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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.

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]

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]

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]

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]

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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]

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]

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]

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]

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]

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.

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

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.

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.

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]

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]

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]

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]

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]

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]

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]

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.

This editorial highlights the significance of old-growth forests in southeast Europe for forest management. The authors stress the importance of studying these forests, which have developed naturally without human intervention, to gain knowledge that can be applied to commercial forests. The authors also emphasize the threats to forest ecosystems, such as climate change and invasive pests, and the need to protect and preserve old-growth forests. They discuss the differences between old-growth forests and commercial forests in terms of structural diversity and biodiversity. The editorial concludes by underscoring the ecological and socioeconomic importance of old-growth forests in southeast Europe and the need for further research in this field. [Extracted from the article]

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.

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.

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.

Snow and watershed models typically do not account for forest structure and shading; therefore, they display substantial uncertainty when attempting to account for forest change or when comparing hydrological response between forests with varying characteristics. This study collected snow water equivalent (SWE) measurements in a snow-dominated forest in Colorado, the United States, with variable canopy structure. The SWE measurements were integrated with 1 m Lidar derived canopy structure metrics and incoming solar radiation to create empirical SWE offset equations for four canopy structure groupings (forest gaps, south-facing forest edges, north-facing forest edges, and the interior forest) that varied in size compared to an open area. These simple equations indirectly integrate terrain shading and canopy shading and were able to estimate 40 to 70% of SWE variation in a heterogenous forested environment. The equations were then applied to a snow melt model with a 100 m grid size by applying the area-weighted average of SWE offsets from the four canopy structure groupings in each model cell. This tiled model configuration allowed for the model to better represent the subgrid heterogeneity of a forest environment that can be seen through an ensemble or range of potential outputs rather than a singular estimate. [ABSTRACT FROM AUTHOR]

Spectral diversity (SD) in reflectance can be used to estimate plant taxonomic diversity (TD) according to the Spectral Variation Hypothesis (SVH). However, contrasting relationships between SD and TD have been reported by different studies. Indeed, multiple factors may affect SD, including spatial and spectral scales, vegetation characteristics and the adopted SD computational method. Here, we tested the SVH over 171 plots within a large and heterogeneous forest area in North-Eastern Italy using Sentinel-2 data, aiming at identifying possible factors affecting the strength and direction of SD-TD relationship. SD was determined using 'biodivMapR' (BD) and 'rasterdiv' (RD) R packages and 38 possible combinations of SD indices, at both α (within a community) and β (among communities) levels, and computational parameters accounting for spatial and spectral scales. Information on vegetation structure was either retrieved from ground-based or LiDAR data. A Random Forest approach was used to disentangle the relationships between SD, TD and vegetation structure, and to identify the best combination of SD computational parameters. At the α-level, we found negative relationship between TD and RD SD indices, which was mainly driven by the presence of gaps within the forest canopy. As regards BD, we found that this algorithm reduced background contribution on SD and was able to differentiate major forest types (broadleaves vs conifers), but derived α-SD indices were marginally correlated with α-TD. At the β-level, we observed a statistically significant positive correlation between BD SD indices and TD (maximum r = 0.24). Finally, we found stronger correlations and R2 when SD indices were calculated using smaller computation windows and over a larger pixels extraction area. Our findings suggest that vegetation cover and structure play a major role, with respect to inter-species spectral differences, in determining α-SD, and that SD might better capture differences in species composition at the landscape-level rather than the richness of individual communities. [ABSTRACT FROM AUTHOR]

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Recent studies suggest that arthropod diversity in German forests is declining. Currently, different national programs are being developed to monitor arthropod trends and to unravel the effects of forest management on biodiversity in forests. To establish effective long-term monitoring programs, a set of drivers of arthropod diversity and composition as well as suitable species groups have to be identified. To aid in answering these questions, we investigated arthropod data collected in four Hessian forest reserves (FR) in the 1990s. To fully utilize this data set, we combined it with results from a retrospective structural sampling design applied at the original trap locations in central European beech (Fagus sylvatica) forests. As expected, the importance of the different forest structural, vegetation, and site attributes differed largely between the investigated arthropod groups: beetles, spiders, Aculeata, and true bugs. Measures related to light availability and temperature such as canopy cover or potential radiation were important to all groups affecting either richness, composition, or both. Spiders and true bugs were affected by the broadest range of explanatory variables, which makes them a good choice for monitoring general trends. For targeted monitoring focused on forestry-related effects on biodiversity, rove and ground beetles seem more suitable. Both groups were driven by a narrower, more management-related set of variables. Most importantly, our study approach shows that it is possible to utilize older biodiversity survey data. Although, in our case, there are strong restrictions due to the long time between species and structural attribute sampling. [ABSTRACT FROM AUTHOR]

Precise estimates of dendrometric and morphometric variables are indispensable for effective forest resource conservation and sustainable utilization. This study focuses on modeling the relationships between shape (morphometric), dimension (dendrometric) and density (N) to assess the sustainability of forest resources. It sheds light on the current state of site characteristics, reproduction, and the structure of Araucaria angustifolia trees at selected forest remnants across multiple sites in Santa Catarina, Southern Brazil. Individual trees and their dendrometric variables, such as the diameter at breast height (d), height (h), crown base height (cbh), annual periodic increment (API) in growth rings, and morphometric variables, including four radii of the crown in cardinal directions, were evaluated. These measurements allowed us to calculate various morphometric indices and crown efficiency, enabling the assessment of both vertical and horizontal structural conditions. Statistical analysis confirmed a positive relationship of the crown volume (cv) and crown surface area (csa) with the crown length (cl). Conversely, the crown efficiency, density, increment rate, and reproductive structure production declined. These morphometric relationships emphasize the complex dynamics within these forest ecosystems, irrespective of the chosen site, indicating that horizontal and vertical forest structures have stagnated and have been characterized by limited change in the last ten years. Such results raise concerns about sustainability, highlighting the need for proper conservation measures and sustainable forest management practices. Our findings underscore the need for substantial adjustments in the structure and dynamics of the forest, particularly on selected rural properties where this tree species is abundant, to ensure long-term sustainability. [ABSTRACT FROM AUTHOR]

Forest canopy height data are crucial for estimating forest carbon storage and assessing forest ecology. By utilizing satellite imagery, canopy height data obtained from airborne or spaceborne LiDAR have been expanded from footprint and plot levels to spatially continuous elevation mapping of forests. However, current research suggests that estimating forest canopy height without forest type data presents a challenge in how to effectively integrate multi-source LiDAR data and ensure the samples adequately represent various forest types for higher estimation accuracy. Therefore, this study proposes a forest canopy height estimation method that considers forest structure and integrates multi-source LiDAR data to overcome the challenge. First, a stratified sampling method based on forest structure (SSMFS) was proposed to select training samples and enhance their representativeness. Second, we combined GEDI and ATL08 data to create a multi-source spaceborne LiDAR dataset, enhancing geographic coverage and increasing canopy height samples. Third, the spaceborne LiDAR-based canopy height estimation model incorporates previously unconsidered canopy openness features and uses SSMFS to select training samples. Finally, we improved spaceborne LiDAR canopy height accuracy by creating a residual correction model that adjusts for differences between airborne scanner (ALS) and spaceborne LiDAR estimates. This study, conducted in Zhangwu County, achieved an accuracy of R2 = 0.71, MAE = 1.20 m, and RMSE = 1.71 m. These results show a 51.06% increase in R2, a 26.38% decrease in MAE, and a 24.00% decrease in RMSE compared to recent research. In summary, this study profoundly amplifies predictive accuracy, providing a clear advantage in the delineation of regional forest canopy maps. [ABSTRACT FROM AUTHOR]

Accurate structural information about forests, including canopy heights and diameters, is crucial for quantifying tree volume, biomass, and carbon stocks, enabling effective forest ecosystem management, particularly in response to changing environmental conditions. Since late 2018, NASA's Global Ecosystem Dynamics Investigation (GEDI) mission has monitored global canopy structure using a satellite Light Detection and Ranging (LiDAR) instrument. While GEDI has collected billions of LiDAR shots across a near-global range (between 51.6°N and >51.6°S), their spatial distribution remains dispersed, posing challenges for achieving complete forest coverage. This study proposes and evaluates an approach that generates high-resolution canopy height maps by integrating GEDI data with Sentinel-1, Sentinel-2, and topographical ancillary data through three machine learning (ML) algorithms: random forests (RF), gradient tree boost (GB), and classification and regression trees (CART). To achieve this, the secondary aims included the following: (1) to assess the performance of three ML algorithms, RF, GB, and CART, in predicting canopy heights, (2) to evaluate the performance of our canopy height maps using reference canopy height from canopy height models (CHMs), and (3) to compare our canopy height maps with other two existing canopy height maps. RF and GB were the top-performing algorithms, achieving the best 13.32% and 16% root mean squared error for broadleaf and coniferous forests, respectively. Validation of the proposed approach revealed that the 100th and 98th percentile, followed by the average of the 75th, 90th, 95th, and 100th percentiles (AVG), were the most accurate GEDI metrics for predicting real canopy heights. Comparisons between predicted and reference CHMs demonstrated accurate predictions for coniferous stands (R-squared = 0.45, RMSE = 29.16%). [ABSTRACT FROM AUTHOR]

Amazon rainforests are critical for providing a wide range of ecosystem services. In the Southeastern Peruvian Amazon; however, goldmining activities are causing severe soil degradation and forest loss. We analyzed aboveground biomass (AGB), forest structure, and species diversity recovery during secondary succession in 179 forest plots. Our study provides the first field-based quantification of AGB recovery following the abandonment by two types of goldmining (heavy machinery and suction pumping) in Madre de Dios (Peru). We found that successional secondary forests in areas subjected to suction pumping were more resilient than those in areas subjected to heavy machinery. After 20 years, mean AGB in suction pumping mining areas had reached 56% of reference forest AGB, while in areas of heavy machinery mining it was only 18%. Mining type, stand age, and distance from the forest edge had a significant effect on AGB. The influence of the distance from the forest edge on AGB varies according to mining type because the effects of species diversity on AGB are mediated by the distance from the forest edge. Our results clearly showed the dynamics of AGB recovery across a secondary succession after goldmining, and the contrasting responses of AGB between the two mining types. Our study disentangles the importance of key factors in forest recovery after mining and improves understanding of the resilience of biomass accumulation in these highly degraded ecosystems. [ABSTRACT FROM AUTHOR]

Forest insect outbreaks cause large changes in ecosystem structure, composition, and function. Humans often respond to insect outbreaks by conducting salvage logging, which can amplify the immediate effects, but it is unclear whether logging will result in lasting differences in forest structure and dynamics when compared with forests affected only by insect outbreaks. We used 15 years of data from an experimental removal of Tsuga canadensis (L.) Carr. (Eastern hemlock), a foundation tree species within eastern North American forests, and contrasted the rate, magnitude, and persistence of response trajectories between girdling (emulating mortality from insect outbreak) and timber harvest treatments. Girdling and logging were equally likely to lead to large changes in forest structure and dynamics, but logging resulted in faster rates of change. Understory light increases and community composition changes were larger and more rapid in the logged plots. Tree seedling and understory vegetation abundance increased more in the girdled plots; this likely occurred because seedlings grew rapidly into the sapling‐ and tree‐size classes after logging and quickly shaded out plants on the forest floor. Downed deadwood pools increased more after logging but standing deadwood pools increased dramatically after girdling. Understory light levels remained elevated for a longer time after girdling. Perhaps because the window of opportunity for understory species to establish was longer in the girdled plots, total species richness increased more in the girdled than logged plots. Despite the potential for greater diversity in the girdled plots, Betula lenta L. (black birch) was the most abundant tree species recruited into the sapling‐ and tree‐size classes in both the girdled and logged plots and is poised to dominate the new forest canopy. The largest difference between the girdling and logging treatments—deadwood structure and quantity—will persist and continue to bolster aboveground carbon storage and structural and habitat diversity in the girdled plots. Human responses to insect outbreaks hasten forest reorganization and remove structural resources that may further alter forest response to ongoing climate stress and future disturbances. [ABSTRACT FROM AUTHOR]

Although Atlantic Forest is diverse and heavily fragmented, little is known about the impact of edges created from fragmentation on forest structure and plant diversity. Our investigation of vegetation at agricultural edges aimed to determine edge width, to compare effects of edge influence and topography and to assess patterns of diversity. We collected data on forest structure, plant groups, plant families, and vertical vegetation structure in 2 × 2 m contiguous plots along 250 m transects across the edges of 24 fragments approx. 70 km west of São Paulo, Brazil. We used randomization tests to estimate the magnitude and distance of edge influence, generalized linear mixed models to assess the effect of topography, and wavelet analysis to evaluate spatial patterns. Although there was evidence of edge degradation (lower diversity and cover of most plant groups compared to interior forest) and edge sealing (abrupt changes at the edge particularly for leafy vertical diversity), edge influence did not extend very far with a distance of edge influence of 20 m or less for most variables. Less extensive edge influence compared to other tropical forests was not explained by topography (slope) but could be due to more extensive fragmentation and land use history. The use of multiple approaches to studying forest edges provided complementary information to improve our understanding of the structure of anthropogenic edges in Atlantic Forest. Signs of edge degradation suggest that edge influence should be considered in conservation planning even though edges are narrow. [ABSTRACT FROM AUTHOR]

The European bison (Bison bonasus L.) is a species that has been successfully reintroduced in several European countries and requires ongoing monitoring to protect and restore its biodiversity. This study examines the population dynamics of free-ranging European bison in the Vânători Neam? Natural Park in Romania, taking into account various local biotic and abiotic factors. Data collected from the collars of several European bison from 2014 to 2019, as well as vector maps of the study area, was used for the analysis. Furthermore, the study identifies the influence of slope and altitude on the distribution patterns of the European bison throughout the seasons. While a confluence of biotic and abiotic factors shapes exploration behavior, the study underscores that food supply and land use exert the most pronounced impact on the sustainability and growth of the European bison population. Despite an exhaustive analysis of correlations between various biotic and abiotic factors with recorded temperatures, no significant results reflecting discernible trends or linkages induced by temperature were obtained. The analysis of GPS collar data revealed that, in the research area, the European bison inhabited areas with forest vegetation for about 74% of their time and places without it for 26%. Over 80% of the localization patterns were found in wooded areas, with fewer than 20% found in non-forest areas, year after year. The age class distribution research shows that the European bisons have a significant preference (more than 55%) for old-growth forests that are older than 100 years. This preference is present in all seasons except winter, when the preference for these types of forests decreases. On the other hand, forests that are younger and less than 60 years old are preferred in the summer and spring, which offers important information on the diverse habitat preferences of the European bison population. [ABSTRACT FROM AUTHOR]

Canopy structural complexity is a key emergent property of forest ecosystems that directly relates to their ability to store carbon, cycle water and nutrients, and provide habitat for biodiversity. However, we lack a general framework for quantifying the structural complexity of forest canopies, and it remains to be seen if there are simple rules that explain the huge variation in canopy structure that we observe across the world's forests.A leading candidate for characterizing differences in structural complexity among forest ecosystems are fractal scaling laws, or measures of statistical self‐similarity. If forest canopies were fractal in nature, their structural attributes could be distilled into a single coefficient—their fractal dimension—which could then be used to directly compare structural differences within and across biomes.To test this idea, we used airborne laser scanning (ALS) data acquired across nine landscapes in Australia that span a huge environmental gradient and include everything from dry shrublands, tropical savannas, dense rainforests, to 90 m tall Mountain Ash forests. Using the ALS data, we built high‐resolution 3D canopy height models of each landscape so that we could quantify how closely they followed fractal scaling laws, based on a comprehensive set of fractal dimension estimators.Across all ecosystem types, fractal scaling assumptions were consistently violated, with clear and systematic differences between real‐world forest canopies and simulated fractal surfaces. However, deviations from fractality did vary predictably among sites, with forests in less arid environments, dominated by tall trees with large crowns, exhibiting a higher degree of self‐similarity across scales.Synthesis: Our study conclusively shows that canopy surfaces are not fractal beyond the scale of individual tree crowns. Nevertheless, we still observed ecologically meaningful and generalisable patterns in forest structure across scales, which closely reflect biophysical and physiological constraints on tree size and architecture. These patterns point the way towards a more general framework for characterizing ecological complexity. [ABSTRACT FROM AUTHOR]

Context: Global change is leading to more frequent and intense tree damage and mortality events. Drought-induced tree mortality is occurring worldwide leading to broad-scale events, but the spatial patterns of tree damage and mortality, their underlying drivers and their variation over time is largely unknown. Objectives: We investigated the spatial patterns of tree damage and mortality across Mediterranean forests of the Iberian Peninsula, the underlying effects of stand structure and climate, and how the spatial patterns and relationships with underlying drivers changed over time. Methods: We used the Spanish Forest Inventory to analyse the autocorrelation in tree damage and mortality across forest types, hurdle-gamma models to quantify the effect of stand structure and climate on tree damage and mortality, and cross-correlograms to assess their spatial dependence and its change over time. Results: We observed a greater magnitude and a stronger autocorrelation in tree damage than mortality, with positive aggregation up to 20 kms. There was a spatial dependence between tree damage and mortality with their drivers, with spatial aggregation increasing with water availability, drought intensity and stand structure. The spatial dependence of tree damage and mortality with the underlying drivers increased over time, particularly for drought intensity. Conclusions: Our results suggest that the combined effect of intense competition and drought could favour more extensive die-off and tree mortality events, providing key information for identifying vulnerable areas and the planning of adaptation measures. [ABSTRACT FROM AUTHOR]

Abstract 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

Abstract 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.

Process-based forest models combine biological, physical, and chemical process understanding to simulate forest dynamics as an emergent property of the system. As such, they are valuable tools to investigate the effects of climate change on forest ecosystems. Specifically, they allow testing of hypotheses regarding long-term ecosystem dynamics and provide means to assess the impacts of climate scenarios on future forest development. As a consequence, numerous local-scale simulation studies have been conducted over the past decades to assess the impacts of climate change on forests. These studies apply the best available models tailored to local conditions, parameterized and evaluated by local experts. However, this treasure trove of knowledge on climate change responses remains underexplored to date, as a consistent and harmonized dataset of local model simulations is missing.Here, our objectives were (i) to compile existing local simulations on forest development under climate change in Europe in a common database, (ii) to harmonize them to a common suite of output variables, and (iii) to provide a standardized vector of auxiliary environmental variables for each simulated location to aid subsequent investigations. Our dataset of European stand- and landscape-level forest simulations contains over 1.1 million simulation runs representing 135 million simulation years for more than 13,000 unique locations spread across Europe. The data were harmonized to consistently describe forest development in terms of stand structure (dominant height), composition (dominant species, admixed species), and functioning (leaf area index). Auxiliary variables provided include consistent daily climate information (temperature, precipitation, radiation, vapor pressure deficit) as well as information on local site conditions (soil depth, soil physical properties, soil water holding capacity, plant-available nitrogen). The present dataset facilitates analyses across models and locations, with the aim to better harness the valuable information contained in local simulations for large-scale policy support, and for fostering a deeper understanding of the effects of climate change on forest ecosystems in Europe.

Multiple scattering of laser ray leads to distance calculation error and accumulated intensity error in LiDAR waveform. This study quantitatively assesses multiple scattering in LiDAR canopy waveform and explores its relationship with sensor configurations, forest structure, and leaf biochemistry. Airborne LiDAR measurements were conducted to verify the presence of multiple scattering in spaceborne LiDAR waveforms. A Monte Carlo-based radiative transfer model, DART, was applied to simulate laser multiple scattering. The simulated results were compared with the actual measurements and further revealed how forest characteristics and sensor parameters influence multiple scattering. Results show that multiple scattering contributes significantly (∼30% of canopy subwaveform and ∼18% of total waveform) when there are sufficient scatterers in a large LiDAR footprint, and the scatterers possess a reasonable spatial distribution and a strong albedo at the laser wavelength; contribution of multiple scattering is typically less than 5% for small-footprint LiDAR; multiple scattering of large-footprint LiDAR increases with forest leaf area index and fractional coverage, while also varying with laser pointing angle, crown shape, and leaf angle distribution; leaf biochemical contents affect multiple scattering only at their sensitive wavelength. These findings help to quantitatively explain errors in the simulation, validation, and interpretation of LiDAR signal.

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.