National Forest Research Articles

Food patch selection by the largest European herbivore

AbstractThe scaling relationship between digestive system and body mass shapes foraging strategies in herbivores. The Jarman–Bell principle and the forage maturation hypothesis (FMH), two of the most important predictions formulated in herbivore foraging ecology, have been used to explain this relationship.We aimed to test these predictions for the largest European herbivore—the European bison-which is highly sexually dimorphic and recognised as a refugee species in non-optimal forest habitats. We conducted our study in the Białowieża Primeval Forest, where the European bison has been recognized as a refugee species. We estimated vegetation quality and biomass along the foraging paths of male and female European bison, and the vegetation biomass at random points in different habitats and months. We found no support for the Jarmann-Bell principle at the intraspecific level in European bison, as males and females foraged on patches of similar crude protein and fibre content. However, females selected for higher biomass. The quality of vegetation on the bison foraging patches showed a significant spatio-temporal variation. It increased from May to July and then gradually decreased. It also increased with the fertility and wetness of the habitats being the highest in wet forests. Mean biomass on European bison foraging patches (131.5 g/m2), was significantly lower than on random plots (210.5 g/m2), which supported the FMH. Our study suggests that in rich forest habitats, the abundant food resources available in summer may not constrain the metabolic rate/gut capacity ratio of either sex, potentially leading to dietary segregation. In contrast to open habitats, where vegetation maturation is less temporally variable, European bison in rich forest habitats may optimise patch selection and energy intake by tracking a habitat- and species-specific changes in the plant phenology. Understanding the relationship between European bison forage selection and forage nutritional constraints in forest habitats may be critical for conservation programmes, as European bison are better adapted to more open habitats and are recognised as refugee species in suboptimal forest habitats.

Integration of field measurements with unmanned aerial vehicle to predict forest inventory metrics at tree and stand scales in natural pure Crimean pine forests

ABSTRACT Inventorying forest ecosystems is an essential part of forest management planning. However, it is quite costly and time-consuming, particularly for larger areas. Recently, significant developments have been made in unmanned aerial vehicle (UAV) technology to improve the cost and time efficiency in forest inventory. Therefore, UAV images have become one of the inventory tools that produces data with high spatial resolution in determining forest resources. This study aims to investigate the contribution of UAV data to forest inventory in a case study area with a total of 30 sample plots located in pure and natural Crimean pine (Pinus nigra J.F. Arnold ssp. pallasiana (Lamb.) Holmboe) stands in the Black Sea backward region of Türkiye. Total tree height (h) and stem volume (v) were recorded at individual tree level (n = 367), and the number of trees (N), mean height (hmean), top height (htop), stand basal area (BA) and stand volume (V) were calculated at sample plot level (n = 30) from both the field and UAV-based data. Pearson’s correlation coefficients (r) for h and v were 0.96 and 0.72, respectively, the highest correlation at the sample plot level was observed for the hmean - htop (r = 0.96), while the lowest correlation was found for BA (r = 0.54). The suitability of the observation and prediction values was assessed using a t-test at both individual tree and sample plot levels. According to the t-test results, the observation and prediction values for h, v, hmean, htop, BA and V metrics were found to be compatible (p > 0.05), but not for N (p < 0.05). Overall results indicated that UAV technology has a potential to be used in forest inventory and can contribute to the determination of individual tree and stand metrics. Thereby, it saves cost and time in forest inventory studies and helps monitoring the dynamic structure of the forest ecosystem with an effective approach in forest inventory.

Wildland fire fuels database for Corsican - Mediterranean Forest stand types

Wildfires represent a major concern for the forests in the Mediterranean basin regions since global climate change enhances the frequency and the severity of this natural ecological disturbance agent. Wildfire models (fire spread or fire behavior models) can be of valuable help for fire risk assessment as well as for planning mitigation actions which mainly concern forest management. These models need generalized vegetation (fuel) data sets at the scales which are significant for fire/forest management. However, despite of the efforts to develop methods for generating global fuel data sets, there is a lack of fine resolution regional scale fuel data sets for the Mediterranean basin. The aim of this work is to build a fuel data set based on existing data sources for fire/forest management, risk assessment and decision-support purposes. Thus, this paper presents a database (building methods and data) of wildland fuels for Mediterranean basin vegetation stands types and in particular for those in Corsica, which gathers together most common input parameters needed by wildfire’s models at several vegetation scales (i.e., stand, elements, particles). Moreover, an example is provided where simulations are run with a specific wildfire model by using the fuel input data extracted from the database. Fuel attributes have been defined to be meaningful at regional/landscape scales and are representative of stand-level characteristics. National (French) Forest Inventory (NFI) data have been mainly used for determining the fuel attributes for forest stand types. The harmonization of European NFIs generates a reliable data source available to extend and generalize the methodology presented herein to other regions or countries.

Modelling Diameter at Breast Height Distribution for Eight Commercial Species in Natural-Origin Mixed Forests of Ontario, Canada

Diameter at breast height (DBH) is a unique attribute used to characterize forest growth and development for forest management planning and to understand forest ecology. Forest managers require an array of DBHs of forest stands, which can be reconstructed using selected probability distribution functions (PDFs). However, there is a lack of practices that fit PDFs of sub-dominating species grown in natural mixed forests. This study aimed to fit PDFs and develop predictive models for PDF parameters, so that the predicted distribution would represent dynamic forest structures and compositions in mixed forest stands. We fitted three of the simplest forms of PDFs, log-normal, gamma, and Weibull, for the DBH of eight tree species, namely balsam fir (Abies balsamea [L.] Mill.), eastern white pine (Pinus strobus L.), paper birch (Betula papyrifera Marshall), red maple (Acer rubrum L.), red pine (Pinus resinosa Aiton), trembling aspen (Populus tremuloides Michx), and white spruce (Picea glauca [Moench] Voss), all grown in natural-origin mixed forests in Ontario province, Canada. We estimated the parameters of the PDFs as a function of DBH mean and standard deviation for these species. Our results showed that log-normal fit the best among the three PDFs. We demonstrated that the predictive model could estimate the recovered parameters unbiasedly for all species, which can be used to reconstruct the DBH distributions of these tree species. In addition to prediction, the cross-validated R2 for the DBH mean ranged between 0.76 for red maple and 0.92 for red pine. However, the R2 for the regression of the standard deviation ranged between 0.00 for red pine and 0.69 for sugar maple, although it produced unbiased predictions and a small mean absolute bias. As these mean and standard deviations are regressed with dynamic covariates (such as stem density and stand basal area), in addition to climate and static geographic variables, the predicted DBH distribution can reflect change over time in response to management or any type of disturbance in the regime of the given geography. The predictive model-based DBH distributions can be applied to the design of appropriate silviculture systems for forest management planning.

Trends and Climate‐Sensitivity of Phenology in China's Natural and Planted Forests

AbstractForest is the largest carbon sink on land, and the timing of forest phenology profoundly influences carbon uptake. However, disparities in the timing and sensitivity of forest phenology, particularly between planted forests (PF) and natural forests (NF), have yet to be determined. In this study, we analyzed the phenological trends and the climate‐sensitivities of China's NF and PF from 2000 to 2022 using field survey data and satellite data. We found that the start of the growing season (start of the season (SOS)) in NF and PF advanced significantly by 0.07 and 0.08 days yr−1, while the end of the growing season (end of the season (EOS)) in both NF and PF delayed by 0.06 days yr−1. Moreover, precipitation and air temperature jointly influenced the timing of phenological events. The pre‐season precipitation delayed the EOS by 0.009 days mm−1 and 0.006 days mm−1 in NF and PF. Moreover, the SOS was more sensitive to precipitation in PF than in NF in arid areas (i.e., pre‐season precipitation &lt;100 mm). Air temperature significantly advanced the SOS by 0.74 and 0.95 days °C−1 in NF and PF and delayed the EOS by 0.89 and 0.71 days °C−1 in NF and PF. Additionally, the sensitivities of SOS and EOS to air temperature significantly differed between NF and PF. Our findings demonstrate a significant phenological discrepancy between PF and NF. The results suggest that NF and PF responded differently to climate variability, implying forest origin should be considered in the projection of carbon sequestration capability under changing climate.

Soil microbes deal with the nitrogen deposition enhanced phosphorus limitation by shifting community structure in an old-growth subtropical forest

Elevated atmospheric nitrogen (N) deposition potentially enhances the degree of phosphorus (P) limitation in tropical and subtropical forests. However, it remains elusive that how soil microorganisms deal with the N deposition-enhanced P limitation. We collected soils experienced 9 years of manipulative N input at various rates (0, 40, and 80 kg N ha−1 y−1) in an old-growth subtropical natural forest. We measured soil total and available carbon (C), N and P, microbial biomass C, N and P, enzyme activities involved in C, N and P acquisition, microbial community structure, as well as net N and P mineralization. Additionally, we calculated element use efficiency and evaluated microbial homeostasis index. Our findings revealed that N input increased microbial biomass C:P (MBC:P) and N:P (MBN:P) ratios. The homeostasis indexes of MBC:P and MBN:P were 0.68 and 0.75, respectively, indicating stoichiometric flexibility. Interestingly, MBC:P and MBN:P correlated significantly with the fungi:bacteria ratio (F:B), not with N and P use efficiencies, net N and P mineralization, and enzyme C:P (EEAC:P) and N:P (EEAN:P) ratios. Furthermore, EEAC:P and EEAN:P correlated positively with F:B but did not negatively correlate with the C:P and N:P ratios of available resources and microbial biomass. The effects of N deposition on MBC:P, MBN:P and EEAN:P became insignificant when including F:B as a covariate. These findings suggest that microbes flexibly adapted to the N deposition enhanced P limitation by changing microbial community structure, which not only alter microbial biomass C:N:P stoichiometry, but also the enzyme production strategy. In summary, our research advances our understanding of how soil microorganisms deal with the N deposition-enhanced soil P limitation in subtropical forests.

Community assembly of ectomycorrhizal fungal communities in pure and mixed Pinus massoniana forests

Ectomycorrhizal (EcM) fungi play an important role in nutrient cycling and community ecological dynamics and are widely acknowledged as important components of forest ecosystems. However, little information is available regarding EcM fungal community structure or the possible relationship between EcM fungi, soil properties, and forestry activities in Pinus massoniana forests. In this study, we evaluated soil properties, extracellular enzyme activities, and fungal diversity and community composition in root and soil samples from pure Pinus massoniana natural forests, pure P. massoniana plantations, and P. massoniana and Liquidambar gracilipes mixed forests. The mixed forest showed the highest EcM fungal diversity in both root and bulk soil samples. Community composition and co-occurrence network structures differed significantly between forest types. Variation in the EcM fungal community was significantly correlated with the activities of β-glucuronidase and β−1,4-N-acetylglucosaminidase, whereas non-EcM fungal community characteristics were significantly correlated with β-1,4-glucosidase and β-glucuronidase activities. Furthermore, stochastic processes predominantly drove the assembly of both EcM and non-EcM fungal communities, while deterministic processes exerted greater influence on soil fungal communities in mixed forests compared to pure forests. Our findings may inform a deeper understanding of how the assembly processes and environmental roles of subterranean fungal communities differ between mixed and pure plantations and may provide insights for how to promote forest sustainability in subtropical areas.

Land use predicition accuracy of different supervised classifiers over agriculture and livestock economy-based municipality in Brazil

The dynamics of land use and land cover (LULC) are of great importance for the management of natural resources, sustainable development and urban planning over geographic space, and this condition is sometimes supported by geoprocessing and remote sensing techniques. In addition, machine learning methods automate the classification and modeling of spatialized prediction processes on orbital images, and with the high precision and adherence of these data, important results and conclusions are the result of these methods. The decision for which classification typology presents the best results is related to the application and considering the LULC prediction as input to a cellular automata (CA) network, the performances of Classification and Regression Tree (CART), Random Forest (RF) and Minimum Distance (MID) for predicting land use and occupation in Sinop, Brazil were assessed. Using the median of the reference years 2013 and 2015 to create a transition potential modelling (TPM) neural network, and then predict a scenario in 2017, the performance was verified with Kappa and global accuracy (OA) statistics. With the highest performance, the RF typology reached the best performance in an area of mostly agricultural occupation, separated into four classes (native forest, urban area, water and bare soil/agricultural activity). The errors inherent to each classifier were decisive for a greater prediction error, where the other classifiers (CART and MID) mistakenly classified the urban area class, but which statistically were not gross errors. Considering the ground truth and the best statistical performance, the prediction of land use and occupation for a scenario as seen in Sinop potentially achieves better results with the Random Forest classifier.

Next-gen regional fire risk mapping: Integrating hyperspectral imagery and National Forest Inventory data to identify hot-spot wildland-urban interfaces

The increasing threat of high-severity wildfires in Mediterranean Wildland-Urban Interface (WUI) areas demands to develop effective fire risk assessment and management strategies. Simultaneously, the newfound accessibility of spaceborne hyperspectral data represents a significant potential for generating fire severity assessments, whereas National Forest Inventories (NFI) offer a vast dataset related to vegetation and fuel loads, which is essential for shaping the planning and strategies of forest services. This research work aims to advance the state-of-the-art in WUI fire risk mapping in the western Mediterranean Basin by combining PRISMA spaceborne hyperspectral data and Spanish NFI data. The proposed methodology had three main stages: (i) fire severity assessment at local scale (a wildfire) by using PRISMA hyperspectral data and Multi-Endmember Spectral Mixture Analysis (MESMA) leveraging field-based measurements of the Composite Burn Index (70 plots); (ii) development of a high fire severity probability map at regional scale from the extrapolation of a Random Forest predictive model calibrated from fire severity estimates, NFI data and topo-climatic variables at local scale (overall accuracy = 92 %; Kappa = 0.8); and (iii) identification and characterization of zones that concentrate WUIs with high probability of high fire severity if a fire event occurs (hot-spot WUIs) by crossing the information from the previous regional high fire severity probability map and a WUI cartography developed at regional scale. Study area was Castilla y León Autonomous Region (larger Spanish region, 94,226 km2), where the second-largest extreme Spanish wildfire event (28,000 ha) occurred. We identified hot-spot WUIs so that stakeholders and decision-makers could (i) prioritize resources and interventions for effective fire management and mitigation, (ii) allocate resources for prevention, and (iii) plan evacuation measures to safeguard lives and property. This study contributes to the development of next-generation fire risk assessment methods that combine remote sensing technologies with comprehensive ground-level datasets.

Current status of tree species diversity at Abu Gadaf Natural Forest Reserve, Blue Nile Region – Sudan

Current status of tree species diversity at Abu Gadaf Natural Forest Reserve, Blue Nile Region – Sudan

The Impact of Social Forestry on Communities and Forest Areas Salimbutan Lubuk Alung

Indonesia is one of the countries that has the largest area of ​​natural forest in the world and is often referred to as one of the world's lungs which contributes oxygen for the survival of living things. Even though forests have many benefits, if the benefits from forests are not managed properly and correctly, they can have negative impacts and can lead to disasters and conflicts, apart from that, there is a fairly high level of deforestation and poverty is still often found in communities in and around forests. made various parties including the Ministry of Environment and Forestry try to encourage community empowerment programs. One of these community empowerment programs is Social Forestry. This type of research uses a quantitative approach with a descriptive design in the form of a case study. The population used in this research is the area and managers of LPHN Salimbutan Lubuk Alung, Padang Pariaman Regency. The samples from this research are all areas managed by LPHN Lintasutan Lubuk Alung. The instruments used in this research were observations and questionnaires. The results of this research show that in 2017 the level of deforestation at the Salimbutan LPHN increased by 9.7 Ha, then in 2018 it decreased by 2.6 Ha, in 2019 by 0.8 Ha, in 2020 by 1.4 Ha, in 2021 with a total of 0.8 Ha, and then in 2022 it will increase again with a total of 4.7 Ha. Meanwhile, community income before the training and assistance was still relatively low with a percentage of 11.16%. The data after training and assistance was carried out in the Salimbutan LPHN area, community income increased by 28.08% in 2022 and 57.38% in 2023

Impacts of Land-Use Change from Primary Forest to Farmland on the Storage of Soil Organic Carbon

Impacts of Land-Use Change from Primary Forest to Farmland on the Storage of Soil Organic Carbon

Light drives nitrogen fixation in tropical montane cloud forests in Costa Rica

Tropical montane cloud forests are high altitude ecosystems characterized by very high ambient humidity, which favors organisms that depend on the environment for their water status, such as bryophytes and their nitrogen-fixing symbionts. Bryophyte-associated N2 fixation is a major source of new N in several northern environments, but their contributions to the N cycle in other ecosystems is still poorly understood. In this work, we evaluated N2 fixation rates associated with epiphytic bryophytes growing along the stems of pumpwood trees (Cecropia sp.) as well as in surrounding litter and soil from a primary and a secondary cloud forests in the Talamanca Mountain Range, Costa Rica. Nitrogen fixation was significantly higher in substrates from the secondary forest compared to those from the primary forest. Overall, N2 fixation rates associated with epiphytic bryophytes were 57 times those of litter and 270 times what was measured in soil. Further, light intensity was the major factor influencing N2 fixation rates in all substrates. Increased access to light in disturbed cloud forests may therefore favor bryophyte-associated N2 fixation, potentially contributing to the recovery of these ecosystems.

What fire didn’t take away: plant growth-promoting microorganisms in burned soils of old-growth Nothofagus forests in Los Andes Cordillera

What fire didn’t take away: plant growth-promoting microorganisms in burned soils of old-growth Nothofagus forests in Los Andes Cordillera

Characteristics, Sources, and Mechanisms of Soil Respiration under Simulated Rainfall in a Native Karst Forest in Southwestern China

Rainfall significantly affects soil respiration rates by altering microbial activity and organic matter decomposition. In karst regions, it also impacts carbonate dissolution and precipitation, further influencing soil CO2 flux. Investigating the mechanism of rainfall’s impact on soil respiration is essential for accurately evaluating and predicting changes in terrestrial ecosystems. However, our understanding of the interaction between rainfall and soil respiration in the extensive karst ecosystems of southwestern China remains limited. This study conducted field-based simulated rainfall experiments to examine variations in soil respiration rates and elucidate the associated control mechanisms through stable carbon isotope composition analysis. Simulated rainfall significantly increased the CO2 release via soil respiration. We observed significant differences in the δ13C value of soil-respired CO2 before and after simulated rainfall. Following the rain, the δ13C of soil-respired CO2 was enriched compared to that before the rain. Through isotope data analysis, we found that the increased soil CO2 emissions were primarily driven by heterotrophic respiration, likely stimulated via changes in soil moisture, affecting microbial growth conditions. Furthermore, the variation in soil moisture affected carbonate dissolution and precipitation, potentially increasing the soil CO2 release after rainfall. In conclusion, these findings expand our understanding of rainfall’s effects on soil respiration in the native karst forests of southwestern China, contributing to the prediction of carbon cycling processes in such ecosystems. The data from this study have significant implications for addressing the release of greenhouse gases in efforts to combat climate change.

Impacts and implications of anthropogenic activities on mangrove forests: A review

The purpose of this paper is to review and evaluate published literature on the causes, impacts, implications and mitigation strategies of anthropogenic activities on mangrove forests. A systematic method was utilized to access research works of literature on “Impacts and implications of anthropogenic activities on mangrove forests”. A total of fifty-three (53) research papers published between the years 1975 to 2024 was accumulated and used for this review. A subjective approach was used to select the topics: impact and implications of anthropogenic activities on mangrove forests. In this paper, five (5) roles of mangroves were assessed and presented. Anthropogenic activities that threaten mangrove forests such as land conversion, mining and logging activities and household and industrial activities were also evaluated. Further, four (4) detrimental anthropogenic activities and the potential impacts they have on natural mangrove forests and the ecosystems they thrive in was also reported on. In addition, this paper reviewed some mitigation strategies for sustainable management and conservation of our mangroves, their ecosystems and the coastlines. The published works of literature established that reduced mangrove forests, altered mangrove composition, unequal species abundance, mass mangrove mortality, pollution in mangrove areas, rising sea levels, global climate change and coastal erosion or abrasion are all effects of anthropogenic activity that have a negative impact on mangrove forests and the ecosystems. This review highlights the fact that more studies on the impact and implications of anthropogenic activities on mangroves should be done in neotropical countries since there are paucity of such information on research and published data in these biodiversity rich regions.

Placing 21st Century Warming in Southern California, USA in a Multi-Century Historical Context

Warming in southern California during the 21st century is unprecedented in the instrumental record. To place this warming in a multi-century historical context, we analyzed tree ring data sampled from Jeffrey pine (Pinus jeffreyi) and sugar pine (Pinus lambertiana) collected from minimally disturbed, old-growth high-elevation forests within Mt. San Jacinto State Park California, USA. Based on a calibration/verification period of 1960–2020 between earlywood radial growth and California Climate Division 6 climate data, we reconstructed annual (November–October) minimum temperature (Tmin) from 1658 to 2020. During the 61-year calibration/verification period, instrumental Tmin increased (r = 0.69, p &lt; 0.01) and was positively associated with annual radial growth (r = 0.71, p &lt; 0.01). Using regime shift analysis, we found that the 363-year reconstruction revealed Tmin stability until 1958 and then decreased until 1980, followed by the two warmest regimes (1981–2007, 2008–2020) on record. The last 13-year period was 0.77 °C warmer than the multi-century average with nine of the ten warmest years in the reconstruction recorded. These results suggest that 21st century warming in southern California is unique in the context of the past four centuries, indicating the rarity of exceptional warmth captured in the tree ring record.

Plant diversity in old-growth woods: the case of the forest edges of the Favorita Park in Palermo (north-western Sicily, Italy)

This article presents the results of a study on plant diversity at different levels in residual forest stands, located in the historical Favorita Park in Palermo, Italy (established and named in 1799 by King Ferdinand IV of the House of Bourbon). These forest aspects have naturally evolved for over two centuries, under minimal conditions of anthropogenic disturbance (e.g. deforestation, fires, grazing activities, etc.). This is especially true in the area known as “Bosco Niscemi”, spread over about 8.5 hectares, in the centre of the park. Bosco Niscemi is characterized by the widespread presence of old trees, abundant necromass and litter. In this study, four different soil profiles were analysed, and classified as follows: (i) Solimovic Regosol (Arenic); ii) Eutric Arenosol (Chromic); (iii) and (iv) Skeletic Regosol (Ochric). From a phytosociological point of view, four forest communities have been identified, two of which are described as new associations (Viburno tini-Phillyreetum latifoliae ass. nova and Teucrio flavi-Phillyreetum latifoliae ass. nova). The species richness was also found to be noteworthy, with the co-occurrence of several taxa (phanerogams and cryptogams) that are of biogeographic interest or rare in Sicily, including: i) vascular flora (e.g. Viburnum tinus and Arbutus unedo, both very rare throughout the western sector of Sicily); ii) bryophytes [Cryphaea heteromalla (new record of a very rare species in Sicily) as well as Hypnum cupressiforme and Leptodon smithii, also never previously found at such low altitudes]; iii) lichens (e.g. Bacidia rosella, Gyalecta derivata, Ramalina roesleri and Waynea stoechadiana); iv) mushrooms (e.g. Eichleriella leucophaea, only known location in Italy). Based on the scientific documentation produced in this study, these woods can be reported as “old-growth forests” to be included in the “National Network” that has been recently established in Italy (Ministerial Decree of 23 June 2023). This area might serve as an ideal control for urban environmental studies, given its pristine ecological setting.

Forest Worker Households in the NFPP: Enhancing Sustainable Livelihoods through Capital and Transformation

The persistent conflict between strict conservation and community welfare highlights the growing need to address sustainable livelihoods in forest protection programs. The Natural Forest Protection Program (NFPP) is a comprehensive forest protection program spearheaded by the Chinese government. It is designed to facilitate the conservation and restoration of forest ecosystems through a range of interventions, including logging ban, management, tending, and afforestation efforts. Drawing upon longitudinal micro-level household survey data spanning five consecutive years from 2017 to 2021, this research quantifies the sustainable livelihood levels of frontline participants in the NFPP by examining two dimensions: livelihood capital stock and livelihood transformation capacity. Additionally, it investigates the internal differentiation phenomenon within this cohort. The findings suggest that forest worker households engaged in tasks related to forest management, tending, and afforestation are the frontline participants in the NFPP, in contrast to management, technical, and service personnel. Moreover, these forest worker households exhibit a pattern characterized by a higher livelihood capital stock but a lower livelihood transformation capacity compared to non-forest worker households. Furthermore, within forest worker households, there is a significant group differentiation phenomenon, resulting in inter-group differentials in the sustainable livelihood levels based on geographical and seniority stratification criteria. The developers of the global forest protection program should prioritize addressing the sustainable livelihood issues of frontline participants in the program, especially the real problem of mismatches between livelihood capital stock and livelihood transformation capacity. This can be achieved through designing income incentives, stimulating consumption, and other means to enhance the relatively disadvantaged position of frontline participants while balancing the coordination and fairness of the protection program based on the aspects of both protection and development.

“Hg distribution and accumulation in soil and vegetation in areas impacted by artisanal gold mining in the Southern Amazonian region of Madre de Dios, Peru.”

Artisanal and small-scale gold mining (ASGM) is the primary global source of anthropogenic mercury (Hg) emissions. It has impacted the Amazon rainforest in the Peruvian region of Madre de Dios. However, few studies have investigated Hg's distribution in terrestrial ecosystems in this region. We studied Hg's distribution and its predictors in soil and native plant species from artisanal mining sites. Total Hg concentrations were determined in soil samples collected at different depths (0–5 cm and 5–30 cm) and plant samples (roots, shoots, leaves) from 19 native plant species collected in different land cover categories: naked soil (L1), gravel piles (L2), natural regeneration (L3), reforestation (L4), and primary forest (L5) in the mining sites. Hg levels in air were also studied using passive air samplers. The highest Hg concentrations in soil (average 0.276 and 0.210 mg kg−1 dw.) were found in the intact primary forest (L5) at 0–5 cm depth and in the plant rooting zones at 5–30 cm depth, respectively.Moreover, the highest Hg levels in plants (average 0.64 mg kg−1 dw) were found in foliage of intact primary forest (L5). The results suggest that the forest in these sites receives Hg from the atmosphere through leaf deposition and that Hg accumulates in the soil surrounding the roots. The Hg levels found in the plant leaves of the primary forest are the highest ever recorded in this region, exceeding values found in forests impacted by Hg pollution worldwide and raising concerns about the extent of the ASGM impact in this ecosystem. Correlations between Hg concentrations in soil, bioaccumulation in plant roots, and soil physical-chemical characteristics were determined. Linear regression models showed that the soil organic matter content (SOM), pH, and electrical conductivity (EC) predict the Hg distribution and accumulation in soil and bioaccumulation in root plants.