Search Results (17)

Prescribed fires are a management strategy involving the controlled application of fire to achieve specific ecological objectives. In the pine–oak forests in west-central Mexico, we conducted an experimental low-severity prescribed fire to assess its effects on hummingbird diversity. We hypothesized that low-severity prescribed fire would enhance both taxonomic and functional diversity by modifying understory vegetation structure and increasing floral resource availability. To test this, we performed point count censuses in both low-severity prescribed fire and fire-suppressed sites where wildfire had been excluded for over 40 years. Taxonomic diversity was assessed using Hill numbers to estimate true diversity across different abundance weights, while functional diversity was evaluated through indices such as functional richness, functional evenness, and functional divergence. Our results indicated that low-severity prescribed fires did not affect overall hummingbird diversity as both low-severity prescribed fire sites and fire-suppressed sites exhibited comparable species richness. However, sites with low-severity prescribed fire and concave summits showed a significantly higher abundance of common and highly abundant species. Notably, species richness did not align with functional richness, as the fire-suppressed site exhibited the highest functional diversity. These findings suggest that hummingbird community structure is influenced by a combination of fire history, topography, vegetation structure, and floral resource availability. We recommend maintaining a heterogeneous forest matrix, incorporating patches with fire suppression, and areas subjected to prescribed fires of varying severity. This multifaceted approach enhances both taxonomic and functional biodiversity, promoting habitat heterogeneity and ensuring the persistence of diverse hummingbird assemblages in fire-prone ecosystems. Full article

Moso bamboo (Phyllostachys edulis (Carrière) J. Houz.) forests are a vital forest type in subtropical China. This study investigates the diversity, floristic composition, and phylogenetic structure of understory vegetation in these bamboo forests within evergreen broad-leaved forests of eastern subtropical China. Using grid-based sampling, we calculated species diversity and phylogenetic indices, and employed correlation analysis, redundancy analysis, and structural equation modeling to assess the effects of canopy closure, soil properties, and topography. The understory exhibited high species richness, with shrub layer demonstrating phytogeographic characteristics predominantly associated with tropical distribution types, while the herbaceous layer is characterized by temperate distribution types. Canopy closure and environmental factors significantly influenced shrub diversity, showing a clustered phylogenetic structure (NTI > 0, NRI > 0) and a negative correlation with species diversity. In contrast, the herb layer displayed a divergent phylogenetic structure (NTI < 0, NRI < 0), shaped by neutral stochastic processes, reflecting endemic taxa and interspecific interactions. These findings emphasize the need for targeted management practices to conserve understory biodiversity, focusing on enhancing floristic and phylogenetic diversity while protecting endemic species and their ecological interactions. Full article

Forest ecosystems are incredibly valuable, and understory terrain is crucial for estimating various forest structure parameters. As the demand for monitoring forest ecosystems increases, quickly and accurately understanding the spatial distribution patterns of understory terrain has become a new challenge. This study used ICESat-2 data as a reference and validation basis, integrating multi-source remote sensing data (including Landsat 8, ICESat-2, and SRTM) and applying machine learning methods to accurately estimate the sub-canopy topography of the study area. The results from the random forest model show a significant improvement in accuracy compared to traditional SRTM products, with an R² of 0.99, ME of 0.22 m, RMSE of 3.59 m, and STD of 3.59 m. In addition, we assessed the accuracy of understory topography estimates for different landforms, canopy heights, forest cover types, and forest coverage. The results demonstrate that the estimation results are minimally impacted by ground elevation, forest cover type, and forest coverage, indicating good stability. This approach holds promise for accurately estimating understory terrain at regional and global scales, providing crucial support for monitoring and protecting forest ecosystems. Full article

Understory terrain plays a multi-faceted role in ecosystems, biodiversity, and productivity in forests by influencing different major factors, such as hydrological processes, soils, climate, and light conditions. Strong illuminants (e.g., sunlight) from ground surfaces and atmosphere can introduce additional photons into the ATLAS system. These photons can, consequently, be mistakenly identified as laser photons reflected from ground surfaces. The presence of such ambient light, particularly under low-photon-count conditions, can significantly increase elevation measurement errors. In this context, this study aims to propose a method for extracting reliable understory elevation control points under varying forest conditions, based on the parameter attributes of ICESat-2/ATLAS data. The overall filtered data resulted in a coefficient of determination (R²), root mean square error (RMSE), and standard deviation (STD) of 0.99, 2.77 m, and 2.42 m, respectively. The greatest accuracy improvement was found in the Puerto Rico study area, showing decreases in the RMSE and STD values by 2.68 and 2.67 m, respectively. On the other hand, canopy heights and slopes exhibited relatively large impacts on noise interferences. In addition, there were decreases in the RMSE and STD values by 4.57 and 4.64 m, respectively, under the very tall canopy category, whereas under steep slope conditions, the RMSE and STD values of the filtering results decreased by 4.59 and 4.34 m, respectively. The proposed method can enhance the overall accuracy of elevation data, allowing for the significant extraction of understory elevation control points, ultimately optimizing forest management practices and improving ecological assessments. Full article

The understory fine dead fuel moisture content (DFMC) is an important reference indicator for regional forest fire warnings and risk assessments, and determining it on a large scale is a critical goal. It is difficult to estimate understory fine DFMC directly from satellite images due to canopy shading. To address this issue, we used canopy meteorology estimated by Landsat images in combination with explanatory variables to construct random forest models of in-forest meteorology, and then construct random forest models by combining the meteorological factors and explanatory variables with understory fine DFMC obtained from the monitoring device to (1) investigate the feasibility of Landsat images for estimating in-forest meteorology; (2) explore the feasibility of canopy or in-forest meteorology and explanatory variables for estimating understory fine DFMC; and (3) compare the effects of each factor on model accuracy and its effect on understory fine DFMC. The results showed that random forest models improved in-forest meteorology estimation, enhancing in-forest relative humidity, vapor pressure deficit, and temperature by 50%, 34%, and 2.2%, respectively, after adding a topography factor. For estimating understory fine DFMC, models using vapor pressure deficit improved fit by 10.2% over those using relative humidity. Using in-forest meteorology improved fits by 36.2% compared to canopy meteorology. Including topographic factors improved the average fit of understory fine DFMC models by 123.1%. The most accurate model utilized in-forest vapor pressure deficit, temperature, topographic factors, vegetation index, precipitation data, and seasonal factors. Correlations indicated that slope, in-forest vapor pressure deficit, and slope direction were most closely related to understory fine DFMC. The regional understory fine-grained DFMC distribution mapped according to our method can provide important decision support for forest fire risk early warning and fire management. Full article

In mountain tropical forests, understory herbs have received little attention compared to trees, and their commonness and rarity are virtually unknown. We studied ground herbs to explore how they are assembled in a full one-hectare plot and to test the influence of light intensity (LI) and topographic habitats in species composition. The plot is a humid montane forest located in the Pasochoa Volcano, at 3300 m. We found 43 genera and 50 perennial species (30 angiosperms in 17 families, and 20 ferns). Interestingly, herbs are 64% richer in species than trees in the same study plot (50 vs. 32). Herbs were mostly obligately terrestrial (70% of the species), while 30% were fallen climbers and epiphytes rooted in the ground. Across the forest, there were 31,119 individuals that covered 8.5% of the ground. We concluded that both LI and topography shaped the species distribution, the floristic composition, and the community structure of ground herbs. For instance, 12% of the species were exclusively found in places with high LI; the rest of species grew in medium- to low-LI environments. Concerning rarity, we found that 39% of the species are rare (judging by botanical collections; <100), which stresses the need of conservation strategies for this group of plants. Full article

Forest ecosystem attributes and their spatial variation across the landscape have the potential to subsequently influence variations in fire behavior. Understanding this variation is critical to fire managers in their ability to predict fire behavior and rate of spread. However, a fine-scale description of fuel patterns and their relationship with overstory and understory attributes for north-central Appalachia is lacking due to the complicated quantification of variations in topography, forest attributes, and their interactions. To better understand the fire environment in north-central Appalachia and provide a comprehensive evaluation based on fine-scale topography, ninety-four plots were established across different aspects and slope positions within an oak–hickory forest located in southeast Ohio, USA, which historically fell within fire regime group I with a fire return interval ranging from 7 to 26 years. The data collected from these plots were analyzed by four components of the fire environment, which include the overstory, understory, shrub and herbaceous layers, surface fuels, and fuel conditions. The results reveal that fuel bed composition changed across aspects and slope position, and it is a primary factor that influences the environment where fire occurs. Specifically, the oak fuel load was highest on south-facing slopes and in upper slope positions, while maple fuel loads were similar across all aspects and slope positions. Oak and maple basal areas were the most significant factors in predicting the oak and maple fuel load, respectively. In the shrub and undergrowth layers, woody plant coverage was higher in upper slope positions compared to lower slope positions. Overstory canopy closure displayed a significant negative correlation with understory trees/ha and woody plant variables. The findings in this study can provide a better understanding of fine-scale fuel bed and vegetation characteristics, which can subsequently feed into fire behavior modeling research in north-central Appalachia based on the different characterizations of the fire environment by landscape position. Full article

Understory topography serves as a crucial data source, playing an instrumental role in numerous forest ecosystem applications. However, the use of synthetic aperture radar interferometry and optical stereo for the acquisition of ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), SRTM (Shuttle Radar Topography Mission), and GLO-30 (Copernicus Digital Elevation Model) DEM presents unique challenges, particularly in forested environments. These challenges are primarily due to limitations in penetration capability and the effects of foreshortening. ICESat-2/ATLAS, with its higher spatial sampling rate and strong penetrability, presents a new opportunity for estimating forest height parameters and understory terrain. We assessed the vertical accuracy of ASTER, SRTM, GLO-30, and ATLAS in the forest study areas of the United States compared to the reference dataset DTM provided by G-LiHT and we will further discuss the influence of different ground altitudes, forest types, slopes, and aspects on vertical accuracy. The study reveals that in a forested environment, ICESat-2 ATL03 exhibits the highest accuracy at the footprint scale, with a correlation coefficient (R²) close to 1 and Root Mean Square Error (RMSE) = 1.96 m. SRTM exhibits the highest accuracy at the regional scale, with an R² close to 0.99, RMSE = 11.09 m. A significant decrease in accuracy was observed with increasing slope, especially for slopes above 15°. With a sudden increase in altitude, such as in mountainous situations, the accuracy of vertical estimation will significantly decrease. Aspect and forest cover indeed influence the accuracy of the four DEM products, but this influence lacks a clear pattern. Our results show that ICESat-2 and SRTM data might show sufficient and stable vertical accuracy in a forested environment. Full article

Understory vegetation is one of the most important links for improving forest biodiversity, and its restoration is conducive to sustainable forest development, energy flow, and nutrient cycling. However, little is known about the developmental dynamics and main driving factors of the long-time series coverage, biomass, diversity, and species composition of plantation understory vegetation. In a case study of three typical plantations, with a natural secondary forest as reference in the Loess Plateau of China, we collected understory vegetation from a Robinia pseudoacacia Linn. deciduous broad-leaved plantation, Pinus tabulaeformis Carr. evergreen coniferous plantation, and mixed plantation with an age span of 10 to 50 years. (1) The understory plantation coverage and biomass results of stands with different ages showed the R. pseudoacacia plantation to be significantly higher than the P. tabulaeformis plantation, and the species diversity of the P. tabulaeformis plantation changed the most with the stand age. However, the growth resource imbalance, and drastic changes in the stands’ environment caused by excessive intraspecific competition in the early stage of the P. tabulaeformis plantation vegetation restoration, are the main reasons that make the species diversity of undergrowth vegetation of P. tabulaeformis plantation lower than that of other stand types. (2) The understory species composition of the plantations revealed their degree of community stability. Compared to the R. pseudoacacia plantation and P. tabulaeformis plantation, the mixed plantation had higher stability, and its species composition closely resembled a natural secondary forest. The community stability of the P. tabulaeformis plantation was the lowest because it had the lowest coverage, biomass, and species diversity of understory vegetation. However, the understory species composition of the three plantation types converged, which was due to atypical species contribution. (3) The dynamic changes of canopy and soil nutrients were the main driving factors affecting the R. pseudoacacia plantation understory vegetation species composition. Stand density and elevation limited the understory vegetation communities of P. tabulaeformis plantation restoration. Soil bulk density is the key factor affecting understory vegetation in mixed plantations, and this effect weakens with the stand age. In future studies, the focus should be on the converged action and further development trend of atypical species, choosing an appropriate recovery strategy (active or passive), and providing more possibilities for the intensive management of vegetation under different plantations. Full article

Stand structure, which links function and management, plays a crucial role in regulating forest ecosystems and influencing biodiversity. Nevertheless, knowledge of the effect of climate change on stand structure and plant diversity is still poorly understood on a large scale. To explore the effects of various climate conditions on stand structure–plant diversity, we conducted a comprehensive analysis of data from 1272 plots across China’s temperate and subtropical forests. Leveraging the structural equation model (SEM), we explored the direct and indirect effects of climate, topography, and tree diversity on understory woody and herbaceous plants with respect to stand structure. Furthermore, we evaluated the effect size of stand structure on understory vegetation diversity under different climatic zones. Our results showed that tree size variation (CV DBH) and stem density (SD) were the key drivers for understory woody plants, while the stand structure complexity index (SSCI) was more important for understory herbaceous diversity. Furthermore, the positive effects of stand structure differed across various climate zones and were enhanced with an increase in the climatic gradient. For instance, the impact of SD on understory woody plants, as well as the influence of the SSCI on the diversity of understory herbaceous vegetation, were both strengthened. These findings raise our awareness of the pressing need to manage stand structure heterogeneity differently across different climate zones, and different management also needs to be implemented among different understory plant types. It becomes evident that distinct forest management measures must be applied under future climate change and forest management practices in order to preserve biodiversity. Full article

Forest aboveground biomass (AGB) plays an important role in regulating the global carbon cycle and is thus an essential component of ecosystem functioning. In the relationships between biodiversity and ecosystem functioning (BEF), studies have shown that many biotic factors (e.g., species, functional traits, and large trees) and abiotic factors have significant impacts on AGB. However, the relative strength of these affecting factors remains unclear. In this study, we analyzed woody plants (diameter at breast height [DBH] ≥ 1 cm) within a 1.6 ha plot in an old-growth subtropical natural forest in southern China. We used structural equation models to test the effects of tree diversity (species, phylogenetic, functional, and size inequality), functional composition, large trees, and environmental factors (topography, soil nutrients, and understory light) on AGB. Our results indicated that size inequality, the community-weighted mean of maximum DBH (CWM_MDBH), and large trees had significant, positive effects on AGB (p < 0.001), while lower soil phosphorus content was found to promote an increase in AGB. Furthermore, large trees, which were mostly composed of dominant tree species, were the main driver of AGB, and the effect of functional composition (e.g., CWM_MDBH) on AGB was substantially reduced by large trees. We argue that the selection effect plays a key role in regulating BEF relationships in subtropical natural forests and conclude that retaining large-diameter trees and dominant species, along with sustaining a complex stand structure, are key measures for improving productivity. Full article

Multivariate analysis was conducted to explore the moist temperate forests of the Shangla district, Khyber Pakhtunkhwa. The prime objective was to quantitatively describe and differentiate the vegetation groups and the factors that determine the boundaries and composition of plant communities in the Shangla district. This was achieved by sampling all common species in a complex vegetation mosaic coinciding with local gradients in topography and soil distribution. Ward’s clustering dendrogram demonstrated four significant vegetation clusters with respect to environmental effects. These four major groups of the tree vegetation were superimposed on the ordination plane: 1. Pinus wallichiana, the dominant group associated with Abies pindrow; 2. Abies pindrow and the Picea smithiana group; 3. Dominant Cedrus deodara associated with the Pinus wallichiana, Abies pindrow, Picea smithiana, and Quercus baloot group; 4. Pinus roxberghii pure group. The key controlling factors for each group were the environmental characteristics (i.e., edaphic factors, topographic factors, soil physical properties, and soil nutrients). The results revealed elevation (p <0.001) to be the prominent factor in the composition of plant communities. Furthermore, pH, soil moisture, maximum water holding capacity, and soil physical properties (sand, silt, and clay) also showed a significant (p < 0.05) relationship with vegetation. The other environmental factor did not show a significant relationship with vegetation. Ward’s cluster dendrogram of understory species also demonstrated four groups. Group 1 comprises two subgroups, a and b, with the highest number of species, i.e., Digeteria sanguinalis, Fragaria nubicola, Verbascum Thapsus, Pinus wallichiana seedlings, and Polygonatum multiflorium, respectively. The second large group contains twenty-five species out of eight stands, and the dominant species was Tagetis minuta. Eighteen species out of six stands were found in group 3, which was considered the smallest group. Group 4 consisted of seven stands containing twenty-four species of ground flora, with Anaphalis scopulosa followed by Adiantum venustum as the dominant species. The environmental characteristics of the understory vegetation showed a resemblance with the tree communities. With the exception of elevation, the other factors did not show a significant correlation. Full article

Global biodiversity is threatened by unprecedented and increasing anthropogenic pressures, including habitat loss and fragmentation. LiDAR can become a decisive technology by providing accurate information about the linkages between biodiversity and ecosystem structure. Here, we review the current use of LiDAR metrics in ecological studies regarding birds, mammals, reptiles, amphibians, invertebrates, bryophytes, lichens, and fungi (BLF). We quantify the types of research (ecosystem and LiDAR sources) and describe the LiDAR platforms and data that are currently available. We also categorize and harmonize LiDAR metrics into five LiDAR morphological traits (canopy cover, height and vertical distribution, understory and shrubland, and topographic traits) and quantify their current use and effectiveness across taxonomic groups and ecosystems. The literature review returned 173 papers that met our criteria. Europe and North America held most of the studies, and birds were the most studied group, whereas temperate forest was by far the most represented ecosystem. Globally, canopy height was the most used LiDAR trait, especially in forest ecosystems, whereas canopy cover and terrain topography traits performed better in those ecosystems where they were mapped. Understory structure and shrubland traits together with terrain topography showed high effectiveness for less studied groups such as BLF and invertebrates and in open landscapes. Our results show how LiDAR technology has greatly contributed to habitat mapping, including organisms poorly studied until recently, such as BLF. Finally, we discuss the forthcoming opportunities for biodiversity mapping with different LiDAR platforms in combination with spectral information. We advocate (i) for the integration of spaceborne LiDAR data with the already available airborne (airplane, drones) and terrestrial technology, and (ii) the coupling of it with multispectral/hyperspectral information, which will allow for the exploration and analyses of new species and ecosystems. Full article

The ecological productivity of the Robinia pseudoacacia L. (RP) widely cultivated on the Loess hilly region has been widely questioned with its aging. Soil microbial biomass (SMB) plays a key role in soil nutrient dynamics and productivity of the ecosystems. Understanding the main ecological drivers of SMB is supposed to be of importance for ecosystem functioning of RP in the Loess hilly region. In this study, we identified the most influential factors affecting SMB at 2 layers (0–10 cm and 10–30 cm) using forward selection in terms of plant characteristics (forest age, tree height, diameter at breast height, tree canopy, crown base height, herb height, herb number, herb coverage, herb ground diameter and herb diversity), soil physiochemical characteristics (soil bulk density, pH, water content, soil organ carbon, soil total and available nutrient content) and topographical properties (elevation, aspect and slope). We also analyzed individual and interactive effects (plant–soil, plant–topography, soil–topography, plant–soil–topography) using general linear model (GLM) analysis. Among all plant variables, tree canopy and understory richness had the greatest impact on SMB. The soil variables with the greatest impact on SMB were bulk density and available phosphorus. Elevation was the most important topographic factor affecting SMB. When we considered the interactive effects among plant, soil physicochemical and topographical variables on SMB, a significant interaction effect occurred at a depth of 10–30 cm soil layer. We concluded that individual effects of abiotic and biotic factors significantly affect SMB at 0–10 cm, while the interaction of these factors mainly played roles at 10–30 cm. These results provide a basis for maintaining soil health and productivity using efficient SMB by providing suitable abiotic and biotic habitats. Full article

Climate, topography, and tree structure have different effects on plant diversity that vary with spatial scale. In this study, we assessed the contribution of these drivers and how they affect the vascular plant richness of different functional groups in a temperate forest ecosystem in Northeast China. We investigated about 0.986 million plants from 3160 sites to quantify the impact of annual mean temperature, sunshine duration, annual precipitation, standard deviation of diameter at breast height, and forest type on richness of vascular plants (total species, tree, treelet, shrub, and herb, separately) using the gradient boosting model. The results show that annual mean temperature had the strongest impact on plant richness. The tree richness peaked at intermediate annual mean temperature and sunshine duration and increased with annual precipitation. The Shannon diversity index and Simpson dominance index increased with annual precipitation and standard deviation of diameter at breast height, decreased with sunshine duration, and peaked at intermediate annual mean temperature and forest type. The total richness and understory richness increased with annual mean temperature and standard deviation of diameter at breast height and peaked at intermediate sunshine duration and annual precipitation. A comprehensive mechanism was found to regulate the plant diversity in forest ecosystems. The relationship between tree richness and annual mean temperature with latitudinal effect could be affected by the differences in number and size of tree individuals, indicating that plant diversity varies with the utilization of energy. The force driving plant richness varied with the functional group due to the different environmental resource requirements and the life history strategies of plants layers. Full article