Forest Conservation Research Articles

Timber resources and management characteristics of economic forestry management areas and other forests in South Korea: post-reforestation challenges

ABSTRACT Over the past 50 years South Korea has made considerable strides in economic development and reforestation by converting barren land into dense forests. Despite these achievements, the forestry sector still faces significant challenges in economic viability and resource utilization. Economic Forestry Management Areas (EFMAs) were established to enhance sustainable timber production and address these challenges. This study evaluates the effectiveness of EFMAs in comparison to other forest areas, focusing on timber resources and management characteristics. The findings reveal that, although EFMAs show a higher intensity of management operations than other forest areas, improvements in timber quality and productivity remain limited. This highlights an important insight: despite increased management efforts, substantial progress in enhancing the quality and quantity of timber resources has not yet been achieved. This outcome may be attributed to the relatively short time since EFMAs were first established, which has not allowed sufficient time for significant developments in timber resource enhancement. To address these challenges, this study recommends increasing the proportion of economically valuable coniferous species and expanding artificial forest coverage through well-coordinated reforestation and management efforts. Additionally, developing precise, location-specific management data is crucial to avoid overlapping activities and to enhance the efficiency of forest management. These strategic improvements will not only boost the productivity of EFMAs but also support sustainable forestry practices, facilitating South Korea’s transition into the post-reforestation era.

Wildfires: Burning our way to a 'hot house Earth'?

A new global analysis shows that wildfires turn temperate and boreal forests into major emitters of greenhouse gases- instead of storing carbon. Without sustainable forest fire management, forest fires may amplify climate change, leading to irreversible ecological changes.

Effectiveness of Digital Technology in Enhancing Blue Tourism Sustainability in Tanzania

The study examined the effectiveness of digital technology in enhancing blue tourism sustainability in Tanzania. Specifically, the study examined the use of solar panel technology, use of artificial intelligence and use of health technologies. The study employed a desk research method which involves the collection of data from various literature sources such as documents, scripts, previous research findings, journals, and related books. Qualitative content analysis was utilized as the method for analyzing the data. The findings revealed that solar panel technology is one of the most renewable and green energy sources in reducing greenhouse gas emissions and mitigating climate change. The findings also revealed that Artificial Intelligence has released up new tourism opportunities with positive effects on the economy, society, and environment. The study concludes that integration of solar power reduces Carbon footprints and increased appeal to budget conscious travelers. Artificial Intelligent enhances visitor’s management, safety, and eco-friendly experiences. Online platforms improve access to information about overseas care and empower patients to make informed decisions. The study recommends that the development and integration of renewable energy sources, like solar, wind, and hydropower, into the energy mix should be given top priority by government and tourism companies. Programs supporting sustainable agriculture and forestry conservation/reforestation should be put into place. Strategies to economic development and environmental sustainability should also be put into place such as encouraging eco-tourism, efficient manufacturing, and sustainable urban planning. The Ministry of Tourism of Tanzania should promote the use of AI technologies in the creation, use, and manufacturing of tourism products.

A framework for upscaling aboveground biomass from an individual tree to landscape level and qualifying the multiscale spatial uncertainties for secondary forests

ABSTRACT Secondary forests, a typical forest type in the sub-frigid zone of Northeast China, have significant potential for carbon sequestration. Accurate estimation of the Aboveground Biomass (AGB) of secondary forests and assessment of multiscale uncertainties are crucial for promoting Reduced Emissions from Deforestation and Degradation. This study developed a novel framework to upscale the AGB estimation from the tree to the landscape level and assessed multiscale uncertainties based on multi-platform laser scanning data and Unmanned Aerial Vehicle (UAV) hyperspectral images. The framework included two stages: (1) quantifying multiple uncertainties (uncertainties of individual tree crown delineation, individual tree parameters estimation, and tree species classification) in individual tree-based AGB estimation using Monte Carlo simulations; (2) upscaling the plot to the landscape level estimated AGB using the Nonlinear Simultaneous Equation (NSE) with error-in-variables and quantifying the uncertainties of model residuals, model parameters, and model independent variables. The findings revealed a high accuracy from tree to plot AGB estimation (R2: 0.75, Root Mean Square Error (RMSE): 6.65 Mg/ha, relative RMSE (rRMSE): 5.40%), with the total and relative uncertainties of 16.85 Mg/ha and 16.29%, respectively, with the highest uncertainty (9.73 Mg/ha) observed in tree species classification. The AGB estimation using NSE achieved an R2 of 0.69, with an RMSE of 9.91 Mg/ha and an rRMSE of 10.43% from the plot to landscape level; and the uncertainties caused by model parameters, independent variables, and residuals were 5.52 Mg/ha, 14.56 Mg/ha, and 25.25 Mg/ha, respectively, accounting for 3.46%, 24.09%, and 72.45% of the total uncertainty. This study develops a framework for large-scale AGB estimation of mixed forests based on the individual tree approach and uncertainty quantification of multiscale estimates and provides a foundation for precise forestry, sustainable forest management, and carbon neutrality.

Leveraging the Internet of Things, Remote Sensing, and Artificial Intelligence for Sustainable Forest Management

Sustainable forest management is vital for addressing climate change, biodiversity loss, and deforestation. Human-induced stresses on forest ecosystems demand innovative approaches to ensure long-term health and productivity. This study explores how cutting-edge technologies, including the Internet of Things (IoT), remote sensing, and artificial intelligence (AI), enhance sustainable forest management practices. Researchers reviewed 196 studies published between 2021 and 2024 from IEEE Xplore Digital Library, MDPI, Taylor & Francis, ScienceDirect, Frontiers, Springer, SAGE, Hindawi, Nature, Wiley Online Library, and Google Scholar. The findings highlight IoT devices like drones, enabling real-time data collection on temperature, humidity, soil moisture, and tree growth, facilitating continuous forest monitoring. Remote sensing technologies, utilizing satellite imagery and aerial surveys, deliver high-resolution data for large-scale forest assessments, including forest cover changes, biomass estimation, and early detection of illegal logging. When integrated with AI, these tools enhance predictive modeling, data analysis, and decision-making, leading to more effective forest management strategies. The study also identifies challenges such as data security concerns, bandwidth limitations, interoperability issues, and high costs. Despite these barriers, IoT, remote sensing, and AI present transformative potential for improving forest resilience, carbon sequestration, and biodiversity conservation. These technologies are crucial in preserving forest ecosystems and mitigating climate change impacts by advancing real-time monitoring, optimizing resource allocation, and enabling data-driven decisions.

Epistemological dimensions of Indigenous honey collection in the Kattunaicken community of South India.

Indigenous ecological knowledge (IEK) has proven effective in environmental governance, forest management, and sustainable development, yet it is threatened by globalization and rapid social-ecological changes. In southern India, I investigated the engagement of the Kattunaicken community with the forest, particularly through honey collection, to explore the connection between their Indigenous epistemological identity and their role in caring for the forest and its inhabitants. I conducted 48 interviews and accompanied 11 forest walks as part of walking ethnography with male community members, who are primarily involved in honey collection within the Wayanad district of Kerala. The Kattunaicken identity was intrinsically linked to their knowledge of the forest, with reciprocal epistemological interactions between the community and forest entities (trees, animals, and bees). Honey collection emerged as an epistemological endeavor, manifesting their Indigenous identity through the collective "knowing" of the forest that encompassed sensorial, ethical, and metaphysical dimensions that facilitated harmonious coexistence and care for the forest and its inhabitants. The Kattunaicken world of knowing challenges extractivist interpretations of nontimber forest product collection, emphasizing the importance of Indigenous epistemologies in shaping alternative knowledge construction for forest conservation. Their epistemological framework highlights care as an active process emerging from collective understanding and negotiation among all entities within their shared epistemic realm, fostering a harmonious coexistence that transcends conservation efforts.

Community Social Capital in Supporting Ecosystem Recovery in Rawa Kidang of Way Kambas National Park

Ecosystem recovery plays a central role in environmental and forest conservation efforts, including efforts made in a conservation area that is a national park. This effort requires support from the communities living around the national park. Therefore, the presence of the community can become a form of capital, which is generally referred to as social capital and will determine the success of a program. Elements of social capital consist of trust, norms, and networks is one form of capital that is very valuable in addition to economic capital, physical capital, and human capital. The purpose of this research is to analyze the level of social capital that exists in the community and how the characteristics of respondents affect social capital. This research is located in Labuhan Ratu VII Village, Labuhan Ratu Subdistrict, East Lampung Regency from November to December 2023. Based on the results of the analysis, it is known that the trust element and the social network element are in the medium category, while the norm element is in the high category. Education and length of stay are respondent characteristics that influence community social capital in supporting ecosystem recovery in Rawa Kidang, Way Kambas National Park (WKNP). Increasing social capital can be done through two characteristics that affect community social capital in the form of informal education such as training. People who live for a long time will affect the sustainability of WKNP so training can be carried out for a long period of time in a structured and intensive manner towards sustainable WKNP management. The results of this study can be a consideration for WKNP managers in implementing Permen LHK policy no 14 of 2023 concerning business and/or activity arrangements in nature conservation areas, nature reserve areas and hunting arks because they must have a strong social foundation.

Spatiotemporal patterns of annual clear-cutting distribution in tropical and subtropical regions of China with time series Landsat and CCDC

ABSTRACT Spatial and temporal clear-cutting distribution is an important data source for investigating forest dynamics and formulating policies related to timber production, land use, and sustainable development. Subtropical and tropical regions of China due to their favorable weather conditions for tree growth are important timber providers, but the spatial distribution of annual clear-cutting areas was unavailable. This research leveraged dense time-series Landsat images (1986–2022) and a novel approach combining Continuous Change Detection and Classification (CCDC) with Random Forest to produce the first annual 30 m resolution clear-cutting map for the China’s subtropical and tropical regions. The per-polygon approach based on the sub-compartment and field survey data was used to evaluate the developed clear-cutting product. The spatiotemporal distribution of clear-cutting areas was further analyzed along longitude and latitude as well as in different provinces. The results indicated that the combination of CCDC and Random Forest successfully detected spatial and temporal distribution of clear-cutting areas with an overall accuracy of 75.6% based on sub-compartments and 93.3% based on field polygons. The cumulative clear-cutting area between 1987 and 2021 was approximately 21.26 × 106 ha, with a mean annual clear-cutting area of 0.61 × 106 ha in China’s subtropics and tropics. About 10.24% of subtropical/tropical vegetation experienced clear-cutting and most areas (85.94%) were clear-cut once. Clear-cutting activities gradually clustered at low latitudes and moderate longitudes, represented by Guangxi Province. More frequent clear-cutting was observed during the later period (2001–2021). This research contributes to an improved understanding of forest dynamics over time at large scale, and provides scientific data to make proper decisions for forest sustainability.

ASSESSMENT OF THE STATE OF SOME ANTI-EROSION INSTALLATIONS IN THE DAIRA OF HONAINE (WILAYA OF TLEMCEN – ALGERIA)

Water erosion is responsible for the degradation of more than half of the world's soils. In northern Algeria, it is widespread, particularly in the western region, which requires mechanical and biological developments to control it. The objective of this work is to evaluate the state and effectiveness of some anti-erosion developments, carried out in a coastal region: that of the Daira de Honaine (wilaya of Tlemcen). The methodology adopted was based on direct field observations, photo taking and data collection at the Forest Conservation in the wilaya of Tlemcen and the district of Remchi. The results obtained showed that most of the anti-erosion installations, carried out in 5 stations in the study region, are in very good condition. This demonstrates the perseverance of foresters, particularly in the creation of gabion thresholds and ridges, as well as the will of farmers in the good management and conservation of agricultural lands.

How Can State-Owned Forest Farms Promote Sustainable Forest–Village Cooperation? A Configuration Analysis Based on the Resource Orchestration Perspective

How Can State-Owned Forest Farms Promote Sustainable Forest–Village Cooperation? A Configuration Analysis Based on the Resource Orchestration Perspective

Asymmetric sensitivity of boreal forest resilience to forest gain and loss.

Forest gains and losses may have unequal effects on forest resilience, particularly given their distinct temporal dynamics. Here, we quantify the sensitivities of boreal forest resilience to forest cover gain and loss using a resilience indicator derived from the temporal autocorrelation (TAC) of the kernel normalized difference vegetation index from 2000 to 2020. Our findings unveil pronounced asymmetric sensitivities, with stronger sensitivity to forest loss (-4.26 ± 0.14 × 10-3; TAC increase per 1% forest cover loss) than to forest gain (-1.65 ± 0.12 × 10-3; TAC decrease per 1% forest cover gain). Locally, ~73% of the boreal forest exhibits negative sensitivity, indicating enhanced resilience with forest cover gain and vice versa, especially in intact forests compared to managed ones. This sensitivity is affected by various trajectories in forest cover change, stemming primarily from temporal asynchrony in the recovery rates of various ecosystem functions. The observed asymmetry underscores the importance of prioritizing forest conservation over reactive management strategies following losses, ultimately contributing to more sustainable forest management practices.

Assessing the Greenhouse Gas Mitigation Potential of Harvested Wood Products in Romania and Their Contribution to Achieving Climate Neutrality

Forests mitigate greenhouse gas (GHG) emissions by capturing CO₂ and storing it as carbon in various forms, including living biomass, dead wood, soil, and forest litter. Importantly, when trees are harvested, a portion of the above-ground biomass is converted into harvested wood products (HWPs), which can retain carbon for decades. With approximately 7 million hectares of forest (30% of its land area), Romania significantly contributes to the country’s carbon budget through the HWP pool. Using country-specific data from 1961 to 2022 and an IPCC method, we tracked HWP carbon storage and projected future scenarios to evaluate the category’s significance in achieving the 2050 climate target. During this period, the carbon stored in Romanian HWPs more than doubled from 28.20 TgC to 60.76 TgC, with sawnwood products as major contributors. Fluctuations were influenced by domestic policies, market dynamics, and industry changes, notably after the 1990s. Annual carbon inflow dipped to 0.65 TgC in 1994 and peaked at 2.54 TgC in 2013. By analyzing the scenarios, we demonstrated that a moderate growth trajectory in carbon inflow, combined with a focus on producing long-lived wood products, could double carbon stock changes by 2050 to 4.4 TgC—roughly 4% of the country’s current total emissions excluding the LULUCF sector. Additionally, based on sustainable forest management practices in Romania, this approach would significantly enhance the carbon pool and its importance in achieving the country’s climate policies.

Fluxes of Cadmium, Chromium, and Lead Along with Throughfall and Stemflow Vary Among Different Types of Subtropical Forests

The interaction between forests and precipitation plays a crucial role in the material cycling of forest ecosystems. Atmospheric deposition and rainfall leaching promote the transfer of heavy metals to the forest floor, while canopy exchange may potentially slow this process. Therefore, studying heavy metal fluxes and their influencing factors, along with canopy rainfall partitioning, is essential for understanding forest material cycling. We conducted a year-long experiment to examine the dynamics of chromium (Cr), cadmium (Cd), and lead (Pb) concentrations and fluxes in four types of forests (Cunninghamia lanceolata plantations, Castanopsis carlesii plantations, Cas. carlesii natural forests, and Cas. carlesii secondary forests) located in the subtropical regions of southeast China. Results showed that (1) the annual mean concentrations of Cr, Cd, and Pb were 167.6, 13.8, and 6180.5 μg L−1 in the throughfall and 204.7, 28.4, and 2251.1 μg L−1 in the stemflow, respectively, and the annual fluxes of Cr, Cd, and Pb through throughfall were 29.3, 2.4, and 847.7 g ha−1, respectively, and were 1.7, 0.2, and 12.7 g ha−1 through stemflow, respectively; (2) the concentrations of these heavy metals associated with throughfall did not vary between forest types, but their fluxes were highest in Cas. carlesii natural forests; (3) Cr concentration and flux were higher during the rainy than dry seasons, while Cd and Pb concentrations and fluxes showed an opposite trend. Overall, our results indicate that the fluxes of heavy metals along with rainfall partitioning were highest in natural forests and are primarily controlled by meteorological factors, indicating that the conversion of natural forests to other forest types will substantially change the fluxes of heavy metals along with hydrological processes. These results will contribute to a better understanding of the natural fluxes of heavy metals in forest ecosystems and are valuable for sustainable forest management, particularly in the context of forest type transformation.

The Impact of Shifting from Subsistence to Cash Crops on the Livelihoods of the Soliga Tribe in India

Efforts to protect and conserve nature are essential worldwide. However, in many protected forests, local communities have lived for centuries, relying on the environment for food, income, shelter, and cultural practices. Conservation regulations often impact their livelihoods. This study examines the challenges and opportunities for sustaining the livelihoods of Soliga tribe farmers in Biligiri Ranganatha Swamy Temple (BRT) Tiger Reserve, South India. A participatory workshop with five researchers and 25 diverse farmers (male, female, young, and old) discussed: a) current agricultural and livelihood practices; b) key drivers of land-use changes; and c) necessary actions for sustainable livelihoods. Findings showed that shifting from traditional subsistence crops to cash crops like coffee, pepper, and floriculture reduced food availability but increased cash income. Non-timber forest products such as honey, berries, nuts, and firewood significantly supplemented household needs. Challenges included low coffee yields due to disease and poor soil, changing rainfall patterns, limited land, and wildlife crop damage. The forest conservation policies enforced during the recent decades have limited their access to resources and traditional lands, affecting their food security and economic stability. Additionally, limited access to education and employment opportunities restricts their social and economic mobility. Ongoing research and support are needed to enhance sustainable farming techniques such as mulching, composting, intercropping, integrated pest management, and controlling Lantana spp. to achieve resilient farming systems.

Analyzing the Impact of Geoenvironmental Factors on the Spatiotemporal Dynamics of Forest Cover via Random Forest

Understanding the dynamic relationships between geoenvironmental factors and forest vegetation cover is crucial for effective forest management and planning. This study investigates the spatiotemporal dynamics of forest cover in the Duhok District in the Kurdistan Region of Iraq over a decade (2013–2023), emphasizing the impact of geoenvironmental factors via Random Forest algorithms and Landsat data. This research integrates datasets including fractional vegetation cover (FVC), groundwater levels, climate data, topography, and soil moisture data, offering a comprehensive analysis of the factors influencing forest cover. The results show that in 2013, altitude and rainfall were the primary factors influencing FVC, with areas of higher altitudes and adequate rainfall exhibiting up to 30% denser forest cover. By 2023, soil moisture and groundwater levels had emerged as the dominant factors, with soil moisture levels accounting for 25% of the variation in FVC. This shift underscores the increasing importance of water management strategies to maintain forest health. The Random Forest model demonstrated high predictive accuracy, achieving an R2 value of 0.918 (RMSE of 0.016 and MAE of 0.013) for 2013 and 0.916 (RMSE of 0.018 and MAE of 0.014) for 2023, underscoring the model’s robustness in handling nonlinear ecological processes. This study’s insights are crucial for guiding sustainable forest management practices and assisting decision-makers in formulating strategies for resource management, environmental preservation, and future planning. This study underscores the necessity of adaptive management strategies that consider evolving climatic and hydrological conditions, emphasizing continuous monitoring and advanced technologies to ensure the resilience of forest ecosystems.

Quantification of Forest Regeneration on Forest Inventory Sample Plots Using Point Clouds from Personal Laser Scanning

The presence of sufficient natural regeneration in mature forests is regarded as a pivotal criterion for their future stability, ensuring seamless reforestation following final harvesting operations or forest calamities. Consequently, forest regeneration is typically quantified as part of forest inventories to monitor its occurrence and development over time. Light detection and ranging (LiDAR) technology, particularly ground-based LiDAR, has emerged as a powerful tool for assessing typical forest inventory parameters, providing high-resolution, three-dimensional data on the forest structure. Therefore, it is logical to attempt a LiDAR-based quantification of forest regeneration, which could greatly enhance area-wide monitoring, further supporting sustainable forest management through data-driven decision making. However, examples in the literature are relatively sparse, with most relevant studies focusing on an indirect quantification of understory density from airborne LiDAR data (ALS). The objective of this study is to develop an accurate and reliable method for estimating regeneration coverage from data obtained through personal laser scanning (PLS). To this end, 19 forest inventory plots were scanned with both a personal and a high-resolution terrestrial laser scanner (TLS) for reference purposes. The voxelated point clouds obtained from the personal laser scanner were converted into raster images, providing either the canopy height, the total number of filled voxels (containing at least one LiDAR point), or the ratio of filled voxels to the total number of voxels. Local maxima in these raster images, assumed to be likely to contain tree saplings, were then used as seed points for a raster-based tree segmentation, which was employed to derive the final regeneration coverage estimate. The results showed that the estimates differed from the reference in a range of approximately −10 to +10 percentage points, with an average deviation of around 0 percentage points. In contrast, visually estimated regeneration coverages on the same forest plots deviated from the reference by between −20 and +30 percentage points, approximately −2 percentage points on average. These findings highlight the potential of PLS data for automated forest regeneration quantification, which could be further expanded to include a broader range of data collected during LiDAR-based forest inventory campaigns.

Productivity of coniferous forests evaluated by remote sensing and field-based models

Abstract The inadequacy of Norway spruce (NS) monocultures in fulfilling ecosystem services, due to deteriorating health and consequent forest ecosystem collapses, has recently become a concerning issue for the forestry sector in Central Europe. This study investigates spruce forest productivity using in-situ field observations (DendroNetwork) and satellite remote sensing (RS) data in the Czech Republic during the past two decades. Tree growth index and moderate resolution imaging spectroradiometer net primary productivity were used to assess productivity at the national scale along an elevation gradient. The analyses of spatiotemporal variation in carbon dynamics indicate a decreasing trend in productivity in 51% and 80% (2800 km2 and 4200 km2) of the spruce stands from the RS and in-situ data, respectively. The increasing trend is particularly evident in montane regions, where satellite data identified an elevation threshold around 900 m a.s.l. while in-situ data 700 m a.s.l. In these energy-limited montane areas, NS may benefit from increased temperatures and may remain relatively safe from drought stress. The decreasing trends detected by both approaches indicate deteriorating conditions for NS in lower elevations. This study suggests that combining in-situ and RS data provides an efficient and robust way to estimate forest productivity at the national scale. The anticipated response of spruce forests associated with declines in productivity and growth in areas with low to moderate elevation points to the adverse effects of climate change by shifting the ecological optimum to higher elevations. We conclude that detailed mapping of forest response to changing environmental conditions is critical to support sustainable forest management and decision-making, ultimately reflecting the need to adopt adequate strategies to mitigate the impacts of climate change and related natural and anthropogenic disturbances.

Evaluating Annual Growth Dynamics Across Forest Types Using Permanent Sample Plots in Yellapur Forest Division, Karnataka, India

Annual tree increment requires repeated assessments on permanent sample plots, which are crucial for evaluating changes in tree size and stem volume. These plots enable long-term monitoring to estimate timber production and analyse growth dynamics. This study examines the current annual increment of growth parameters like tree height, GBH, basal area, and volume in dry, moist deciduous, and semi-evergreen forests within the Yellapur Forest Division, Uttara Kannada, Karnataka. The Permanent sample plots were utilized to assess growth patterns across various forest types. Total of nine one-hectare permanent plots were established across the forest types with three plots laid in each forest type and divided into subplots of size 33.33m× 33.33m and data were collected for tree parameters including girth at breast height and tree height using digital laser hypsometer. Measurements were conducted over two consecutive years i.e., in 2022 and repeated in 2023 to calculate CAI. The results revealed significant variation among forest types. Moist deciduous forests exhibited the highest CAI in height (0.45 m/year) and GBH (1.84 cm/year), while dry deciduous forests showed lower increments due to water stress and leafless periods. Semi-evergreen forests, although with dense canopy competition, demonstrated moderate growth rates. The basal area increment was highest in moist deciduous forests (0.93 m²/ha/year), reflecting favourable moisture and nutrient conditions. The tree volume increased significantly in moist deciduous forests (22.80 m³/ha/year). Variations in basal area and volume suggest that environmental conditions and anthropogenic activities, such as litter collection, impacted growth across forest types. These insights are critical for developing sustainable forest management practices and mitigating the impact of anthropogenic activities on forest ecosystems. This research underscores the importance of monitoring permanent plots to understand forest dynamics and supports sustainable forest management strategies.

Floodplain forests drive fruit-eating fish diversity at the Amazon Basin-scale

Unlike most rivers globally, nearly all lowland Amazonian rivers have unregulated flow, supporting seasonally flooded floodplain forests. Floodplain forests harbor a unique tree species assemblage adapted to flooding and specialized fauna, including fruit-eating fish that migrate seasonally into floodplains, favoring expansive floodplain areas. Frugivorous fish are forest-dependent fauna critical to forest regeneration via seed dispersal and support commercial and artisanal fisheries. We implemented linear mixed effects models to investigate drivers of species richness among specialized frugivorous fishes across the ~6,000,000 km2 Amazon Basin, analyzing 29 species from 9 families (10,058 occurrences). Floodplain predictors per subbasin included floodplain forest extent, tree species richness (309,540 occurrences for 2,506 species), water biogeochemistry, flood duration, and elevation, with river order controlling for longitudinal positioning along the river network. We observed heterogeneous patterns of frugivorous fish species richness, which were positively correlated with floodplain forest extent, tree species richness, and flood duration. The natural hydrological regime facilitates fish access to flooded forests and controls fruit production. Thus, the ability of Amazonian floodplain ecosystems to support frugivorous fish assemblages hinges on extensive and diverse seasonally flooded forests. Given the low functional redundancy in fish seed dispersal networks, diverse frugivorous fish assemblages disperse and maintain diverse forests; vice versa, diverse forests maintain more fish species, underscoring the critically important taxonomic interdependencies that embody Amazonian ecosystems. Effective management strategies must acknowledge that access to diverse and hydrologically functional floodplain forests is essential to ensure the long-term survival of frugivorous fish and, in turn, the long-term sustainability of floodplain forests.

Multiscale Sensitivity Analysis of Landscape Fragmentation in Plantation Forests on the Loess Plateau

ABSTRACTEffectively quantifying plantation forest fragmentation is crucial for vegetation restoration and management on the Loess Plateau. Forest area density (FAD) effectively measures fragmentation, with multiscale sensitivity analysis essential for determining the appropriate window scale threshold. This study uses plantation forest data from different geomorphological types (hilly gully and plateau gully region) of the Loess Plateau. Various window sizes are applied to evaluate the FAD threshold curve and its derivative for determining a stable fragmentation threshold, while a structural equation model is employed to analyze the drivers of forest fragmentation. The results are as follows: (1) FAD variability decreases with increasing window size, leveling off after reaching the threshold. (2) Thresholds vary by forest type and county. (3) Two principal components explain 81.27% of the window stabilization threshold variation, with strong correlations between similar fragmentation types. (4) Fragmentation slowed between 2000 and 2022, with hilly gully region more fragmented than plateau gully region. (5) Stand structure mitigates fragmentation, with topographic and climate change influences showing heterogeneity. “Core” maintain stability, while “Islet” drive fragmentation. This study emphasizes the importance of scale‐specific analysis and adaptive management strategies in mitigating forest fragmentation. This study supports optimizing ecological restoration and targeted conservation strategies, promoting sustainable forest management on the Loess Plateau.