Spatial decision-support tools are necessary for assessment and management of threats to biodiversity, which in turn is necessary for biodiversity conservation. In conjunction with the U.S. Geological Survey-Biological Resources Division's Species at Risk program, we developed a GIS-based spatial decision-support tool for relative risk assessments of threats to biodiversity on the U.S. Army's White Sands Missile Range and Fort Bliss (New Mexico and Texas) due to land uses associated with military missions of the two bases. The project tested use of spatial habitat models, land-use scenarios, and species-specific impacts to produce an assessment of relative risks for use in conservation planning on the 1.2 million-hectare study region. Our procedure allows spatially explicit analyses of risks to multiple species from multiple sources by identifying a set of hazards faced by all species of interest, identifying a set of feasible management alternatives, assigning scores to each species for each hazard, and mapping the distribution of these hazard scores across the region of interest for each combination of species/management alternatives. We illustrate the procedure with examples. We demonstrate that our risk-based approach to conservation planning can provide resource managers with a useful tool for spatial assessment of threats to species of concern.
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http://dx.doi.org/10.1111/j.0272-4332.2004.00521.x | DOI Listing |
Sci Rep
January 2025
Department of Mapping and Cadastre, Savur Vocational School, Mardin Artuklu University, 47080, Mardin, Turkey.
Site selection for agricultural products is critical for agricultural planning, productivity, and farmers. Site selection is also critical for agricultural sustainability, as it helps ensure the efficient use of natural resources and avoids environmental degradation. This research proposes an evaluation model for walnut cultivation in the Savur (Mardin, Turkey) district in the Southeastern Anatolia region.
View Article and Find Full Text PDFArtif Intell Med
January 2025
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy; Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
The heterogeneity of psychiatric disorders makes researching disorder-specific neurobiological markers an ill-posed problem. Here, we face the need for disease stratification models by presenting a generalizable multivariate normative modelling framework for characterizing brain morphology, applied to bipolar disorder (BD). We used deep autoencoders in an anomaly detection framework, combined for the first time with a confounder removal step that integrates training and external validation.
View Article and Find Full Text PDFAppl Environ Microbiol
January 2025
Department of Forest Mycology and Plant Pathology, Uppsala BioCenter, Swedish University of Agricultural Sciences, Uppsala, Sweden.
In Sweden, reforestation of managed forests relies predominantly on planting nursery-produced tree seedlings. However, the intense production using containerized cultivation systems (e.g.
View Article and Find Full Text PDFSci Total Environ
January 2025
Geological Survey of Denmark and Greenland (GEUS), Department of Hydrology, Copenhagen, Denmark.
Machine learning (ML) methods continue to gain traction in hydrological sciences for predicting variables at large scales. Yet, the spatial transferability of these ML methods remains a critical yet underexamined aspect. We present a metamodel approach to obtain large-scale estimates of drain fraction at 10 m spatial resolution, using a ML algorithm (Gradient Boost Decision Tree).
View Article and Find Full Text PDFInt J Cardiovasc Imaging
January 2025
Shanxi Cardiovascular Hospital, 18 Yifen Street, Taiyuan, 030024, Shanxi, China.
Amid an aging global population, heart failure has become a leading cause of hospitalization among older people. Its high prevalence and mortality rates underscore the importance of accurate mortality prediction for swift disease progression assessment and better patient outcomes. The evolution of artificial intelligence (AI) presents new avenues for predicting heart failure mortality.
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