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Forest management is an important component of global change as more than half of the world's forests are managed for human use. Although the effect of forest management on taxonomic diversity is well‐studied, we do not fully understand its impact on functional diversity. Understanding this is important to better predict how ecosystem processes will respond to global change scenarios and to implement efficient conservation actions. We conducted two large‐scale (~81 800 km2) research projects over 4 years in temperate forests of the northeastern USA to investigate how the functional structure of bird and mammal communities are affected by forest disturbance. We surveyed 85 bird species distributed in 115 sites using point counts, and 14 mammal species across 197 sites using camera traps. For each species, we selected functional traits that summarize key features of their biology, and for each site, we collected data on the level of forest disturbance based on forest loss events. We found that functional richness increased with forest disturbance for mammals but not for birds. Our results also showed that niche breadth (diet), morphological (body mass and wing length), and physiological (litter size) factors were the main determinants of the functional structure of both groups. These findings emphasize the complexity of making predictions about responses to forest management given the heavy dependence on the context and taxa studied. Overall we observed a limited response of functional diversity to forest management, which might indicate that the environmental changes generated by forest management in this region are less extreme than deforestation or conversion of natural forest to plantations of exotic species. Nonetheless, our results underscore the importance of investigating the effects of forestry on individual traits to develop strategies for managing for ecosystem functions. [ABSTRACT FROM AUTHOR]

Integrating Light Detection And Ranging (LiDAR) and Hyperspectral Imaging (HSI) enhances the assessment of tropical forest degradation and regeneration, which is crucial for conservation and climate mitigation strategies. This study optimized procedures using combined airborne LiDAR, HSI data, and machine learning algorithms across 12 sites in the Brazilian Amazon, covering various environmental and anthropogenic conditions. Four forest classes (undisturbed, degraded, and two stages of second-growth) were identified using Landsat time series (1984–2017) and auxiliary data. Metrics from 600 samples were analyzed with three classifiers: Random Forest, Stochastic Gradient Boosting, and Support Vector Machine. The combination of LiDAR and HSI data improved classification accuracy by up to 12% compared with single data sources. The most decisive metrics were LiDAR-based upper canopy cover and HSI-based absorption bands in the near-infrared and shortwave infrared. LiDAR produced significantly fewer errors for discriminating second-growth from old-growth forests, while HSI had better performance to discriminate degraded from undisturbed forests. HSI-only models performed similarly to LiDAR-only models (mean F1 of about 75% for both data sources). The results highlight the potential of integrating LiDAR and HSI data to improve our understanding of forest dynamics in the context of nature-based solutions to mitigate climate change impacts. [ABSTRACT FROM AUTHOR]

The degradation, fragmentation, and depletion of forests have occurred all over the world, simultaneously, with the exponential growth of the human population. The present study assesses the forest disturbance regime of Chinnar Wildlife Sanctuary (CWS), an ecologically significant biodiversity hotspot of Western Ghats (WG) through satellite image-based forest and land cover mapping and landscape metric analysis. The on-screen analysis based on field knowledge, facilitated, mapping of seven types of vegetation cover. The majority of forest area was covered by dry deciduous forest (39.2 km2 or 43.15%), whereas the minimum area was covered by disturbed forest (2.05 km2 or 2.26%). The disturbance index (DI) was calculated by linearly combining the land use land cover (LULC), road and settlement closeness, fire risk, porosity, patchiness interspersion, normalized difference vegetation index (NDVI), and fragmentation through a probabilistic weight-based linear additive model. The values of the DI (120–310) reclassified into four classes viz. low, moderate, high, and very high. The results revealed that 74.62 km2 (82.52%) of the forest area has low to moderate disturbance, while the rest of the forest area that is 15.8 km2 (17.48%) has high to very high disturbance. These analyses are based on remote sensing and GIS crucial for forest conservation and management strategies formulation. [ABSTRACT FROM AUTHOR]

The identification of climate anomalies associated with large-scale stand disturbances can help inform climate-focused forest management. In the spring and summer of 2018, an unusual spike in balsam fir (Abies balsamea) mortality was reported in multiple areas across its southern distribution range limit, from Wisconsin, United States, to New Brunswick, Canada. Such an event was previously reported in 1986 in the study region and referred to as Stillwell's Syndrome. To identify the role of climate anomalies in the 2018 event, we used monthly climate anomalies as explanatory variables in a Random Forest model predicting the presence of mortality from air and field-based surveys across the Maritimes region of eastern Canada. Results were validated by (1) comparing common climate predictors of mortality from this model and a separate Random Forest model fitted on the 1986 event, and (2) using the 2018 model to predict areas of mortality in 1986. Both the 1986 and 2018 models identified multiple common climate anomalies. Areas with unusually high water deficit and temperatures in the previous growing season, followed by thick April snowpack and high May temperatures the same year, were associated with balsam fir mortality. Such climate anomalies have been previously associated with water stress and desiccation in trees. When using the 2018 model to map the occurrence of mortality in 1986 using historical climate data, we report a 95% accuracy in prediction (kappa = 0.88). The approach used here in conjunction with mapped records of past stand disturbances could help with understanding the impacts of future climate anomalies and adaptive management strategies to deal with these events. [ABSTRACT FROM AUTHOR]

Background and Aims Not all plant–pollinator interactions are mutualistic, and in fact deceptive pollination systems are widespread in nature. The genus Arisaema has a pollination system known as lethal deceptive pollination, in which plants not only attract pollinating insects without providing any rewards, but also trap them until they die. Many Arisaema species are endangered from various disturbances, including reduction in forest habitat, modification of the forest understorey owing to increasing deer abundance, and plant theft for horticultural cultivation. We aimed to theoretically investigate how lethal deceptive pollination can be maintained from a demographic perspective and how plant and pollinator populations respond to different types of disturbance. Methods We developed and analysed a mathematical model to describe the population dynamics of a deceptive plant species and its victim pollinator. Calibrating the model based on empirical data, we assessed the conditions under which plants and pollinators could coexist, while manipulating relevant key parameters. Key Results The model exhibited qualitatively distinct behaviours depending on certain parameters. The plant becomes extinct when it has a low capability for vegetative reproduction and slow transition from male to female, and plant–insect co-extinction occurs especially when the plant is highly attractive to male insects. Increasing deer abundance has both positive and negative effects because of removal of other competitive plants and diminishing pollinators, respectively. Theft for horticultural cultivation can readily threaten plants whether male or female plants are frequently collected. The impact of forest habitat reduction may be limited compared with that of other disturbance types. Conclusions Our results have emphasized that the demographic vulnerability of lethal deceptive pollination systems would differ qualitatively from that of general mutualistic pollination systems. It is therefore important to consider the demographics of both victim pollinators and deceptive plants to estimate how endangered Arisaema populations respond to various disturbances. [ABSTRACT FROM AUTHOR]

Uncrewed Aircraft Systems (UAS) have been recognized as powerful tools for providing monitoring data supporting mitigating and adapting to various abiotic and biotic forest disturbances. However, their suitability and commercial viability have been significantly limited by the necessity for operation primarily within visual line of sight (VLOS). Efforts are currently ongoing to establish frameworks for beyond visual line of sight (BVLOS) UAS operation. This study represents the first successful application of hydrogen-powered BVLOS airship UAS technology for monitoring forest disturbances. The test area covered 1.3 km2 of conserved forest area in North Karelia, Finland, seriously affected by bark beetles and other disturbance agents. Monitoring flights were conducted in spring, summer, and autumn in BVLOS setting operated from a command centre situated approximately 75 km distance from the test area. Deep learning models were applied to study the tree health in the disturbance area, with the special focus on examining the scalability of machine learning models. Multispectral imagery outperformed RGB imagery in classifying trees as healthy, infested, and dead spruce trees, as well as non-spruce tree classes. With multispectral imagery, the F1-scores were 0.88–0.94, 0.80, 0.92–0.96, and 0.75–0.82 for healthy, infested, and dead spruce trees, and non-spruces, respectively. Transfer learning and fine-tuning, which adapted previously trained models to new study areas, improved results compared to training models from scratch and thus contributed to the development of scalable UAS remote sensing methods in mitigating forest disturbances on a larger scale. [ABSTRACT FROM AUTHOR]

Cold-temperate forests (CTFs) are not only an important source of wood but also provide significant carbon storage in China. However, under the increasing pressure of human activities and climate change, CTFs are experiencing severe disturbances, such as logging, fires, and pest infestations, leading to evident degradation trends. Though these disturbances impact both regional and global carbon budgets and their assessments, the disturbance patterns in CTFs in northern China remain poorly understood. In this paper, the Genhe forest area, which is a typical CTF region located in the Inner Mongolia Autonomous Region, Northeast China (with an area of about 2.001 × 104 km2), was selected as the study area. Based on Landsat historical archived data on the Google Earth Engine (GEE) platform, we used the continuous change detection and classification (CCDC) algorithm and considered seasonal features to detect forest disturbances over nearly 30 years. First, we created six inter-annual time series seasonal vegetation index datasets to map forest coverage using the maximum between-class variance algorithm (OTSU). Second, we used the CCDC algorithm to extract disturbance information. Finally, by using the ECMWF climate reanalysis dataset, MODIS C6, the snow phenology dataset, and forestry department records, we evaluated how disturbances relate to climate and human activities. The results showed that the disturbance map generated using summer (June–August) imagery and the enhanced vegetation index (EVI) had the highest overall accuracy (88%). Forests have been disturbed to the extent of 12.65% (2137.31 km2) over the last 30 years, and the disturbed area generally showed a trend toward reduction, especially after commercial logging activities were banned in 2015. However, there was an unusual increase in the number of disturbed areas in 2002 and 2003 due to large fires. The monitoring of potential widespread forest disturbance due to extreme drought and fire events in the context of climate change should be strengthened in the future, and preventive and salvage measures should be taken in a timely manner. Our results demonstrate that CTF disturbance can be robustly mapped by using the CCDC algorithm based on Landsat time series seasonal imagery in areas with complex meteorological conditions and spatial heterogeneity, which is essential for understanding forest change processes. [ABSTRACT FROM AUTHOR]

Spatially explicit global estimates of forest carbon storage are typically coarsely scaled. While useful, these estimates do not account for the variability and distribution of carbon at management scales. We asked how climate, topography, and disturbance regimes interact across and within geopolitical boundaries to influence tree biomass carbon, using the perhumid region of the Pacific Coastal Temperate Rainforest, an infrequently disturbed carbon dense landscape, as a test case. We leveraged permanent sample plots in southeast Alaska and coastal British Columbia and used multiple quantile regression forests and generalized linear models to estimate tree biomass carbon stocks and the effects of topography, climate, and disturbance regimes. We estimate tree biomass carbon stocks are either 211 (SD = 163) Mg C ha−1 or 218 (SD = 169) Mg C ha−1. Natural disturbance regimes had no correlation with tree biomass but logging decreased tree biomass carbon and the effect diminished with increasing time since logging. Despite accounting for 0.3% of global forest area, this forest stores between 0.63% and 1.07% of global aboveground forest carbon as aboveground live tree biomass. The disparate impact of logging and natural disturbance regimes on tree biomass carbon suggests a mismatch between current forest management and disturbance history. [ABSTRACT FROM AUTHOR]

In the absence of forest ecosystem time series data, monitoring proxies such as the enhanced vegetation index (EVI) can inform the capacity of forests to provide ecosystem services. We used MODIS-derived EVI at 250 m and 16-day resolution and Breaks for Additive and Seasonal Trend (BFAST) algorithms to monitor forest EVI changes (breaks and trends) in and around the Algonquin Provincial Park (Ontario, Canada) from 2003 to 2022. We found that relatively little change occurred in forest EVI pixels and that most of the change occurred in non-protected forest areas. Only 5.3% (12,348) of forest pixels experienced one or more EVI breaks and 27.8% showed detectable EVI trends. Most breaks were negative (11,969, 75.3%; positive breaks: 3935, 24.7%) with a median magnitude of change of −755.5 (median positive magnitude: 722.6). A peak of negative breaks (2487, 21%) occurred in the year 2013 while no clear peak was seen among positive breaks. Most breaks (negative and positive) and trends occurred in the eastern region of the study area. Boosted regression trees revealed that the most important predictors of the magnitude of change were forest age, summer droughts, and warm winters. These were among the most important variables that explained the magnitude of negative (R2 = 0.639) and positive breaks (R2 = 0.352). Forest composition and protection status were only marginally important. Future work should focus on assessing spatial clusters of EVI breaks and trends to understand local drivers of forest vegetation health and their potential relation to forest ecosystem services. [ABSTRACT FROM AUTHOR]

The Amazon forest contains globally important carbon stocks, but in recent years, atmospheric measurements suggest that it has been releasing more carbon than it has absorbed because of deforestation and forest degradation. Accurately attributing the sources of carbon loss to forest degradation and natural disturbances remains a challenge because of the difficulty of classifying disturbances and simultaneously estimating carbon changes. We used a unique, randomized, repeated, very high-resolution airborne laser scanning survey to provide a direct, detailed, and high-resolution partitioning of aboveground carbon gains and losses in the Brazilian Arc of Deforestation. Our analysis revealed that disturbances directly attributed to human activity impacted 4.2% of the survey area while windthrows and other disturbances affected 2.7% and 14.7%, respectively. Extrapolating the lidar-based statistics to the study area (544,300 km²), we found that 24.1, 24.2, and 14.5 Tg C y-1 were lost through clearing, fires, and logging, respectively. The losses due to large windthrows (21.5 Tg C y-1) and other disturbances (50.3 Tg C y-1) were partially counterbalanced by forest growth (44.1 Tg C y-1). Our high-resolution estimates demonstrated a greater loss of carbon through forest degradation than through deforestation and a net loss of carbon of 90.5 ± 16.6 Tg C y-1 for the study region attributable to both anthropogenic and natural processes. This study highlights the role of forest degradation in the carbon balance for this critical region in the Earth system. [ABSTRACT FROM AUTHOR]

Anthropogenic and climatic variables combine to alter river flow regimes worldwide, which can influence shifts in species composition. Exacerbation of drought events in southeastern United States floodplains may have varying impacts on survival and regeneration of high bottomland, low bottomland, and swamp forest species, causing distribution shifts between these forest types. We tested 7-month-old seedlings of the swamp species water tupelo (Nyssa aquatica), Ogeechee tupelo (Nyssa ogeche), and pop ash (Fraxinus caroliniana), and the low bottomland species overcup oak (Quercus lyrata) and water hickory (Carya aquatica) in a controlled experiment with increasing drought treatment. We compared timing to 95% mortality and determined lethal soil moisture thresholds for species. Swamp species were no more vulnerable to mortality under increasing drought than low bottomland hardwood species found in neighboring wet floodplain habitats, suggesting that duration of drought may not be a main driver of compositional shifts between these forests. Study Implications : Natural flows of river systems around the world have been altered by humans and a changing climate. Increased intensity of drought events can affect tree species growing adjacent to rivers and influence their ability to reproduce, thus changing the composition of floodplain forests. We tested the drought tolerance of three swamp species accustomed to frequent flooding and two low bottomland hardwood species accustomed to less frequent flooding to see which species might be more affected by drought. We observed no differences between these two groups, suggesting that greater flood tolerance may not imply greater drought intolerance. [ABSTRACT FROM AUTHOR]

Natural disturbances are important drivers of forest dynamics, and canopy gaps are their fingerprints in forest ecosystems. Gaps form and persist because of the interplay of tree mortality and regeneration. They can have long‐lasting impacts on ecosystems, yet the temporal dynamics of gap formation and closure remains poorly quantified.We analysed 11,331 canopy gaps and their changes through time across 3999 ha of unmanaged temperate mountain forests at Berchtesgaden National Park (Germany). We assessed gap formation and closure using three repeat lidar acquisitions between 2009 and 2021, analysing canopy height changes at 1 m horizontal resolution. Our objective was to determine the dominant mode of gap formation, distinguishing the creation of new gaps from the expansion of existing ones. Additionally, we studied the rate of gap closure, considering closure from tree regeneration and lateral crown expansion.Gap formation was primarily driven by gap expansion rather than the initiation of new gaps. Gap expansion accounted for 81.3% of gap formation, although new gaps were on average twice as large as gap expansions. Only 1.4% of gaps did not expand over the 12‐year study period, and Norway spruce forests had the highest rate of gap expansion.Overall, gap closure rate (0.74 ha 100 ha−1 year−1) was higher than gap formation (0.58 ha 100 ha−1 year−1) in our study system. Ingrowth of the regenerating tree cohort was the primary mode of gap closure, with lateral crown expansion accounting for 20% of all gap area closed. Mixed‐species stands had the highest rate of gap closure, and gaps <0.1 ha closed faster than larger gaps.Synthesis. While canopy openings are generally small in the European Alps, we show that they keep growing over multiple years, underlining that gap expansion is an important driver of temperate forest dynamics. Canopy gaps closed faster than they were created, highlighting the resilience of European mountain forests to natural disturbances. However, as disturbances are projected to increase under climate change, this resilience might be challenged in the future, requiring a continuous monitoring of gap dynamics as an important early warning indicator of forest change. [ABSTRACT FROM AUTHOR]

Annual tree rings are widely recognized as valuable tools for quantifying and reconstructing historical forest disturbances. However, the influence of climate can complicate the detection of disturbance signals, leading to limited accuracy in existing methods. In this study, we propose a random under-sampling boosting (RUB) classifier that integrates both tree-ring and climate variables to enhance the detection of forest insect outbreaks. The study focused on 32 sites in Alberta, Canada, which documented insect outbreaks from 1939 to 2010. Through thorough feature engineering, model development, and tenfold cross-validation, multiple machine learning (ML) models were constructed. These models used ring width indices (RWIs) and climate variables within an 11-year window as input features, with outbreak and non-outbreak occurrences as the corresponding output variables. Our results reveal that the RUB model consistently demonstrated superior overall performance and stability, with an accuracy of 88.1%, which surpassed that of the other ML models. In addition, the relative importance of the feature variables followed the order RWIs > mean maximum temperature (Tmax) from May to July > mean total precipitation (Pmean) in July > mean minimum temperature (Tmin) in October. More importantly, the dfoliatR (an R package for detecting insect defoliation) and curve intervention detection methods were inferior to the RUB model. Our findings underscore that integrating tree-ring width and climate variables as predictors in machine learning offers a promising avenue for enhancing the accuracy of detecting forest insect outbreaks. [ABSTRACT FROM AUTHOR]

The return of snow accumulation and ablation processes in regenerating forests to pre‐disturbance conditions, collectively referred to as hydrological recovery, has been investigated in past decades through manual snow surveys in adjacent open, juvenile, and mature stands. The outcomes of such studies provide a general understanding of hydrological recovery but lack transferability to areas where stand structure and terrain conditions differ from the reference sites. The application of mobile terrestrial LiDAR to investigate peak snow water equivalent (SWE) and ablation rates beneath regenerating trees in a space‐for‐time substitution study design provides new insights on the process of hydrological recovery in snowmelt forests of British Columbia, Canada. Outcomes of this study better quantify the influence of tree growth on peak SWE and ablation rate at both the tree and stand level for north aspect mixed conifer stands. Recovery of these two processes differ with recovery of Peak SWE beginning when the trees in a stand reach 3 m in height and recovery of ablation rates beginning once trees reach 5 m in height. Additionally, the process of negative ablation recovery in early juvenile stands reported in previous studies is herein clearly observed, providing an improved understanding of forest canopy effects on hydrological recovery in juvenile stands. The methods used in this study, which are internationally applicable, increase transferability of outcomes to stands where canopy characteristics (i.e., height, crown cover, and heterogeneity) are not represented in reference sites. [ABSTRACT FROM AUTHOR]

Stream temperature governs many aquatic ecosystem processes and plays a key role in determining the distribution of cold‐water amphibians and cool‐ and cold‐water fish, including salmonids. Decades of research have focused on the effects of forestry and forest disturbance on stream temperature, and new projects are underway or being planned in jurisdictions including the Provinces of Alberta and British Columbia, Canada, and Washington State, USA. The objective of this paper is to provide a critical review of methodologies employed in previous studies. The review initially focuses on the range of metrics used to quantify stream thermal regimes and the factors that control stream temperature variability in time and space, then focuses on sampling and analytical methodologies used to quantify stream temperature response to forestry activity and forest disturbance. Empirical methods include sampling in time, space, or both, and may or may not include pre‐ and post‐harvest data. Process‐based mechanistic and hybrid empirical‐mechanistic models have also been applied. The advantages and disadvantages of these approaches are discussed, and recommendations provided to support the design and execution of future studies. [ABSTRACT FROM AUTHOR]

The timing, location, intensity, and drivers of forest disturbance and recovery are crucial for developing effective management strategies and policies for forest conservation and ecosystem resilience. Although many algorithms and improvement methods have been developed, it is still difficult to guarantee the detection accuracy for forest disturbance and recovery patterns in southern China due to the complex climate and topography, faster forest recovery after disturbance, and the low availability of noise-free Landsat images. Here, we improved the LandTrendr parameters for different provinces to detect forest disturbances and recovery trajectories based on the LandTrendr change detection algorithm and time-series Landsat images on the GEE platform, and then applied the secondary random forest classifier to classify the forest disturbance and recovery patterns in southern China during 1990–2020. The accuracy evaluation indicated that our approach and improved parameters of the LandTrendr algorithm can increase the detection accuracy for both the spatiotemporal patterns and multiple events of forest disturbance and recovery, with an overall accuracy greater than 86% and a Kappa coefficient greater than 0.91 for different provinces. The total forest loss area was 1.54 × 105 km2 during 1990–2020 (4931 km2/year); however, most of these disturbed forests were recovered and only 6.39 × 104 km2 was a net loss area (converted to other land cover types). The area with two or more times of disturbance events accounted for 11.50% of the total forest loss area. The total forest gain area (including gain after loss and the afforestation area) was 5.44 × 105 km2, among which, the forest gain area after loss was 8.94 × 104 km2, and the net gain area from afforestation was 4.55 × 105 km2. The timing of the implementation of forestry policies significantly affected the interannual variations in forest disturbance and recovery, with large variations among different provinces. The detected forest loss and gain area was further compared against with inventory and other geospatial products, and proved the effectiveness of our method. Our study suggests that parameter optimization in the LandTrendr algorithm could greatly increase the accuracy for detecting the multiple and lower rate disturbance/recovery events in the fast-regrowing forested areas. Our findings also offer a long-term, moderate spatial resolution, and precise forest dynamic data for achieving sustainable forest management and the carbon neutrality goal in southern China. [ABSTRACT FROM AUTHOR]

Background and aims: In mature forests, tree and shrub mycorrhizal associations integrate plant and fungal functional traits, making these relationships important controls on soil nitrogen availability. Whether these plant-fungal effects are observed in forests during early succession following disturbances is largely unexplored. We quantify differences in soil nitrogen availability under an ectomycorrhizal tree (Betula lenta) and an ericoid mycorrhizal shrub (Kalmia latifolia) and explore the potential for known mechanisms, such as the availability of soil carbon, to explain the patterns observed. Methods: We analyze variables indicative of soil nitrogen and carbon availability for incubated soil samples collected from under ecto- or ericoid mycorrhizal plants within a recently harvested temperate forest. Specific measures include net nitrogen mineralization, nitrification, and carbon mineralization rates; active microbial biomass; particulate and mineral-associated soil organic carbon and nitrogen concentrations; and carbon and nitrogen concentrations in plant tissues. Results: Net nitrogen mineralization and nitrification rates were lower under ericoid shrubs than ectomycorrhizal trees. Soil carbon availabilities were similar, suggesting that mechanisms other than carbon accumulation likely create nitrogen limitation under the ericoid shrub in early successional forests. Ectomycorrhizal plant tissues also had higher nitrogen contents and lower carbon-to-nitrogen ratios than ericoid shrubs, providing further support for greater soil nitrogen limitation in the ericoid plots. Conclusion: Our findings suggest local heterogeneity of plant mycorrhizal associations, or at least plant functional types, will be important for understanding differences in soil nitrogen availability in early versus mid- to late-successional forests and hence forest ecosystem responses to intensifying disturbance regimes. [ABSTRACT FROM AUTHOR]

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Urban forests are an important element of nature in highly urbanised areas. Due to their specificity (e.g. fragmentation, disturbances, alien propagule pressure), they are particularly susceptible to the penetration of alien species. The aim of this study was to determine the share of alien species in the tree stand structure of urban forests in the city of Katowice (Upper Silesian Industrial Region). A total of 50 woody species were recorded in the analysed stands, including 31 native species and 19 of alien origin. The share of alien species in the stands of individual forests was significant, as they constituted from 15% to 44% of the total number. Among the 20 trees most frequently included in the stand, as many as six are species of alien origin (Robinia pseudoacacia L., Padus serotina (Ehrh.) Borkh., Acer negundo L., Quercus rubra L., Juglans regia L. and Fraxinus pennsylvanica Marshall). The most abundant species were Robinia pseudoacacia, Quercus rubra and Padus serotina. The local potential of alien woody species was determined using the index of their abundance in the understory; the highest value of the average abundance index was obtained by Padus serotina, Robinia pseudoacacia, Acer negundo and Quercus rubra. Most alien tree species were more likely to inhabit forest edges (ecotone zone). [ABSTRACT FROM AUTHOR]

This study identifies 38 cases of windthrows in the Amazonia to explore the relationship between windthrows and the characteristics (storm passing time, cloud top temperature, and maximum precipitation) of mesoscale convective systems (MCSs) that produced them. Most of windthrow cases in this study occurred in August and September. The storm passing time is positively correlated with the size of windthrows. MCSs with colder cloud top temperature (with a mean at 206 K)—indicating deeper convection—resulted in large windthrows, while those with warm cloud top (with a mean above 230 K) resulted in relatively small windthrows except for windthrows in the western Amazonia. No significant relationship is found between maximum precipitation intensity and the area of windthrows.

The identification of climate anomalies associated with large-scale stand disturbances can help inform climate-focused forest management. In the spring and summer of 2018, an unusual spike in balsam fir (Abies balsamea) mortality was reported in multiple areas across its southern distribution range limit, from Wisconsin, United States, to New Brunswick, Canada. Such an event was previously reported in 1986 in the study region and referred to as Stillwell’s Syndrome. To identify the role of climate anomalies in the 2018 event, we used monthly climate anomalies as explanatory variables in a Random Forest model predicting the presence of mortality from air and field-based surveys across the Maritimes region of eastern Canada. Results were validated by (1) comparing common climate predictors of mortality from this model and a separate Random Forest model fitted on the 1986 event, and (2) using the 2018 model to predict areas of mortality in 1986. Both the 1986 and 2018 models identified multiple common climate anomalies. Areas with unusually high water deficit and temperatures in the previous growing season, followed by thick April snowpack and high May temperatures the same year, were associated with balsam fir mortality. Such climate anomalies have been previously associated with water stress and desiccation in trees. When using the 2018 model to map the occurrence of mortality in 1986 using historical climate data, we report a 95% accuracy in prediction (kappa = 0.88). The approach used here in conjunction with mapped records of past stand disturbances could help with understanding the impacts of future climate anomalies and adaptive management strategies to deal with these events.

Time series analysis of medium-resolution multispectral satellite imagery is critical to investigate forest disturbance dynamics at the landscape scale. In particular, the spatial, temporal, and radiometric consistency of Landsat time series data provides unprecedented insight into past disturbances that occurred over the last four decades. Several Landsat time series-based algorithms have been developed to automate the detection of forest disturbances. However, automated detection of non-stand-replacing disturbances based on Landsat time series remains a challenging task due to the difficulty of effectively separating them from spectral noise. Here, we present the High-dimensional detection of Landscape Dynamics (HILANDYN) algorithm, which exploits spatial and spectral information provided by Landsat time series to detect forest disturbance dynamics retrospectively. A novel and unsupervised procedure for changepoint detection in high-dimensional time series allows HILANDYN to perform the temporal segmentation of inter-annual time series into linear trends. The algorithm embeds a noise filter to remove spurious changepoints caused by residual spectral noise in the time series. We tested HILANDYN to detect disturbances that occurred in the forests of the European Alps over a period of 39 years, i.e. between 1984 and 2022, and evaluated its accuracy using a validation dataset of 3000 plots randomly located inside and outside the disturbed patches. We compared HILANDYN with the Bayesian Estimator of Abrupt change, Seasonality, and Trend (BEAST), which is a well-established and high-performing time series-based algorithm for changepoint detection. The quantitative results highlighted that the number of bands, i.e. original Landsat bands and spectral indices, included in the high-dimensional time series and the threshold controlling the significance of changepoints strongly influenced the user’s accuracy (UA). Conversely, changes in the combinations of bands primarily affected the producer’s accuracy (PA). HILANDYN achieved an F1 score of 0.801, which increased to 0.833 when we activated the noise filter, allowing the algorithm to balance UA (83.1%) and PA (83.5%). The qualitative results showed that disturbed forest patches detected by HILANDYN were characterized by a high spatio-temporal consistency, regardless of the disturbance severity. Furthermore, our algorithm was able to detect forest patches associated with secondary disturbances, such as salvage logging, that occur in close succession with respect to the primary event. The comparison with BEAST evidenced a similar sensitivity of the algorithms to non-stand-replacing events, as both achieved comparable PA. However, BEAST struggled to balance UA and PA when using a single parameter set, achieving a maximum F1 score of 0.717. Moreover, the computational efficiency of BEAST in processing high-dimensional time series was very limited due to its univariate nature based on the Bayesian approach. The adaptability of HILANDYN to detect a wide range of disturbance severities using a single parameter set and its computational efficiency in handling high-dimensional time series promotes its scalability to large study areas characterized by heterogeneous ecological conditions.

Habitat loss and degradation are key drivers of the current biodiversity crisis. Most research focuses on the question of which traits allow species to persist in degraded habitats. We asked whether a species' trophic position or niche width influences the resilience of species in degraded habitats and to what extent habitat degradation affects trophic interactions between species. We used nitrogen isotope ratios (15N:14N, expressed as δ15N value) to quantify and compare trophic positions and niche widths of understory birds inhabiting old‐growth and young secondary forests in the Pacific lowlands of Costa Rica. We found that a species' trophic position rather than its trophic niche width determined its persistence in secondary forests. Species feeding at lower trophic levels in old‐growth forests were less likely to persist in secondary forests than those occupying a higher trophic position in old‐growth forests. This pattern is likely induced by the occurrence of relatively large‐bodied habitat specialists with a flexible and high‐trophic level diet in secondary forests. These habitat specialists likely caused generalist bird species to lower their trophic position relative to conspecifics in old‐growth forests. Regarding trophic niche widths, species in secondary forests tend to have larger niche widths than old‐growth forest species. However, as old‐growth forest specialists and generalists did not differ in their niche widths, no systematic effect of trophic niche width on species persistence after forest disturbance was found. This is the first study that shows a systematic effect of trophic position on the persistence of a wide range of bird species in a disturbed forest ecosystem. It therefore provides important insights into species' responses to habitat degradation and the conservation value of secondary forests. To improve habitat quality for old‐growth forest birds and facilitate avian seed dispersal, the creation of large contiguous forest patches should be prioritised when implementing reforestation measures. [ABSTRACT FROM AUTHOR]

The rapid and accurate detection of forest disturbances in temperate forests has become increasingly crucial as policy demands and climate pressure on these forests rise. The cloud-penetrating Sentinel-1 radar constellation provides frequent and high-resolution observations with global coverage, but few studies have assessed its potential for mapping disturbances in temperate forests. This study investigated the sensitivity of temporally dense C-band backscatter data from Sentinel-1 to varying management-related disturbance intensities in temperate forests, and the influence of confounding factors such as radar backscatter signal seasonality, shadow, and layover on the radar backscatter signal at a pixel level. A unique network of 14 experimental sites in the Netherlands was used in which trees were removed to simulate different levels of management-related forest disturbances across a range of representative temperate forest species. Results from six years (2016–2022) of Sentinel-1 observations indicated that backscatter seasonality is dependent on species phenology and degree of canopy cover. The backscatter change magnitude was sensitive to medium- and high-severity disturbances, with radar layover having a stronger impact on the backscatter disturbance signal than radar shadow. Combining ascending and descending orbits and complementing polarizations compared to a single orbit or polarization was found to result in a 34% mean increase in disturbance detection sensitivity across all disturbance severities. This study underlines the importance of linking high-quality experimental ground-based data to dense satellite time series to improve future forest disturbance mapping. It suggests a key role for C-band backscatter time series in the rapid and accurate large-area monitoring of temperate forests and, in particular, the disturbances imposed by logging practices or tree mortality driven by climate change factors. [ABSTRACT FROM AUTHOR]

Bark beetles are a principal source of tree mortality in conifer forests, with beetle distribution and beetle-associated tree mortality increasing in frequency and extent. While bark beetles are associated with large-scale outbreaks that affect landscape structure, function, and wood quality, they are also drivers of important ecological processes that modify forest ecosystems. Bark beetle activity may affect biogeochemistry and forest decomposition processes by mediating microbial and detrital communities and by facilitating the turnover of deadwood. The turnover of deadwood in bark beetle-attacked forests has important implications for forest biogeochemical cycling, as dead wood releases CO2 into the atmosphere and carbon, nitrogen, and other nutrients into surrounding soils. However, our understanding of how initial physical, chemical, and biotic changes to bark beetle-attacked trees affect the succession of detrital organisms and decomposition of beetle-generated deadwood remains poor. Furthermore, the relationship between woody decomposition and landscape-level changes in biogeochemical processes in forest ecosystems following bark beetle activity is not well unified. This review article bridges this divide and provides an interdisciplinary perspective on tree mortality, ecological succession, and woody decomposition mediated by bark beetles. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China Normal University (Natural Science Edition) / Huanan Shifan Daxue Xuebao (Ziran Kexue Ban) is the property of Journal of South China Normal University (Natural Science Edition) Editorial Office 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.)

The utilization of radiative transfer models for interpreting remotely sensed data to evaluate forest disturbances is a cost-effective approach. However, the current radiative transfer modeling approaches are either too abstract (e.g., 1D models) or too complex (detailed 3D models). This study introduces a novel multilayer heterogeneous 3D radiative transfer framework with medium complexity, termed MART3D, for characterizing forest disturbances. MART3D generates 3D canopy structures accounting for the within-crown clumping by clustering leaves, which is modeled as a turbid medium, around branches, applicable for forests of medium complexity, such as temperate forests. It then automatically generates a multilayer forest with grass, shrub and several layers of trees using statistical parameters, such as the leaf area index and fraction of canopy cover. By employing the ray-tracing module within the well-established LargE-Scale remote sensing data and image Simulation model (LESS) as the computation backend, MART3D achieves a high accuracy (RMSE = 0.0022 and 0.018 for red and Near-Infrared bands) in terms of the bidirectional reflectance factor (BRF) over two RAMI forest scenes, even though the individual structures of MART3D are generated solely from statistical parameters. Furthermore, we demonstrated the versatility and user-friendliness of MART3D by evaluating the band selection strategy for computing the normalized burn ratio (NBR) to assess the composite burn index over a forest fire scene. The proposed MART3D is a flexible and easy-to-use tool for studying the remote sensing response under varying vegetation conditions. [ABSTRACT FROM AUTHOR]

Copyright of Biotropica 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.)

Climate change is already reducing carbon sequestration in Central European forests dramatically through extensive droughts and bark beetle outbreaks. Further warming may threaten the enormous carbon reservoirs in the boreal forests in northern Europe unless disturbance risks can be reduced by adaptive forest management. The European spruce bark beetle (Ips typographus) is a major natural disturbance agent in spruce‐dominated forests and can overwhelm the defences of healthy trees through pheromone‐coordinated mass‐attacks.We used an extensive dataset of bark beetle trap counts to quantify how climatic and management‐related factors influence bark beetle population sizes in boreal forests. Trap data were collected during a period without outbreaks and can thus identify mechanisms that drive populations towards outbreak thresholds.The most significant predictors of bark beetle population size were the volume of mature spruce, the extent of newly exposed clearcut edges, temperature and soil moisture. For clearcut edge, temperature and soil moisture, a 3‐year time lag produced the best model fit. We demonstrate how a model incorporating the most significant predictors, with a time lag, can be a useful management tool by allowing spatial prediction of future beetle population sizes.Synthesis and Applications: Some of the population drivers identified here, i,e., spruce volume and clearcut edges, can be targeted by adaptive management measures to reduce the risk of future bark beetle outbreaks. Implementing such measures may help preserve future carbon sequestration of European boreal forests. [ABSTRACT FROM AUTHOR]

Climate change has accelerated the frequency of catastrophic wildfires; however, the drivers that control the time‐to‐recover of forests are poorly understood. We integrated remotely sensed data, climate records, and landscape features to identify the causes of variability in the time‐to‐recover of canopy leaf area in southeast Australian eucalypt forests. Approximately 97% of all observed burns between 2001 and 2014 recovered to a pre‐fire leaf area index (±0.25 sd) within six years. Time‐to‐recover was highly variable within individual wildfires (ranging between ≤1 and ≥5 years), across burn seasons (90% longer January to September), and year of fire (median time‐to‐recover varying four‐fold across fire years). We used the logistic growth function to estimate the leaf area recovery rate, burn severity, and the long‐term carrying capacity of leaf area. Time‐to‐recover was most correlated with the leaf area recovery rate. The leaf area recovery rate was largest in areas that experienced high burn severity, and smallest in areas of intermediate to low burn severity. The leaf area recovery rate was also strongly accelerated by anomalously high post‐fire precipitation, and delayed by post‐fire drought. Finally we developed a predictive machine‐learning model of time‐to‐recover (R2: 0.68). Despite the exceptionally high burn severity of the 2019–2020 Australian megafires, we forecast the time‐to‐recover to be only 15% longer than the average of previous fire years. Plain Language Summary: Australian eucalypt forests have evolved different strategies to recover from fire. While the meteorological drivers of bushfire are reasonably well understood, the various processes explaining how long a forest takes to recover from fire are not. We investigated a range of static (landscape) and dynamic (vegetation condition or meteorological) factors that could influence how long a forest's canopy leaf area would take to recover from fire. "Time‐to‐recover" after fire is highly variable, ranging from less than 1 year to more than 5 years even within an individual burn location. More intense fires cause greater forest canopy damage and generally (but not always) lead to longer recovery times, whereas wetter conditions after the fire can accelerate recovery. Using these factors and others, we developed a model capable of predicting the time‐to‐recover and applied it to the 2019–2020 Australian megafires. Our analysis suggests the canopy damage caused by these fires was far more severe than fires in years prior. This would normally lead to a prolonged time‐to‐recover, however we predict that anomalously high rainfall in the year following the fires will shorten recovery time, compensating for the high burn severity. Ultimately we predict the time‐to‐recover will be only slightly longer than average. Key Points: Pre‐fire leaf area, burn severity, and post‐fire meteorological conditions combine to determine time‐to‐recover after fireLarge geographic variation in time‐to‐recover can be explained by mean climate and landscape differencesTime‐to‐recover can be predicted with high accuracy using information limited to the first year following fire [ABSTRACT FROM AUTHOR]

The Norway spruce is one of the most important tree species in Europe. This tree species has been put under considerable pressure due to the ongoing impacts of climate change. Meanwhile, frequent droughts and pest outbreaks are reported as the main reason for its dieback, resulting in severe forest cover loss. Such was the case with Norway spruce forests within the Kopaonik National Park (NP) in Serbia. This study aims to quantify, spatially and temporally, forest cover loss and to evaluate the sensitivity of various vegetation indices (VIs) in detecting drought-induced response and predicting the dieback of Norway spruce due to long-lasting drought effects in the Kopaonik NP. For this purpose, we downloaded and processed a large number of Landsat 7 (ETM+), Landsat 8 (OLI), and Sentinel 2 (MSI) satellite imagery acquired from 2009 to 2022. Our results revealed that forest cover loss was mainly driven by severe drought in 2011 and 2012, which was later significantly influenced by bark beetle outbreaks. Furthermore, various VIs proved to be very useful in monitoring and predicting forest health status. In summary, the drought-induced response detected using various VIs provides valuable insights into the dynamics of forest cover change, with implications for monitoring and conservation efforts of Norway spruce forests in the Kopaonik NP. [ABSTRACT FROM AUTHOR]

Knowledge regarding the response of soil invertebrate communities to typhoon disturbance is limited, although it is known that soil invertebrates are sensitive to forest disturbances and that tropical cyclones (typhoons/hurricanes) are the most destructive natural disasters affecting the structure and function of forest ecosystems. To fill this knowledge gap, soil invertebrates in both litter and topsoil layers were investigated in four representative subtropical coastal forests of eastern China one week before the first typhoon (Hinnamnor) (T1), one day after the first typhoon (Hinnamnor) (T2), one day after the second typhoon (Muifa) (T3), and one week after the second typhoon (Muifa) (T4) in September 2022. Typhoon disturbances decreased the density and taxa abundance of soil invertebrate communities in litter layer, but the first typhoon disturbance increased these values in the topsoil layer. One week after the second typhoon disturbance, soil invertebrate communities in the litter layer showed a gradual recovery trend. Meanwhile, the soil invertebrate communities in the litter layer were more sensitive to typhoon disturbances than those in the topsoil layer. Furthermore, the responses of the soil invertebrate communities to the typhoon disturbances varied greatly with the forest types. The invertebrate densities in the litter layer decreased by 62.1%, 63.53%, 47.01%, and 46.92% in Chinese fir, second broad-leaved, mixed, and bamboo forests, respectively. Particularly, these two non-catastrophic typhoons significantly altered the functional group composition of detrital food webs in the short term, and the proportion of phytophages in detrital food webs in the litter layer increased after the typhoon disturbances. In conclusion, the effects of typhoon disturbances on soil invertebrate communities vary greatly with forest type and soil layer, and soil invertebrate communities can gradually recover after typhoon disturbances. The legacy effects of typhoon disturbances on the functional group composition of detrital food webs may influence carbon and nutrient cycling in forest ecosystems. [ABSTRACT FROM AUTHOR]

Forests are important ecosystems that face threats from climate change and global environmental shifts, with invasive alien plant species being a significant concern. Some of these invasive species have already become established, while others are in the process of naturalisation. Although forests are a relatively stable ecosystem, extreme weather events increase their vulnerability to change, and clearings left after natural disturbances are particularly susceptible to invasion by alien plant species (IAPS). We created risk maps of two species that have spread rapidly in the last decade: American pokeweed (Phytolacca americana) and the tree of heaven (Ailanthus altissima). We prepared a generalised linear model based on the occurrence data collected within the LIFE ARTEMIS project. Eleven environmental variables were used to determine habitat characteristics. We constructed two models for each species: one covering the entirety of Slovenia and the other specifically for the forested areas in Slovenia, with the latter incorporating forest-specific variables (such as forest sanitation felling and monocultures). We observed the presence of both species at lower altitudes and in close proximity to water sources. American pokeweed tends to occur nearer to railways, while the presence of the tree of heaven is associated with areas lacking carbonate parent material and influenced by land use patterns. In forested areas, the occurrence of American pokeweed is influenced by forest habitat characteristics, such as disturbances caused by extreme weather events or the prevalence of Norway spruce monocultures. In contrast, the occurrence of the tree of heaven is influenced by more general environmental variables, such as altitude and proximity to railways. Consequently, we have generated risk maps for the entirety of Slovenia and separately for forested areas, both of which indicate similar levels of risk, particularly for the tree of heaven. The risk map for American pokeweed highlights numerous vulnerable areas, especially forest edges, which are highly susceptible to invasion. Furthermore, there is a higher likelihood of this species occurring in areas that have undergone sanitation felling. This study suggests that the production of risk maps of IAPS could be improved by focussing on habitat types and taking into account habitat-specific variables. This approach could enhance the early detection and management of these invasive species. [ABSTRACT FROM AUTHOR]

Forest fires represent a natural element in the dynamics of forest ecosystems. This study investigated the impact of a large-scale forest fire in 2022 (ca. 1300 ha) on epigeic ground beetles (Coleoptera: Carabidae). The research was conducted in coniferous forests at six pairwise study sites: burnt and unburnt dead spruce from bark beetles, burnt and unburnt clear cut, and burnt and unburnt healthy sites. Each site was replicated in four plots, with two pitfall traps deployed within each plot. In total, 48 pitfall traps (6 × 4 × 2) were installed in April 2023. It was tested how individual sites affected the similarity of ground beetle communities, whether they contained similar life guilds, and how significantly large-scale fire affects the abundance of pyrophilous ground beetles. A total of 5952 individuals and 63 species were recorded. We observed a significant decline in abundance at clear-cut and dead spruce burnt sites (73% and 77.5%, respectively) compared to the unburnt sites. Conversely, abundance increased by 88% at the burnt healthy site compared to the unburnt healthy site. Additionally, significant differences in the number of species per trap and species richness diversity (q = 0, q = 1, q = 2) were found only between burnt and unburnt healthy sites. In general, the highest species richness in the comparison of all study sites was at unburnt clear-cut and burnt healthy sites. Communities of ground beetles responded considerably to the fire, differing significantly from unburnt sites, and demonstrating a high degree of similarity. The original healthy spruce stands had highly homogeneous communities. On the contrary, any disturbance (bark beetle calamity, clear-cut) resulted in an increase in the alpha, beta, and gamma diversities of the ground beetle communities. Burnt sites attracted pyrophilous species (Sericoda quadripunctata, Pterostichus quadrifoveolatus) at very low abundances, with the highest activity in the second half of the season. In conclusion, ground beetles demonstrated a strong short-term response to large-scale fire, forming specific communities. However, pyrophilous ground beetles were unable to occupy a large-scale fire area due to the initial low abundance. Understanding post-fire processes can provide important guidance for management in areas designated for biodiversity enhancement. [ABSTRACT FROM AUTHOR]

Globally, wildfires burn an average of approximately 5.5 Mha of forest per year. Deriving a detailed inventory of forest fuel conditions is critical to managing resources both before and during a fire. However, data products that form the basis of these inventories often come from disparate sources, may not be subject to update, or may not capture information relevant to fuels and fire behaviour. Satellite-derived data products present a valuable solution to these concerns due to exhaustive spatial coverage and capacity for frequent temporal revisit. In this study, we modeled annual burn probability in Canadian forests for a 27-year period (1994–2020) using satellite-derived data products informing on topographic, biotic, and climatic variables. While not predicting the presence or absence of a wildfire, a high burn probability as output from the models showed strong correspondence with actual burned areas. Results indicated that biotic variables representing canopy cover and stand volume were among the most important to estimate burn probability, with the most relevant climatic variables being spring precipitation and the annual range of temperature. Analysis of the temporal trends in burn probability highlighted that 15% of tiles analyzed had significantly declining trends in mean burn probability across the study period. These decreasing trends did not always correspond to reductions in total burned area, potentially indicating a shift in forest composition of some regions and a change in the conditions which burn in others. This study highlights the importance and potential of annual burn probability mapping in order to capture these longer-term trends as well as changes in climatic conditions, which are more dynamic than other biotic or topographic drivers of burn probability. This study demonstrates the use of freely available satellite-derived datasets to generate maps of burn probability across a large, structurally and compositionally diverse forested extent, aimed to inform effective fire management. [ABSTRACT FROM AUTHOR]

Forest disturbances can result in very different canopies that carry elevated albedo, thus causing substantial cooling effects on the climate. Unfortunately, the resulting dynamic global warming potential from altered albedo (GWPΔα) is poorly understood. We examined and modeled the changes in albedo over time after disturbances (i.e., forest age) by forest type, disturbance type and geographic location using direct measurements from 107 sites in temperate and boreal regions. Albedo in undisturbed forests was used as the reference to calculate albedo changes (Δα) and GWPΔα after a disturbance. We found that age is a significant factor for predicting albedo amid the obvious regulations from forest type and geographic locations. We found the strongest cooling GWPΔα in the first 10 years after a disturbance, but it decreased rapidly with time. The changes in GWPΔα were very different from the chronosequence of net ecosystem production (NEP). In the first decade after disturbances, GWPΔα was negative (i.e., cooling) and surprisingly larger in magnitude, with an average of −0.609 kg CO2 m−2 yr−1, compared to NEP of −0.166 kg CO2 m−2 yr−1. Albedo continued to decrease and approached pre‐disturbance levels until around 50 years, resulting in a nearly zero GWPΔα. This research illustrates that many forests in temperate and boreal regions can be considered significant cooling agents by taking into account the high albedo of young forests following disturbances. Plain Language Summary: After forest disturbances, the removal of dense canopies exposes brighter surfaces, leading to higher surface albedo. Changes in albedo, which affect the reflection of solar energy from the Earth's surface, can have different climate impacts of greenhouse gases emissions like CO2. In this study, we used in situ observations to model how albedo changes over time following forest disturbances. We also assessed the climate impacts of these albedo changes, taking into account their temporal dynamics. Our results show that the age of the forest is the most significant factor influencing albedo dynamics after disturbances. However, the trajectory of albedo dynamics also depends on the forest types and disturbance types. We found that the elevated albedo in forests after disturbances accounts for substantial cooling effects in the first 20 years that is comparable to the effect of net ecosystem production (NEP). Understanding the dynamic of albedo following forest disturbances is essential for quantifying the climate impacts, especially as more forests are experiencing disturbances induced by human activities. Key Points: Age, forest type, and disturbance type significantly influence post‐disturbance albedo dynamics, with age having the most substantial impactElevated albedo in forests after disturbances exerts a dominant cooling effect within the first 20 years of successionQuantifying the climate impact of changing albedo post‐disturbance should consider the successional trajectory, as its effects are highly time‐sensitive [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. 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.)

Precise and prompt information on forest disturbances in the tropics is critical to support law enforcement and protect tropical forests. In 2023, medium resolution ALOS-2 ScanSAR data (∼100 m spatial resolution) was made available for Southeast Asia, marking the first freely accessible large-area L-Band dataset. We assessed its potential for large-area forest disturbance mapping and its combination with high-resolution C-band Sentinel-1 data (∼20 m spatial resolution). We mapped forest disturbances in Sumatra, Indonesia for the year 2021 based on ALOS-2 ScanSAR data and Sentinel-1 data separately, and subsequently combined the mapped disturbances. Forest disturbances detected by both C-band and L-band SAR using a probabilistic change algorithm were combined at the product level by merging both sets of detections. The added benefit of combining both sensors was particularly evident in higher detection rates, as indicated by an improved producer accuracy (78.9 ± 11.9 %) compared to detections based on single sensor ALOS-2 ScanSAR (40.8 ± 6.3 %) and Sentinel-1 data (63.3 ± 9.6 %). Both combined and single sensor detections showed negligible false detections. ALOS-2 ScanSAR showed advantages for overcoming limited capability of Sentinel-1 to detect large-sized disturbance events characterized by post-disturbance tree remnants, occurring at locations with large-scale agricultural clearings. The medium resolution of approximately 100 m restricts the detection capability of ALOS-2 ScanSAR data for small-scale disturbances, resulting in missed detections. ALOS-2 ScanSAR detections showed a delay of up to 17.8 days compared to detections based solely on Sentinel-1 data. Combining ALOS-2 ScanSAR and Sentinel-1-based disturbances resulted in improved detection timeliness, with an average improvement of up to 16.5 days compared to Sentinel-1-based detections. Furthermore, we observed improved detection rates for our ALOS-2 ScanSAR-based detections compared to those of the ALOS-2 ScanSAR-based JICA-JAXA Forest Early Warning System in the Tropics (JJ-FAST) forest disturbance alerting product. This suggests that the potential of ALOS-2 ScanSAR data in operational forest monitoring systems is not currently fully realized. Comparing the SAR-based disturbance detections from this study with existing optical-based forest disturbance products (GFC and GLAD-L) suggested improved detection accuracies by overcoming sensor-specific omission errors when using combined SAR and optical data. The demonstrated potential of L-Band ALOS-2 ScanSAR data for improving operational forest monitoring efforts using C-band radar and optical satellites is expected to be amplified by upcoming L-Band satellite missions like NiSAR (2024) and ROSE-L (2028), which will provide freely accessible L-Band data with higher spatial resolution.

Crown damage can account for over 23% of canopy biomass turnover in tropical forests and is a strong predictor of tree mortality; yet, it is not typically represented in vegetation models. We incorporate crown damage into the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), to evaluate how lags between damage and tree recovery or death alter demographic rates and patterns of carbon turnover. We represent crown damage as a reduction in a tree's crown area and leaf and branch biomass, and allow associated variation in the ratio of aboveground to belowground plant tissue. We compare simulations with crown damage to simulations with equivalent instant increases in mortality and benchmark results against data from Barro Colorado Island (BCI), Panama. In FATES, crown damage causes decreases in growth rates that match observations from BCI. Crown damage leads to increases in carbon starvation mortality in FATES, but only in configurations with high root respiration and decreases in carbon storage following damage. Crown damage also alters competitive dynamics, as plant functional types that can recover from crown damage outcompete those that cannot. This is a first exploration of the trade-off between the additional complexity of the novel crown damage module and improved predictive capabilities. At BCI, a tropical forest that does not experience high levels of disturbance, both the crown damage simulations and simulations with equivalent increases in mortality does a reasonable job of capturing observations. The crown damage module provides functionality for exploring dynamics in forests with more extreme disturbances such as cyclones and for capturing the synergistic effects of disturbances that overlap in space and time.

Accurate estimation of Timber Product Output (TPO) is important for carbon budget accounting, since wood products can act as delayed release carbon pools. However, the existing timber harvest data in the US relies on Forest Service’s TPO survey, and the survey does not happen every year. In this study, we proposed a methodological framework to produce annual TPO volume estimates for seven southeastern states (North Carolina, South Carolina, Alabama, Florida, Georgia, Mississippi, and Tennessee) of the US by integrating TPO survey data, Landsat Time Series Stacks (LTSS), and National Land Cover Database (NLCD). First, a forest disturbance product was derived based on Vegetation Change Tracker (VCT) algorithm using LTSS from 1985 to 2016. Then, by linking the predictor variables derived from the disturbance data and the TPO survey data, two regression algorithms were tested and compared, and Random Forest was selected to create TPO estimation models for different wood types. The results show that from 1986 to 2015, the region produced more than 5 × 109 m3 wood products, including 3.7 × 109 m3 softwood products and 1.6 × 109 m3 hardwood products. The derived TPO estimates had large spatial variations among the counties within each state as well as large temporal variations across the study period. The TPO data derived through this study can provide an observational basis for calculating the amount of C transferred from the standing biomass to the wood products through logging and for partitioning of harvested C among different wood product pools.

The European spruce bark beetle (Ips typographus L.) is a biotic disturbance that devastates forest environmental services, and its activities are exacerbated due to climate change. Accordingly, researchers seek workflows using remote sensing imagery for bark beetle detection in the early stage of the attack, enabling proactive management. Most previous studies attempted to detect attacks with pixel-based approaches. This study explores the applicability of pixels’ spatial information, using kernels, for early bark beetle detection in south Sweden. Four vegetation indices, Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Distance Red and SWIR (NDRS), and Chlorophyll Carotenoid Index (CCI), were derived from Sentinel-2 images and time-series of the coefficient of variation (CV) were calculated, followed by interpolation and smoothing to eliminate gaps and reduce noise. The CV time series were fed to a change detection algorithm called Detecting Breakpoints and Estimating Segments in Trend (DBEST). Detection accuracies ranged from 83.80% to 87.89%, with the highest related to NDVI, followed by NDRS. Detection dates mainly fell in June and July, 6–7 weeks after the bark beetle swarming. NDRS performed slightly better in detecting the attacks earlier, with an average detection date of 29th June. NDVI obtained higher detection accuracies for pine, spruce, and mixed conifer forests in nonwetland areas, dominating the study area. In general, the detection accuracies increased as the number of attacked trees and pixels increased in kernels. Results demonstrated the applicability of kernel-based early bark beetle attack detection, which can elucidate a new paradigm for bark beetle studies.

Integrating Light Detection And Ranging (LiDAR) and Hyperspectral Imaging (HSI) enhances the assessment of tropical forest degradation and regeneration, which is crucial for conservation and climate mitigation strategies. This study optimized procedures using combined airborne LiDAR, HSI data, and machine learning algorithms across 12 sites in the Brazilian Amazon, covering various environmental and anthropogenic conditions. Four forest classes (undisturbed, degraded, and two stages of second-growth) were identified using Landsat time series (1984–2017) and auxiliary data. Metrics from 600 samples were analyzed with three classifiers: Random Forest, Stochastic Gradient Boosting, and Support Vector Machine. The combination of LiDAR and HSI data improved classification accuracy by up to 12% compared with single data sources. The most decisive metrics were LiDAR-based upper canopy cover and HSI-based absorption bands in the near-infrared and shortwave infrared. LiDAR produced significantly fewer errors for discriminating second-growth from old-growth forests, while HSI had better performance to discriminate degraded from undisturbed forests. HSI-only models performed similarly to LiDAR-only models (mean F1 of about 75% for both data sources). The results highlight the potential of integrating LiDAR and HSI data to improve our understanding of forest dynamics in the context of nature-based solutions to mitigate climate change impacts.

Cold-temperate forests (CTFs) are not only an important source of wood but also provide significant carbon storage in China. However, under the increasing pressure of human activities and climate change, CTFs are experiencing severe disturbances, such as logging, fires, and pest infestations, leading to evident degradation trends. Though these disturbances impact both regional and global carbon budgets and their assessments, the disturbance patterns in CTFs in northern China remain poorly understood. In this paper, the Genhe forest area, which is a typical CTF region located in the Inner Mongolia Autonomous Region, Northeast China (with an area of about 2.001 × 104 km2), was selected as the study area. Based on Landsat historical archived data on the Google Earth Engine (GEE) platform, we used the continuous change detection and classification (CCDC) algorithm and considered seasonal features to detect forest disturbances over nearly 30 years. First, we created six inter-annual time series seasonal vegetation index datasets to map forest coverage using the maximum between-class variance algorithm (OTSU). Second, we used the CCDC algorithm to extract disturbance information. Finally, by using the ECMWF climate reanalysis dataset, MODIS C6, the snow phenology dataset, and forestry department records, we evaluated how disturbances relate to climate and human activities. The results showed that the disturbance map generated using summer (June–August) imagery and the enhanced vegetation index (EVI) had the highest overall accuracy (88%). Forests have been disturbed to the extent of 12.65% (2137.31 km2) over the last 30 years, and the disturbed area generally showed a trend toward reduction, especially after commercial logging activities were banned in 2015. However, there was an unusual increase in the number of disturbed areas in 2002 and 2003 due to large fires. The monitoring of potential widespread forest disturbance due to extreme drought and fire events in the context of climate change should be strengthened in the future, and preventive and salvage measures should be taken in a timely manner. Our results demonstrate that CTF disturbance can be robustly mapped by using the CCDC algorithm based on Landsat time series seasonal imagery in areas with complex meteorological conditions and spatial heterogeneity, which is essential for understanding forest change processes.

Key message: Annual stem CO2 efflux increases with stem wood production rates and are inhibited by daily moisture stress. Abstract: Tropical forests cycle a large amount of CO2 between the land and atmosphere, with a substantial portion of the return flux due tree respiratory processes. However, in situ estimates of woody tissue respiratory fluxes and carbon use efficiencies (CUEW) and their dependencies on physiological processes including stem wood production (Pw) and transpiration in tropical forests remain scarce. Here, we synthesize monthly Pw and daytime stem CO2 efflux (ES) measurements over 1 year from 80 trees with variable biomass accumulation rates in the central Amazon. On average, carbon flux to woody tissues, expressed in the same stem area normalized units as ES, averaged 0.90 ± 1.2 µmol m−2 s−1 for Pw, and 0.55 ± 0.33 µmol m−2 s−1 for daytime ES. A positive linear correlation was found between stem growth rates and stem CO2 efflux, with respiratory carbon loss equivalent to 15 ± 3% of stem carbon accrual. CUEW of stems was non-linearly correlated with growth and was as high as 77–87% for a fast-growing tree. Diurnal measurements of stem CO2 efflux for three individuals showed a daytime reduction of ES by 15–50% during periods of high sap flow and transpiration. The results demonstrate that high daytime ES fluxes are associated with high CUEW during fast tree growth, reaching higher values than previously observed in the Amazon Basin (e.g., maximum CUEW up to 77–87%, versus 30–56%). The observations are consistent with the emerging view that diurnal dynamics of stem water status influences growth processes and associated respiratory metabolism.

Timely detection of insect infestation (or other disturbance) in a forest is vital for an adequate response plan to be developed. To determine the status of an active infestation of southern pine beetle (Dendroctonus frontalis) in the Bienville National Forest, WorldView-2 imagery was utilized. Principal components analysis (PCA) was performed and correlated with spectral reflectance bands to assess differences between the classification of spectral reflectance bands and principal components. Unsupervised classification of combinations of principal components (e.g., combining principal components 1 and 2, principal component 1 alone, and principal component 2 alone) was performed and compared with combinations of principal component correlations with spectral reflectance bands (e.g., all bands, bands 1–5, bands 6–8, and bands 2, 4, and 5). Combining principal components 1 and 2 was more accurate than other methods, closely followed by spectral bands 1–5. Employing PCA will aid resource managers in quickly detecting areas of active insect infestation and allow them to deploy adequate response measures to prevent or mitigate continued outbreaks. [ABSTRACT FROM AUTHOR]

In recent decades, the western United States (U.S.) has experienced increasing magnitudes and frequencies of natural land cover disturbances that impact water budget partitioning. Post‐disturbance hydrologic response is often variable at the stream outlet and is difficult to detect and quantify with traditional before–after control–impact studies. This study uses a modified version of the U.S. Geological Survey's Monthly Water Balance Model (MWBM) to simulate and separate the hydrologic response to several forest disturbances, including (1) wildfire, (2) forest conversion (subalpine to mid‐elevation forest) and (3) a climate that is hotter and drier than present in the Rio Grande Headwaters (RGH) in Colorado, U.S. (this climate scenario was derived from an ensemble of climate scenarios from the Coupled Model Intercomparison Project phases 3 and 5, which were selected based on water stress potential in the state of Colorado). We leverage historic post‐disturbance vegetation data in the RGH to add quantitative vegetation representation to the MWBM, then modeled synthetic future (2021–2050) streamflow scenarios as both single and compound disturbances. Relative to a baseline scenario, modeled scenarios predict several changes to average annual water trends over the final simulation decade (2041–2050); (1) decreases in average annual water yield under a hot and dry climate (−14%), except during the rising limb of annual snowmelt; (2) increases in average annual water yield (+32%) and peak runoff under a fire simulation; and (3) increases in average annual water yield (+24%) along with earlier and higher peak runoff under compound (fire + hot/dry) conditions. These findings show the strengths of hydrologic models in separating compound disturbance signals at the stream outlet and a need for quantitative vegetation representation within models to adequately represent dynamic disturbance conditions. [ABSTRACT FROM AUTHOR]

The recent expansion of mountain pine beetle (MPB, Dendroctonus ponderosae) from its native range in western North America into northern boreal pine forests in Alberta, Canada has resulted in conditions for tree regeneration that are dramatically different from those after wildfire, the predominant natural disturbance in these forests. We assessed natural regeneration post-MPB for northern boreal lodgepole pine sites in Alberta, Canada via intensive surveys of small plots at 33 severely attacked pine stands and using data from 205 permanent sample plots representing various site types and levels of MPB mortality. We used model selection to identify factors explaining regeneration. Overall, pine regeneration was very poor 6–9 years post-MPB; only 42% of the 33 intensively surveyed plots and only 9% of the 205 permanent plots had pine seedlings. This poor regeneration is attributed to high levels of cone serotiny in these populations, unsuitable regeneration microsites due to undisturbed litter or feathermoss layers, and competition from the residual canopy and understory vegetation. Other species (aspen, birch, poplar, and black and white spruce) were found on most sites, either as post-attack regeneration or regeneration established in advance. Without intervention, many of these stands will likely transition away from pine, to broadleaf and other conifer species. [ABSTRACT FROM AUTHOR]

Several efforts have been made to model forest structure and estimate global aboveground biomass (AGB) magnitude and distribution. However, AGB models still show large uncertainties, especially over tropical mountains that are difficult to survey and understudied. Although forest disturbance and land use greatly influence forest structure and AGB accumulation, they are rarely considered in forest structure and AGB models. Including the effect of forest disturbance and land use could improve model performance. In this study, we used Landsat time series analysis and forest inventory data to assess and model the effect of small‐scale disturbance and land use on tropical montane forest structure and AGB. We explored two approaches: abrupt forest change detection, using the algorithm Breaks For Additive Seasonal Trend (BFAST), and gradual vegetation change by calculating the variation of remote sensing vegetation indices over time. We found that tropical montane landscapes are very dynamic and heterogenous. Out of the 284 forest plots analyzed, 27% had abrupt disturbances at least once between 1993 and 2014, and some of them showed abrupt disturbances several times. Forest plots with abrupt disturbances exhibit statistically significant lower basal area, tree height, and AGB than plots without abrupt disturbances. Using linear mixed‐effects models, we found that the number of breakpoints, annual Normalized Difference Vegetation Index SD, and Normalized Difference Water Index minimum values over time were the most informative variables for predicting forest structure, particularly basal area (adjusted R2 = 0.61). These variables are easy to calculate and could add significant power to forest structure and AGB models. In conclusion, incorporating small‐scale disturbance to model forest structure and AGB at regional scales is feasible through the identification of recent abrupt and gradual forest change using remote sensing time series. Including recent forest disturbance variables as predictors in forest structure models can greatly improve biomass predictability and mapping in dynamic landscapes. [ABSTRACT FROM AUTHOR]

The long‐term effects of intensive forest harvest on sensitive demographic stages of the American black bear (Ursus americanus) have been often overlooked. Much of Maine, USA, is covered in forests that are hospitable to bears and commercial timber harvest. To investigate the potential effects of differing intensities of disturbance on black bears, and on females with cubs particularly, we designed a large‐scale natural experiment with 197 motion‐sensitive camera sites dispersed over representative forest stands in northern and central Maine. Using multi‐state occupancy models, we distinguished the overall trends in space use by females with young versus adult bears without young. Forest disturbance at large spatial scales was positively associated with the probability of use for both demographic groups and the availability of hardwood trees was an additional important factor for habitat use by females with young. Our study illustrates the use of motion‐sensitive cameras to monitor and understand habitat use by distinct life‐history stages of animals living in human‐modified landscapes, and results indicate that managers can maintain black bear habitat in areas of active forest harvest by ensuring the availability of hardwood species. [ABSTRACT FROM AUTHOR]