Search Results (161)

This study advances vegetation and forest mapping in temperate mixed forests by integrating airborne hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data, overcoming the limitations of conventional multispectral imaging. Employing a Digital Canopy Height Model (DCHM) derived from LiDAR, our approach integrates these structural metrics with hyperspectral spectral information, alongside detailed remote sensing data extraction. Through machine learning-based clustering, which combines both structural and spectral features, we successfully classified eight specific tree species, community boundaries, identified dominant species, and quantified their abundance, contributing to precise vegetation and forest type mapping based on predominant species and detailed attributes such as diameter at breast height, age, and canopy density. Field validation indicated the methodology’s high mapping precision, achieving overall accuracies of approximately 98.0% for individual species identification and 93.1% for community-level mapping. Demonstrating robust performance compared to conventional methods, this novel approach offers a valuable foundation for National Forest Ecology Inventory development and significantly enhances ecological research and forest management practices by providing new insights for improving our understanding and management of forest ecosystems and various forestry applications. Full article

(1) Background: Understanding how forest management practices regulate hydrological cycles is critical for sustainable water resource management and addressing global water crises. However, the effects of light-felling (selective thinning) on hydrological processes in temperate mixed forests remain poorly understood. This study comprehensively evaluated the impacts of light-felling intensity levels on three hydrological layers (canopy, litter, and soil) in mid-rotation Korean pine (Pinus koraiensis) forests managed under the “planting conifer and preserving broadleaved trees” (PCPBT) system on Changbai Mountain, China. (2) Methods: Hydrological processes—including canopy interception, throughfall, stemflow, litter interception, soil water absorption, runoff, and evapotranspiration—were measured across five light-felling intensity levels (control, low, medium, heavy, and clear-cutting) during the growing season. The stand structure and precipitation characteristics were analyzed to elucidate the driving mechanisms. (3) Results: (1) Low and heavy light-felling significantly increased the canopy interception by 18.9%~57.0% (p < 0.05), while medium-intensity light-felling reduced it by 20.6%. The throughfall was significantly decreased 10.7% at low intensity but increased 5.3% at medium intensity. The stemflow rates declined by 15.8%~42.7% across all treatments. (2) The litter interception was reduced by 22.1% under heavy-intensity light-felling (p < 0.05). (3) The soil runoff rates decreased by 56.3%, 16.1%, and 6.5% under the low, heavy, and clear-cutting intensity levels, respectively, although increased by 27.1% under medium-intensity activity (p < 0.05). (4) The monthly hydrological dynamics shifted from bimodal (control) to unimodal patterns under most treatments. (5) The canopy processes were primarily driven by precipitation, while litter interception was influenced by throughfall and tree diversity. The soil processes correlated strongly with throughfall. (4) Conclusions: Low and heavy light-felling led to enhanced canopy interception and reduced soil runoff and mitigated flood risks, whereas medium-intensity light-felling supports water supply during droughts by increasing the throughfall and runoff. These findings provide critical insights for balancing carbon sequestration and hydrological regulation in forest management. Full article

Oak forests are an important part of temperate European ecosystems, where they are actively improving biodiversity, carbon storage, and ecological stability. However, current concerns such as climatic changes, and especially rising temperatures and changing precipitation patterns, are impacting their resilience. In this context, our study intends to evaluate the impact of climatic variability on temperate oak forests, focusing on the influence of temperature and precipitation. This covers different sites that have different environmental conditions. By using both a bibliometric approach and a systematic analysis of publications that have studied the influence of climate change on oak forests, our study has identified specific species and site responses to climate stressors. Furthermore, we have also evaluated trends in drought sensitivity. All these aspects have allowed us to understand and suggest improvements for the impact of climate change on the resilience and productivity of oak ecosystems. We have analyzed a total number of 346 publications that target the impact of climate change on oak forests. The articles were published between 1976 and 2024, with the majority originating from the USA, Spain, Germany, and France. These studies were published in leading journals from Forestry, Environmental Sciences, and Plant Sciences, among which the most cited journals were Forest Ecology and Management, the Journal of Biogeography, and Global Change Biology. As for the keywords, the most frequent ones were climate change, drought, growth, forest, and oak. However, we have observed a trend towards drought sensitivity, which indicates the intensification of climate changes on oak ecosystems. Moreover, this trend was more present in central and southern regions, which further highlights the impact of regional conditions. As such, certain local factors (soil properties, microclimate) were also taken into account in our study. Our literature review focused on the following aspects: Oak species affected by climate change; Impact of drought on oak forests; Influence of climate change on mixed forests containing oaks; Effects of climate change on other components of oak ecosystems; Radial growth of oaks in response to climate change; Decline of oak forests due to climate change. Our results indicate that oak forests decline in a process caused by multiple factors, with climate change being both a stressor and a catalyst. Across the globe, increasing temperatures and declining precipitation affect these ecosystems in their growth, functions, and resistance to pathogens. This can only lead to an increased forest decline. As such, our results indicate the need to implement forest management plans that take into account local conditions, species, and climate sensitivity. This approach is crucial in improving the adaptivity of oak forests and mitigating the impact of future climate extremes. Full article

Soil microorganisms are crucial in global biogeochemical cycles, impacting ecosystems’ energy flows and material cycling. This study, via high-throughput sequencing in four forests—the original Larix gmelinii (Rupr.) Kuzen. forest (LG), the conifer–broad-leaved mixed Pinus sylvestris var. mongolica Litv. forest (PS), the original pure Betula platyphylla Sukaczev forest (BP), and the original pure Populus L. forest (PL) in Shuanghe National Nature Reserve, Daxing’anling mountains—explored soil microbial community structures and diversities. The results indicated that the BP and PL forests had the lowest soil bacterial ACE and Chao1 indices, and the original pure birch forest’s Shannon index was higher than that of the poplar forest. The soil’s fungal Chao1 index of the birch forest was higher than that of the larch forests. Bradyrhizobium and Roseiarcus were the dominant soil bacterial genera; the dominant soil fungal genera were Podila, Russula, and Sebacina. RDA and mantel analyses indicated that soil microbial community structures varied across forest types mainly because of the effective phosphorous and pH levels, soil’s total nitrogen level, and available phosphorus level. This study offers a scientific foundation for cold-temperate-forest ecosystem management regarding soil microbial diversity and community structural changes in different forest types. Full article

Mountain forests in biodiversity hotspots show complex responses to climate and topographic gradients. However, the effect of synergistic controls of elevation and climate on Net Primary Productivity (NPP) dynamics remain insufficiently quantified in complex mountains. Southwest China’s mountains are Asia’s most biodiverse temperate region with pronounced vertical ecosystem stratification, representing a critical continental carbon sink. This study investigated the spatiotemporal dynamics and driving mechanisms of NPP in Southwest China’s typical mountain ecosystems over the past three decades using a high-resolution modeling framework integrated with relative importance analysis, a Geodetector, and an elevation-dependent model. The results showed that (1) NPP revealed a significant increasing trend, rising from 634 ± 325 to 748 ± 348 g C m⁻² yr⁻¹ (mean rate 4 g C m⁻² yr⁻¹) from 1990 to 2018. Spatially, the most rapid increases occurred in eastern regions. (2) Rising CO₂ and climate warming (dominate 17% regions) drove interannual NPP growth, with elevation thresholds dictating driver dominance. The CO₂ governed low elevation, while temperature controlled higher elevation (>4800 m). (3) The elevation-dependent model revealed a more complex and nonlinear relationship between NPP and elevation, identifying three distinct phases: the saturation phase (<500 m) with negligible decay of NPP; the transition phase (500–3500 m) with linear decline (NPP loss of 29 g C m⁻² yr⁻¹ per 100 m); and the collapse phase (>3500 m) with continuously attenuated NPP losses (NPP average loss of 10.5 g C m⁻² yr⁻¹ per 100 m) reflecting high-elevation vegetation adaptation to extreme conditions. (4) Land cover dominated NPP spatial heterogeneity and was amplified by interactions with elevation and temperature, highlighting a vegetation–climate–topography coupling mechanism that critically shapes productivity patterns. Biodiversity-rich widespread mixed forests underpinned the region’s high productivity. Mountain protection should focus on protecting existing evergreen forests from fragmentation, while forestation should prioritize the establishment of biodiversity-rich mixed forest. These findings established a comprehensive framework for spatiotemporal analysis of driving mechanisms and enhanced the understanding of NPP dynamics in complex mountain ecosystems, informing sustainable management priorities in mountain regions. Full article

In recent years, the cultivation techniques of large-diameter forests have garnered increasing attention due to their significant ecological and economic values. However, the effects of small-scale latitudinal changes on the species distribution and community composition of large-diameter trees remain poorly understood. This study aims to investigate the effects of narrow latitudinal gradients on the species composition and structure of large-diameter forests. Investigating these impacts provides critical insights for silvicultural species selection and forest structure optimization, particularly in the context of global warming, and is essential for the sustainable development of large-diameter forests. In this study, three forest communities along a small-scale latitudinal gradient in subtropical China were selected to study the community structure of large-diameter trees by analyzing species composition and species diversity. The community structure was also studied by analyzing species rank curves, the diameter structure, PCoA, MRPP, and indicator species. The results revealed that as latitude increased, the proportion of rare species rose from 43.8% in LL (low-latitude) to 63.2% in HL (high-latitude) areas, while the stem density of dominant species and the number of stems per species also increased. Additionally, species composition homogeneity decreased (based on PCoA and MRPP analysis), age-class structures became more complex, and the proportion of tropical genera gradually declined, whereas temperate genera increased. These findings indicate that small-scale latitudinal variation is a key driver of changes in the composition and structure of large-diameter forests. Currently, the northern Guangdong region is suitable for large-diameter forest development, with Fagaceae species (particularly Castanopsis and Lithocarpus) showing high potential. Specifically, Castanopsis eyrei, Castanopsis fissa, and Ternstroemia gymnanthera are well-suited for large-diameter stand cultivation in Guangdong. For mixed large-diameter forests, Machilus chinensis, Cinnamomum porrectum, and Schima superba are recommended as optimal associated species. However, as global warming progresses, the suitability of tree species for afforestation may shift, necessitating adaptive management strategies. Full article

Forest mapping using remote sensing has made considerable progress over the past decade, but substantial uncertainties remain in complex regions, particularly where terrain and climate vary dramatically. Yunnan Province, China, represents such a challenging case, with its diverse climatic zones ranging from tropical to temperate and its topography spanning over 6500 m in elevation. These factors contribute to substantial variation in vegetation types, complicating the accurate identification of forest cover through remote sensing. This study aims to enhance forest mapping in Yunnan by leveraging multi-temporal remote sensing data from Sentinel-2 and Landsat 8/9 imagery, incorporating key phenological stages—such as the leaf greening (GRN) period, as well as the senescence, defoliation, and foliation (SDF) stages of deciduous forests—along with kNDVI and terrain factors. A random forest (RF) classifier was applied on the Google Earth Engine (GEE) platform to create a 10 m resolution forest map (LS2-RF). This map achieved an overall accuracy of 96.35% when validated with 1572 ground samples, significantly outperforming existing global datasets, such as Dynamic World (73.88%) and WorldCover (87.66%). These maps agreed well in extensive forested areas; discrepancies were noted in mixed land types, including farmland, urban areas, and regions with fragmented landscapes. In 2022, Yunnan’s forest cover was 60.40%, with higher coverage in the southwestern region and lower in the northeast. The largest forested area was found in Pu’er City, while the smallest was in Yuxi City. Forests were most abundant at elevations between 1500 and 2500 m (occupying 52.29% of the total forest area) and slopes of 15° to 25° (occupying 39.19% of the total forest area). Conversely, forest cover was lowest in areas below 500 m elevation (occupying 0.64% of the total forest area) and on slopes less than 5° (occupying 2.40% of the total forest area). The analysis also revealed a general trend of increasing forest cover with decreasing latitude and longitude, with peak forest coverage at mid-elevations and slopes, followed by a decline at higher elevations. The resultant forest map provides valuable data for ecological assessments, forest conservation initiatives, and informed policy decision-making. Full article

Forest Orchidaceae are important for European temperate forests, yet their distribution and abundance have so far interested limited research. In three pure or mixed silver fir stands in the Foreste Casentinesi National Park (NP) (Northern Apennines, Italy) we analysed how structural traits in mature and old-growth forests affected orchid communities in terms of abundance of the main genera, trophic strategy and rarity in the NP. We established three 20 × 60 m plots to quantify the structure of living and dead tree community, including a set of old-growth attributes connected to large trees, deadwood, and established regeneration. In each plot, we measured the abundance of all orchid species and explored their behaviour according to the trophic strategy (autotrophy/mixotrophy, obligate mycoheterotrophy), rarity within the NP, and threatened status according to the IUCN Red List. We used multivariate ordination and classification techniques to assess plot similarities according to forest structure and Orchid Community and identify the main structural factors related to orchid features. The main structural factors were used as predictors of community traits. Forest composition (i.e., the dominance/abundance of silver fir) affected the presence of the main orchid genera: Epipactis were abundant in silver fir-dominated forests, Cephalanthera in mixed beech and fir forests. Interestingly, Cephalanthera could become limited even in beech-dominated conditions if fir regeneration was abundant and established. Old-growth attributes like the density of deadwood and large tree volume were important determinants of the presence of rare and mycoheterotrophic species. Our results provided a first quantitative description of forest reference conditions to be used in the protection and restoration of threatened and rare orchid species. Full article

Biodiversity maintenance mechanisms have been central to the study of community ecology, and the negative density dependence effect plays an important role in maintaining species diversity in forest communities. However, the strength and direction of the negative density dependence effect may change at different latitudinal gradients, and theory predicts that the negative density dependence effect increases with decreasing latitude. Using three provinces in northeastern China as the study target, we selected forest ecosystems in 15 locations according to the latitude gradient and analyzed the mixing of large- and small-diameter trees and adjacent tree species at different latitudinal gradients by the second-order characteristic function of mark mingling (The species mingling was used as “constructed marks” and we developed a second-order characteristic function of mark mingling useful for comparing spatial species mingling via random assignment of species patterns at specific ecological scales). It was found that the tree species mixed level of the large trees was higher, that of the small trees was lower in the stands at the middle and low latitudes (40, 41, and 43), and the tree species mixed level of the large or small trees was lower in the stands at high latitudes (45 and 46). Also, the level of mixing of large trees with surrounding tree species was significantly different among latitudes within the small scale (0–5 m). More importantly, the peak value of the difference in the second-order characteristic function of mark mingling (Δv(r)) of the stand increased gradually with decreasing latitude. The results indicated that the difference in tree species mixing degree between large and small trees was increasing, and this phenomenon was more obvious at the small scale (0–10 m). In general, we found that the negative density dependence effect in the late successional forest system showed a variation trend with latitude gradient, which showed that with the decrease in latitude, the negative density dependence effect in the stands was increasing. The results showed that in temperate forests, in low-latitude stands (40–43° N), there is significant peak in species mingling differences at small scales (0–10 m). Spatial heterogeneity thinning should be prioritized, and rare tree species should be replanted within a 10 m radius to alleviate intraspecific competition. In contrast, in high-latitude stands (45–46° N), human disturbance should be reduced to maintain the natural community structure. These measures can provide precise management strategies for regional biodiversity conservation. This study revealed the response of the intensity of the negative density dependence effect to changes in latitudinal gradients, and provides new ideas for maintaining and controlling regional species diversity. Full article

Old-growth forests are essential for maintaining biodiversity, as they are formed by the complexity of diverse forest structures, such as broad variations in tree height and diameter (DBH) and conditions of living and dead trees, leading to various ecological niches. However, many efforts of old-growth forest mapping from LiDAR have targeted only one specific forest structure (e.g., stand height, basal area, or stand density) by deriving information through a large number of LiDAR metrics. This study introduces a novel approach for identifying old-growth forests by optimizing a set of selected LiDAR standards and structural metrics. These metrics effectively capture the arrangement of multiple forest structures, such as canopy heterogeneity, multilayer canopy profile, and canopy openness. To determine the important LiDAR standard and structural metrics in identifying old-growth forests, multicollinearity analysis using the variance inflation factor (VIF) approach was applied to identify and remove metrics with high collinearity, followed by the random forest algorithm to rank which LiDAR standard and structural metrics are important in old-growth forest classification. The results demonstrate that the LiDAR structural metrics (i.e., advanced LiDAR metrics related to multiple canopy structures) are more important and effective in distinguishing old- and second-growth forests than LiDAR standard metrics (i.e., height- and density-based LiDAR metrics) using the European definition of a 150-year stand age threshold for old-growth forests. These structural metrics were then used as predictors for the final classification of old-growth forests, yielding an overall accuracy of 78%, with a true skill statistic (TSS) of 0.58 for the test dataset. This study demonstrates that using a few structural LiDAR metrics provides more information than a high number of standard LiDAR metrics, particularly for identifying old-growth forests in mixed temperate forests. The findings can aid forest and national park managers in developing a practical and efficient old-growth forest identification and monitoring method using LiDAR. Full article

Tree species are important factors affecting the carbon sequestration capacity of forests and maintaining the stability of ecosystems, but trees are widely distributed spatially and located in complex environments, and there is a lack of large-scale regional tree species classification models for remote sensing imagery. Therefore, many studies aim to solve this problem by combining multivariate remote sensing data and proposing a machine learning model for forest tree species classification. However, satellite-based laser systems find it difficult to meet the needs of regional forest species classification characters, due to their unique footprint sampling method, and SAR data limit the accuracy of species classification, due to the problem of information blending in backscatter coefficients. In this work, we combined Sentinel-1 and Sentinel-2 data to construct a machine learning tree classification model based on optical features, vegetation spectral features, and PolSAR polarization observation features, and propose a forest tree classification feature selection method featuring the Hilbert–Huang transform for the problem of mixed information on the surface of SAR data. The PSO-RF method was used to classify forest species, including four temperate broadleaf forests, namely, aspen (Populus L.), maple (Acer), peach tree (Prunus persica), and apricot tree (Prunus armeniaca L.), and two coniferous forests, namely, Chinese pine (Pinus tabuliformis Carrière) and Mongolian pine (Pinus sylvestris var. mongolica Litv.). In this study, some experiments were conducted using two Sentinel-1 images, four Sentinel-2 images, and 550 measured forest survey sample data points pertaining to the forested area of Fuxin District, Liaoning Province, China. The results show that the fusion model constructed in this study has high accuracy, with a Kappa coefficient of 0.94 and an overall classification accuracy of 95.1%. In addition, this study shows that PolSAR data can play an important role in forest tree species classification. In addition, by applying the Hilbert–Huang transform to PolSAR data, other feature information that interferes with the perceived vertical structure of forests can be suppressed to a certain extent, and its role in the classification of forest species, combined with PolSAR, should not be ignored. Full article

Global climate change, characterized by nitrogen (N) deposition and precipitation reduction, can disrupt soil microbial stoichiometry and soil nutrient availability, subsequently affecting soil nutrient cycles. However, the effects of N deposition and precipitation reduction on microbial stoichiometry and the soil nutrient status in temperate forests remain poorly understood. This study addresses this gap through a 10-year field trial conducted in a Korean pine mixed forest in northeastern China where three treatments were applied: precipitation reduction (PREC), nitrogen addition (N50), and a combination of nitrogen addition with precipitation reduction (PREC-N50). The results showed that N50 and PREC significantly increased carbon-to-phosphorus (C/P) and nitrogen-to-phosphorus (N/P) imbalances, thereby exacerbating microbial P limitation, while PREC-N50 did not alter the nutrient imbalances. PREC decreased soil water availability, impairing microbial nutrient acquisition. Both N50 and PREC influenced soil enzyme stoichiometry, leading to increasing the ACP production. The results of redundancy analysis indicated that microbial nutrient status, enzymatic activity, and composition contributed to the variations in nutrient imbalances, suggesting the adaption of microorganisms to P limitation. These results highlight that N addition and precipitation reduction enhanced microbial P limitation, boosting the shifts of microbial elemental composition, enzyme production, and community composition, and subsequently impacting on forest nutrient cycles. Full article

Saltmarshes provide important ecosystem services, including coastline protection, but face decline due to human activities and climate change. There are increasing efforts to conserve and restore saltmarshes worldwide. Our study evaluated the effectiveness of Sentinel-2 satellite imagery to monitor landcover changes using a saltmarsh restoration project undergoing its 9th to 12th year of recovery in the megatidal Bay of Fundy in Maritime Canada. Specifically, in 2019–2022, five satellite images per growing season were acquired. Random Forests classification for 13 landcover classes (ranging from bare mud to various plant communities) achieved a high overall classification accuracy, peaking at 96.43% in 2021. Field validation points confirmed this, with high validation accuracies reaching 93.02%. The classification results successfully distinguished ecologically significant classes, such as Spartina alterniflora–S. patens mix. Our results reveal the appearance of high marsh species in restoration sites and elevational-based zonation patterns, indicating progression. They demonstrate the potential of Sentinel-2 imagery for monitoring saltmarsh restoration projects in north temperate latitudes, aiding management efforts. Full article

Freeze–thaw events are predicted to be more frequent in temperate forest ecosystems. Whether and how freeze–thaw cycles change soil greenhouse gas fluxes remains elusive. Here, we compared the fluxes of three soil greenhouse gases (CO₂, CH₄, and N₂O) across the spring freeze–thaw (SFT) period, the growing season (GS), and the annual (ALL) period in a temperate broad-leaved Korean pine mixed forest in the Changbai Mountains in Jilin Province, Northeastern China from 2019 to 2020. To assess the mechanisms driving the temporal variation of soil fluxes, we measured eleven soil physicochemical factors, including temperature, volumetric water content, electrical conductivity, gravimetric water content, pH, total carbon, total nitrogen, total-carbon-to-total-nitrogen ratio, nitrate (NO₃⁻), ammonium (NH₄⁺), and dissolved organic carbon, all of which play crucial roles in soil carbon (C) and nitrogen (N) cycling. Our findings indicate that the soil in this forest functioned as a source of CO₂ and N₂O and as a sink for CH₄, with significant differences in greenhouse gas (GHG) fluxes among the SFT, GS, and ALL periods. Our results suggest freeze–thaw events significantly but distinctly impact soil C and N cycling processes compared to normal growing seasons in temperate forests. The soil N₂O flux during the SFT (0.65 nmol m⁻² s⁻¹) was 4.6 times greater than during the GS (0.14 nmol m⁻² s⁻¹), likely due to the decreased NO₃⁻ concentrations that affect nitrification and denitrification processes throughout the ALL period, especially at a 5 cm depth. In contrast, soil CO₂ and CH₄ fluxes during the SFT (0.69 μmol m⁻² s⁻¹; −0.61 nmol m⁻² s⁻¹) were significantly lower than those during the GS (5.06 μmol m⁻² s⁻¹; −2.34 nmol m⁻² s⁻¹), which were positively influenced by soil temperature at both 5 cm and 10 cm depths. Soil CO₂ fluxes increased with substrate availability, suggesting that the total nitrogen content at 10 cm depth and NH₄⁺ concentration at both depths were significant positive factors. NO₃⁻ and NH₄⁺ at both depths exhibited opposing effects on soil CH₄ fluxes. Furthermore, the soil volumetric water content suppressed N₂O emissions and CH₄ oxidation, while the soil gravimetric water content, mainly at a 5 cm depth, was identified as a negative predictor of CO₂ fluxes. The soil pH influenced CO₂ and N₂O emissions by regulating nutrient availability, particularly during the SFT period. These findings collectively contribute to a more comprehensive understanding of the factors driving GHG fluxes in temperate forest ecosystems and provide valuable insights for developing strategies to mitigate climate change impacts. Full article

We evaluated the long-term impacts of various forest management practices on the structure and biodiversity of Estonian hemiboreal forests, a unique ecological transition zone between temperate and boreal forests, found primarily in regions with cold winters and moderately warm summers, such as the northern parts of Europe, Asia, and North America. The study examined 150 plots across stands of different ages (65–177 years), including commercial forests and Natura 2000 habitat 9010* “Western Taiga”. These plots varied in stand origin—multi-aged (trees of varying ages) versus even-aged (uniform tree ages), management history—historical (practices before the 1990s) and recent (post-1990s practices), and conservation status—protected forests (e.g., Natura 2000 areas) and commercial forests focused on timber production. Data on forest structure, including canopy tree diameters, deadwood volumes, and species richness, were collected alongside detailed field surveys of vascular plants and bryophytes. Management histories were assessed using historical maps and records. Statistical analyses, including General Linear Mixed Models (GLMMs), Multi-Response Permutation Procedures (MRPP), and Indicator Species Analysis (ISA), were used to evaluate the effects of origin, management history, and conservation status on forest structure and species composition. Results indicated that multi-aged origin forests had significantly higher canopy tree diameters and deadwood volumes compared to even-aged origin stands, highlighting the benefits of varied-age management for structural diversity. Historically managed forests showed increased tree species richness, but lower deadwood volumes, suggesting a biodiversity–structure trade-off. Recent management, however, negatively impacted both deadwood volume and understory diversity, reflecting short-term forestry consequences. Protected areas exhibited higher deadwood volumes and bryophyte richness compared to commercial forests, indicating a small yet persistent effect of conservation strategies in sustaining forest complexity and biodiversity. Indicator species analysis identified specific vascular plants and bryophytes as markers of long-term management impacts. These findings highlight the ecological significance of integrating historical legacies and conservation priorities into modern management to support forest resilience and biodiversity. Full article