Forest Ecosystem Health Research Articles

From Field Data to Practical Knowledge: Investigating the Bioecology of the Oak Lace Bug-An Invasive Insect Species in Europe.

Corythucha arcuata, commonly known as the oak lace bug (OLB), is an insect species originally native to North America that has become an invasive species of significant concern in Europe. This invasive pest has been observed in various European countries, raising concerns about its impact on forest ecosystems. In 2015, it was first documented in Romania, further highlighting the need for research on its bioecology and life cycle. This study investigated the bioecology of the OLB in the southern region of Romania, focusing on its life cycle, development, and population dynamics. The results indicated that the OLB has three generations per year and overwinters in the adult stage in sheltered locations. Temperature significantly influenced the timing of egg hatching, nymph appearance, and adult development, with variation observed between generations. Additionally, a life table analysis provided insights into the population dynamics of the OLB in its natural environment, revealing variation in egg laying trends across generations. This research contributes to a better understanding of the OLB's bioecology and provides essential data for forest managers developing science-based management strategies to mitigate its impact. By elucidating the life cycle and development patterns of the OLB in southern Romania, this study aids in the development of predictive models and life tables tailored to the region. These findings empower forest managers with the knowledge needed to make informed decisions for effective OLB management, ultimately preserving the health of forest ecosystems.

Total and component forest aboveground biomass inversion via LiDAR-derived features and machine learning algorithms

Forest aboveground biomass (AGB) and its biomass components are key indicators for assessing forest ecosystem health, productivity, and carbon stocks. Light Detection and Ranging (LiDAR) technology has great advantages in acquiring the vertical structure of forests and the spatial distribution characteristics of vegetation. In this study, the 56 features extracted from airborne LiDAR point cloud data were used to estimate forest total and component AGB. Variable importance–in–projection values calculated through a partial least squares regression algorithm were utilized for LiDAR-derived feature ranking and optimization. Both leave-one-out cross-validation (LOOCV) and cross-validation methods were applied for validation of the estimated results. The results showed that four cumulative height percentiles (AIH30,AIH40, AIH20, and AIH25), two height percentiles (H8 and H6), and four height-related variables (Hmean, Hsqrt, Hmad, and Hcurt) are ranked more frequently in the top 10 sensitive features for total and component forest AGB retrievals. Best performance was acquired by random forest (RF) algorithm, with R2 = 0.75, root mean square error (RMSE) = 22.93 Mg/ha, relative RMSE (rRMSE) = 25.30%, and mean absolute error (MAE) = 19.26 Mg/ha validated by the LOOCV method. For cross-validation results, R2 is 0.67, RMSE is 24.56 Mg/ha, and rRMSE is 25.67%. The performance of support vector regression (SVR) for total AGB estimation is R2 = 0.66, RMSE = 26.75 Mg/ha, rRMSE = 28.62%, and MAE = 22.00 Mg/ha using LOOCV validation and R2 = 0.56, RMSE = 30.88 Mg/ha, and rRMSE = 31.41% by cross-validation. For the component AGB estimation, the accuracy from both RF and SVR algorithms was arranged as stem > bark > branch > leaf. The results confirmed the sensitivity of LiDAR-derived features to forest total and component AGBs. They also demonstrated the worse performance of these features for retrieval of leaf component AGB. RF outperformed SVR for both total and component AGB estimation, the validation difference from LOOCV and cross-validation is less than 5% for both total and component AGB estimated results.

Microbial biomarkers of tree water status for next-generation biomonitoring of forest ecosystems.

Next-generation biomonitoring proposes to combine machine-learning algorithms with environmental DNA data to automate the monitoring of the Earth's major ecosystems. In the present study, we searched for molecular biomarkers of tree water status to develop next-generation biomonitoring of forest ecosystems. Because phyllosphere microbial communities respond to both tree physiology and climate change, we investigated whether environmental DNA data from tree phyllosphere could be used as molecular biomarkers of tree water status in forest ecosystems. Using an amplicon sequencing approach, we analysed phyllosphere microbial communities of four tree species (Quercus ilex, Quercus robur, Pinus pinaster and Betula pendula) in a forest experiment composed of irrigated and non-irrigated plots. We used these microbial community data to train a machine-learning algorithm (Random Forest) to classify irrigated and non-irrigated trees. The Random Forest algorithm detected tree water status from phyllosphere microbial community composition with more than 90% accuracy for oak species, and more than 75% for pine and birch. Phyllosphere fungal communities were more informative than phyllosphere bacterial communities in all tree species. Seven fungal amplicon sequence variants were identified as candidates for the development of molecular biomarkers of water status in oak trees. Altogether, our results show that microbial community data from tree phyllosphere provides information on tree water status in forest ecosystems and could be included in next-generation biomonitoring programmes that would use in situ, real-time sequencing of environmental DNA to help monitor the health of European temperate forest ecosystems.

THE EFFECTIVENESS OF CURRENT NATIONAL POLICIES ON INTEGRATED FOREST MANAGEMENT AND CLIMATE CHANGE: SYSTEMATIC LITERATURE REVIEW (SLR)

This research topic focuses on exploring anthropological insights with reference to national policies on integrated forest management and climate change. Forests are a crucial element of ecosystem which provide a range of ecological, economic, and social benefits, and play a significant role in mitigating climate change. Integrated forest management (IFM), which involves the sustainable use of forests through a combination of conservation and development activities, is an important strategy for ensuring the long-term health and productivity of forest ecosystems. This research seeks to identify the socio-cultural factors that shape forest management practices, as well as the impacts of climate change on forest-dependent communities. The methodology of the study was systematic literature review (SLR), for which, researcher has collected seventeen papers from Google Scholar covering the period from 2012 to 2022 for SLR, focusing on the themes of integrated forest management, climate change and policies, specifically from Pakistan. This study underscores the significant positive impact that IFM can have on the environment and climate, fostering ecosystem-friendly conditions, along the need for ongoing assessment in ensuring its continual effectiveness in fostering sustainable practices and positive outcomes for both ecosystems and human societies. The insights gained from this research can inform the development of national policies that take into account the needs and perspectives of forest-dependent communities and promote sustainable forest management practices that are responsive to the impacts of climate change. Regular monitoring and evaluation of IFM practices may help to promote sustainable integrated forest management which will in turn improve our environment and ecosystem friendly climate and a better human life. Key words: Effectiveness, Forest Management, Integrated Forest Management, National Policies, Climate Change

Bacterial and Fungal Community Profiling of Karst Ecosystem in Basey, Samar, Philippines Using Shotgun Metagenomic Approach

The Philippines has an abundance of karst or forest over limestone landscapes, but only a small percentage of them have been studied and protected. Although the flora and fauna of karst forests are diverse and unique, little is known about the microflora that inhabits this ecosystem. The taxonomic and functional composition of bacteria and fungi in soil samples from three locations within three karst forest zones in Basey, Samar, Philippines were analyzed using Illumina shotgun metagenome sequencing. Proteobacteria and Actinobacteria bacterial sequences were most prevalent in the karst soil, followed by those of the Firmicutes, Acidobacteria, Chloroflexi, Planctomycetes, Cyanobacteria, Verrumicrobia, Bacteriodetes, and Deinococcus-Thermus. The most abundant fungal sequences belonged to Ascomycota, followed by Basidiomycota. An average of 33 million predicted protein features was detected across all sites. Enzyme pathways for nitrogen and sulfur metabolism, and several carbon fixation pathways, appeared nearly complete. To our knowledge, this is the first report to provide baseline information on the microbial community and their possible roles in karst forest ecosystem health in the Philippines, which may lead to identifying new microbes with specialized metabolism and promoting biodiversity conservation of karst forests in the Philippines. Furthermore, correlation analysis with plant diversity will reveal plant-microbe interaction leading to the understanding of the adaptation, abundance, survival, and diversity of microorganisms and plants.

Spatial-temporal mapping of forest vegetation cover changes along highways in Brunei using deep learning techniques and Sentinel-2 images

Infrastructure development is a leading driver of forest cover loss in the tropics, resulting in a significant decrease in biodiversity. With recent advancements in digital image processing and satellite imagery techniques, this study presents an approach for tracking forest cover alterations along a recently constructed highway in Brunei. Specifically, this paper harnesses the power of cutting-edge deep learning (DL) models; U-Net and Deeplabv3+, to classify forest and non-forest features from Sentinel-2 satellite images captured along the Telisai–Lumut highway between 2018 and 2022. A computational performance analysis and network comparison revealed that the Deeplabv3+ had a remarkable semantic segmentation performance, with a mean intersection over union of 0.9247, followed by an ensemble of four networks, with a mean intersection over union of 0.9171. The best model consistently outperformed other models in segmenting forest and non-forest features, with an intersection over union (IOU) of 0.9463 and 0.9031, respectively. Moreover, deploying hyperparameter optimization allowed us to assess the model's sensitivity to different hyperparameters for better results. Furthermore, forest change statistics were computed using the best model's generated forest change maps, and the results indicated a 16.7% gain in the overall forest vegetation cover along the highway, demonstrating the validity of the results. These reproducible findings will provide forest managers and ecologists with near real-time information on the status, health, and extent of forest ecosystems. This study's results have critical implications for using DL to monitor forest cover changes and will be of great interest to the scientific community as they open new avenues for future research and underline the need for continued innovation in this important field.

Spatial-Temporal Evolution, Trade-Offs and Synergies of Ecosystem Services in the Qinba Mountains

A scientific understanding of the trade-offs and synergies of ecosystem services is the prerequisite for maintaining the structure, function and health of forest ecosystems, which is conducive to promoting the “win-win” goal of economic development and ecological protection. As an important ecological function area in China, the Qinba Mountain region is responsible for important ecological services such as biodiversity conservation and water conservation, and exploring the trade-offs and synergistic relationships of ecosystem services is important for ecological conservation and high-quality development in this region. This paper analyzes the spatial and temporal characteristics of water conservation, soil conservation, carbon sequestration, and habitat quality services and their synergistic/balancing relationships in the Qinba Mountain region from 1990 to 2020 using tools such as the InVEST model, ArcGIS, and Matlab. The results showed that (1) the overall trend of water connotation, soil conservation and carbon sequestration in the Qinba Mountains is increasing, while the trend of habitat quality is fluctuating and decreasing. The spatial distribution pattern of water conservation and soil conservation services was “high in the southwest and low in the northeast”, while the spatial pattern of habitat quality services was the opposite; the spatial distribution pattern of carbon sequestration services was “low in the middle and high around”. (2) Habitat quality in the Qinba Mountains has a trade-off relationship with water connotation and soil conservation, as water connotation–soil conservation is a synergistic relationship, while carbon sequestration is unrelated to water connotation–soil conservation and habitat quality. (3) The area of habitat quality–water conservation showed a decreasing trend; the area of habitat quality–soil conservation showed an increasing trend; the area of habitat quality-water conservation showed a decreasing trend; the area of habitat quality-soil conservation showed an increasing trend; the area of water conservation-soil conservation service synergistic relationship showed a decreasing and then increasing trend; while the area of carbon sequestration service and In most of the regions, carbon sequestration, soil conservation and habitat quality services are not related to each other.

Effects of decreased precipitation and thinning on soil respiration in a temperate forest: A one-year field experiment in Central China

Silvicultural practices are essential for alleviating the impacts of drought on the health and structure of forest ecosystems. However, it is yet uncertain how precipitation reduction and forest management together affect soil carbon (C) cycling. Therefore, we carried out a one-year field manipulative experiment to examine the influences of decreased precipitation and thinning on soil respiration (Rs) and its heterotrophic component (Rh) in a coniferous-broadleaved mixed forest in a warm temperate-subtropical climate transition zone in Central China. We found that thinning significantly suppressed Rs by 13.9%, but decreased precipitation by 25% did not affect it, and decreased precipitation by 50% significantly decreased it by 18.6%. Thinning significantly reduced Rh by 12.2%, and decreased precipitation by 25% and 50% significantly decreased Rh by 15.9% and 23.5%, respectively. The decreasing Rh was primarily to blame for the reduction in Rs. Reduced soil moisture and microbial biomass C were the key regulators of declines in Rh and Rs caused by decreased precipitation or thinning. There was no evidence of an interactive impact between decreased precipitation and thinning on Rs or Rh. Our findings indicate that decreased precipitation and thinning may alleviate soil C release in the short term and enhance their potential benefits for soil C accumulation under future climate change scenarios.

Nitrogen Deposition May Benefit to Larix olgensis Root Soils

Atmospheric nitrogen deposition affects the health of forest ecosystems by altering soil microbial activity. However, the effects of nitrogen addition levels, morphology and ecosystem type on whether nitrogen addition is beneficial or detrimental to soil health is controver-sial, and most studies have focused on the negative effects on microbial structure. Based on this, this study conducted a four-year experiment of nitrogen (NaNO3) addition at two levels (10 and 20 kg N hm−2·yr−1) in the understory soil of Larix olgensis in northeastern China to study soil microbial properties, soil enzyme activities, and to analyze soil physi-cochemical properties and the correlation between them. The results showed that nitrogen addition reduced soil pH and increased soil NH4+-N and NO3−-N contents, thus promoting the activities of Urease (Ure), Acid phosphatase (ACP) and N-Acetamidoglucosidase (NAG) and inhibiting the activity of Leucine aminopeptidase (LAP) in soil, further improving the diversity and richness of soil microorganisms and increasing the dominant taxa of beneficial microorganisms. This may be due to soil acidification caused by the addition of nitrogen, which increases the effectiveness of nitrogen in the soil, improving soil properties, moving soil health in a beneficial direction, promoting beneficial microbial activity, and making the soil more suitable for the growth of the acid-loving tree species L. olgensis. In general, N addition favored the development of soil bacterial communities and the maintenance of soil nutrient status, and had a positive effect on the soil nutrient status of L. olgensis. The results of this study may provide an important scientific basis for adaptive management of forest ecosystems in the context of global nitrogen deposition.

The relationship between tree species and wood colonising fungi and fungal interactions influences wood degradation

Deadwood is one of the main terrestrial carbon (C) pools and its decomposition is fundamental in biogeochemical cycles. As fungi are the main wood degraders, a study was carried out on deadwood-colonising fungal community changes as a function of host plants and wood degradation. Deadwood fungal communities were investigated in adjacent woody plantations of hybrid poplar, willow and black locust established in the early 2000 s and abandoned for fifteen years. Lying deadwood was estimated in the three plantations and wood decay (WD) visually assessed using a 3-class scale. Fungal colonization of wood samples was assessed using culture-based methods and identified with morphological and molecular features. Wood colonising fungal communities differed in composition both between host plants and WD classes (P < 0.001). Ligninolytic white rot fungal species (Basidiomycota) prevailed in black locust and poplar, where they co-colonised all three wood decay classes in association with soft rot fungal agents. In willow, Daldinia chilidiae, a ligninolytic ascomycete, co-colonised deadwood with fungal species associated with tree dieback (Botryosphaeriaceae); whilst soft rot fungi principally colonised the highest wood degradation class. Results showed high fungal preference towards tree species that was most evident in ligninolytic fungi. Moreover, different wood decay paths were deduced from fungal changes across the three wood degrading classes and the necromass distribution in those classes. The co-habitation of white rot and soft rot fungi in all the wood decay classes of poplar and black locust indicated a continuum decay process; whilst the prevalence of soft rot fungi in the almost decomposed class in willow, suggested sequential succession of primary and secondary phases in deadwood degradation. Wood degradation is influenced by plant species and the native fungal microbiome and is mediated by multiple biotic interactions and environmental factors, which supports the use of wood colonising fungi as indicators of forest ecosystem health.

Measuring forest health at stand level: A multi-indicator evaluation for use in adaptive management and policy

Assessments of forest ecosystem health for use in adaptive management need an integrative multi-indicator examination at the stand scale. To assess forest health, we exained multiple forest indicators including diversity, age structure, regeneration, and edaphic factors of the dominant and associated tree species in their natural forest habitats. A stratified random cluster sampling strategy was used to gather vegetation samples from the five main forest types in the Zabarwan Mountain Range—Acacia forest (ACFT), Broad leaved forest (BLFT), Oak forest (OKFT), Pinus wallichiana forest (PWFT), and Scrub forest (SRFT). The Pearson method and canonical correspondence analysis (CCA) were used to investigate the relationship between tree species and edaphic factors. A total of 22 tree species were found, of which 13 were exotic and 9 were native. The proportion of exotic species was highest in OKFT (85%), followed by BLFT (75%), and the least (50%) SRFT. The BLFT forest type had the highest Shannon diversity while the lowest was the SRFT. ACFT and BLFT forest types have significantly higher Shannon diversity indexes than other forest types. Based on the density-girth class distribution, ACFT & SRFT forest types showed an Inverse-J distribution pattern, indicating a stable population structure. The dominant tree species, such as Populus alba in BLFT, demonstrated comparatively no regeneration, whereas Parrotiopsis jacquemontiana in SRFT, Pinus wallichiana in PWFT, Quercus robur in OKFT, and Robinia pseudoacacia demonstrated adequate regeneration performance. Overall exotic tree species such as Robinia pseudoacacia, Prunus cerasifera, Celtis australis, and Ailanthus altissima showed high/sufficient regeneration performance. The average seedling/tree value for all forest types in the area was 2.14, with the highest value at BLFT (3.61) and the lowest value at SRFT (0.71). In the CCA it showed that SRFT forests were greatly influenced by salinity and organic carbon, whereas ACFT and OKFT forests had comparable habitat preferences and were mutually influenced by electrical conductance and phosphorus availability. Prunus cerasifera was the only species positively associated with available calcium. By combining the data of numerous field-based indicators into a single integrated study, our research will give decision-makers an update on a forest's current and anticipated health.

A dataset of ecological factors in the distribution area of Tianshan spruce in Yili region in 2014

Tianshan spruce (Picea schrenkiana var. tianschanica ) is an important tree for water conservation, windbreak and sand fixation in the mountains of Xinjiang, and plays an important role in the formation and maintenance of forest ecosystem in Xinjiang. Ecological factors such as temperature, precipitation, soil and topography are important data bases for studying forest ecosystem. Based on multi-source data such as remote sensing image, DEM, meteorological data and soil data in Yili region of Xinjiang, we used multi-scale segmentation, nearest neighbor classification and spatial analysis to generate a dataset of ecological factors in the distribution area of Tianshan spruce in Yili region in 2014, covering six ecological factors, namely temperature, precipitation, sunshine duration, slope, slope direction and soil type. In this dataset, strict procedures of data quality control were carried out by TTA Mask accuracy verification and other methods to ensure the accuracy and reliability of the data. The dataset can provide data support for forest ecosystem health assessment.

A Novel Vegetation Index Approach Using Sentinel-2 Data and Random Forest Algorithm for Estimating Forest Stock Volume in the Helan Mountains, Ningxia, China

Forest stock volume (FSV) is a major indicator of forest ecosystem health and it also plays an important part in understanding the worldwide carbon cycle. A precise comprehension of the distribution patterns and variations of FSV is crucial in the assessment of the sequestration potential of forest carbon and optimization of the management programs of the forest carbon sink. In this study, a novel vegetation index based on Sentinel-2 data for modeling FSV with the random forest (RF) algorithm in Helan Mountains, China has been developed. Among all the other variables and with a correlation coefficient of r = 0.778, the novel vegetation index (NDVIRE) developed based on the red-edge bands of the Sentinel-2 data was the most significant. Meanwhile, the model that combined bands and vegetation indices (bands + VIs-based model, BVBM) performed best in the training phase (R2 = 0.93, RMSE = 10.82 m3ha−1) and testing phase (R2 = 0.60, RMSE = 27.05 m3ha−1). Using the best training model, the FSV of the Helan Mountains was first mapped and an accuracy of 80.46% was obtained. The novel vegetation index developed based on the red-edge bands of the Sentinel-2 data and RF algorithm is thus the most effective method to assess the FSV. In addition, this method can provide a new method to estimate the FSV in other areas, especially in the management of forest carbon sequestration.

The Trade-Offs and Synergies of Ecosystem Services in Pinus massoniana Lamb. Plantations in Guangxi, China

A scientific understanding of the synergistic and trade-off relationships among ecosystem services (ESs) is essential for maintaining the structure, function, and health of plantation forest ecosystems. This understanding facilitates effective ecosystem management practices, and helps identify the types, intensities, and spatial and temporal distribution characteristics of interactions among ESs, which is critical for regional development planning, ecological supplementation, and the maximization of economic benefits. In this study, we used correlation analysis, bivariate spatial autocorrelation, and hot spot analysis to comprehensively analyze the synergistic and trade-off relationships between ESs in Pinus massoniana (PM) plantations in Guangxi Paiyang Forest Farm from 2009 to 2018, across both time and space. The study showed that the ESs in PM plantations in Guangxi Paiyang Forest Farm maintained significant positive correlation (synergy), with a mutually reinforcing relationship among services. Notably, the regulating services shifted from weak synergy to weak trade-off relationships over time. From the bivariate spatial autocorrelation analysis, it is clear that the overall trade-off synergistic relationship among the four ESs is basically consistent with the correlation analysis results. From the distribution of multiple ES hot spots, we determined that the number of small groups that can provide three to four service hot spots in Guangxi Paiyangshan Forest Farm is greater. The spatial distribution of cold–hot spots of various ESs varied, and the distribution of cold–hot spots of supply services and regulation services of carbon sequestration and oxygen release was similar.

Long-Read Sequencing Analysis Revealed the Impact of Forest Conversion on Soil Fungal Diversity in Limu Mountain, Hainan.

Soil fungi are essential to soil microorganisms that play an important role in the ecosystem's soil carbon cycle and mineral nutrient transformation. Understanding the structural characteristics and diversity of soil fungal communities helps understand the health of forest ecosystems. The transition from tropical rainforest to artificial forest greatly impacts the composition and diversity of fungal communities. Hainan Limushan tropical rainforest National Park has a large area of artificial forests. Ecologists have conducted in-depth studies on the succession of animals and plants to regenerate tropical rainforests. There are few reports on the diversity of soil fungi and its influencing factors in the succession of tropical rainforests in Limu Mountain. In this study, 44 soil samples from five different stands were collected in the tropical rainforest of Limushan, Hainan. High-throughput sequencing of rDNA in its region was used to analyze fungal communities and study their α and β diversity. Analysis of variance and multiple regression models was used to analyze soil variables and fungal functional groups to determine the effects of interaction between fungi and environmental factors. A total of 273,996 reads and 1290 operational taxonomic units (OTUs) were obtained, belonging to 418 species, 325 genera, 159 families, eight phyla, 30 classes, and 73 orders. The results showed that the composition of soil fungal communities in the five stands was similar, with ascomycetes accounting for 70.5% and basidiomycetes accounting for 14.7%. α and β diversity analysis showed that soil fungi in Limushan tropical rainforest had high abundance and diversity. Multiple regression analysis between soil variables and functional groups showed that organic matter, TN, TP, TK, and AK were excellent predictors for soil fungi. TP was the strongest predictor in all functional groups except soil saprotroph. Organic matter and total nitrogen were the strongest predictors of soil rot. The transformation from tropical rainforest to artificial forest in Limushan did not change the soil fungal community structure, but the richness and diversity of soil fungi changed. The forest transformation did not lead to decreased soil fungal abundance and diversity. Different vegetation types and soil properties affect the diversity of soil fungal communities. We found that Caribbean pine plantations can improve soil fungal diversity, while long-term Eucalyptus spp. plantations may reduce soil fungal diversity.

Soil bacterial community is more sensitive than fungal community to canopy nitrogen deposition and understory removal in a Chinese fir plantation.

Soil microorganisms are key regulators for plant growth and ecosystem health of forest ecosystem. Although previous research has demonstrated that soil microorganisms are greatly affected by understory nitrogen (N) addition, little is known about the effects of canopy N addition (CNA) and understory management on soil microorganisms in forests. In this study, we conducted a full designed field experiment with four treatments: CNA (25 kg N ha–1 year–1), understory removal (UR), canopy N addition, and understory removal (CNAUR) (25 kg N ha–1 year–1), and control in a Chinese fir plantation. High-throughput sequencing and qPCR techniques were used to determine the abundance, diversity, and composition of bacterial and fungal communities in three soil layers. Our results showed that CNA increased bacterial diversity in the 10–20 cm soil layer but decreased bacterial abundance in the 20–40 cm soil layer and fungal diversity in the 0–10 cm soil layer. UR increased bacterial abundance only in the 20–40 cm soil layer. CNA, not UR significantly altered the compositions of soil bacterial and fungal community compositions, especially in the 0–20 cm soil layer. CNA sharply reduced the relative abundance of copiotrophic taxa (i.e., taxa in the bacterial phylum Proteobacteria and the orders Eurotiales and Helotiales in the fungal phylum Ascomycota) but increased the relative abundance of oligotrophic taxa (i.e., in the bacterial phylum Verrucomicrobia). RDA analysis revealed that soil pH, DON, and DOC were the main factors associated with the variation in bacterial and fungal communities. Our findings suggest that short-term CNA changes both soil bacterial and fungal communities, with stronger responses in the surface and middle soil than in the deep soil layer, and that UR may enhance this effect on the soil bacterial abundance. This study improves our understanding of soil microorganisms in plantations managed with understory removal and that experience increases in N deposition.

Understanding the conservation challenges and needs of culturally significant plant species through Indigenous Knowledge and species distribution models

Indigenous People in the Klamath River Basin have cared for and utilized ecosystems and component resources since time immemorial, proactively conserving species through continuous use and stewardship. Though many culturally significant plants are still tended and used by Indigenous people, many species are also experiencing prolonged stress from colonial forest management practices and environmental change. By integrating western and Indigenous ways of knowing, as part of a participatory and collaborative research and extension project, we present an approach to informing the conservation of four culturally significant plants (tanoak, evergreen huckleberry, beargrass, and iris) and understanding the influence of bioclimatic factors and stress on Indigenous people’s relationships with plants and the broader forest ecosystem. Mixed methods and ways of knowing generate a detailed assessment of each case study species that presence only species distribution models cannot supply alone. In this study we use MAXENT to model species distributions of our four study species and the flexible coding method in NVivo for qualitative interview and focus group data. Using species distribution models and 127 interviews and focus groups with cultural practitioners, we found significant shifts in huckleberry harvesting times, beargrass and iris cultural use quality, and tanoak acorn availability that must be addressed for the long-term vitality of these species and interconnected cultures and people. Tribes have generations of knowledge, experience, and connection to land that can help inform how to combat stressors and enhance productivity of forest foods and fibers and the health of forest ecosystems.

Foraging strata and dietary preferences of fifteen species of babblers in Sarawak, Malaysia

Babblers are the primary insectivorous birds of the tropical forests in southeastern Asia which have shown to be affected by forest disturbance. Their high diversity, microhabitat specificity and specialised feeding guilds provide a good opportunity for ecological research pertaining to niche segregation. We examined the diet and foraging strata of 15 sympatric babbler species mist-netted in nine forests in Sarawak, eastern Malaysia. Based on 222 birds captured from December 2014 to March 2016, a segregation in foraging strata was found, with half of the species captured frequenting low strata, while only three were found at mid strata and four at high strata. Both species richness and abundance were found to decrease when the foraging height increased. From a total of 136 prey items retrieved from regurgitated and faecal samples of 13 babbler species, we found that Coleoptera (41.5%), Hymenoptera (36.2%), and Araneae (12.3%) formed the major diet of the birds. Diet overlaps among the babblers were relatively low. Our study demonstrated the possible presence of spatial and trophic niche segregation among babblers, and justified their ecological role as indicators of tropical forest ecosystem health, especially in the case of specialists, that deserve further conservation attention.

Uncontrolled Exploitation of Pterocarpus tinctorius Welw. and Associated Landscape Dynamics in the Kasenga Territory: Case of the Rural Area of Kasomeno (DR Congo)

The uncontrolled logging of Pterocarpus tinctorius Welw. in the Kasenga territory in the southeast of the Democratic Republic of the Congo is of significant socioeconomic benefit, but above all, it is a threat to the stability of forest ecosystems. Based on Landsat images from 2009, 2013, 2017 and 2021, the landscape dynamics of the Kasomeno region in the Kasenga territory, a P. tinctorius exploitation area, was quantified using a mapping approach coupled with landscape ecology analysis tools. The results reveal a continuous loss of forest cover over all the periods studied, mostly between 2013 and 2017, primarily through the dissection of patches. Also, through the spatial process of attrition, the fields recorded a regressive dynamic between 2013–2017, a sign of abandonment of agricultural activity in favour of P. tinctorius illegal logging. These landscape dynamics are the consequences of strong anthropic activities in the study area, leading to an important spatial expansion of the savannah. Consequently, the level of landscape disturbance doubled from 0.8 to 1.7 between 2009 and 2021. Our results suggest that, without regulatory enforcement, illegal logging of P. tinctorius seriously compromises forest ecosystem health and household food security in the region.

Measuring forest ecosystem services in Aceh Province for inclusion to local forest resource management plans

The project trained forest managers from Kesatuan Pengelolaan Hutan (KPH) and Social Forestry units in Aceh Province in forest resource data collection and the use of three tools that report important forest ecosystem services. Data were collected in systematic forest plots by local KPH staff and social forestry community members for measuring forest carbon, tree biodiversity and forest ecosystem health. Teams represented nine different forest ecosystems in Aceh Province, Indonesia. Average forest carbon estimates range from a low of 27.14 Mg C ha&lt;sup&gt;-1&lt;/sup&gt; in a coffee agroforestry system to a high of 446.93 Mg C ha&lt;sup&gt;-1&lt;/sup&gt; in a tropical forest area managed under social forestry. Tree biodiversity ranged from a low of one species as expected in a coastal mangrove forest to a high of 35 species in the tropical forest area managed under social forestry. Forest health conditions on average for the nine areas were mostly healthy, with a few noted in fair condition and one considered to be in poor condition. Resources provided at the local level would enable mainstreaming of field data collection for measuring these forest ecosystem services, and continued and expanded training should be considered to meet national demand.