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Animals disperse seeds in various ways that affect seed deposition sites and seed survival, ultimately shaping plant species distribution, community composition, and ecosystem structure. Some animal species can disperse seeds through multiple pathways (e.g., defecation, regurgitation, epizoochory), each likely producing distinct seed dispersal outcomes. We studied how seed traits (size and toughness) interact with disperser species to influence seed dispersal pathway and how this ultimately shapes the proportion of seeds deposited in various habitat types. We focused on three frugivorous species of duikers (African forest antelopes) in the Dja Faunal Reserve, a tropical rainforest in southern Cameroon. Duikers can both defecate and regurgitate seeds, the latter predominantly occurring during rumination at their bedding sites (or "nests"). We located duiker nests and dungs along 18 linear 1-km-transects to assess: (1) how seed traits affect the likelihood of dispersal via defecation versus regurgitation, (2) if defecated versus regurgitated seeds are deposited at different rates in different forest types (assessed by indigenous Baka), microhabitats, and forest structural attributes (measured by drone lidar), and (3) if these differ between three duiker species that vary in size and diel activity patterns. We found that duikers predominantly defecated small seeds (10 mm length), the latter including 25 different plant species. The three duiker species varied in their nesting habits, with nocturnal bay duikers (Cephalophus dorsalis) nesting in dense understory vegetation at proportions 3-4 times higher than Peter's and yellow-backed duikers (Cephalophus callipygus and Cephalophus silvicultor). As a result, bay duikers deposited larger regurgitated seeds at a higher rate in habitats with denser understory where lianas and palms predominate and near fallen trees. This directed regurgitation seed deposition likely plays an important and unique role in forest succession and structure. This study highlights the importance of ungulate seed dispersal by regurgitation, a vastly understudied process that could impact many ecosystems given the prevalence of ruminating ungulates worldwide.

Lack of fire and increasingly wet conditions in eastern North America are thought to interact in a way that depresses the recruitment of fire-adapted species like Quercus and Carya while promoting the growth and recruitment of mesophytic species like Acer. This process, referred to as forest mesophication, has tended to focus on the consequences for Quercus with more limited attention on Carya. Additionally, forest mesophication has the potential to alter traditional forest community and topographic relationships as environmental conditions continue to shift. This study questioned if Carya populations have responded to forest mesophication over the last 38 yr. Also, this study questioned if long-term changes in forest composition maintained traditional aspect-species relationships in response to forest mesophication. In Athens County, Ohio, forest surveys of Carya and associated overstory, midstory, sapling, and seedling vegetation were conducted in 1983 and 2021. Survey results from 1983 and 2021 were analyzed to determine shifts in Carya population structure and shifts in forest community composition and diversity. Carya populations decreased by approximately 50% since the original 1983 survey across all species, topographic positions, and aspects. Current populations are composed of mostly large individuals with little evidence of recruitment over the last several decades. The midstory and sapling forest layers have experienced decreases in species diversity, mostly through increases in Acer and Fagus grandifolia Ehrh., and decreases in Quercus and Carya. Species compositions from the sapling and seeding layers retained aspect differences and reflected overstory species compositions. The midstory and overstory layers have experienced shifts in species composition over time with the midstory layer no longer retaining topographic and aspect relationships with species composition. Changes in forest composition may be trending towards a loss in topographic distinction in species composition for forest communities in the region. Carya populations appear unlikely to remain a significant portion of the forest community given the observed lack of recruitment into larger size classes past the sapling stage. Long-term changes in forest composition, due in large part to mesophication, are becoming evident and have the potential to drastically alter forest composition in the very near future without direct intervention. [ABSTRACT FROM AUTHOR]

Forest types correspond to differences in structural characteristics and species composition that influence biomass and biodiversity values, which are essential measurements for ecological monitoring and management. However, differentiating forest types in tropical regions remains a challenge. This study aimed to improve forest type extent mapping by combining structural information from discrete full-waveform LiDAR returns with multitemporal images. This study was conducted in a tropical forest region over complex terrain in north-eastern Tanzania. First, structural classes were generated by applying time-series clustering algorithms. The results showed four different structural clusters corresponding to forest types, montane–humid forest, montane–dry forest, submontane forest, and non-forest, when using the Kshape algorithm. Kshape considers the shape of the full-sequence LiDAR waveform, requiring little preprocessing. Despite the overlap amongst the original clusters, the averages of structural characteristics were significantly different across all but five metrics. The labeled clusters were then further refined and used as training data to generate a wall-to-wall forest cover type map by classifying biannual images. The highest-performing model was a KNN model with 13 spectral and 3 terrain features achieving 81.7% accuracy. The patterns in the distributions of forest types provide better information from which to adapt forest management, particularly in forest–non-forest transitional zones. [ABSTRACT FROM AUTHOR]

Young tropical secondary forests play an important role in the local and global carbon cycles because of their large area and rapid biomass accumulation rates. This study examines how environmental conditions and forest attributes shape biomass compartments and the productivity of young tropical secondary forests. We compared 36 young secondary forest stands that differed in the time since agricultural land abandonment (2.3–3.6 years) from dry and wet regions in Ghana. We quantified biomass stocks in living and dead stems, roots, and soil, and aboveground biomass and litter productivity. We used structural equation models to evaluate how macroclimate, soil nutrients (N, P), and forest attributes (structure, diversity, and functional composition) affect ecosystem functioning. After three years of succession, tropical wet forests stored on average 115 t biomass ha−1 (the sum of aboveground living and dead biomass, belowground fine root biomass, and soil organic matter), and dry forests stored 99 t ha−1. These values represent 31% (in the wet forest) and 39% (in the dry forest) of the biomass compared with neighboring old‐growth forests. The majority of forest ecosystem biomass was stored in the soil (70%) and aboveground living vegetation (25%). Macroclimate strongly shaped forest attributes, which in turn determined biomass stocks and productivity. Soil phosphorus strongly increased litter production and soil organic matter, confirming that it is a limiting element in tropical ecosystems. Tree density and species diversity increased forest biomass stocks, suggesting crown packing and complementary resource use enhance forest functioning. A more conservative trait composition (high wood density) increased biomass stocks but reduced productivity, indicating that quantity, identity, and quality of species affect ecosystem functioning. [ABSTRACT FROM AUTHOR]

Large herbivorous mammals strongly influence vegetation structure by creating and maintaining open areas and causing disturbance within closed woody habitats. The herbivores alive today in Europe are only a small remnant of the large species that existed in high diversity and abundance before modern humans. The extinction of so many large herbivores during the last 50,000 years, and the loss of megaherbivores (body weight ≥1000 kg) from most of the continent before the Pleistocene–Holocene transition, is likely to have had cascading effects on vegetation structure and composition.To evaluate these effects within the European temperate forest biome, we examine the abundance change of three important European woody taxa (deciduous oaks, Quercus spp.; hazel, Corylus [mainly C. avellana]; and yew, Taxus baccata) before and after the late‐Quaternary downgrading of the region's large herbivore fauna. These taxa are disturbance‐favoured, depend on canopy openings for regeneration, and tend to decline in closed dense forests. Quercus and Corylus may thrive in systems affected by megafauna herbivory or fire, whilst Taxus is fire‐sensitive but can thrive in grazed systems. Using pollen‐based reconstructions (REVEALS), we investigated the proportional cover of these three focal taxa in the Last Interglacial (129,000–116,000 before present [BP]) and early–mid‐Holocene (8700–5700 BP).We found that woodlands in the Last Interglacial exhibited higher cover of Corylus and Taxus relative to the Holocene, with the former reaching very high percentage cover; meanwhile, Quercus had a consistent, moderately high percentage cover in both periods. Furthermore, we found that the cover of Corylus and Taxus appeared to be influenced more by unmeasured, non‐climatic factors than Quercus.Synthesis: The abundance of Taxus suggests a limited role of fire; whilst the observed levels of Taxus, Corylus and Quercus align with the potential influence of megafauna herbivory; however, a direct quantitative link remains to be established. Our results suggest that vegetation was structurally and compositionally affected by differences in disturbance regimes between the Last Interglacial and early–mid‐Holocene, with the loss of diverse disturbance regimes likely contributing to the divergence of Holocene vegetation from long‐term ecological baselines. [ABSTRACT FROM AUTHOR]

BackgroundAimsMethodsResultsConclusionsPolylepis forests are highly endangered tropical high Andean ecosystems. P. rugulosa has had the most significant decrease in spatial distribution compared to other Polylepis species and requires adequate restoration measures.This study evaluated the relationship between P. rugulosa seedling density and biotic and abiotic factors across east- and west-facing slopes to identify optimum conditions for the regeneration and restoration of the species.We assessed structural, edaphic, and topographic variables in 58 plots of 600 m2, microsite characteristics in 204 quadrats of 1 m2 and related them observed to P. rugulosa seedling density.The density and basal area of P. rugulosa adults were higher on west-facing slopes than on east-facing slopes. On both aspects, seedling density was positively related to the height and basal area of the adult trees and negatively related to sand content and the cover of ground vegetation (shrubs and herbs). Seedling density on east-facing slopes was positively related to soil organic matter content, and it was negatively related to it on west-facing slopes. Regarding microsite characteristics, the height of the nearest adult tree and litter cover were positively related to seedling density.Restoration of P. rugulosa should consider the contrasting effects of abiotic and biotic factors on seedling density on east- vs. west-facing slopes to optimise the recruitment and growth. [ABSTRACT FROM AUTHOR]

Remote sensing can provide continuous spatiotemporal information about vegetation to inform wildlife habitat estimates, but these methods are often limited in availability or lack adequate resolution to capture the three‐dimensional vegetative details critical for understanding habitat. The Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne light detection and ranging system (LiDAR) that has revolutionized the availability of high‐quality three‐dimensional vegetation measurements of the Earth's temperate and tropical forests. To date, wildlife‐related applications of GEDI data or GEDI‐fusion products have been limited to estimate species habitat use, distribution, and diversity. Here, our goal was to expand the use of GEDI‐based applications to wildlife demography by evaluating if GEDI data fusions could aid in characterizing demographic parameters of wildlife. We leveraged a recently published dataset of GEDI‐fusion forest structures and capture–mark–recapture data to estimate the density and survival of two small mammal species, Humboldt's flying squirrel (Glaucomys oregonensis) and Townsend's chipmunk (Neotamias townsendii), from three studies in western Oregon spanning 2014–2021. We used capture histories in Huggins robust design models to estimate apparent annual survival and density as a derived parameter. We found strong support that both flying squirrel and chipmunk density were associated with GEDI‐fusion forest structures of foliage height diversity and plant area volume density in the 5–10 m strata for flying squirrels and proportionately higher plant area volume density in the 0–20 m strata for chipmunks, as well as other spatiotemporal factors such as elevation. We found weak support that apparent annual survival was associated with GEDI‐fusion forest structures for flying squirrels but not for chipmunks. We demonstrate further utility of these methods by creating spatially explicit density maps of both species that could aid management and conservation policies. Our work represents a novel application of GEDI data to evaluate wildlife demography and produce continuous spatially explicit density predictions for these species. We conclude that aspects of small mammal demography can be explained by forest structure as characterized via GEDI data fusions. [ABSTRACT FROM AUTHOR]

We examined the abundance of five small rodent species, the chestnut white-bellied rat (Niviventer fulvescens), red spiny rat (Maxomys surifer), long-tailed giant rat (Leopoldamys sabanus), house mouse (Mus musculus), and black rat (Rattus rattus), and the stand structure of primary and secondary forest stands resulting from two types of post-fire silvicultural management practices in the Phou Khao Khauy National Protected Area (PKKNPA), Lao PDR. Post-fire silvicultural practices contributed to dramatically converting the structure of forests. Coverage of overstory, midstory, and ground vegetation, number of tree stems, woody seedlings, snags, and volume of coarse woody debris all had significant differences among study sites. We captured 456 individual small rodents during the dry and rainy seasons. The mean number of small rodents captured in primary forests was significantly higher than that in two secondary forests. In addition, there were more rodents in burned rice field stands than in burned rubber plantation stands. The value of overstory to understory vegetation coverage and the volume of coarse woody debris were strongly associated with small-mammal abundance in the PKKNPA. Thus, post-fire silvicultural practices should take the differences in small rodent abundance as well as forest structure into account. Long-term experiments may help illuminate the potential effects of management strategies after forest fires in the Lao PDR. [ABSTRACT FROM AUTHOR]

Abstract Traditional approaches of forest classifications were based on tree species composition, but recently combine phenology and climate to characterise functional (cyclic and seasonal greenness) rather than structural or compositional components (phenoclusters). The objective was to compare the conservation value (capacity to support more native biodiversity) and provision of ecosystem services (ES) in different phenocluster categories of Nothofagus antarctica forests in Tierra del Fuego (Argentina). We used available models (ES, potential biodiversity) and ground-truth data of 145 stands, comparing phenocluster values using uni- and multivariate analyses. Conservation value and capacity to supply ES significantly varied among phenocluster categories: (i) cultural, regulating, and provisioning ES and potential biodiversity at landscape level, (ii) soil carbon and nitrogen, (iii) dominant height, crown cover, basal area, total volume, and domestic animal stock, and (iv) understory plant richness and cover at stand level. These differences are linked to the forest capacity to support more native biodiversity and ES. Besides, multivariate analyses supporting the split of this forest type into four phenocluster subtypes (coast, highland, ecotone with other types, and degraded or secondary forests). Our findings suggest the needs of specific management and conservation proposals, based on phenoclusters rather than forest types defined by tree canopy-cover composition.

Abstract Forests display tremendous structural diversity, shaping carbon cycling, microclimates and terrestrial habitats. An important tool for forest structure assessments are canopy height models (CHMs): high resolution maps of canopy height obtained using airborne laser scanning (ALS). CHMs are widely used for monitoring canopy dynamics, mapping forest biomass and calibrating satellite products, but surprisingly little is known about how differences between CHM algorithms impact ecological analyses. Here, we used high‐quality ALS data from nine sites in Australia, ranging from semi‐arid shrublands to 90‐m tall Mountain Ash canopies, to comprehensively assess CHM algorithms. This included testing their sensitivity to point cloud degradation and quantifying the propagation of errors to derived metrics of canopy structure. We found that CHM algorithms varied widely both in their height predictions (differences up to 10 m, or 60% of canopy height) and in their sensitivity to point cloud characteristics (biases of up to 5 m, or 40% of canopy height). Impacts of point cloud properties on CHM‐derived metrics varied, from robust inference for height percentiles, to considerable errors in above‐ground biomass estimates (~50 Mg ha−1, or 10% of total) and high volatility in metrics that quantify spatial associations in canopies (e.g. gaps). However, we also found that two CHM algorithms—a variation on a ‘spikefree’ algorithm that adapts to local pulse densities and a simple Delaunay triangulation of first returns—allowed for robust canopy characterisation and should thus create a secure foundation for ecological comparisons in space and time. We show that CHM choice has a strong impact on forest structural characterisation that has previously been largely overlooked. To address this, we provide a sample workflow to create robust CHMs and best‐practice guidelines to minimise biases and uncertainty in downstream analyses. In doing so, our study paves the way for more rigorous large‐scale assessments of forest structure and dynamics from airborne laser scanning.

This study evaluates global radar‐derived digital elevation models (DEMs), namely the Shuttle Radar Topography Mission (SRTM), NASADEM and GLO‐30 DEMs. We evaluate their accuracy over bare‐earth terrain and characterize elevation biases induced by forests using global Lidar measurements from the Ice, Cloud, and Land Elevation Satellite (ICESat)'s Geoscience Laser Altimeter System (GLAS), the Global Ecosystem Dynamics Investigation (GEDI) and the ICESat‐2 Advanced Topographic Laser Altimeter System (ATLAS) instruments collected on locally flat terrain. Our analysis is based on error statistics calculated for each 1°×1° $1{}^{\circ}\times 1{}^{\circ}$ DEM tile, which are then summarized as global error percentiles, providing a regional characterization of DEM quality. We find NASADEM to be a significant improvement upon the SRTM V3. Over bare ground areas, the mean elevation bias and root mean square error (RMSE) improved from 0.68 to 2.50 m respectively to 0.00 and 1.5 m as compared to ICESat/GLAS. GLO‐30 is more accurate with bare ground elevation bias and RMSE were below 0.05 and 0.55 m. Similar improvements were observed when compared to GEDI and ICESat‐2 measurements. The DEM biases associated with the presence of vegetation vary linearly with canopy height, and more closely follow the 50th $5{0}^{th}$ percentile of Lidar Relative Height (RH50). Other factors such as canopy density, radar frequency and Lidar technology also contribute to observed elevation biases. This global analysis highlights the potential of various technologies for mapping of Earth's topography, and the need for more advanced remote sensing observations that can resolve vegetation structure and sub‐canopy ground elevation. Plain Language Summary: We compare the accuracy of three global maps of Earth's surface topography that were derived from spaceborne radar missions, namely, SRTM, NASADEM, and GLO30. The accuracy is evaluated using spaceborne Lidar data sets from three missions: ICESAT, ICESAT‐2, and GEDI. Lidars are typically more accurate in estimating elevation of the ground, but their measurements are like sparse point measurements. We evaluated the radar‐derived DEMs separately over bare ground regions and forests. We found that NASADEM is a significant improvement over the SRTM DEM with an error of 1.5 m in bare ground areas. But the GLO30 DEM is more accurate with error of only 0.55 m. The presence of forests causes radar‐derived elevation to be higher than the ground, somewhere mid‐canopy. Key Points: NASA's Digital Elevation Model is better than the Shuttle Radar Topography Mission Digital Elevation Model, but GLO‐30 is the most accurateAll radar‐derived elevations exhibit a strong correlation with forest canopy height with bias similar to the Lidar's relative height 50The Ice, Cloud and land Elevation Satellite‐2 Lidar measurements provide a better Lidar alternative for detecting the ground below forest [ABSTRACT FROM AUTHOR]

Climate change is expected to alter both forest stand- and landscape-level dynamics through a change in environmental characteristics. While numerous studies have employed models to assess the ecological and/or economic impacts of such changes on forests throughout the world, there is need to further refine such analyses. In this paper, we contribute to this literature by coupling an ecological (PICUS–LANDIS II) modeling framework with an economic (computable general equilibrium) model to better account for the economic impact associated with climate-induced impacts on forest stand- and landscape-level structure and composition dynamics. Applying this framework to a case-study region of New Brunswick, Canada, we estimate that climate change will reduce softwood supply by 16%–73% and impact hardwood supply in the range of −2% to +4% by 2150. The change in wood supply is estimated to reduce the value of the softwood and hardwood forestry and logging sector output by up to 51% and 17%, respectively, by 2150. These sector-level impacts may lead to a 0.08%–0.88% reduction in annual gross domestic product by 2150. The methodological advances established in this study can be used to better inform future forest management and economic plans that aim to lessen both the ecological and economic impact of climate change. [ABSTRACT FROM AUTHOR]

Satellite data are increasingly used to provide information to support forest monitoring and reporting at varying levels of detail and for a range of attributes and spatial extents. Forests are dynamic environments and benefit from regular assessments to capture status and changes both locally and over large areas. Satellite data can provide products relevant to forest science and management on a regular basis (e.g. annually) for land cover, disturbance (i.e. date, extent, severity, and type), forest recovery (e.g. quantification of return of trees following disturbance), and forest structure (e.g. volume, biomass, canopy cover, stand height), with products generated over large areas in a systematic, transparent, and repeatable fashion. While pixel-based outcomes are typical based upon satellite data inputs, many end users continue to require polygon-based forest inventory information. To meet this information need and have a spatial context for forest inventory attributes such as tree species assemblages, we present a new work-flow to produce a novel spatially explicit, stand-level satellite-based forest inventory (SBFI) in Canada applying image segmentation approaches to generate spatially unique forest stands (polygons), which are the fundamental spatial unit of management-level inventories. Thus, SBFI offers spatial context to aggregate and generalize other pixel-based forest data sets. Canada has developed a National Terrestrial Ecosystem Monitoring System (NTEMS) that utilizes medium spatial resolution imagery, chiefly from Landsat, to annually characterize Canada's forests at a pixel level from 1984 until present. These NTEMS datasets are used to populate SBFI polygons with information regarding status (e.g. current land cover type, dominant tree species, or total biomass) as well as information on dynamics (e.g. has this polygon been subject to change, when, by what, and if so, how is the forest recovering). Here, we outline the information drivers for forest monitoring, present a set of products aimed at meeting these information needs, and follow to demonstrate the SBFI concept over the 650-Mha extent of Canada's forest-dominated ecosystems. In so doing, the entirety of Canada's forest ecosystems (managed and unmanaged) were mapped using the same data, attributes, and temporal representation. Moreover, the use of polygons allows for the generation of attributes such as tree species composition, and total biomass and wood volume in a stand-scale format familiar to landscape managers and suitable for strategic planning. The data, methods, and outcomes presented here are portable to other regions and input data sources, and the national SBFI outcomes for Canada are available via open access. [ABSTRACT FROM AUTHOR]

Forests display tremendous structural diversity, shaping carbon cycling, microclimates and terrestrial habitats. An important tool for forest structure assessments are canopy height models (CHMs): high resolution maps of canopy height obtained using airborne laser scanning (ALS). CHMs are widely used for monitoring canopy dynamics, mapping forest biomass and calibrating satellite products, but surprisingly little is known about how differences between CHM algorithms impact ecological analyses.Here, we used high‐quality ALS data from nine sites in Australia, ranging from semi‐arid shrublands to 90‐m tall Mountain Ash canopies, to comprehensively assess CHM algorithms. This included testing their sensitivity to point cloud degradation and quantifying the propagation of errors to derived metrics of canopy structure.We found that CHM algorithms varied widely both in their height predictions (differences up to 10 m, or 60% of canopy height) and in their sensitivity to point cloud characteristics (biases of up to 5 m, or 40% of canopy height). Impacts of point cloud properties on CHM‐derived metrics varied, from robust inference for height percentiles, to considerable errors in above‐ground biomass estimates (~50 Mg ha−1, or 10% of total) and high volatility in metrics that quantify spatial associations in canopies (e.g. gaps). However, we also found that two CHM algorithms—a variation on a 'spikefree' algorithm that adapts to local pulse densities and a simple Delaunay triangulation of first returns—allowed for robust canopy characterisation and should thus create a secure foundation for ecological comparisons in space and time.We show that CHM choice has a strong impact on forest structural characterisation that has previously been largely overlooked. To address this, we provide a sample workflow to create robust CHMs and best‐practice guidelines to minimise biases and uncertainty in downstream analyses. In doing so, our study paves the way for more rigorous large‐scale assessments of forest structure and dynamics from airborne laser scanning. [ABSTRACT FROM AUTHOR]

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Seasonal foliage display (leaf phenology) is a key determinant of ecosystem function. Variation in land surface phenology, observed via space‐borne remote sensing, can be explained at broad spatial scales by climate, but we lack understanding of how vegetation structure and floristic diversity mediates these relationships. This lack of understanding hampers our ability to predict changes in phenology and therefore ecosystem function, in light of rapid ongoing shifts in biodiversity and ecosystem structure due to land use and climate change.We combined a network of 619 vegetation monitoring sites across seasonally dry tropical deciduous woodlands in Zambia with land surface phenology metrics to investigate the role of tree species diversity, composition and vegetation structure on patterns of land surface phenology, including the phenomenon of pre‐rain green‐up.Tree species diversity was associated with earlier pre‐rain green‐up, a longer growing season, and greater cumulative foliage production. Independent of diversity, proportional abundance of Detarioideae species (subfamily of Fabaceae) was associated with a longer growing season by facilitating earlier pre‐rain green‐up. Woodland stands with larger trees green up earlier, suggesting access to deep groundwater reserves. Senescence metrics showed variation among sites but were not well‐explained by precipitation, temperature, structure or diversity.Synthesis: Tree diversity, composition and structure are co‐determinants of seasonal patterns of foliage display in seasonally dry tropical deciduous woodlands, as measured via land surface phenology, at regional scale. Our study identifies both a niche complementarity effect whereby diverse woodlands exhibit longer growing seasons, as well as a mass‐ratio effect whereby detarioid species drive earlier pre‐rain green‐up, providing insights into the mechanisms underlying the biodiversity ecosystem function relationship in this biome. We stress the importance of considering biotic controls on ecosystem functioning in the next generation of earth‐system models predicting the response of communities to global change. [ABSTRACT FROM AUTHOR]

Tree thinning and the application of prescribed surface fire are widespread forest restoration strategies used to regain ecological structure and function throughout dry forests of the western United States. Though such treatments are increasingly applied to broad extents, their effects on forest ecosystems are commonly evaluated at individual experimental sites or treatment units rather than large, operational landscapes. We evaluated the responses of forest structure, regeneration, old‐tree mortality, and tree growth to forest restoration for 21 years in a landscape‐scale (2114 ha) experiment in a Ponderosa pine (Pinus ponderosa)‐Gambel oak (Quercus gambelii) forest in northern Arizona, United States. Relative to the start of the experiment in 1996, tree density and basal area (BA) in the treated area were reduced by 56 and 38%, respectively, at the end of the study period compared to the untreated control. Conifer seedling densities generally declined and sprouting hardwoods increased following treatment. Mortality of old oak trees was significantly higher in the treated area compared to the control, likely due to fire‐caused injury during the prescribed burning. Mean annual BA increment of individual trees was 93% higher in the treated area than in the control. Our study provides new information on Ponderosa pine forest responses to restoration treatments at broad spatial scales and under realistic operational conditions. Results from this study can help inform landscape‐scale restoration projects in dry, fire‐dependent forests. [ABSTRACT FROM AUTHOR]

Old-growth forests are essential to preserve biodiversity and play an important role in sequestering carbon and mitigating climate change. However, their existence across Europe is vulnerable due to the scarcity of their distribution, logging, and environmental threats. Therefore, providing the current status of old-growth forests across Europe is essential to aiding informed conservation efforts and sustainable forest management. Remote sensing techniques have proven effective for mapping and monitoring forests over large areas. However, relying solely on remote sensing spectral or structural information cannot capture comprehensive horizontal and vertical structure complexity profiles associated with old-growth forest characteristics. To overcome this issue, we combined spectral information from Sentinel-2A multispectral imagery with 3D structural information from high-density point clouds of airborne laser scanning (ALS) imagery to map old-growth forests over an extended area. Four features from the ALS data and fifteen from Sentinel-2A comprising raw band (spectral reflectance), vegetation indices (VIs), and texture were selected to create three datasets used in the classification process using the random forest algorithm. The results demonstrated that combining ALS and Sentinel-2A features improved the classification performance and yielded the highest accuracy for old-growth class, with an F1-score of 92% and producer's and user's accuracies of 93% and 90%, respectively. The findings suggest that features from ALS and Sentinel-2A data sensitive to forest structure are essential for identifying old-growth forests. Integrating open-access satellite imageries, such as Sentinel-2A and ALS data, can benefit forest managers, stakeholders, and conservationists in monitoring old-growth forest preservation across a broader spatial extent. [ABSTRACT FROM AUTHOR]

Seasonal forests are characterized by seasonal dynamics that influence the growth and ecophysiology of forest species. Erythroxylum pauferrense is an understory species endemic to the Northeastern region of Brazil, with a distribution limited to Paraíba, Brazil. In this study, how the physiological characteristics of E. pauferrense vary in response to seasonal changes in an open ombrophilous forest of the Brazilian Atlantic Forest was investigated. Precipitation, air and soil temperature, and leaf area index were monitored and correlated with gas exchange, chlorophyll fluorescence, chlorophyll indices, and leaf morphofunctional attributes. The results show that E. pauferrense exhibits ecophysiological plasticity, adjusting its photosynthesis rates, stomatal conductance, and water use efficiency according to seasonal changes. During the rainy season, photosynthesis and stomatal conductance were higher than in the dry season, indicating more excellent photosynthetic activity due to increased water availability. Water use efficiency varied, with more efficient use in the dry season, which is crucial for survival in conditions of low water availability. Thus, this study contributes to understanding the ecology of endemic understory species in seasonal tropical forests, such as Erythroxylum pauferrense. [ABSTRACT FROM AUTHOR]

The accurate mapping of age structure and access to spatially explicit information are essential to optimal planning and policy-making for forest ecosystems, including forest management and sustainable economic development. Specifically, surveying and mapping the age structure of forests is crucial for calculating the carbon sequestration capacity of forest ecosystems. However, spatial heterogeneity and limited accessibility make forest age mapping in mountainous areas challenging. Here, we present a new workflow using ICESat-2 LiDAR data integrated with multisource remote sensing imagery to estimate forest age in Shangri-La, China. Two methods—a climate-driven exponential model and a random forest algorithm—are compared to infer the age structure of the five dominant species in Shangri-La. The climate-driven model, with an R2 of 0.67 and an RMSE of 12.79 years, outperforms the random forest model. The derived wall-to-wall forest age map at 30 m resolution reveals that nearly all forests in Shangri-La are mature or overmature, especially among the high-elevation species Abies fabri (Mast.) Craib and Picea asperata Mast., compared with Pinus yunnanensis Franch., Quercus aquifolioides Rehd. and E.H. Wils. and Pinus densata Mast., where the age structure is more evenly distributed across different elevation ranges. Younger forests are frequently found around human settlements and along the Jinsha River valley, whereas older forests are located in remote and high-elevation areas that are less disturbed. The combined use of active and passive remote sensing data has resulted in substantial improvements in the spatial detail and accuracy of wall-to-wall age mapping, which is expected to be a cost-effective approach for supporting forest management and carbon accounting in this important ecological region. The method developed here can be scaled to other mountain areas both to understand the age patterns and structure of mountain forests and to provide critical information for forestation, reforestation and carbon accounting in surface-to-high mountain areas, which are increasingly crucial for climate mitigation. [ABSTRACT FROM AUTHOR]

The Brazilian Amazon has been a focus of land development with large swaths of forests converted to agriculture. Forest degradation by selective logging and fires has accompanied the agricultural frontier and has resulted in significant impacts on Amazonian ecosystems. Changes in forest structure resulting from forest disturbances have large impacts on the surface energy balance, including on land surface temperature (LST) and evapotranspiration (ET). This study's objective is to assess the effects of forest disturbances on water fluxes and forest structure in a transitional forest site in the Southern Amazon. We used ET and LST products from MODIS and Landsat 8 and GEDI-derived forest structure data to address our research questions. We found that disturbances induced seasonal water stress, more pronounced in croplands/pastures than in forests (differences up to 20% in the dry season), and more pronounced in second-growth and recently burned areas than in logged and intact forests (differences up to 12% in the dry season). Moreover, ET and LST were negatively related, with more consistent relationships across disturbance classes in the dry season (R2: 0.41–0.87) than in the wet season (R2: 0.18–0.49). Forest and cropland or pasture classes showed contrasting relationships in the dry season. Finally, we found that forest structure exhibited stronger relationships with ET and LST in the most disturbed forests (R2: 0.01–0.43) than in the least disturbed forests (R2

Mortality of remaining trees tends to increase after logging, and adapting forest management to climate change is another significant challenge to ensure the sustainability of forests, timber production, and provision of ecosystem services. This study investigated mortality in remaining trees (DBH ≥ 20 cm) in different ecological groups (pioneer, light-demanding, and shade-tolerant) during different phases of forest monitoring (initial, intermediate, and final), as well as their relationship with structural, climatic, and environmental characteristics. The research was conducted in five managed areas in the Brazilian Amazon (Jari, Km114, Km67, Peteco, and Moju) with continuous monitoring over six to 33 years. We analyzed a total of 212 permanent plots containing 62 pioneer species, 250 light-demanding species, and 304 shade-tolerant species. The mortality rate for the three monitoring phases in logged forests was 1.27 %, 1.49 %, and 0.75 % year−1, respectively, while the rate in unlogged forests was 1.04 %, 1.33 %, and 0.50 % year−1. In post-logging forests, mortality was high for pioneer and light-demanding species, especially in the initial phase. For shade-tolerant species, mortality rate was influenced by soil silt and sand content, presence of phosphorus, and total carbon content. In unlogged forests, forest structure variables such as average height and total basal area, soil characteristics like phosphorus content, and climate factors such as intensity of dry months were most important for pioneer and light-demanding species. In the intermediate and final monitoring phases in logged forests, tree mortality was related to soil characteristics like sand and clay content, total carbon, and cation exchange capacity. Extreme climatic events such as severe drought, El Niño, and extreme temperatures also influenced tree mortality, along with time elapsed after logging. These findings highlight the complex interaction between soil characteristics, climate, and forest structure and tree mortality and ecology. These correlations are crucial to understand mortality processes in logged and unlogged forests, underscoring the importance of these factors for managing and conserving these ecosystems in the face of extreme climatic events and forest management practices.

This study aims to reveal the relationships between understory coverage (principally, weed growth) beneath the canopies of Sumatran eucalyptus plantations and L-band backscatter data gathered by the PALSAR-2 Synthetic Aperture Radar (SAR) onboard the ALOS-2 satellite. Statistical analysis in conjunction with in situ measurements of forest structural parameters reveals that the SAR polarization ratio is significantly negatively correlated with understory coverage in forests taller than or equal to 10 m or above 2 years of age. Our field investigations confirm that a decrease in canopy coverage takes place some 2 years after transplanting, which in turn permits increased penetration (decreased attenuation) of microwave signals thereby enabling the SAR to detect changes in understory coverage. In addition, both growth and unmanaged remnants of the understory vegetation in older forests reduce evaporation from the ground, which in turn stabilizes soil-moisture levels throughout the year and allows co-polarized backscatter data (which might otherwise be dominated by soil-moisture changes) to contain significant information on the underlying vegetation. No correlation between the understory and SAR backscatter was recorded in younger forests undergoing programs of intensive weed management. This is presumably because strong canopy and trunk backscatter obscured the response from understory vegetation and because artificial changes were induced in vegetation water content as a result of the application of herbicides which in turn reduced the accuracy of some of our field measurements of understory coverage. This research nevertheless demonstrates an important potential application of active satellite microwave remote sensing. We show that satellite-based L-band SAR can be used as a tool to investigate coverage percentages of understory layers. This synoptic technique reduces the need for repeated field surveys across large areas of forest. It can enhance ecosystem assessment and improve understory coverage estimates in sparse forests and in various types of plantations.

Effective planning for natural resource management and wildlife conservation requires detailed information on vegetation structure at landscape scales and how structure is influenced by land-use practices. In many forested landscapes, the largest impacts of land use on forest structure are driven by forest management activities, which can include invasive species control, prescribed fire, partial harvests (e.g. shelterwood harvests) or overstory removals and clearcuts. Active timber management is often used to achieve forest conservation objectives, but to be used effectively, managers require knowledge of harvest frequency and extent in adjacent forests and at landscape scales. In this paper, we develop a timber harvest mapping workflow using machine learning (XGBoost algorithm) and single campaign airborne light detection and ranging (LiDAR) surveys for the state of Pennsylvania, USA. We show that harvest type (shelterwood and overstory removals) can be mapped at high accuracy (overall accuracy = 94.9%), including broad age classes defined by the number of years since harvest. Errors of omission (false negatives) were lowest for recent (30 m from an edge). The expectation that these harvest treatments would be rarer in private forests was not supported by the model results, which is likely due to the model’s inability to distinguish between alternative natural processes (weather damage, wildfire, pathogens, etc.) and forest treatment types (high-grading and firewood collection) that result in similar forest structures to the trained classes in the XGBoost model. This study provides a framework and validation for combining approachable machine-learning techniques with large-campaign LiDAR to accurately predict forest structure with application to a host of forestry, natural resource, and conservation-related problems. Future efforts that refine the model’s ability to better distinguish between more complex harvest classes and natural processes would be valuable.

Canopy cover (CC) quantifies the proportion of canopy materials projected vertically onto the ground surface. CC is a crucial canopy structural variable and is commonly used in many ecological and climatic models. The vertical CC profile product is currently available from the Global Ecosystem Dynamics Investigation (GEDI). However, detailed information about the accuracy and uncertainty of the GEDI vertical CC profile product remains limited. The objective of this study is to validate the GEDI CC product over selected forest sites using reference values derived from digital hemispherical photography (DHP), airborne laser scanning (ALS) point clouds, and simulated waveforms. The accuracy of CC was quantified and analyzed regarding GEDI observation conditions, waveform processing, and estimation methods. The results show that the GEDI total CC correlates well with those estimated from DHP, ALS, and simulated waveform data (r2 = 0.65, 0.71, and 0.71, respectively) but is systematically underestimated (bias = −0.05, −0.11, and −0.07, respectively) based on reference data. Compared with the ALS-estimated CC, needleleaf forest shows the highest correlation for vertical CC (r2 ≥ 0.65) and shrubland shows the lowest bias for total CC (bias = −0.13). The mean absolute error (MAE) of the GEDI CC decreases from 0.15 to 0.09 as the estimation height increases from ground to 35 m. The GEDI total CCs derived from the waveform interpretation algorithms A2 and A6 display the highest r2 (≥ 0.6) and smallest RMSE (≤ 0.23) compared to those of the other algorithms. The CC accuracy increases with beam sensitivity and decreases with increasing canopy cover. The GEDI CC was improved at moderate CC values using a canopy-to-ground backscattering coefficient ratio (ρv/ρg) determined with the regression method. The partial difference between GEDI CC and ALS CC is attributed to definitional discrepancies. Further improvement of the CC algorithm can be made by using vegetation-specific waveform processing algorithms and realistic ρv/ρg values.

Abstract Spending time in forests benefits human well‐being, but the importance of forest characteristics on well‐being is unclear. This knowledge could help guide forest management decisions to improve outcomes for both people and nature. The overall aim of this study was to investigate how psychological restoration, defined as psychological recovery processes in nature, may be supported by forest characteristics. We (1) investigated how perceptions of restoration (perceived restorativeness) were linked to specific forest characteristics. More specifically, we selected attributes included in nature protection legislation in Germany (beauty, diversity and uniqueness) as the basis to evaluate how forest characteristics were related to perceived restorativeness. Additionally, we (2) tested differences in the assessments of these attributes between forestry experts and people from the general public. Based on the results of the first two objectives (1, 2), we (3) predicted how forest management that affects forest characteristics may impact psychological restoration today and in the future. We developed a perceived restorativeness model based on attributes stated in the German Nature Conservation Act and specific forest structure variables. Drawing from the literature, we included perceived naturalness as an additional key predictor for restoration. Forestry experts and participants from the general public were then asked to rate computer‐generated forest stand pictures on these attributes and restorativeness. We found that all attributes were positively associated with perceived restorativeness, but perceived beauty was most important. Perceived uniqueness was statistically significant, but the strength of the relationship was weak. Mixed forests were rated as most beautiful, while coniferous forest stands were rated as least beautiful. The general public gave higher ratings than forestry experts on all attributes, but the pattern was similar. Based on participant ratings, forests left without management (Set‐aside), followed by forests with management aiming for resilience to climate change (Adaptation forestry), both supporting biodiversity conservation, showed the highest perceived restorativeness over the course of 100 years. Based on our results, it could be recommendable to increase forest diversity, especially in areas with many visitors. However, more nuanced knowledge involving diverse stakeholders is needed to inform forest management decisions on landscape level. Read the free Plain Language Summary for this article on the Journal blog.

Abstract Assessing the dynamics of forest structure complexity is a critical task in times of global warming, biodiversity loss and increasing disturbances in order to ensure the resilience of forests. Recent studies on forest biodiversity and forest structure emphasize the essential functions of deadwood accumulation and diversification of light conditions for the enhancement of structural complexity. The implementation of an experimental patch‐network in managed broad‐leaved forests within Germany enables the standardized analysis of various aggregated and distributed treatments characterized through diverse deadwood and light structures. To monitor the dynamics of enhanced forest structure complexity as seasonal and trend components, dense time‐series from high spatial resolution imagery of Sentinel‐1 (Synthetic‐Aperture Radar, SAR) and Sentinel‐2 (multispectral) are analyzed in time‐series decomposition models (BEAST, Bayesian Estimator of Abrupt change, Seasonal change and Trend). Based on several spatial statistics and a comprehensive catalog on spectral indices, metrics from Sentinel‐1 (n = 84) and Sentinel‐2 (n = 903) are calculated at patch‐level. Metrics best identifying the treatment implementation event are assessed by change point dates and probability scores. Heterogeneity metrics of Sentinel‐1 VH and Sentinel‐2 NMDI (Normalized Multi‐band Drought Index) capture the treatment implementation event most accurately, with clear advantages for the identification of aggregated treatments. In addition, aggregated structures of downed or no deadwood can be characterized, as well as more complex standing structures, such as snags or habitat trees. To conclude, dense time‐series of complementary high spatial resolution sensors have the potential to assess various aggregated forest structure complexities, thus supporting the continuous monitoring of forest habitats and functioning over time.

We tested the effectiveness of the gradual removal of Scots pine (Pinus sylvestris L.) in former plantations of this species in Roztocze National Park (SE Poland) to support the restoration of natural herbaceous flora and forest structure. We compared 0.5-ha study plots subjected to selective removal of pine trees with control plots excluded from any kind of human intervention for half a century. The observed changes in forest floor vegetation in the converted plots showed naturalization towards habitat-specific species. However, differences in the spatial distribution of trees between the treatment and control plots showed no universal pattern and revealed subtle but positive shifts from regular to random or clustered patterns. The mean tree diameters were higher in plots subjected to Scots pine removal, which resulted from the vigorous growth of tree species, consistent with habitat types. We conclude that forest restoration through the removal of planted trees can support the naturalization of former Scots pine plantations in protected areas. However, the selection of an appropriate method and its intensity are of vital importance. Methods that resemble typical management practices, such as selection thinning, are not always the best approach, as they may preserve or even increase the regular distribution of trees. Therefore, for restoration purposes, we recommend testing other methods that increase spatial heterogeneity, including systematic cutting or emulating natural disturbances. In addition, low-intensity thinning may not be sufficient to support the restoration of natural forest floor vegetation and the variability in forest stand structure.

European beech (Fagus sylvatica L.) forests can have a high variability in plant species richness and abundance, from monospecific stands to highly species-rich communities. To understand what causes the low plant diversity observed in some beech forests, we analyzed the drivers of plant community completeness in 155 vegetation plots. Data were collected in mature, closed-canopy beech forests in Tuscany, central Italy. Site-specific species pools were estimated based on species co-occurrences. We used Generalized Least Squares linear modeling to assess the effects of anthropogenic and environmental drivers on the community completeness of whole communities and on the set of specialist species of beech forests. We also tested the response of the total cover of the herb layer to the selected predictors and related both the predictive and response variables to species composition in a Non-metric Multidimensional Scaling ordination. The community completeness of whole communities and that of beech forest specialists were negatively affected by total beech cover and positively influenced by slope. Moreover, the community completeness of whole communities was negatively impacted by elevation and positively influenced by disturbance frequency. The cover of the herb layer decreased with increasing beech cover, elevation, and precipitation. High community completeness and high cover of the herb layer were associated with the presence of thermophilic species of mixed deciduous woods in low-elevation beech forests. Our results suggest that a low plant community completeness and a low cover of the herb layer are mainly due to the competition by beech itself when it forms pure forests in its ecological optimum. Such competition is better exerted at upper elevations and in sites with low slopes, where beech litter accumulation is a limiting factor for understory species. Such evidence suggests that species absence in mature beech forests is mainly due to natural drivers and should therefore not be considered an indicator of ecological degradation of the forest.

Improving our ability to monitor fragmented tropical ecosystems is a critical step in supporting the stewardship of these complex landscapes. We investigated the structural characteristics of vegetation classes in Ucayali, Peru, employing a co‐production approach. The vegetation classes included three agricultural classes (mature oil palm, monocrop cacao, and agroforestry cacao plantations) and three forest regeneration classes (mature lowland forest, secondary lowland forest, and young lowland vegetation regrowth). We combined local knowledge with spaceborne lidar from NASA's Global Ecosystem Dynamics Investigation mission to classify vegetation and characterize the horizontal and vertical structure of each vegetation class. Mature lowland forest had consistently higher mean canopy height and lower canopy height variance than secondary lowland forest (μ = 29.40 m, sd = 6.89 m vs. μ = 20.82 m, sd = 9.15 m, respectively). The lower variance of mature forest could be attributed to the range of forest development ages in the secondary forest patches. However, secondary forests exhibited a similar vertical profile to mature forests, with each cumulative energy percentile increasing at similar rates. We also observed similar mean and standard deviations in relative height ratios (RH50/RH95) for mature forest, secondary forest, and oil palm even when removing the negative values from the relative height ratios and interpolating from above‐ground returns only (mean RH50/RH95 of 0.58, 0.54, and 0.53 for mature forest, secondary forest, and oil palm, respectively) (p <.0001). This pattern differed from our original expectations based on local knowledge and existing tropical forest succession studies, pointing to opportunities for future work. Our findings suggest that lidar‐based relative height metrics can complement local information and other remote sensing approaches that rely on optical imagery, which are limited by extensive cloud cover in the tropics. We show that characterizing ecosystem structure with a co‐production approach can support addressing both the technical and social challenges of monitoring and managing fragmented tropical landscapes. [ABSTRACT FROM AUTHOR]

Assessing the dynamics of forest structure complexity is a critical task in times of global warming, biodiversity loss and increasing disturbances in order to ensure the resilience of forests. Recent studies on forest biodiversity and forest structure emphasize the essential functions of deadwood accumulation and diversification of light conditions for the enhancement of structural complexity. The implementation of an experimental patch‐network in managed broad‐leaved forests within Germany enables the standardized analysis of various aggregated and distributed treatments characterized through diverse deadwood and light structures. To monitor the dynamics of enhanced forest structure complexity as seasonal and trend components, dense time‐series from high spatial resolution imagery of Sentinel‐1 (Synthetic‐Aperture Radar, SAR) and Sentinel‐2 (multispectral) are analyzed in time‐series decomposition models (BEAST, Bayesian Estimator of Abrupt change, Seasonal change and Trend). Based on several spatial statistics and a comprehensive catalog on spectral indices, metrics from Sentinel‐1 (n = 84) and Sentinel‐2 (n = 903) are calculated at patch‐level. Metrics best identifying the treatment implementation event are assessed by change point dates and probability scores. Heterogeneity metrics of Sentinel‐1 VH and Sentinel‐2 NMDI (Normalized Multi‐band Drought Index) capture the treatment implementation event most accurately, with clear advantages for the identification of aggregated treatments. In addition, aggregated structures of downed or no deadwood can be characterized, as well as more complex standing structures, such as snags or habitat trees. To conclude, dense time‐series of complementary high spatial resolution sensors have the potential to assess various aggregated forest structure complexities, thus supporting the continuous monitoring of forest habitats and functioning over time. [ABSTRACT FROM AUTHOR]

Spending time in forests benefits human well‐being, but the importance of forest characteristics on well‐being is unclear. This knowledge could help guide forest management decisions to improve outcomes for both people and nature.The overall aim of this study was to investigate how psychological restoration, defined as psychological recovery processes in nature, may be supported by forest characteristics. We (1) investigated how perceptions of restoration (perceived restorativeness) were linked to specific forest characteristics. More specifically, we selected attributes included in nature protection legislation in Germany (beauty, diversity and uniqueness) as the basis to evaluate how forest characteristics were related to perceived restorativeness. Additionally, we (2) tested differences in the assessments of these attributes between forestry experts and people from the general public. Based on the results of the first two objectives (1, 2), we (3) predicted how forest management that affects forest characteristics may impact psychological restoration today and in the future.We developed a perceived restorativeness model based on attributes stated in the German Nature Conservation Act and specific forest structure variables. Drawing from the literature, we included perceived naturalness as an additional key predictor for restoration. Forestry experts and participants from the general public were then asked to rate computer‐generated forest stand pictures on these attributes and restorativeness.We found that all attributes were positively associated with perceived restorativeness, but perceived beauty was most important. Perceived uniqueness was statistically significant, but the strength of the relationship was weak. Mixed forests were rated as most beautiful, while coniferous forest stands were rated as least beautiful. The general public gave higher ratings than forestry experts on all attributes, but the pattern was similar. Based on participant ratings, forests left without management (Set‐aside), followed by forests with management aiming for resilience to climate change (Adaptation forestry), both supporting biodiversity conservation, showed the highest perceived restorativeness over the course of 100 years.Based on our results, it could be recommendable to increase forest diversity, especially in areas with many visitors. However, more nuanced knowledge involving diverse stakeholders is needed to inform forest management decisions on landscape level. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Reestablishing appropriate tree species is an important step in converting off‐site monocultures into woodlands. Species conversion is often necessary, as off‐site exotic species rarely function like woodland species. However, when off‐site tree species are native, and functionally redundant to woodland species, conversion may be unnecessary. To explore this possibility in the southeastern United States, we reviewed the literature on trait differences among the primary southern pines and qualitatively assessed the effect of their identity at the species and stand‐levels. In this region, woodland restoration focuses on removing loblolly (Pinus taeda) and slash pine (P. elliottii) to reestablish longleaf (P. palustris) or shortleaf pine (P. echinata). Our review found minimal variation among species in understory flammability, fire resistance at maturity, and Red‐cockaded Woodpecker (Leuconotopicus borealis) habitat at the stand‐level. Longleaf and shortleaf pine were generally more resistant to abiotic and pest disturbance at the tree‐level; however, stand‐level differences in wind, drought, and boring insect resistance among southern pines growing in open forests were considered minimal. Retaining loblolly and slash pine will improve stand‐level productivity in the short term, but creates regeneration problems due to low juvenile fire resistance and resilience. Ice resistance and long‐term carbon sequestration will also likely be reduced by retaining loblolly and slash pine. Collectively, these results suggest that southern pine species are generally interchangeable at the stand‐level in woodlands; although woodlands featuring loblolly and slash pine may be less stable in the face of disturbance than those dominated by shortleaf and longleaf pine. [ABSTRACT FROM AUTHOR]

In Japan, secondary forests associated with agriculture (satoyama) had been maintained traditionally by small-scale clearcutting and short-rotation coppice forestry. After forest management was discontinued due to modernization, shade-intolerant, deciduous trees such as Q. serrata and Q. variabilis have become dominant in many secondary forests of southwestern Japan. In recent years, however, tree death from Japanese oak wilt has become wide-spread. To identify forest communities that will be most affected by oak wilt, we investigated stand structure and species composition in a 64-ha unmanaged secondary forest in Kobe, Japan. We identified three forest communities using cluster analysis of 27 survey plots. We then analyzed and visualized vegetation similarity among the communities using non-metric multidimensional scaling (nMDS). In secondary forests, Pinus densiflora–Rhododendron macrosepalum and Quercus serrata–Quercus variabilis communities, which established after pine wilt were identified. We also found communities dominated by Cryptomeria japonica, a plantation species. We predicted that Quercus serrata–Quercus variabilis communities will experience significant vegetation change after oak wilt and become low-statured, evergreen-deciduous forests. These communities, therefore, should be prioritized for active management by small-scale clear cutting to prevent further spread of oak wilt and subsequent biodiversity decline. [ABSTRACT FROM AUTHOR]

Forest fires darken snow albedo and degrade forest structure, ultimately reducing peak snow–water storage, and advancing snowmelt timing for up to 15 years following fire. To date, no volumetric estimates of watershed‐scale postfire effects on snow–water storage and snowmelt timing have been quantified over decades of postfire recovery. Using postfire parameterizations in a spatially‐distributed snow mass and energy balance model, SnowModel, we estimated postfire recovery of forest fire effects on snow–water equivalent (SWE) and snowmelt timing over decades following fire. Using this model, we quantified volumetric recovery of forest fire effects on snow hydrology across a chronosequence of eight sub‐alpine forests burned between 2000 and 2019 in the Triple Divide of western Wyoming. We found that immediately following fire, forest fire effects reduced snow–water storage by 6.8% (SD = 11.2%) and advanced the snow disappearance date by 31 days (SD = 9 days). Across the 15‐year recovery following fire, forest fire effects reduced snow–water storage by 4.5% (SD = 11.4%). Postfire effects on snow hydrology generally recovered over time, but still persisted beyond 15‐years following fire due to the observed postfire shift from forest to open meadow. Estimates of postfire reductions on peak SWE summed over the entire 15‐year postfire recovery period were 18 times greater than the immediate losses in the first winter following fire alone. These lasting effects of forest fires on snow hydrology decades following fire highlight the importance of postfire parameterizations for more accurate watershed‐scale volumetric estimates of forest fire effects on snow–water resources. [ABSTRACT FROM AUTHOR]

Agroforestry systems (AFS) are important for biodiversity conservation outside protected areas. The presence of shade trees in AFS form structurally complex habitats that provide food for many species. Habitat complexity is considered an essential biodiversity variable and its characterization is now possible using remote sensing techniques, including 3D point clouds derived from images obtained with unmanned aerial vehicles (UAVs). However, studies evaluating the relationship between canopy structure and variables derived from 3D clouds are rare for AFS, especially for the tropical Andes. Here, we show how six important variables of canopy structure can be predicted across a canopy structure gradient from AFS with cacao and coffee to a natural forest using characteristics extracted from the 3D point clouds and multiple linear regression. For leaf area index the best model obtained an R2 of 0.82 with a relative RMSE = 24%, for canopy cover an R2 of 0.81 and relative RMSE = 13%, for above-ground biomass an R2 of 0.81 and relative RMSE = 10%, the density of shade trees was predicted with an R2 of 0.66 and relative RMSE = 34%, the mean height and the standard deviation of height in the canopy obtained an R2 of 0.82 and 0.79 respectively, and relative RMSE of 18% for both. The approach presented in this study allows an accurate characterization of the canopy structure of AFS using UAVs, which can be useful for assessing above-ground biomass and biodiversity in tropical agricultural landscapes to monitor sustainable management practices and derive payments for ecosystem services. [ABSTRACT FROM AUTHOR]

With limited national financing for conservation, there is an increasing interest in using biodiversity offset funds to strengthen protected area management. Offsetting measures can potentially be used in the restoration of degraded protected areas. However, there are concerns related to the uncertainty of restoration outcomes and time-lags before the expected benefits can be observed. Using a case of the Gangu Central Forest Reserve in central Uganda, we contribute empirical findings showing the potential and limitations of biodiversity offsetting by means of the restoration of a degraded forest reserve. We use forest cover change analysis and community surveys to determine forest changes after eight years of offset implementation, and forest inventories to analyse the current forest structure and composition to ascertain taxonomic diversity recovery. The results revealed that biodiversity offsetting led to a 21% increase in Tropical High Forest cover, and enhanced restoration of forest species composition and diversity. However, attaining permanence of the restoration benefits requires the regulation of community forest resource access and use. Strengthening forest management capacity to monitor the offset sites and compensating impacted communities for foregone forest resource benefits are crucial for the successful implementation of biodiversity offsets. [ABSTRACT FROM AUTHOR]

Aim: Schizomids are one of the less-known arachnid groups in terms of their natural history and ecology. However, due to their remarkable short-range endemic distribution, they may be vulnerable to climate change and habitat loss. In Colombia, although the national IUCN red list of threatened invertebrates has categorized species of schizomids as vulnerable (VU), this assessment was based on expert criteria. Therefore, information about the ecology of schizomids is critical for a more accurate reassessment of their conservation status. In this study, we describe the habitat of two species of Surazomus in endangered Andean tropical forests of Colombia after a sampling effort of 15.12 m2 (n = 168 soil samples) and the collection of 6999 soil fauna individuals from the samples. We analyzed soil fauna communities associated with schizomids as well as different forest and environmental variables from permanent plots installed a decade ago in the Sabana de Bogotá region. Detailed information on climate, plant communities, and forest structure was obtained from these plots. Thus, we provide the first comprehensive habitat description of schizomids including both above- and belowground compartments. We found that each species lives in specific habitats with different soil fauna communities, suggesting a potential association between geographical fidelity and habitat conditions. This result could indicate that schizomids are highly sensitive to dramatic environmental changes, such as those experienced in the Andean region of Colombia. Implications for insect conservation: Our study is valuable for the future reassessment of the conservation status of schizomids in the country, particularly considering that the previous categorization was based on expert criteria. Since habitat conditions and soil fauna communities are species-specific, schizomids could be disproportionately vulnerable to climate change and human disturbances in the Colombian Andes. [ABSTRACT FROM AUTHOR]

Sea level rise (SLR) is causing vegetation regime shifts on both the seaward and landward sides of many coastal ecosystems, with the eastern coast of North America experiencing accelerated impacts due to land subsidence and the weakening of the Gulf Stream. Tidal wetland ecosystems, known for their significant carbon storage capacity, are crucial but vulnerable blue carbon habitats. Recent observations suggest that SLR rates may exceed the threshold for elevation gain primarily through vertical accretion in many coastal regions. Therefore, research has focused on mapping the upslope migration of marshes into suitable adjacent lands, as this landward gain may be the most salient process for estimating future wetland resiliency to accelerated rates of SLR. However, our understanding of coastal vegetation characteristics and dynamics in response to SLR is limited due to a lack of in situ data and effective mapping strategies for delineating the boundaries, or ecotones, of these complex coastal ecosystems. In order to effectively study these transitioning ecosystems, it is necessary to employ reliable and scalable landscape metrics that can differentiate between marsh and coastal forests. As such, integrating vegetation structure metrics from light detection and ranging (lidar) could enhance traditional mapping strategies compared to using optical data alone. Here, we used terrestrial laser scanning (TLS) to measure changes in forest structure along elevation gradients that may be indicative of degradation associated with increased inundation in the Delaware Bay estuary. We analyzed a set of TLS‐derived forest structure metrics to investigate their relationships with elevation, specifically seeking those that showed consistent change from the forest edge to the interior. Our findings revealed a consistent pattern between elevation and the plant area index (PAI), a metric that holds potential for enhancing the delineation of complex coastal ecosystem boundaries, particularly in relation to landward marsh migration. This work provides support for utilizing lidar‐derived forest structural metrics to enable a more accurate assessment of future marsh landscapes and the overall coastal carbon sink under accelerated SLR conditions. [ABSTRACT FROM AUTHOR]

Understanding and mimicking regeneration processes in forests is crucial to sustainable forestry and forest conservation, since they largely determine the structural and ecophysiological traits as well as the ecosystem goods and services of forest stands. The techniques employed in achieving tree regeneration include the active manipulation of forest structure and are formally described by silvicultural systems. In the past, most silvicultural systems were developed locally and the corresponding authors used names and terminology that greatly varied and were often ambiguous. In addition, although local developments, silvicultural systems were often presented as “package deals” and individual components were not sufficiently defined to allow for adaptations in applications elsewhere. We critically reviewed the basic components and variants of silvicultural systems as well as their combinations in order to develop a unifying terminology that allows a better communication of regeneration methods and inspires the continued creation of new ones. Finally we applied the terminology elaborated in our review to an example of classifying silvicultural systems from Poland in order to show how existing silvicultural systems can be more clearly re-interpreted. We found that our review and analysis opened new insights on silvicultural systems that pave the way to more detailed and systematic future research in regeneration techniques. Silvicultural systems applied to high forests are often, with few modifications, also applicable to coppice forests and vice versa. Silvicultural systems also form an important element of close-to-nature or continuous cover forestry (CCF), as they contribute to diversifying forest structure by introducing new tree cohorts and the way how rigorously silvicultural systems are applied in various countries much depends on the time elapsed since the adoption of CCF.