Aim: Well-managed semi-arid forests help offset global change by storing significant amounts of carbon above- and belowground and maintaining hydrological cycles. Larger trees have been the focus of many studies due to their carbon storage and habitat quality, yet recruitment and small trees are important components of ecosystem resilience and recovery. Here, we study the impacts of disturbances (including harvesting) on recruitment, mortality and growth for a mixed conifer-broadleaf semi-arid forest type using long-term data.
Location: Pilliga Forest in New South Wales, inland eastern Australia.
Taxon: forests.
Methods: We used data from permanent sample plots (PSPs) spanning 55 years, calculated stand structure, gains and losses and determined reasons for tree death (harvesting, fire, wind, drought and other effects). We extracted climate and fire data for the PSP locations using spatial analysis.
Results: Stocking of studied forests remained stable (modest increase in basal area and stem density), despite harvesting and wildfires over 6 decades. Compared to stands in the 1940s and prior to European settlement, current forests are composed of more trees per unit area, and these trees have smaller diameters. Recruitment and sustained presence of small trees have buffered impacts of recurring drought, fire and harvesting. Fires are a common feature of the studied ecosystems and fire impacts have increased in the past 20 years, especially in unmanaged stands, where fires have reduced tree carbon by >50%.
Main Conclusions: Recruitment and growth of small trees are critical to offset carbon losses due to fire, drought and harvesting. All size classes have important ecological values in semi-arid forests and must be included in long-term monitoring programmes. Long-term data offer unique insights into combined effects of climate change, management and disturbances, especially for fire-prone ecosystems, where small trees are often susceptible to fire.
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http://dx.doi.org/10.1111/jbi.14522 | DOI Listing |
Plants (Basel)
January 2025
Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
Precision pesticide application mainly relies on canopy volume, resulting in varied application effectiveness across different density areas of orchard trees. This study examined pesticide application effectiveness based on the spray wind, canopy volume, and leaf area within the canopy, providing variable bases for precise regulation of spray wind and pesticide dosage. The study addresses the knowledge gap by utilizing laser detection and ranging (LiDAR) to measure the thickness and leaf area of orchard tree canopies.
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January 2025
Department of Medical Oncology, Faculty of Medicine, İstinye University, İstanbul 34010, Turkey.
Background: Although higher-generation TKIs are associated with improved progression-free survival in advanced NSCLC patients with EGFR mutations, the optimal selection of TKI treatment remains uncertain. To address this gap, we developed a web application powered by a reinforcement learning (RL) algorithm to assist in guiding initial TKI treatment decisions.
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Biology (Basel)
January 2025
Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.
To date, standard rRNA marker genes have had limited success in resolving the phylogeny of the phylum Chytridiomycota. Whereas the conserved and easily alignable ribosomal small subunit 18S rRNA gene had problems resolving nodes relating orders, the internal transcribed spacer 2 (ITS2) has been claimed to not be alignable for this group of organisms. Although the ITS2 is a fast-evolving locus, its secondary structure is well conserved.
View Article and Find Full Text PDFSci Rep
January 2025
College of Mechanical Engineering, Anhui Institute of Information Technology, Wuhu, 241199, Anhui, China.
To address the challenge of accurately capturing tool wear states in small sample scenarios, this paper proposes a tool wear prediction method that combines XGBoost feature selection with a PSO-BP network. In order to solve the problem of input feature selection and parameter selection in BP neural network, a double-layer programming model of input feature and parameter selection is established, which is solved by XGBoost and PSO. Initially, vibration and cutting force signals from CNC machining are preprocessed using time-domain segmentation, Hampel filtering, and wavelet denoising.
View Article and Find Full Text PDFJ Imaging
December 2024
Department of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea.
The geometric feature characterization of fruit trees plays a role in effective management in orchards. LiDAR (light detection and ranging) technology for object detection enables the rapid and precise evaluation of geometric features. This study aimed to quantify the height, canopy volume, tree spacing, and row spacing in an apple orchard using a three-dimensional (3D) LiDAR sensor.
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