negative relationship with height, becoming absent or outnumbered at higher strata. Our study site has already presented evidence of recent transmission of the wild yellow fever virus, supporting the need to carefully monitor the emergence of febrile diseases among residents in the surrounding areas and the local population. Full article

The wall-to-wall prediction of fuel structural characteristics conducive to high fire severity is essential to provide integrated insights for implementing pre-fire management strategies designed to mitigate the most harmful ecological effects of fire in fire-prone plant communities. Here, we evaluate the potential of high point cloud density LiDAR data from the Portuguese áGiLTerFoRus project to characterize pre-fire surface and canopy fuel structure and predict wildfire severity. The study area corresponds to a pilot LiDAR flight area of around 21,000 ha in central Portugal intersected by a mixed-severity wildfire that occurred one month after the LiDAR survey. Fire severity was assessed through the differenced Normalized Burn Ratio (dNBR) index computed from pre- and post-fire Sentinel-2A Level 2A scenes. In addition to continuous data, fire severity was also categorized (low or high) using appropriate dNBR thresholds for the plant communities in the study area. We computed several metrics related to the pre-fire distribution of surface and canopy fuels strata with a point cloud mean density of 10.9 m⁻². The Random Forest (RF) algorithm was used to evaluate the capacity of the set of pre-fire LiDAR metrics to predict continuous and categorized fire severity. The accuracy of RF regression and classification model for continuous and categorized fire severity data, respectively, was remarkably high (pseudo-R² = 0.57 and overall accuracy = 81%) considering that we only focused on variables related to fuel structure and loading. The pre-fire fuel metrics with the highest contribution to RF models were proxies for horizontal fuel continuity (fractional cover metric) and the distribution of fuel loads and canopy openness up to a 10 m height (density metrics), indicating increased fire severity with higher surface fuel load and higher horizontal and vertical fuel continuity. Results evidence that the technical specifications of LiDAR acquisitions framed within the áGiLTerFoRus project enable accurate fire severity predictions through point cloud data with high density. Full article

We developed new measures of structural complexity using single point terrestrial laser scanning (TLS) point clouds. These metrics are depth, openness, and isovist. Depth is a three-dimensional, radial measure of the visible distance in all directions from plot center. Openness is the percent of scan pulses in the near-omnidirectional view without a return. Isovists are a measurement of the area visible from the scan location, a quantified measurement of the viewshed within the forest canopy. 243 scans were acquired in 27 forested stands in the Pacific Northwest region of the United States, in different ecoregions representing a broad gradient in structural complexity. All stands were designated natural areas with little to no human perturbations. We created “structural signatures” from depth and openness metrics that can be used to qualitatively visualize differences in forest structures and quantitively distinguish the structural composition of a forest at differing height strata. In most cases, the structural signatures of stands were effective at providing statistically significant metrics differentiating forests from various ecoregions and growth patterns. Isovists were less effective at differentiating between forested stands across multiple ecoregions, but they still quantify the ecological important metric of occlusion. These new metrics appear to capture the structural complexity of forests with a high level of precision and low observer bias and have great potential for quantifying structural change to forest ecosystems, quantifying effects of forest management activities, and describing habitat for organisms. Our measures of structure can be used to ground truth data obtained from aerial lidar to develop models estimating forest structure. Full article

Shade coffee farms in southwest Ethiopia are known to host high levels of avian biodiversity. However, these farms vary in terms of forest management, which affects their understory, mid-story, crown cover, and canopy closure, and hence their structural complexity. Such differences in vegetation structure can potentially affect the survival of specialist bird species, and shade coffee farms may not equally contribute to avian biodiversity conservation. This study aimed to investigate how avian community composition, richness, and the relative abundance of different bird functional guilds relate to structural differences in vegetation shaped by forest management. Bird guild classification was based on bird species forest dependence, diet type, migration status, nest type, foraging, and nesting strata, and bird communities were surveyed using the Timed Species Counts (TSCs) method. Species turnover in bird communities was evaluated using detrended correspondence analysis and redundancy analysis, whereby multiple regression models were used to examine bird guild responses to vegetation structure. Total bird species richness and relative abundance did not respond to vegetation structure. However, the richness of forest specialists and understory foragers, and the relative abundance of mid-high foragers, all positively related to tree diameter at breast height (DBH) and crown cover, whereas the relative abundance of species with medium levels of forest dependency, mid-high/canopy foragers, and open-nesters were positively related to basal area and canopy cover. This study demonstrates that the relative value of shade coffee farms for avian biodiversity conservation depends on the type of forest management, and that bigger trees with larger crown cover provide a habitat of higher quality to habitat specialist birds. Full article

The U.S. Forest Service, Forest Inventory and Analysis (FIA) program is tasked with making and reporting estimates of various forest attributes using a design-based network of permanent sampling plots. To make its estimates more precise, FIA uses a technique known as post-stratification to group plots into more homogenous classes, which helps lower variance when deriving population means. Currently FIA uses a nationally available map of tree canopy cover for post-stratification, which tends to work well for forest area estimates but less so for structural attributes like volume. Here we explore the use of new statewide digital aerial photogrammetric (DAP) point clouds developed from stereo imagery collected by the National Agricultural Imagery Program (NAIP) to improve these estimates in the southeastern mixed hardwood forests of Tennessee and Virginia, United States (U.S.). Our objectives are to 1. evaluate the relative quality of NAIP DAP point clouds using airborne LiDAR and FIA tree height measurements, and 2. assess the ability of NAIP digital height models (DHMs) to improve operational forest inventory estimates above the gains already achieved from FIA’s current post-stratification approach. Our results show the NAIP point clouds were moderately to strongly correlated with FIA field measured maximum tree heights (average Pearson’s r = 0.74) with a slight negative bias (−1.56 m) and an RMSE error of ~4.0 m. The NAIP point cloud heights were also more accurate for softwoods (R²s = 0.60–0.79) than hardwoods (R²s = 0.33–0.50) with an error structure that was consistent across multiple years of FIA measurements. Several factors served to degrade the relationship between the NAIP point clouds and FIA data, including a lack of 3D points in areas of advanced hardwood senescence, spurious height values in deep shadows and imprecision of FIA plot locations (which were estimated to be off the true locations by +/− 8 m). Using NAIP strata maps for post-stratification yielded forest volume estimates that were 31% more precise on average than estimates stratified with tree canopy cover data. Combining NAIP DHMs with forest type information from national map products helped improve stratification performance, especially for softwoods. The monetary value of using NAIP height maps to post-stratify FIA survey unit total volume estimates was USD 1.8 million vs. the costs of installing more field plots to achieve similar precision gains. Overall, our results show the benefit and growing feasibility of using NAIP point clouds to improve FIA’s operational forest inventory estimates. Full article

In this study, we used data from a thinning trial conducted on 34 different sites and 102 sample plots established in pure and even-aged Pinus radiata and Pinus pinaster stands, to test the potential use of low-density airborne laser scanning (ALS) metrics and terrestrial laser scanning (TLS) metrics to provide accurate estimates of variables related to surface and canopy fires. An exhaustive field inventory was carried out in each plot to estimate the main stand variables and the main variables related to fire hazard: surface fuel loads by layers, fuel strata gap, surface fuel height, stand mean height, canopy base height, canopy fuel load and canopy bulk density. In addition, the point clouds from low-density ALS and single-scan TLS of each sample plot were used to calculate metrics related to the vertical and horizontal distribution of forest fuels. The comparative performance of the following three non-parametric machine learning techniques used to estimate the main stand- and fire-related variables from those metrics was evaluated: (i) multivariate adaptive regression splines (MARS), (ii) support vector machine (SVM), and (iii) random forest (RF). The selection of the best modeling approach was based on a comparison of the root mean square error (RMSE), obtained by optimizing the parameters of each technique and performing cross-validation. Overall, the best results were obtained with the MARS techniques for data from both sensors. The TLS data provided the best results for variables associated with the internal characteristics of canopy structure and understory fuel but were less reliable for estimating variables associated with the upper canopy, due to occlusion by mid-canopy foliage. The combination of ALS and TLS metrics improved the accuracy of estimates for all variables analyzed, except the height and the biomass of the understory shrubs. The variability demonstrated by the combined use of both types of metrics ranged from 43.11% for the biomass of duff litter layers to 94.25% for dominant height. The results suggest that the combination of machine learning techniques and metrics derived from low-density ALS data, drawn from a single-scan TLS or a combination of both metrics, may represent a promising alternative to traditional field inventories for obtaining valuable information about surface and canopy fuel variables at large scales. Full article