Primary forests are of paramount importance for biodiversity conservation and the provision of ecosystem services. In Europe, these forests are scarce and threatened by human activities. However, a comprehensive assessment of the magnitude of disturbances in these forests is lacking, due in part to their incomplete mapping. We sought to provide a systematic assessment of disturbances in primary forests in Europe based on remotely sensed imagery from 1986 to 2020. We assessed the total area disturbed, rate of area disturbed, and disturbance severity, at the country, biogeographical, and continental level. Maps of potential primary forests were used to mitigate gaps in maps of documented primary forests. We found a widespread and significant increase in primary forest disturbance rates across Europe and heightened disturbance severity in many biogeographical regions. These findings are consistent with current evidence and associate the ongoing decline of primary forests in Europe with human activity in many jurisdictions. Considering the limited extent of primary forests in Europe and the high risk of their further loss, urgent and decisive measures are imperative to ensure the strict protection of remnants of these invaluable forests. This includes the establishment of protected areas around primary forests, expansion of old-growth zones around small primary forest fragments, and rewilding efforts.
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http://dx.doi.org/10.1111/cobi.14404 | DOI Listing |
Tree Physiol
January 2025
Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden.
Although the separate effects of water and nitrogen (N) limitations on forest growth are well known, the question of how to predict their combined effects remains a challenge for modeling of climate change impacts on forests. Here, we address this challenge by developing a new eco-physiological model that accounts for plasticity in stomatal conductance and leaf N concentration. Based on optimality principle, our model determines stomatal conductance and leaf N concentration by balancing carbon uptake maximization, hydraulic risk and cost of maintaining photosynthetic capacity.
View Article and Find Full Text PDFTransplantation
January 2025
Department of Thoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu, China.
Background: Primary graft dysfunction (PGD) develops within 72 h after lung transplantation (Lung Tx) and greatly influences patients' prognosis. This study aimed to establish an accurate machine learning (ML) model for predicting grade 3 PGD (PGD3) after Lung Tx.
Methods: This retrospective study incorporated 802 patients receiving Lung Tx between July 2018 and October 2023 (640 in the derivation cohort and 162 in the external validation cohort), and 640 patients were randomly assigned to training and internal validation cohorts in a 7:3 ratio.
BMC Psychiatry
January 2025
Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Background: Mental disorders are increasingly prevalent, leading to increased medical expenditures. To refine the reimbursement of medical costs for inpatients with mental disorders by health insurance, an accurate prediction model is essential. Per-diem payment is a common internationally implemented payment method for medical insurance of inpatients with mental disorders, necessitating the exploration of advanced machine learning methods for predicting the average daily hospitalization costs (ADHC) based on the characteristics of inpatients with mental disorders.
View Article and Find Full Text PDFNPJ Precis Oncol
January 2025
Department of Pathology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Tumors of unknown origin (TUO) generally result in poor patient survival and are clinically difficult to address. Identification of the site of origin in TUO patients is paramount to their improved treatment and survival but is difficult to obtain with current methods. Here, we develop a random forest machine learning TUO methylation classifier using a large number of primary and metastatic tumor samples.
View Article and Find Full Text PDFConserv Biol
January 2025
Marine Science Institute, University of California, Santa Barbara, Santa Barbara, California, USA.
Marine protected areas (MPAs) are widely implemented tools for long-term ocean conservation and resource management. Assessments of MPA performance have largely focused on specific ecosystems individually and have rarely evaluated performance across multiple ecosystems either in an individual MPA or across an MPA network. We evaluated the conservation performance of 59 MPAs in California's large MPA network, which encompasses 4 primary ecosystems (surf zone, kelp forest, shallow reef, deep reef) and 4 bioregions, and identified MPA attributes that best explain performance.
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