Forest Survey Research Articles

Tracking foresters and mapping tree stem locations with decimeter-level accuracy under forest canopies using UWB

This paper presents a new method for tracking foresters and mapping tree stem locations under forest canopies utilizing ultra-wideband (UWB) data, in which four estimators (collector, estimator, locator, and extractor) operate closely together. The collector records all the distance measurements (beacon to beacon and terminal to the beacons); the estimator determines the local coordinates of the beacons automatically and precisely based on the distance measured between them; the locator then determines the trajectory of the terminal/forester using the extended Kalman filter (EKF) with the assistance of non-line-of-sight (NLoS) distance measurement classification and processing; the extractor continuously determines the optimal locations of the tree stems. To fully evaluate the performance of the developed method, experiments were conducted under four forest canopies with different tree densities and species. The experimental results show that the root-mean-squared-errors (RMSEs) of the proposed method for the terminal/forester positioning and individual tree stem location are less than 0.3 m with a stable update rate of 20 Hz, and a coverage of larger than approximately 3,000 m2 with only four UWB beacons under forest canopies. Therefore, it is sufficient for forest surveys and management with reduced human efforts.

A Novel Method for Extracting DBH and Crown Base Height in Forests Using Small Motion Clips

The diameter at breast height (DBH) and crown base height (CBH) are important indicators in forest surveys. To enhance the accuracy and convenience of DBH and CBH extraction for standing trees, a method based on understory small motion clips (a series of images captured with slight viewpoint changes) has been proposed. Histogram equalization and quadtree uniformization algorithms are employed to extract image features, improving the consistency of feature extraction. Additionally, the accuracy of depth map construction and point cloud reconstruction is improved by minimizing the variance cost function. Six 20 m × 20 m square sample plots were selected to verify the effectiveness of the method. Depth maps and point clouds of the sample plots were reconstructed from small motion clips, and the DBH and CBH of standing trees were extracted using a pinhole imaging model. The results indicated that the root mean square error (RMSE) for DBH extraction ranged from 0.60 cm to 1.18 cm, with relative errors ranging from 1.81% to 5.42%. Similarly, the RMSE for CBH extraction ranged from 0.08 m to 0.21 m, with relative errors ranging from 1.97% to 5.58%. These results meet the accuracy standards required for forest surveys. The proposed method enhances the efficiency of extracting tree structural parameters in close-range photogrammetry (CRP) for forestry. A rapid and accurate method for DBH and CBH extraction is provided by this method, laying the foundation for subsequent forest resource management and monitoring.

Distinct features of topsoil carbon fractions across urban forests in eastern China

AbstractRapid urbanization has increased the areas of urban forests that store considerable soil carbon (C). Different soil C fractions may show distinctive contents and spatial patterns in view of their contrasting sensitivities to various drivers. However, current studies on soil C fractions are mostly limited to natural ecosystems and little is known about the large‐scale patterns and drivers of soil C fractions in urban forests. Based on a field survey of urban forests across a north–south transect in eastern China, we analysed the spatial variations and main drivers of topsoil (surface layer, 0–10 cm; subsurface layer, 10–20 cm) C fractions (i.e., soil organic C, SOC; soil inorganic C, SIC; particulate organic C, POC; mineral‐associated organic C, MAOC). Our results showed that topsoil contents of POC, MAOC and SOC changed non‐linearly with latitude, with lowest values occurring in the cities in the warm temperate region. In contrast, SIC content showed the highest values in the warm temperate region. POC instead of MAOC was found to be a major fraction of SOC in urban forests. The spatial variation in topsoil POC content was mainly explained by mean annual temperature, soil clay and silt content, and park age. The spatial variation in MAOC content was mainly explained by soil clay and silt content, mean annual precipitation, mean annual temperature and park age. In contrast, the spatial variation in SIC content was mainly explained by mean annual precipitation and soil pH. These findings demonstrate distinct features of different soil C fractions in urban forests and provide useful implications for urban soil carbon management.

Mixed stands of black spruce (Picea mariana) and tamarack (Larix laricina) offer high secondary growth in eastern boreal forests of Canada

The Canadian boreal forest is still lacking investigation into compatible species mixtures that can lead to higher growth rates than monospecific or pure stands. The effect of species mixtures on stand productivity can vary with stem density, species proportion, site characteristics, and climate variation. We investigated mixed stands of black spruce (Picea mariana) and tamarack (Larix laricina) in eastern Canadian boreal forests. Using modern forest survey data, we analyzed stand basal area variation in 756 mature stands exclusively composed of these two species over a study area spanning 535,000 km². We generated a linear relationship between mixed stand basal area and stem density, and the residual of this relation was used as response variable named ‘residual basal area’. Positive and negative deviations of residual basal area were analysed with generalized additive models and quantile regressions with tamarack basal area, climate, and site characteristics as explanatory variables. The selected model included tamarack basal area, mean seasonal precipitation, degree days, mean annual temperature, stand age, latitude, longitude, and the type of surficial deposit. Tamarack exhibited a linear positive effect on predicted residual basal area. The most significant effect was observed from the interaction between tamarack basal area and stand age, predicting positive residual basal area of 16 m² ha−1 when stand aged was 150 years old and tamarack basal area was between 12 and 17 m² ha−1. Quantile regressions revealed that tamarack could enhance basal area while increasing black spruce stem size and density. Landscape managers may consider the establishment of mixed black spruce and tamarack stands to favor secondary growth in eastern Canadian boreal forests.

Land Cover Classification of Remote Sensing Imagery with Hybrid Two-Layer Attention Network Architecture

In remote sensing image processing, when categorizing images from multiple remote sensing data sources, the deepening of the network hierarchy is prone to the problems of feature dispersion, as well as the loss of semantic information. In order to solve this problem, this paper proposes to integrate a parallel network architecture HDAM-Net algorithm with a hybrid dual attention mechanism Hybrid dual attention mechanism for forest land cover change. Firstly, we propose a fusion MCA + SAM (MS) attention mechanism to improve VIT network, which can capture the correlation information between features; secondly, we propose a multilayer residual cascade convolution (MSCRC) network model using Double Cross-Attention Module (DCAM) attention mechanism, which is able to efficiently utilize the spatial dependency between multiscale encoder features: the spatial dependency between multiscale encoder features. Finally, the dual-channel parallel architecture is utilized to solve the structural differences and realize the enhancement of forestry image classification differentiation and effective monitoring of forest cover changes. In order to compare the performance of HDAM-Net, mountain urban forest types are classified based on multiple remote sensing data sources, and the performance of the model is evaluated. The experimental results show that the overall accuracy of the algorithm proposed in this paper is 99.42%, while the Transformer (ViT) is 96.92%, which indicates that the proposed classifier is able to accurately determine the cover type.The HDAM-Net model emphasizes the effectiveness in terms of accurately classifying the land, as well as the forest types by using multiple remote sensing data sources for predicting the future trend of the forest ecosystem. In addition, the land utilization rate and land cover change can clearly show the forest cover change and support the data to predict the future trend of the forest ecosystem so that the forest resource survey can effectively monitor deforestation and evaluate forest restoration projects.

Tree species classification based on PointNet++ deep learning and true- colour point cloud

ABSTRACT Accurate identification of tree species is the foundation of forest resource surveys and an important research field in forestry remote sensing. The introduction of the PointNet++ deep learning network to point cloud processing provides a new approach for tree species identification. This network can directly compute and train unordered point clouds, greatly reducing the manual selection and extraction process of feature data. However, how to establish a high-quality single tree point cloud dataset is a key issue to be solved. In this study, a 2.5-ha temperate coniferous broad-leaved mixed forest located in Mao’er Mountain Experimental Forest Farm in Heilongjiang Province, China, was investigated. The unmanned aerial vehicle (UAV) RGB image and LiDAR synchronous observation system were used to obtain the true-colour point clouds of the forest. Combining upsampling and downsampling methods, a dataset containing coordinates, normal vectors, RGB, and intensity information was constructed from the original point cloud. Comparative experiments were designed based on resampling algorithms, number of sampling points, feature information, and time cost to find the optimal feature information and processing methods for tree species classification. The results showed that the accuracy of tree species classification by using PointNet++ was greatly improved after adding RGB and point cloud intensity information. The classification accuracy was about 5% higher than that using only coordinate data sets. In addition, the combination of PU-Net downsampling network based on point cloud completion and geometric upsampling method achieved the highest classification accuracy (OA = 0.944) when the number of single trees point cloud was 3072. The algorithm also took a relatively shorter running time. This study demonstrated that the introduction of multi-feature information and the optimization of resampling method can provide new solutions for tree species classification based on the PointNet++ deep learning network.

Model-assisted estimation of domain totals, areas, and densities in two-stage sample survey designs

Model-assisted, two-stage forest survey sampling designs provide a means to combine airborne remote sensing data, collected in a sampling mode, with field plot data to increase the precision of national forest inventory estimates, while maintaining important properties of design-based inventories, such as unbiased estimation and quantification of uncertainty. In this study, we present a comprehensive set of model-assisted estimators for domain-level attributes in a two-stage sampling design, including new estimators for densities, and compare the performance of these estimators with standard poststratified estimators. Simulation was used to assess the statistical properties (bias, variability) of these estimators, with both simple random and systematic sampling configurations, and indicated that 1) all estimators were generally unbiased. and 2) the use of lidar in a sampling mode increased the precision of the estimators at all assessed field sampling intensities, with particularly marked increases in precision at lower field sampling intensities. Variance estimators are generally unbiased for model-assisted estimators without poststratification, while model-assisted estimators with poststratification were increasingly biased as field sampling intensity decreased. In general, these results indicate that airborne remote sensing data, collected as an intermediate level of sampling, can be used to increase the efficiency of national forest inventories in remote regions.

Tree Height Estimation of Chinese Fir Forests Based on Geographically Weighted Regression and Forest Survey Data

Tree Height Estimation of Chinese Fir Forests Based on Geographically Weighted Regression and Forest Survey Data

Food security effects of forest sector participation in rural Liberia

Forests can help rural households cope with food insecurity challenges in the face of climate change while also sequestering carbon and advancing other sustainability objectives in low- and middle-income countries (LMICs). As such, participation in the forestry sector can contribute to achieving the Sustainable Development Goals (SDGs), especially on hunger and food security (SDG 2) in forest rich, but economically poor areas of LMICs. However, analysis of the effects of forest sector participation on food security in such contexts remain limited. Here we estimate the effects of participation in forest-based activities like collection and processing of timber and non-timber forest products (NTFPs) on household food security in Liberia, the most forested country in West Africa. We applied endogenous switching poisson regression to data from 1408 households living in the proximity of forests included in Liberia’s 2019 national household forest survey to estimate the effect of forest sector participation on food security (in terms of reduction in the number of months households reportedly had insufficient food). Results show that forest-sector participation reduced the number of months households had insufficient food by 84% (about 2.7 months). Heterogeneity analyses show that NTFPs affected food security by 80% (about 2.5 months) compared to timber and wood products, which had a 66% effect size (about 2.1 months) on average. Our results provide new, national-scale evidence on the role of forests in enhancing food security in Liberia and suggest the importance of forest sector participation for food security and achieving SDG 2 more generally.

China's future forest carbon sequestration potential under different management scenarios

Enhancing forest carbon storage and carbon sequestration capacity is crucial for achieving carbon neutrality. Scientific forest management can maintain a high level of carbon sequestration capacity in forests, and considering the carbon pool of wood products can extend the time of carbon fixation. However, current predictions of large-scale forest carbon storage and carbon sequestration capacity have overlooked changes in forest carbon absorption with forest age and the buffering effect of wood product carbon pools in the carbon release process. In this paper, we used the Forest Simulation and Optimization System model (FSOS) to analyze the wood supply and carbon sequestration capacities of different management scenarios based on data from the Forest Resources Inventory (2014–2018). The results showed that China's forests could export significant amounts of timber in the future; According to the 9th forest survey report, China's forests produce only 88 million m3 yr-1. Among them, the harvested objects were mainly planted forests, and if all forests (planted forests and natural forests) are involved in forest management planning, the maximum sustainable annual wood supply will reach 286 million m3 yr-1. Moreover, due to the current large proportion of younger forests in China, 358 million m3 of annual wood supply will be achievable in the future (as of the year 2039). Forest management can increase the carbon sequestration capacities in forest ecosystems as well as the wood supply compared to the no management options. In summary, the carbon sequestration potential of unmanaged forests is limited. Appropriate forest management can increase the carbon sequestration potential of forests. The substitution of carbon emission reduction of wood products and bioenergy can also greatly reduce the pressure to achieve carbon neutral strategies.

Extraction of Arbors from Terrestrial Laser Scanning Data Based on Trunk Axis Fitting

Accurate arbor extraction is an important element of forest surveys. However, the presence of shrubs can interfere with the extraction of arbors. Addressing the issues of low accuracy and weak generalizability in existing Terrestrial Laser Scanning (TLS) arbor point clouds extraction methods, this study proposes a trunk axis fitting (TAF) method for arbor extraction. After separating the point cloud data by upper and lower, slicing, clustering, fitting circles, obtaining the main central axis, filtering by distance, etc. The canopy point clouds are merged with the extracted trunk point clouds to precisely separate arbors and shrubs. The advantage of the TAF method proposed in this study is that it is not affected by point cloud density or the degree of trunk curvature. This study focuses on a natural forest plot in Shangri-La City, Yunnan Province, and a plantation plot in Kunming City, using manually extracted data from a standardized dataset of samples to test the accuracy of the TAF method and validate the feasibility of the proposed method. The results showed that the TAF method proposed in this study has high extraction accuracy. It can effectively avoid the problem of trunk point cloud loss caused by tree growth curvature. The experimental accuracy for both plots reached over 99%. This study can provide certain technical support for arbor parameter extraction and scientific guidance for forest resource investigation and forest management decision-making.

The forest biodiversity index (FOBI): monitoring forest biodiversity potential over space and time

Public forest agencies are obligated to take steps to conserve and where possible enhance biodiversity, but they often lack information and tools that support and evidence their decision making. To help inform and monitor impact of management actions and policies aimed at improving forest biodiversity, we have co-developed a quantitative, transparent and repeatable approach for assessing the biodiversity potential of the United Kingdom’s (UK) publicly owned forests over space and time. The FOrest Biodiversity Index (FOBI) integrates several forest biodiversity indicators or ‘metrics’, which characterise management-sensitive woodland and landscape features associated with biodiversity. These are measured or modelled annually using spatially comprehensive forest survey data and other well-maintained spatial environmental datasets. Following metric normalisation and a correlation analysis, a statistically robust selection of these metrics is aggregated using a hierarchical procedure to provide composite index scores. The FOBI metric and index results are provided for every individual public forest, and can be summarised across any reporting region of interest. Compared to existing indicators that rely on sample-based forest data, the results thus better support decisions and obligations at a range of scales, from locally targeted action to national, long-term biodiversity monitoring and reporting. We set out how the FOBI approach and associated bespoke online interfaces were co-developed to meet public forest agency needs in two constituent countries of the UK (England and Scotland), whilst providing a conceptual framework that can be adapted and transferred to other geographic areas and private forests. Example results are reported for England’s public forests for four annual timestamps between 2014 and 2021, which indicate improvements to the biodiversity potential of public forests and surrounding landscapes over this time via increases in their diversity, extent, condition and connectivity.

Estimation of Picea Schrenkiana Canopy Density at Sub-Compartment Scale by Integration of Optical and Radar Satellite Images

This study proposes a novel approach to estimate canopy density in Picea Schrenkiana var. Tianschanica forest sub-compartments by integrating optical and radar satellite data. This effort is aimed at enhancing methodologies for forest resource surveys and monitoring, particularly vital for the sustainable development of semi-arid mountainous areas with fragile ecological environments. The study area is the West Tianshan Mountain Nature Reserve in Xinjiang, which is characterized by its unique dominant tree species, Picea Schrenkiana. A total of 411 characteristic factors were extracted from Gaofen-2 (GF-2) sub-meter optical satellite imagery, Gaofen-3 (GF-3) multi-polarization synthetic aperture radar satellite imagery, and digital elevation model (DEM) data. Consequently, 17 characteristic parameters were selected based on their correlation with canopy density data to construct an estimation model. Three distinct models were developed, including a multiple stepwise regression model (a linear approach), a Back Propagation (BP) neural network model (a neural network-based method), and a Cubist model (a decision tree-based technique). The results indicate that combining optical and radar image characteristics significantly enhances accuracy, with an Average Absolute Percentage Precision (AAPP) value improvement in estimation accuracy from 76.50% (with optical image) and 78.50% (with radar image) to 78.66% (with both). Of the three models, the BP neural network model achieved the highest overall accuracy (79.19%). At the sub-component scale, the BP neural network model demonstrated superior accuracy in low canopy density estimation (75.37%), whereas the Cubist model, leveraging radar image characteristics, excelled in medium density estimations (87.46%). Notably, the integrated Cubist model combining optical and radar data achieved the highest accuracy for high canopy density estimation (89.17%). This study highlights the effectiveness of integrating optical and radar data for precise canopy density assessment, contributing significantly to ecological resource monitoring methodologies and environmental assessments.

Cosmological constraints from the eBOSS Lyman-α forest using the PRIYA simulations

We present new cosmological parameter constraints from the eBOSS Lyman-α forest survey. We use a new theoretical model and likelihood based on the PRIYA simulation suite. PRIYA is the first suite to resolve the Lyman-α forest in a (120 Mpc/h)3 volume, using a multi-fidelity emulation technique. We use PRIYA to predict Lyman-α forest observables with ≲ 1% interpolation error over an 11 dimensional (9 simulated, 2 in post-processing) parameter space. We identify an internal tension within the flux power spectrum data. Once the discrepant data is removed, we find the primeval scalar spectral index measured at a pivot scale of k 0 = 0.78 Mpc-1 to be nP = 1.009+0.027 -0.018 at 68% confidence. This measurement from the Lyman-α forest flux power spectrum alone is in reasonable agreement with Planck, and in tension with earlier eBOSS analyses. The amplitude of matter fluctuations is σ 8 = 0.733+0.026 -0.029 at 68% confidence, in agreement with Dark Energy Survey weak lensing measurements and other small-scale structure probes and in tension with CMB measurements from Planck and ACT. The effective optical depth to Lyman-α photons from our pipeline is in good agreement with earlier high resolution measurements. We find a linear power at z = 3 and k = 0.009 s/km of Δ2 L = 0.302+0.024 -0.027 with a slope n eff = -2.264+0.026 -0.018. Our flux power spectrum only chains prefer a low level of heating during helium reionization. When we add IGM temperature data we find nP = 0.983 ± 0.020 and σ 8 = 0.703+0.023 -0.027. Our chains prefer an early and long helium reionization event, as suggested by measurements from the helium Lyman-α forest. In the near future we will use our pipeline to infer cosmological parameters from the DESI Lyman-α data.

Prediction of prognosis in patients with systemic sclerosis based on a machine-learning model.

The clinical manifestations of systemic sclerosis (SSc) are highly variable, resulting in varied outcomes and complications. Diverse fibrosis of the skin and internal organs, vasculopathy, and dysregulated immune system lead to poor and varied prognoses in patients with SSc subtypes. Therefore, this study aimed to develop a personalized tool for predicting the prognosis of patients with SSc. A cohort of 517 patients with SSc were recruited between January 2009 and November 2021 at Xijing Hospital in China, and 266 patients completed the follow-up and performed in the survival analysis. Risk factors for death were identified using Cox survival analysis and random survival forest-based machine-learning methods separately. The consistency index, area under the curve (AUC), and integrated Brier scores were used to compare the predictive performance of the different prognostic models. The results of Cox-based multivariate regression analysis suggested that pulmonary arterial hypertension, digital ulcer, and Modified Rodnan Skin Score (mRSS) were independent risk factors for poor prognosis in patients with SSc and significant risk factors in random survival forest (RSF) surveys. A nomogram was plotted to evaluate the prognostic risk to facilitate clinical assessment; the RSF model had better predictive performance than the Cox model, with 3- and 5-year AUCs of 0.74 and 0.78, respectively. Machine-learning models can help us better understand the prognosis of patients with SSc and comprehensively evaluate the clinical characteristics of each individual. The early identification of the characteristics of high-risk patients can improve the prognosis of those with SSc. Key Points • Regarding predictive performance, the random survival forest model was more effective than the Cox model and had unique advantages in analyzing nonlinear effects and variable importance. • Machine learning using the simple clinical features of patients with systemic sclerosis (SSc) to predict mortality can guide attending physicians, and the early identification of high-risk patients with SSc and referral to experts will assist rheumatologists in monitoring and management planning.

Utilisation of Sentinel-2A Imagery for Estimation of Mangrove Carbon Stock in Mamminasata Area, South Sulawesi

Population growth and land conversion have led to the degradation of mangrove forests on the southern coast of South Sulawesi, especially the Mamminasata area. Reduced mangroves increase carbon dioxide in the atmosphere. However, data on the potential carbon absorption of mangroves is still lacking. To overcome this, remote sensing is used to estimate carbon reserves. This reseach utilises Sentinel-2A imagery to estimate mangrove carbon stocks in Mamminasata. The image processing process includes radiometric correction, atmospheric correction, image classification, and extraction of NDVI values. The NDVI value is used to classify the density of mangroves into sparse, medium, and dense, covering 1,244.75 hectares. Field data collection was carried out through a survey of forest stand measurements. The results of NDVI transformation show a value range of 0.2 to 0.8 for mangrove objects in the Mamminasata area. The NDVI data on the analysed images were then made into three density classes. The rare density class has a carbon value of 3.56 – 21.16 Ton C/ha, the medium density class is between 21.17 – 31.49 Ton C/ha, and the dense density class is between 31.50 – 39.18 Ton C/ha. Regression analysis shows a strong correlation between NDVI and carbon stock (R² = 0.7134). This study confirms the effectiveness of remote sensing in environmental monitoring and mangrove conservation. These findings support conservation efforts and sustainable management policies by highlighting areas with high carbon sequestration potential.

Forest age estimation using UAV-LiDAR and Sentinel-2 data with machine learning algorithms- a case study of Masson pine (Pinus massoniana)

ABSTRACT The assessment of ecological functions, such as those of forest structure zoning and carbon sinks, heavily relies on forest age classification. Therefore, monitoring forest age is a crucial element of forest resource surveys. With the increased availability of high-quality open-access satellite data and advancements in Unmanned Aerial Vehicle Light Detection and Ranging (UAV-LiDAR) technology, remote sensing has emerged as an essential method for acquiring accurate forest age information. In this study, Sentinel-2 remote sensing data, UAV-LiDAR data, and combined Sentinel-2 and LiDAR data are used as data sources. Three machine learning algorithms, Adaptive Boosting (AdaBoost), Random Forest (RF), and Extreme Random Tree (ERT), are used to predict forest age in a Masson pine (Pinus massoniana Lamb.) forest. The optimal model is used to predict the forest age and simulate the spatial age distribution. The machine learning models based on separate Sentinel-2 and LiDAR data accurately predict the age of the Masson pine forest. Nevertheless, the accuracy of the RF model with combined data was higher than that in other cases, with an accuracy R value of 0.81. The model displayed good stability, and the spatial uncertainty of age estimation was low. Compared with the RF model using only Sentinel-2 data (R = 0.43), the RF model with combined LiDAR and Sentinel-2 data achieved the highest accuracy, with R values 88.37% higher. In addition, the forest canopy structure parameters, such as the average height of the forest stand extracted from UAV-LiDAR data, had a significant impact on the estimation of forest age. Thus, when the combined Sentinel-2 and LiDAR data were used to establish these parameters, the highest accuracy in the estimation of Masson pine was obtained. The findings of this study provide new insights for forest age estimation based on multi-source remote sensing data.

Koala density, habitat, conservation, and response to logging in eucalyptus forest; a review and critical evaluation of call monitoring

ABSTRACT This study is the second in a series that examines the habitat requirements and response to logging of koalas (Phascolarctos cinereus) inhabiting tall eucalypt forests of north-east NSW. It presents the findings of koala population and habitat monitoring surveys in Pine Creek State Forest and Bongil Bongil National Park using a combination of call-counting and direct observation (spotlighting). The 6400 ha study area was mapped into 6 zones of increasing koala habitat quality by ground survey of forest structure and floristics on a 200 m grid. The accuracy of habitat definition and mapping was tested by stratified transect counts of koala calls and sightings over two consecutive years (1997–98). Average koala density increased steeply and significantly, from 0.02 – 0.20 koalas/hectare, with increasing mapped habitat quality based on increasing forest age, structural complexity, local food tree species diversity, history of prior koala occurrence and decreased past logging intensity. This relationship was driven primarily by breeding females, with the number of male koala calls weakly or uncorrelated with koala sightings and mapped habitat quality. Male koalas were more widely and uniformly distributed than females, including areas of low quality, plantation, and intensively logged forest. This finding explains the discrepancy between our results and those of other recent studies which concluded that koalas are tolerant of intensive logging based on modelling of calling male koalas and reliance on an untested assumption that male calling is indicative of female breeding success. Koala density in a subset of the highest quality habitat was relatively stable at 0.28 koalas/ha (3 hectares/koala) over the long term (1997–98 and 2012–2023). Key characteristics of the forest koala population, including low stable density, large home ranges, preference for high food tree diversity and locally unique food trees (including Allocasuarina torulosa and Syncarpia glomulifera), are not adequately explained by existing koala habitat models. We present a new paradigm to explain regional variation in koala distribution, habitat and foraging preferences based on variations in foliage chemistry (toxicity and nutritional value) determined by the duration and stability of local plant-koala interactions in response to past fire, hunting, predation and logging disturbance history. We hypothesize that koala density in stable forest populations is regulated at low levels by a combination of selected and induced increases in leaf toxicity and decreases in leaf nutrition that limit koala browsing to benign levels of about 1-2% of annual leaf production. Large home ranges, complex mature forest structure, high food tree diversity and a specialized or diverse gut microbiome may be essential to allow females to rotate and change food trees frequently to minimize induced toxicity and select individual leaves with sufficient nutrients to support breeding and lactation with minimal risk of predation. High density koala populations (> 0.6/ha) occur primarily in areas where koalas have been introduced or re-introduced to planted habitats and natural areas where aboriginal hunters and dingoes were historically present but are now absent, and where food trees have not been selected for resistance to koala browsing pressure.

Biodiversity and Soil Properties of Saranda Sal Forest: Implications for Conservation and Management

Aims: This study is significant as it elucidates the relationship between floristic composition and soil nutrient availability of India's Saranda Sal (Shorea robusta Gaertn.) forest. Study Design: Vegetation sampling was done following a grid of 5 km x 5 km, and soil sampling was done in each sampling plot using a Z-pattern. Place and Duration of Study: The study was conducted in Saranda Forest of West Singhbhum district, Jharkhand, Eastern India, during 2021-2022 Methodology: Sampling has been designed as grid methods (5 km X 5 km) following a forest survey of India, and vegetation sampling was done by quadrat method. The soil samples were collected from three depths (i.e., surface: 0–30 cm, sub-surface: 30–60 cm and inner: 60-90 cm from each selected site. ANOVAs were used to compare the chemical properties of soil samples from various forests. Pearson correlation analyses to examine the effects of climatic variables on chemical properties of the soil of selected Sal forest as well as their relation with plant diversity. Results: A comprehensive analysis of 5432 vascular plants from 65 species and 34 families was conducted across the 17 sites. Fabaceae is the most dominant family with 07 species. The study also examined soil chemical parameters and micronutrients in different sites and established their relations with vegetation dynamics. Notably, tree density showed a significant positive correlation with soil pH (r=0.59, p<0.05), but a significant negative correlation with Shannon diversity (H') (r=-0.53, p<0.05). Similarly, there was a significant positive correlation of Organic carbon with copper (r=0.59, p<0.05) and iron (r=0.61, p<0.00); however, there was a significant negative correlation with Available Phosphorus (r=-0.52, p<0.05). Conclusion: Research findings underscore the importance of soil nutrients in promoting forest health and growth. Importantly, they can guide the formulation of practical and effective soil-forest management strategies for S. robusta and its associated forests, directly benefiting the forestry and environmental science community.

Forecast Cosmological Constraints with the 1D Wavelet Scattering Transform and the Lyman-α Forest.

We make forecasts for the constraining power of the 1D wavelet scattering transform when used with a Lyman-α forest cosmology survey. Using mock simulations and a Fisher matrix, we show that there is considerable cosmological information in the scattering transform coefficients not captured by the flux power spectrum. We estimate mock covariance matrices assuming uncorrelated Gaussian pixel noise for each quasar at a level drawn from a simple log-normal model. The extra information comes from a smaller estimated covariance in the first-order wavelet power and from second-order wavelet coefficients that probe non-Gaussian information in the forest. Forecast constraints on cosmological parameters from the wavelet scattering transform are more than an order of magnitude tighter than for the power spectrum, shrinking a 4D parameter space by a factor of 10^{6}. Should these improvements be realized with the Dark Energy Spectroscopic Instrument, inflationary running would be constrained to test common inflationary models predicting α_{s}=-6×10^{-4} and neutrino mass constraints would be improved enough for a 5-σ detection of the minimal neutrino mass.