The ecosystem services provided by tropical forests are affected by deforestation. Territorial management strategies aim to prevent and mitigate forest loss. Therefore, modeling potential land use changes is important for forest management, monitoring, and evaluation. This study determined whether there are relationships between forest vulnerability to deforestation (potential deforestation distribution) and the forest management policies applied in the Ecuadorian Amazon. Proxy and underlying variables were used to construct a statistical model, based on the principle of maximum entropy that could predict potential land use changes. Entropy can be seen as a measure of uncertainty for a density function. Receiver operating characteristics (ROC) analysis and the Jackknife Test were used to validate the model. The importance of input variables in the model was determined through: Percent Contribution (PC) and Permutation Importance (PI). The results were compared with prevailing regional forest management strategies. The socioeconomic variables that provided the largest amount of information in the overall model (AUC = 0.81) and that showed most of the information not present in other variables were: "Protected areas-Intangible zone" (PC = 24%, PI = 12.4%), "timber harvesting programs" (PC = 21.7%, PI = 4.7%), "road network" (PC = 18.9%, PI = 7.7%), and "poverty rate" (PC = 3.7%, PI = 6.1%). Also, the biophysical variable "temperature" (PC = 7,9%, PI = 22.3%) provided information in the overall model. The results suggested the need for changes in forest management strategies. Forest policies and management plans should consider integrating and strengthening protected areas and intangible zones, as well as restricting timber harvesting in native forest and establishing forest areas under permanent management. Furthermore, the results also suggested that financial incentive programs to reduce deforestation have to be evaluated because their present distribution is inefficient. In this context, conservation incentive plans need to be revised so that they focus on areas at deforestation risk.
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http://dx.doi.org/10.1016/j.scitotenv.2018.07.028 | DOI Listing |
Neurooncol Adv
November 2024
Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, Saint Louis, Missouri, USA.
Background: Alterations in cellular metabolism affect cancer survival and can manifest in metrics of body composition. We investigated the effects of various body composition metrics on survival in patients with glioblastoma (GBM).
Methods: We retrospectively analyzed patients who had an abdominal and pelvic computed tomography (CT) scan performed within 1 month of diagnosis of GBM (178 participants, 102 males, 76 females, median age: 62.
Ann Thorac Surg Short Rep
December 2024
Sanger Heart & Vascular Institute, Charlotte, North Carolina.
Background: Our remote patient monitoring (RPM) program for adult cardiac surgery patients aims to remove barriers to access, provide continuity of expert care, and increase their time-at-home. The RPM program integrates novel biosensors, an application for audiovisual visits, messaging, biometric data tracking, patient-reported outcomes, and scheduling with the aim of reducing postoperative length of stay and 30-day readmissions, while simultaneously increasing the rate of patients discharged to home.
Methods: Our institutional database was utilized for this retrospective review of 1000 consecutive RPM patients who underwent coronary artery bypass, valve, and coronary artery bypass + valve, at 3 hospitals from July 2019 through April 2023.
PeerJ
January 2025
College of Forestry, Guizhou University, Guiyang, Guizhou, China.
Masson pine ( Lamb.) and Chinese fir ( (Lamb.) Hook.
View Article and Find Full Text PDFTree 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 PDFPlant Methods
January 2025
School of Electronic and Information Engineering, Liaoning Technical University, Huludao, 125105, China.
Apricot trees, serving as critical agricultural resources, hold a significant role within the agricultural domain. Conventional methods for detecting pests and diseases in these trees are notably labor-intensive. Many conditions affecting apricot trees manifest distinct visual symptoms that are ideally suited for precise identification and classification via deep learning techniques.
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