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The structure and fire regime of pre-industrial (historical) dry forests over ~26 million ha of the western USA is of growing importance because wildfires are increasing and spilling over into communities. Management is guided by current conditions relative to the historical range of variability (HRV). Two models of HRV, with different implications, have been debated since the 1990s in a complex series of papers, replies, and rebuttals. The "low-severity" model is that dry forests were relatively uniform, low in tree density, and dominated by low- to moderate-severity fires; the "mixed-severity" model is that dry forests were heterogeneous, with both low and high tree densities and a mixture of fire severities. Here, we simply rebut evidence in the low-severity model's latest review, including its 37 critiques of the mixed-severity model. A central finding of high-severity fire recently exceeding its historical rates was not supported by evidence in the review itself. A large body of published evidence supporting the mixed-severity model was omitted. These included numerous direct observations by early scientists, early forest atlases, early newspaper accounts, early oblique and aerial photographs, seven paleo-charcoal reconstructions, ≥18 tree-ring reconstructions, 15 land survey reconstructions, and analysis of forest inventory data. Our rebuttal shows that evidence omitted in the review left a falsification of the scientific record, with significant land management implications. The low-severity model is rejected and mixed-severity model is supported by the corrected body of scientific evidence. [ABSTRACT FROM AUTHOR]

The increasing level of atmospheric carbon dioxide and its effects on our climate system has become a global environment issue. The forest ecosystem is essential for the stability of carbon in the atmosphere as it operates as a carbon sink and provides a habitat for numerous species. Therefore, our understanding of the structural elements of the forest ecosystem is vital for the estimation of forest biomass or terrestrial carbon stocks. Over the last two decades, light detection and ranging (LIDAR) technology has significantly revolutionized our understanding of forest structures and enhanced our ability to monitor forest biomass. This paper presents a review of metrics for forest biomass estimation, outlines metrics selection methods for biomass modeling, and addresses various assessment criteria for the selection of allometric equations for the aboveground forest biomass estimations, using LIDAR data. After examining one hundred publications written by different authors between 1999 and 2023, it was observed that LIDAR technology has become a dominant data collection tool for aboveground biomass estimation with most studies focusing on the use of airborne LIDAR data for the plot-level analysis on a local scale. Parametric-based models dominated in most studies with coefficient of determination (R2) and root mean square error (RMSE) as assessment criteria. In addition, mean top canopy height (MCH) and quadratic mean height (QMH) were reported as strong predictors for aboveground biomass (AGB) estimation. Pixel-based uncertainty analysis was found to be a reliable method for assessing spatial variations in uncertainties. [ABSTRACT FROM AUTHOR]

The ability to measure the ecological effects of understory fire in the Amazon on a landscape scale remains a frontier in remote sensing. The Global Ecosystem Dynamics Investigation's (GEDI) LiDAR data have been widely suggested as a critical new tool in this field. In this paper, we use the GEDI Simulator to quantify the nuanced effects of understory fire in the Amazon, and assess the ability of on-orbit GEDI data to do the same. While numerous ecological studies have used simulated GEDI data, on-orbit constraint may limit ecological inference. This is the first study that we are aware of that directly compares methods using simulated and on-orbit GEDI data. Simulated GEDI data showed that fire effects varied nonlinearly through the canopy and then moved upward with time since burn. Given that fire effects peaked in the mid-canopy and were often on the scale of 2 to 3 m in height difference, it is unlikely that on-orbit GEDI data will have the sensitivity to detect these same changes. [ABSTRACT FROM AUTHOR]

For vegetation monitoring, it is crucial to understand which changes are caused by the measurement setup and which changes are true representations of vegetation dynamics. UAV–LiDAR offers great possibilities to measure vegetation structural parameters; however, UAV–LiDAR sensors are undergoing rapid developments, and the characteristics are expected to keep changing over the years, which will introduce data inter-operability issues. Therefore, it is important to determine whether datasets acquired by different UAV–LiDAR sensors can be interchanged and if changes through time can accurately be derived from UAV–LiDAR time series. With this study, we present insights into the magnitude of differences in derived forest metrics in savanna woodland when three different UAV–LiDAR systems are being used for data acquisition. Our findings show that all three systems can be used to derive plot characteristics such as canopy height, canopy cover, and gap fractions. However, there are clear differences between the metrics derived with different sensors, which are most apparent in the lower parts of the canopy. On an individual tree level, all UAV–LiDAR systems are able to accurately capture the tree height in a savanna woodland system, but significant differences occur when crown parameters are measured with different systems. Less precise systems result in underestimations of crown areas and crown volumes. When comparing UAV–LiDAR data of forest areas through time, it is important to be aware of these differences and ensure that data inter-operability issues do not influence the change analysis. In this paper, we want to stress that it is of utmost importance to realise this and take it into consideration when combining datasets obtained with different sensors. [ABSTRACT FROM AUTHOR]

With the publication of a Decree that has established a List of the Italian oldgrowth forests, we have analyzed, using text mining software, the state of the art of Italian research in this field. We have analyzed 188 ISI and 72 non-indexed papers or reports. The analysis has identified 165 locations of which more than 50% are on pure beech or mixed beech and silver fir stands. The analysis was focused mainly on structural characteristics and bio-indicators. The current knowledge represents reliable support for the establishment of an Italian network of old-growth forests and this green infrastructure offers important opportunities for research and collaboration between research, natural resource management, and local, regional, and state administrations. [ABSTRACT FROM AUTHOR]

One-third of Germany's land surface area is covered by forest (around 11.4 million hectares), and thus, it characterizes the landscape. The forest is a habitat for a large number of animal and plant species, a source of raw materials, important for climate protection, and a well-being refuge for people, to name just a few of its many functions. During the annual forest condition surveys, the crown condition of German forests is assessed on the basis of field samples at fixed locations, as the crown condition of forest trees is considered an important indicator of their vitality. Since the start of the surveys in 1984, the mean crown defoliation of all tree species has increased, now averaging about 25% for all tree species. Additionally, it shows a strong rise in the rate of dieback. In 2019, the most significant changes were observed. Due to the drastic changes in recent years, efforts are being made to assess the situation of the forest using different remote sensing methods. There are now a number of freely available products provided to the public, and more will follow as a result of numerous projects in the context of earth-observation (EO)-based monitoring and mapping of the forests in Germany. In 2020, the situation regarding the use of remote sensing for the German forest was already investigated in more detail. However, these results no longer reflect the current situation. The changes of the last 3 years are the content of this publication. For this study, 84 citable research publications were thoroughly analyzed and compared with the situation in 2020. As a major result, we found a shift in the research focus towards disturbance monitoring and a tendency to cover larger areas, including national-scale studies. In addition to the review of the scientific literature, we also reviewed current research projects and related products. In congruence to the recent developments in terms of publications in scientific journals, these projects and products reflect the need for comprehensive, timely, large-area, and complementary EO-based information around forests expressed in multiple political programs. With this review, we provide an update of previous work and link it to current research activities. We conclude that there are still gaps between the information needs of forest managers who usually rely on information from field perspectives and the EO-based information products. [ABSTRACT FROM AUTHOR]

Many studies have focused on vegetation across forest edges to study impacts of edges created by human activities on forest structure and composition, or patterns of vegetation at inherent natural edges. Our objective was to create a database of plant‐related variables across different types of edges from various studies (mainly from across Canada, but also in Brazil and Belize) to facilitate edge research. We compiled data on vegetation along more than 300 transects perpendicular to forest edges adjacent to clear‐cuts, burned areas, bogs, lakes, barrens, insect disturbances, and riparian areas from 24 studies conducted over the past three decades. Data were compiled for more than 400 plant species and forest structure variables (e.g., trees, logs, canopy cover). All data were collected with a similar sampling design of quadrats along transects perpendicular to forest edges, but with varying numbers of transects and quadrats, and distances from the edge. The purpose for most of the studies was either to determine the distance of edge influence (edge width) or to explore the pattern of vegetation along the edge to interior gradient. We provide data tables for the cover of plant species and functional groups, the species and size of live and dead trees, the density of saplings, maximum height of functional groups and shrub species, and the cover of functional groups at different heights (vertical distribution of vegetation). The Forest Edge Research Network (FERN) database provides extensive data on many variables that can be used for further study including meta‐analyses and can assist in answering questions important to conservation efforts (e.g., how is distance of edge influence from created edges affected by different factors?). We plan to expand this database with subsequent studies from the authors and we invite others to contribute to make this a more global database. The data are released under a CC0 license. When using these data, we ask that you cite this data paper and any relevant publications listed in our metadata file. We also encourage you to contact the first author if you are planning to use or contribute to this database. [ABSTRACT FROM AUTHOR]

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]

Forests play a significant role for maintaining the biodiversity. In order to manage sustainable forests, tree species history, distribution, and their future prospects are vital. Using standardized quantitative approaches, the age, radial growth, and size class distribution of Abies pindrow (Himalayan fir) were determined from three different al)titudinal sites (i.e. high, middle, and lower). The results indicate that Himalayan fir growing in the high-altitude site (Ayubia, 2 917 m a.s.l.) of moist temperate forests of the Himalayan mountains showed lower radial growth (0.13 cm) than in the middle (Bara Gali, 2 617 m a.s.l.; radial growth = 0.13 cm) and lower (Kuldana, 2 455 m a.s.l.; radial growth = 0.22 cm) altitude sites. Correlation analysis demonstrated that age showed a significant positive correlation (P < 0.001) with diameter at breast height. The tree-ring width chronology (totally 80 core samples) of Himalayan fir was developed from moist temperate forests of Himalayan mountains of Pakistan. At Ayubia site it possesses a long time)span (1703–2020 C.E.), followed by Bara Gali (1862–2020 C.E.) and Kuldana (1864–2020 C.E.). Further, the tree-ring width (TRW) chronology of Ayubia showed a significant positive correlation (P < 0.05) with May and June temperature, and a significant negative correlation (P < 0.05) with June and October precipitation, indicating that summer tempera)tures are the key factor for the radial growth of Himalayan fir. For the Kuldana site, the response of TRW chronology to temperature and precipitation was the same, however, it was significant only for June temperature at Bara Gali. The size class distribution of the high-altitude region (Ayubia) showed a higher number of individuals than the lower alti)tude region, indicating the lowest disturbance conditions. The absence of individuals in the early size classes and the gap in middle and mature size classes indicate a lower regeneration potential and anthropogenic impact. The pointer year analysis indicated that the Bara Gali forest is more sensitive to abnormal climate events than the other sites. Based on the present study, we suggest that proper attention and conservation strategy should be provided to Himalayan fir growing in the moist temperate forests of Pakistan. [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]

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]

• Old-growth definition criteria greatly impacts amount estimates and maps. • Physically-based old-growth definitions are detectable via lidar remote sensing. • Age-based old-growth definitions are difficult to detect using lidar remote sensing. • Age-based old-growth forests are extremely diverse in physical structure. Old-growth forests have been widely studied for decades. The extreme diversity of old forest characteristics has inspired an equally diverse set of old-growth definitions, and makes mapping old-growth difficult across large areas and different forest types. While the use of remote sensing in old-growth research is not new, there is a growing need for large scale mapping to improve understanding of old forest processes and to support old-growth conservation. Old-growth mapping requires definitions that are ecologically relevant to old forests while also transferable to remote sensing data. In this paper we develop a conceptual framework to evaluate three dimensions of old-growth—a temporal dimension related to tree ages, a physical dimension related to tree sizes, and a functional dimension related to forest processes. In the first part of our analysis, we classify forests throughout the eastern US as old or not with respect to each old-growth dimension using existing old-growth definitions and data from the US Forest Inventory and Analysis (FIA) program. We estimate the proportion of forest classified as old within a hexagon grid, resulting in a unique map of old forest proportion (OFP) for each dimension. Subsequently, we use spaceborne lidar data from NASA's Global Ecosystem Dynamics Investigation (GEDI) to reproduce each OFP map in a modeling framework designed to 1) assess the extent to which each dimension of forest oldness can be mapped at large spatial scales, and 2) identify biophysical GEDI variables related to each dimension of forest oldness. We estimate that only 2% of forest classified as old in any dimension satisfied the old criteria in all three dimensions. We found substantial spatial variation in the mapped OFP estimates across the three dimensions, highlighting how definition criteria impacts old-growth maps. We also found that physically old forests were more effectively mapped using GEDI data than functionally or temporally old forests, and that physically old forests were more structurally similar to one another than temporally or functionally old forests. Our modeling results indicate that while remote sensing may be best suited to mapping physical old-growth characteristics, definitions that rely solely on physical characteristics do not adequately represent old forests throughout the eastern US. We propose that future efforts to map old-growth with spaceborne remote sensing data may maximize utility through collaboration between western and indigenous old-growth experts to determine broad yet nuanced approaches that are appropriately tailored to the target variable of old forests. These efforts should balance explicit and ecologically relevant old-growth definitions specifically for mapping that can be linked to remotely sensed data, 2) appropriate spatial resolutions, and 3) flexible quantitative frameworks that encompass the complexities and heterogeneity of old forests. [ABSTRACT FROM AUTHOR]

From an economic perspective, Scots pine (Pinus sylvestris L.) is one of Europe's most important tree species. It is characterized by its wide ecological adaptability across its natural range. This research aimed to evaluate the forest structure, productivity and especially radial growth of heterogenous pine stands in 16 research plots in the Czech Republic, Poland, Spain and Great Britain. The study assessed the tree-ring formation and its relationship to climate change for each country, using 163 dendrochronological samples. The stand volume of mature pine forest ranged between 91 and 510 m3 ha−1, and the carbon sequestration in the tree biomass was 40–210 t ha−1. The stands had a prevailing random distribution of trees, with a high vertical structure close to selection forests (forest stands with typical very diverse height, diameter and age structure). Spectral analyses showed a substantial decrease in fluctuations in the tree-ring index and a loss in natural growth cyclicity in the last thirty years. The results also evinced that mean air temperature was the most important factor influencing the radial growth compared to precipitation totals. Pine thrives in precipitation-stable locations, as shown by the results from Great Britain. The conclusions of this study confirm the fundamental effect of ongoing global climate change on the dynamics and growth of pine forests in Europe. [ABSTRACT FROM AUTHOR]

IntroductionAccording to various censuses, Europe has less than 1.5 million ha of old-growth forests (OGF). Most of them are in the boreal zone, while their presence in the temperate zone is residual and fragmented.In the framework of the EU biodiversity strategy, it has been adopted a broad definition of OGF which includes late-seral forests and forests with some management legacies. However, research purposes need to identify strictly defined OGFs characterized by structure, disturbance history, and processes typical and exclusive of the last stage of the forest dynamic.MethodsThe present paper wants to contribute to this debate by presenting a research network of four mixed (Fagus-Abies-Picea) montane OGFs in the Dinaric Alps (Lom, BiH; Janj, BiH; Perućica, BiH; Biogradska Gora, MNE), summarizing 20 years of multidisciplinary research by focusing on the structural characteristics and the disturbance history of the whole network and their coherency with strict OGF indicators. These sites were selected in relatively structurally uniform study areas, where 142 permanent plots have been established since 2002.Results and discussionThe study sites have a high living (747–1,201 m3 ha−1) and coarse woody debris (CWD) biomass (304–410 m3 ha−1), resulting in the highest forest carbon sink at the continental level (398–484 Mg C ha−1). The presence of large and old trees is one of the critical characteristics of the old-growth stage: in Lom and Perućica, there are 19 trees and 14 ha−1 larger than 1 m at breast height, respectively, and 14 trees and 15 trees ha−1 older than 400 years. In the last three centuries, continuous small-scale disturbances have driven forest dynamics, developing stands characterized by gap-phase dynamics and quasi-equilibrium structure. The Dinaric OGF network presents robust indicators of old-growthness, similar structural characteristics, and dynamic processes across all four sites. Identifying this sub-set of OGF using strict criteria is critical for recognizing conservation priorities and for quantifying, along an old-growthness chronosequence, the current structural differences of managed or recently abandoned forests. Besides, only OGF selected with rigorous criteria can act as a reliable reference for ecological restoration and sustainable forest management as a benchmark for carbon sink and for quantifying the impact of climate change on forests.

Forest management methods and harvest intensities influence wood production, carbon sequestration and biodiversity. We devised different management scenarios by means of stakeholder analysis and incorporated them in the forest growth simulator PREBAS. To analyse impacts of harvest intensity, we used constraints on total harvest: business as usual, low harvest, intensive harvest and no harvest. We carried out simulations on a wall-to-wall grid in Finland until 2050. Our objectives were to (1) test how the management scenarios differed in their projections, (2) analyse the potential wood production, carbon sequestration and biodiversity under the different harvest levels, and (3) compare different options of allocating the scenarios and protected areas. Harvest level was key to carbon stocks and fluxes regardless of management actions and moderate changes in proportion of strictly protected forest. In contrast, biodiversity was more dependent on other management variables than harvesting levels, and relatively independent of carbon stocks and fluxes. [ABSTRACT FROM AUTHOR]

The ecosystem services framework is a convenient approach for identifying and mapping nature's contributions to people, and an accurate assessment of ecosystem services potential is the first step in the decision support process of well-informed land management planning. The approach we use for forest ecosystem services potential assessment in Latvia is based on the principles of the matrix model and biophysical data of the forest inventory database, and it is comparable to other assessments previously carried out in the Baltic Sea Region. The proposed approach supports spatial planning and may be integrated with assessments of other ecosystems based on the same methodological principles. The evaluation results reflect the high spatial heterogeneity of forest types in Latvia. Future work should include integrating ecosystem services flows and demand into the assessment, developing additional indicators for culturally important ecosystem services, and introducing socio-cultural valuation to account for a broader set of stakeholders and values. [ABSTRACT FROM AUTHOR]

The activity characteristics of the village and its symbiotic relationship with the environment play an important role in the sustainable development of the surrounding environment. Miao villages have a long history. In the process of long-term interaction with the surrounding natural environment, Miao villages have formed a unique forest culture, which has an important impact on the distribution of the surrounding forests and plays a crucial role in local forest management. In this study, we took the Miao villages of China that are distributed in Qiandongnan Miao and Dong Autonomous Prefecture of China as an example and constructed a research framework to study the interaction between the natural environment and human activities around the Miao villages and its impact on forest change based on partial least squares structural equation modeling (PLS-SEM) and geographically weighted regression modeling (GWR) methods. The validity and reliability evaluations showed that the PLS-SEM model was reasonable. The results showed that the Miao villages were randomly distributed within 0–2 km and clustered in the 8–10 km buffer zone. The temporal variation of the forest landscape around the Miao villages was small, and the spatial heterogeneity was obvious. Within the 0~2 km buffer zone, the proportion of closed-canopy forest was the largest, and with the increase in the buffer zone radius, the proportion of closed-canopy forest gradually decreased, the open-canopy forest gradually grew, and the proportion of shrubbery and other forests showed an upward trend first and then a downward trend. Temporally, the four forest landscapes did not change much, with closed and open forests sliding, and shrubbery and other forests increasing. Regarding the drivers of forest structure change, topographic factors and landscape patterns had a positive effect on forest structure, while human activities had a negative effect. The influence of topography on human activities in the Miao villages weakened the direct positive effect of topography on forests and promoted the positive effect of the landscape pattern on forests. There were significant spatial differences in the GWR regression coefficients of the effects of different factors on forest structure in 2020, with a pivotal negative correlation between NDVI and night-time light data. In addition, the higher the elevation, the more unfavorable the distribution of open-canopy forests and the better the distribution of closed-canopy forests. The higher the slope, the higher the NDVI but the more unfavorable the distribution of closed-canopy forests. In general, the impacts of Miao villages on forest structure are highly complex and interactive, with both direct and indirect effects. Different factors interact to affect the structure of the forest. The study on the effect of Miao villages on forests is helpful for more targeted forest protection and the formulation of laws and regulations, so as to find a balance between human activities and forest management, in order to maintain the ecological balance of local areas. This study introduced the PLS-SEM model to investigate the impacts of Miao villages on forest structure, which effectively enhanced our understanding of the drivers and impacts of forest change and provides theoretical support and a basis for forest protection and management. [ABSTRACT FROM AUTHOR]

Old-growth forests have been widely studied for decades. The extreme diversity of old forest characteristics has inspired an equally diverse set of old-growth definitions, and makes mapping old-growth difficult across large areas and different forest types. While the use of remote sensing in old-growth research is not new, there is a growing need for large scale mapping to improve understanding of old forest processes and to support old-growth conservation. Old-growth mapping requires definitions that are ecologically relevant to old forests while also transferable to remote sensing data. In this paper we develop a conceptual framework to evaluate three dimensions of old-growth—a temporal dimension related to tree ages, a physical dimension related to tree sizes, and a functional dimension related to forest processes. In the first part of our analysis, we classify forests throughout the eastern US as old or not with respect to each old-growth dimension using existing old-growth definitions and data from the US Forest Inventory and Analysis (FIA) program. We estimate the proportion of forest classified as old within a hexagon grid, resulting in a unique map of old forest proportion (OFP) for each dimension. Subsequently, we use spaceborne lidar data from NASA’s Global Ecosystem Dynamics Investigation (GEDI) to reproduce each OFP map in a modeling framework designed to 1) assess the extent to which each dimension of forest oldness can be mapped at large spatial scales, and 2) identify biophysical GEDI variables related to each dimension of forest oldness. We estimate that only 2% of forest classified as old in any dimension satisfied the old criteria in all three dimensions. We found substantial spatial variation in the mapped OFP estimates across the three dimensions, highlighting how definition criteria impacts old-growth maps. We also found that physically old forests were more effectively mapped using GEDI data than functionally or temporally old forests, and that physically old forests were more structurally similar to one another than temporally or functionally old forests. Our modeling results indicate that while remote sensing may be best suited to mapping physical old-growth characteristics, definitions that rely solely on physical characteristics do not adequately represent old forests throughout the eastern US. We propose that future efforts to map old-growth with spaceborne remote sensing data may maximize utility through collaboration between western and indigenous old-growth experts to determine broad yet nuanced approaches that are appropriately tailored to the target variable of old forests. These efforts should balance explicit and ecologically relevant old-growth definitions specifically for mapping that can be linked to remotely sensed data, 2) appropriate spatial resolutions, and 3) flexible quantitative frameworks that encompass the complexities and heterogeneity of old forests.

Forest structure is a key driver of forest functional processes. The characterization of forest structure across spatiotemporal scales is essential for forest monitoring and management. LiDAR data have proven particularly useful for cost-effectively estimating forest structural attributes. This paper evaluates the ability of combined forest inventory data and low-density discrete return airborne LiDAR data to discriminate main forest structural types in the Mediterranean-temperate transition ecotone. Firstly, we used six structural variables from the Spanish National Forest Inventory (SNFI) and an aridity index in a k-medoids algorithm to define the forest structural types. These variables were calculated for 2770 SNFI plots. We identified the main species for each structural type using the SNFI. Secondly, we developed a Random Forest model to predict the spatial distribution of structural types and create wall-to-wall maps from LiDAR data. The k-medoids clustering algorithm enabled the identification of four clusters of forest structures. A total of six out of forty-one potential LiDAR metrics were utilized in our Random Forest, after evaluating their importance in the Random Forest model. Selected metrics were, in decreasing order of importance, the percentage of all returns above 2 m, mean height of the canopy profile, the difference between the 90th and 50th height percentiles, the area under the canopy curve, and the 5th and the 95th percentile of the return heights. The model yielded an overall accuracy of 64.18%. The producer's accuracy ranged between 36.11% and 88.93%. Our results confirm the potential of this approximation for the continuous monitoring of forest structures, which is key to guiding forest management in this region. [ABSTRACT FROM AUTHOR]

Contemporary reference sites in California's Sierra Nevada represent areas where a frequent, low-intensity fire regime – an integral ecological process in temperate dry forests – has been reintroduced after several decades of fire suppression. Produced by an intact fire regime, forest structural patterns in these sites are likely more resilient to future disturbances and climate, and thus can provide reference conditions to guide management and ecological research. In this paper, we present a set of 119 delineated contemporary reference sites in the Sierra Nevada yellow pine and mixed-conifer zone along with a suite of key remote sensing-derived forest structure metrics representing conditions within these sites. We also provide a set of summary figures for individual reference sites and sites grouped by dominant climate class. We identified restored frequent-fire landscapes using a combination of fire history, burn severity, management history, and forest type datasets and we delineated individual polygons using catchment basins, fire perimeters, and imagery. Reference sites ranged in size from 101-966 ha with a mean size of 240 ha. Where available (for 59 sites), we used airborne lidar datasets to characterize a suite of key forest structure metrics within reference sites. Across all 119 sites, we provide a set of forest structure metrics produced by the California Forest Observatory. Reference sites were categorized based on their dominant climate class to assist users in identifying the most climatically relevant reference conditions for their project or study area. We encourage the use of the reference sites and associated forest structure datasets for guiding ecologically focused forest management and research in the Sierra Nevada.

Human-induced land-use change has resulted in substantial loss and degradation of habitat for forest-dwelling wildlife. The moor macaque Macaca maura , an Endangered primate endemic to Sulawesi, Indonesia, has been observed in a wide array of habitats, including heavily modified areas, but little is known about the quality of these habitats. Here we compared the habitat quality of two areas occupied by moor macaque populations: a previously human-modified karst forest now located within a national park and a heavily modified forest located in an unprotected area. We assessed habitat quality by measuring specific indicators of potential food availability derived from vegetation data and quantified the nutritional environment based on macronutrient analysis of fruit samples collected at the two sites. Although the species richness, diversity and overall stem density of macaque food trees were greater in the protected area forest, total basal area and mean diameter at breast height were greater in the heavily modified forest. Mean metabolizable energy concentrations of fruits eaten by macaques were similar between forests, as were the proportions of protein, lipids and total non-structural carbohydrates. These results provide further support for the notion that heavily modified habitats should not be overlooked for their potential conservation value. To further augment their value, conservation efforts should focus on forest restoration, specifically the planting of fast-growing species that are utilized by both wildlife and people. [ABSTRACT FROM AUTHOR]

Emerging satellite radar and lidar platforms are being developed to produce gridded aboveground biomass (AGB) predictions that are poised to expand our understanding of global carbon stocks and changes. However, the spatial resolution of AGB map products from these platforms is often larger than the available field plot data underpinning model calibration and validation efforts. Intermediate-resolution/extent remotely sensed data, like airborne lidar, can serve as a bridge between small plots and map resolution, but methods are needed to estimate and propagate uncertainties with multiple layers of data. Here, we introduce a workflow to estimate the pixel-level mean and variance in AGB maps by propagating uncertainty from a lidar-based model using small plots, taking into account prediction uncertainty, residual uncertainty, and residual spatial autocorrelation. We apply this workflow to estimate AGB uncertainty at a 100 m map resolution (1 ha pixels) using 0.04 ha field plots from 11 sites across four ecoregions. We compare uncertainty estimates using site-specific models, ecoregion-specific models, and a general model using all sites. The estimated AGB uncertainty for 1 ha pixels increased with mean AGB, reaching 7.8–33.3 Mg ha−1 for site-specific models (one standard deviation), 11.1–28.2 Mg ha−1 for ecoregion-specific models, and 21.1–22.1 Mg ha−1 for the general model for pixels in the AGB range of 80–100 Mg ha−1. Only 3 of 11 site-specific models had a total uncertainty of <15 Mg ha−1 in this biomass range, suitable for the calibration or validation of AGB map products. Using two additional sites with larger field plots, we show that lidar-based models calibrated with larger field plots can substantially reduce 1 ha pixel AGB uncertainty for the same range from 18.2 Mg ha−1 using 0.04 ha plots to 10.9 Mg ha−1 using 0.25 ha plots and 10.1 Mg ha−1 using 1 ha plots. We conclude that the estimated AGB uncertainty from models estimated from small field plots may be unacceptably large, and we recommend coordinated efforts to measure larger field plots as reference data for the calibration or validation of satellite-based map products at landscape scales (≥0.25 ha). [ABSTRACT FROM AUTHOR]

Species diversity has been shown to be influenced by environmental factors, but the mechanism underlying their relationship remains unclear across spatial scales. Based on field investigation data collected from 3077 sample plots in temperate forest ecosystems, we compared tree species richness, evenness and dominance at 10 km × 10 km, 30 km × 30 km and 90 km × 90 km spatial scales. Then, we detected the scale dependence of changes in tree species composition on climate, topography and forest structure using variation partitioning and quantified their contribution to tree diversity with gradient–boosted models (GBMs) and fitted their relationships. The magnitude of tree richness, evenness and dominance significantly increased with spatial scale. Ecological factors jointly accounted for 24.3%, 26.5% and 38.5% of the variation in tree species composition at the three spatial scales, respectively. The annual mean temperature had a strong impact on tree richness, evenness and dominance and peaked at an intermediate scale. Tree evenness and dominance increased with the variation of temperature but had upper and lower limits. Tree richness obviously increased with annual precipitation on multiple scales and decreased with annual sunshine duration at large spatial scales. Tree richness, evenness and dominance obviously increased with the variation in elevation and diameter at breast height at large scales and small scales, respectively. Tree dominance decreased with tree height at a small scale. The dependence of tree diversity on ecological factors increased with spatial scales. Furthermore, different factors exert various controls on tree diversity at different spatial scales, representing a comprehensive mechanism regulating tree diversity. [ABSTRACT FROM AUTHOR]

Copyright of Fire Ecology is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Vertical structure is an important parameter not only for assessment of the naturalness of a forest and several functional parameters, such as biodiversity or protection from avalanches or rockfall, but also for estimating biomass/carbon content. This study analyses the options for assessing vertical forest structure by using airborne (ALS) and spaceborne LiDAR data (GEDI) in a mountainous near-natural forest in the Austrian Alps. Use of the GEDI waveform data (L1B) is still heavily underexploited for vertical forest structure assessments. Two indicators for explaining forest vertical structure are investigated in this study: foliage height diversity (FHD) and number of layers (NoL). For estimation of NoL, two different approaches were tested: break-detection algorithm (BDA) and expert-based assessment (EBA). The results showed that FHD can be used to separate three structural classes; separability is only slightly better for ALS than for GEDI data on a 25 m diameter plot level. For NoL, EBA clearly outperformed BDA in terms of overall accuracy (OA) by almost 20%. A better OA for NoL was achieved using ALS (49.5%) rather than GEDI data (44.2%). In general, OA is limited by difficult terrain and near-natural forests with high vertical structure. The usability of waveform-based structure parameters is, nonetheless, promising and should be further tested on larger areas, including managed forests and simpler stands. [ABSTRACT FROM AUTHOR]

Laser scanning has revolutionized the ability to quantify single-tree morphologies and stand structural variables. In this study, we address the issue of occlusion when scanning a spruce (Picea abies (L.) H.Karst.) and beech (Fagus sylvatica L.) forest with a mobile laser scanner by making use of a unique study site setup. We scanned forest stands (1) from the ground only and (2) from the ground and from above by using a crane. We also examined the occlusion effect by scanning in the summer (leaf-on) and in the winter (leaf-off). Especially at the canopy level of the forest stands, occlusion was very pronounced, and we were able to quantify its impact in more detail. Occlusion was not as noticeable as expected for crown-related variables but, on average, resulted in smaller values for tree height in particular. Between the species, the total tree height underestimation for spruce was more pronounced than that for beech. At the stand level, significant information was lost in the canopy area when scanning from the ground alone. This information shortage is reflected in the relative point counts, the Clark–Evans index and the box dimension. Increasing the voxel size can compensate for this loss of information but comes with the trade-off of losing details in the point clouds. From our analysis, we conclude that the voxelization of point clouds prior to the extraction of stand or tree measurements with a voxel size of at least 20 cm is appropriate to reduce occlusion effects while still providing a high level of detail. [ABSTRACT FROM AUTHOR]

Laurel forests are quite relevant for biodiversity conservation and are among the island ecosystems most severely damaged by human activities. In the past, Canary laurel forests have been greatly altered by logging, livestock and agriculture. The remains of laurel forests are currently protected in the Canary Islands (Spain). However, we miss basic information needed for their restoration and adaptive management, such as tree longevity, growth potential and responsiveness to natural and anthropogenic disturbances. Using dendrochronological methods, we studied how forest dynamic is related to land-use change and windstorms in two well-preserved laurel forests on Tenerife Island. Wood cores were collected from over 80 trees per stand at three stands per forest. We used ring-width series to estimate tree ages and calculate annual basal area increments (BAI), cumulative diameter increases, and changes indicative of released and suppressed growth. Twelve tree species were found in all stands, with Laurus novocanariensis, Ilex canariensis and Morella faya being the most common species. Although some individuals were over 100 years old, 61.8%-88.9% of the trees per stand established between 1940 and 1970, coinciding with a post-war period of land abandonment, rural exodus and the onset of a tourism economy. Some trees have shown growth rates larger than 1 cm diameter per year and most species have had increasing BAI trends over the past decades. Strong growth releases occurred after windstorms at both sites, but the effects of windstorms were site-dependent, with the 1958 storm affecting mainly the eastern tip of the island (Anaga massif) and the 1991 storm the western tip (Teno massif). Given the great ability of laurel forest trees to establish after land use cessation and to increase growth after local disturbances such as windstorms, passive restoration may be sufficient to regenerate this habitat in currently degraded areas. [ABSTRACT FROM AUTHOR]

Quadratic mean diameter is the most frequently reported descriptor of the diameter distribution of forests. As such, it is often used as an indicator of forest stand structure, developmental stage, and ecological and economic potential. However, quadratic mean diameter can be heavily influenced by the presence or absence of large numbers of small stems in lower canopy strata, and it is also sensitive to left-truncation of the diameter distribution, making its interpretation across inventories with different protocols challenging. Here, we examine three alternative expressions of stand diameter: the arithmetic and quadratic mean diameter of the thickest 100 trees per hectare, and the basal area-weighted mean diameter. Using data from the United States Forest Inventory and Analysis program for New York and New England, these alternative expressions showed closer correlation with multiple stand structural variables than did quadratic mean diameter, including merchantable cubic and board foot volume per hectare, aboveground live tree carbon per hectare, and total number of live and dead standing trees greater than 40 cm diameter at breast height per hectare (previously proposed as an index of old-growth structure). Arithmetic and quadratic mean diameter of the thickest 100 trees per hectare showed nearly identical performance, and the strongest correlations across the board. We develop closed-form expressions for these variables when the diameter distribution is a Weibull, and illustrate their behavior relative to quadratic mean diameter for that situation. While the reasons for prevalence of quadratic mean diameter as an indicator remain valid, we suggest that these alternative measures should be more widely reported and analyzed to give a more informative depiction of stand structure and development in complex forests. [ABSTRACT FROM AUTHOR]

The understory is an essential ecological and structural component of forest ecosystems. The lack of efficient, accurate, and objective methods for evaluating and quantifying the spatial spread of understory characteristics over large areas is a challenge for forest planning and management, with specific regard to biodiversity and habitat governance. In this study, we used terrestrial and airborne laser scanning (TLS and ALS) data to characterize understory in a European beech and black pine forest in Italy. First, we linked understory structural features derived from traditional field measurements with TLS metrics, then, we related such metrics to the ones derived from ALS. Results indicate that (i) the upper understory density (5–10 m above ground) is significantly associated with two ALS metrics, specifically the mean height of points belonging to the lower third of the ALS point cloud within the voxel (HM1/3) and the corresponding standard deviation (SD1/3), while (ii) for the lower understory layer (2–5 m above ground), the most related metric is HM1/3 alone. As an example application, we have produced a map of forest understory for each layer, extending over the entire study region covered by ALS data, based on the developed spatial prediction models. With this study, we also demonstrated the power of hand-held mobile-TLS as a fast and high-resolution tool for measuring forest structural attributes and obtaining relevant ecological data. [ABSTRACT FROM AUTHOR]

The increasing level of atmospheric carbon dioxide and its effects on our climate system has become a global environment issue. The forest ecosystem is essential for the stability of carbon in the atmosphere as it operates as a carbon sink and provides a habitat for numerous species. Therefore, our understanding of the structural elements of the forest ecosystem is vital for the estimation of forest biomass or terrestrial carbon stocks. Over the last two decades, light detection and ranging (LIDAR) technology has significantly revolutionized our understanding of forest structures and enhanced our ability to monitor forest biomass. This paper presents a review of metrics for forest biomass estimation, outlines metrics selection methods for biomass modeling, and addresses various assessment criteria for the selection of allometric equations for the aboveground forest biomass estimations, using LIDAR data. After examining one hundred publications written by different authors between 1999 and 2023, it was observed that LIDAR technology has become a dominant data collection tool for aboveground biomass estimation with most studies focusing on the use of airborne LIDAR data for the plot-level analysis on a local scale. Parametric-based models dominated in most studies with coefficient of determination (R2) and root mean square error (RMSE) as assessment criteria. In addition, mean top canopy height (MCH) and quadratic mean height (QMH) were reported as strong predictors for aboveground biomass (AGB) estimation. Pixel-based uncertainty analysis was found to be a reliable method for assessing spatial variations in uncertainties.

With the publication of a Decree that has established a List of the Italian old-growth forests, we have analyzed, using text mining software, the state of the art of Italian research in this field. We have analyzed 188 ISI and 72 non-indexed papers or reports. The analysis has identified 165 locations of which more than 50% are on pure beech or mixed beech and silver fir stands. The analysis was focused mainly on structural characteristics and bio-indicators. The current knowledge represents reliable support for the establishment of an Italian network of old-growth forests and this green infrastructure offers important opportunities for research and collaboration between research, natural resource management, and local, regional, and state administrations.

The Miombo woodland is the most extensive tropical woodland in south-central Africa. However, field sample plot data on forest cover changes, species distribution and carbon stocks in the Miombo ecoregion are inadequate for effective forest management. Owing to logistical challenges that come with field-based inventory methods, remote sensing plays an important role in supplementing field methods to fill in data gaps. Traditional satellite and manned aircraft remote sensing platforms have their own advantages and limitations. The advent of unmanned aerial systems (UASs) has made it possible to acquire forest data at unprecedented spatial and temporal scales. UASs are adaptable to various forest applications in terms of providing flexibility in data acquisition with different sensors (RGB, multispectral, hyperspectral, thermal and light detection and ranging (lidar)) at a convenient time. To highlight possible applications in the Miombo woodlands, we first provide an overview of the Miombo woodlands and recent progress in remote sensing with small UASs. An overview of some potential forest applications was undertaken to identify key prospects and challenges for UAS applications in the Miombo region, which will provide expertise and guidance upon which future applications in the Miombo woodlands should be based. While much of the potential of using UASs for forest data acquisition in the Miombo woodlands remains to be realized, it is likely that the next few years will see such systems being used to provide data for an ever-increasing range of forest applications. [ABSTRACT FROM AUTHOR]

Widespread afforestation is a crucial component of climate mitigation strategies worldwide. This presents a significant opportunity for biodiversity conservation if forests are appropriately managed. Within forests, structural and habitat diversity are known to be critical for biodiversity but pragmatic management recommendations are lacking. We make a comprehensive assessment of the effects of habitat variables on bird populations using data from over 4000 ha of forested landscape. We combine high‐resolution remote sensing data with comprehensive management databases to classify habitat attributes and measure the response of six taxonomic and functional diversity metrics: species richness, Shannon diversity, functional richness, functional evenness, functional divergence, and functional dispersion. We use a novel approach that combines hierarchical partitioning analysis with linear models to determine the relative importance of different habitat variables for each bird diversity metric. The age class of forest stands was consistently the most important variable across all bird diversity metrics, outperforming other structural measures such as horizontal and vertical heterogeneity and canopy density. Shrub density and gap fraction were each significantly associated with one bird diversity metric. In contrast, variables describing within‐stand structural heterogeneity (vertical and horizontal) were generally less important while tree species identity (e.g., conifer or broadleaved) was not significant for any bird diversity metric. Each of the six bird diversity metrics had different patterns of independent variable importance and significance, emphasizing the need to consider multiple diversity metrics in biodiversity assessments. Similarly, the optimal resolution for remote sensing metrics varied between structural variables and bird diversity metrics, suggesting that the use of remote sensing data in biodiversity studies could be greatly improved by first exploring different resolutions and data aggregations. Based on the results from this comprehensive study, we recommend that managers focus on creating habitat diversity at the between‐, rather than exclusively within‐stand scale, such as by creating a matrix of different age classes, to maximize bird diversity. This recommendation for forest managers is powerful yet pragmatic in its simplicity. [ABSTRACT FROM AUTHOR]

Tidal wetland ecosystems and their vegetation communities are broadly controlled by tidal range and inundation frequency. Sea-level rise combined with episodic flooding events are causing shifts in thresholds of vegetation species which reconstructs the plant zonation of the coastal landscape. More frequent inundation events in the upland forest are causing the forest to convert into tidal marshes, and what is left behind are swaths of dead-standing trees along the marsh–forest boundary. Upland forest dieback has been well documented in the mid-Atlantic; however, reliable methods to accurately identify this dieback over large scales are still being developed. Here, we use multitemporal Lidar and imagery from the National Agricultural Imagery Program to classify areas of forest loss in the coastal regions of Delaware. We found that 1197 ± 405 hectares of forest transitioned to non-forest over nine years, and these losses were likely driven by major coastal storms and severe drought during the study period. In addition, we report decreases in Lidar-derived canopy height in forest loss areas, suggesting forest structure changes associated with the conversion from forest to marsh. Our results highlight the potential value of integrating Lidar-derived metrics to determine specific forest characteristics that may help predict future marsh migration pathways. [ABSTRACT FROM AUTHOR]

Dry-mesic old-growth oak forests are widely distributed remnants across the eastern U.S. and are expected to increase in number and extent as second-growth forests mature. In this study, we synthesize published and unpublished information to better define the species, structure and extent of these forests. Mean site tree density for trees ≥10 cm dbh ranged from 341–620 trees ha−1. In the eastern part of the region, most stand basal areas were >23 m2 ha−1, compared to ≤23 m2 ha−1 in the westernmost stands. Overall, woody species diversity was relatively low compared to old-growth oak forests on moister sites, with tree species per forest ranging from 5–18. The most common species among the stands were white oak (Quercus alba), northern red oak (Quercus rubra), and black oak (Quercus velutina). Shrub and vine species per forest ranged from 1–10, with common species or genera including Virginia creeper (Parthenocissus quinquefolia), poison ivy (Toxicodendron radicans), Vaccinium spp., and grapevines (Vitis spp.). Within the southern Appalachian Mountains, rosebay rhododendron (Rhododendron maximum) and mountain laurel (Kalmia latifolia L.) were common. Herbaceous species per stand ranged from 4–51, with the highest richness occurring in a southern Appalachian oak-hickory forest. The maximum within-stand age of the large trees ranged from 170 to over 365 years. The mean density of standing dead trees ≥10 cm dbh ranged from 31–78 ha−1 and the volume of coarse woody debris ≥10 cm in diameter averaged 52 m3 ha−1. We more fully describe the characteristics of these forests and fill gaps in the collective knowledge of this increasingly important forest type. However, over the past 20 years, there has been scant research on these forests, and older research studies have used a variety of research plots and methods. A uniform approach to surveying these sites is needed to gain a better understanding of these forests before we are faced with caring for an increase in old-growth forest areas. [ABSTRACT FROM AUTHOR]

Bats are primarily forest mammals and forest structure may affect their communities through the level of vegetation clutter. Pine plantations are typically even-aged managed forests that lack structural complexity. However, an understory layer can enhance the heterogeneity of these forests, making them suitable for several animal taxa. We hypothesized that species composition, richness, and foraging activity of insectivorous bats in pine plantations vary according to forest structure, specifically with the density of the understory. We measured pine density, Diameter at Breast Height (DBH), canopy closure, and vegetation cover of 29 pine (Pinus halepensis) plantations of the Judean Lowlands, Israel, and collected acoustic data on resident bats. We found that bat species richness and total activity increased in forests with large tree DBH and dense shrubs. Cluttered-habitat species foraged preferentially in forests with large tree DBH and high pine density, while open-habitat species preferred forests with well-developed canopies and dense shrubs. Pipistrellus pipistrellus and Eptesicus serotinus foraged in mature forests with well-developed bushes and these species are endangered in Israel. We conclude that mature planted pine forests with a well-developed under-canopy are suitable foraging grounds for insectivorous bats. Management plans for planted pine forests should consider our findings to support bat populations, including rare and endangered species. [ABSTRACT FROM AUTHOR]

Forest ecosystems are strongly impacted by continuing climate change and increasing disturbance activity, but how forest dynamics will respond remains highly uncertain. Here, we argue that a short time window after disturbance (i.e., a discrete event that disrupts prevailing ecosystem structure and composition and releases resources) is pivotal for future forest development. Trees that establish during this reorganization phase can shape forest structure and composition for centuries, providing operational early indications of forest change. While forest change has been fruitfully studied through a lens of resilience, profound ecological changes can be masked by a resilience versus regime shift dichotomy. We present a framework for characterizing the full spectrum of change after disturbance, analyzing forest reorganization along dimensions of forest structure (number, size, and spatial arrangement of trees) and composition (identity and diversity of tree species). We propose four major pathways through which forest cover can persist but reorganize following disturbance: resilience (no change in structure and composition), restructuring (structure changes but composition does not), reassembly (composition changes but structure does not), and replacement (structure and composition both change). Regime shifts occur when vegetation structure and composition are altered so profoundly that the emerging trajectory leads to nonforest. We identify fundamental processes underpinning forest reorganization which, if disrupted, deflect ecosystems away from resilience. To understand and predict forest reorganization, assessing these processes and the traits modulating them is crucial. A new wave of experiments, measurements, and models emphasizing the reorganization phase will further the capacity to anticipate future forest dynamics. [ABSTRACT FROM AUTHOR]

Tree damage by game browsing is one of the biggest threats to forest ecosystems at the time of climate change and large-scale forest disturbances. The aim of the paper was to determine the effect of browsing by ungulates on the diversity, abundance and species composition of natural regeneration in forest stands dominated by European beech (Fagus sylvatica L.). The research was conducted on 10 permanent research plots in the Krušné hory Mts. in the Czech Republic and Germany. The density of natural regeneration was in the range of 23,300–114,100 recruits ha−1. A higher proportion of silver birch (Betula pendula Roth.) and rowan (Sorbus aucuparia L.) was found in the regeneration compared to the mature stands. A total of 78% of recruits was damaged by browsing. The most frequently damaged tree species were sycamore (Acer pseudoplatanus L.; 98%) and black alder (Alnus glutinosa [L.] Gaertn.; 97%), while Norway spruce (Picea abies [L.] Karst; 31%) and sessile oak (Quercus petraea [Matt.] Liebl.; 50%) were the least affected. Seventy-nine percent of European beech recruits were damaged. The game significantly reduced the height of regeneration by up to 40%, especially by terminal browsing. Browsing also negatively affected the quality and abundance of regeneration. For successful dynamics of species-rich natural forest ecosystems, it is necessary to minimize tree damage by game browsing. These main measures include the reduction of ungulate population levels and the optimization of their age structure and sex ratio, an increase in the number of overwintering enclosures and food fields for game and a change in the political approach to game management with sufficient consideration of forestry interests.

Background and Objectives: In close-to-nature silviculture, snag and/or dead trees have great importance because of their essential role in the conservation of biodiversity in forest ecosystems. In this regard, estimating the volume of snag trees in old-growth forests can be a guide for its maintenance in managed forests. One of the most important questions in recent years is how much snag trees can be recommended for retention in the managed natural forests. Related resources show different results for this variable. Therefore, in a meta-analysis, studies in Hyrcanian forests were collected and analyzed. This is the first meta-analysis study of an important structural factor in close-to-nature silviculture, namely the snag volume in the Hyrcanian forests. Materials and Methods: This study includes design and search strategy articles collection and data extraction, inclusion and exclusion criteria and finally statistical analysis of extracted data. Snag and/or dead tree related literature was searched in national and international database including SID, Science Direct, Web of Science, Scopus, Google Scholar and IranDoc until May 2022. In this study, after a detailed literature review, finally, the data (i.e., mean, standard deviation and sample size) of 16 original studies related to the snag tree volumes in Hyrcanian forests were used, which has 163 samples with a total area of 92.44 hectares. After data extraction, Cochran's Q test and I² index were used to assess the heterogeneity of the studies and choose the appropriate model for analysis. To find the source of heterogeneity, a subgroup analysis was performed based on geographical areas (province) and tree composition. In this study, Egger's and Beggs' tests were employed to investigate the publication bias. Data analysis was performed with Comprehensive meta-analysis ver. 3.7. Results: The average snag volume was estimated at 35.293 m3 /h (95 confidence interval: 24.769 - 45.817) for Hyrcanian forests according to the significance of the Cochrane Q test (P=0.0001) and I2 index (99.735) based on the random effects model. Subgroup analysis showed that the highest snag volume was estimated based on a random model in the Golestan province forests at 50.520 m³ /h (95% confidence interval 45.669 - 55.372). In this study, the publication bias test was not significant (p-value for Egger's and Beggs' tests were 0.28 and 0.24, respectively). Conclusion: The average volume of snag trees in the Hyrcanian forests was estimated to be "average". Thus, the maintenance of at least 35 m³/h of snag tree in Hyrcanian-managed forests can be a guarantee for the preservation of the biological functions of these trees. [ABSTRACT FROM AUTHOR]

Copyright of Conservation Biology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)