The greatest threat to the protected Eurasian lynx () in Central Europe is human-induced mortality. As the availability of lynx prey often peaks in human-modified areas, lynx have to balance successful prey hunting with the risk of encounters with humans. We hypothesized that lynx minimize this risk by adjusting habitat choices to the phases of the day and over seasons. We predicted that (1) due to avoidance of human-dominated areas during daytime, lynx range use is higher at nighttime, that (2) prey availability drives lynx habitat selection at night, whereas high cover, terrain inaccessibility, and distance to human infrastructure drive habitat selection during the day, and that (3) habitat selection also differs between seasons, with altitude being a dominant factor in winter. To test these hypotheses, we analyzed telemetry data (GPS, VHF) of 10 lynx in the Bohemian Forest Ecosystem (Germany, Czech Republic) between 2005 and 2013 using generalized additive mixed models and considering various predictor variables. Night ranges exceeded day ranges by more than 10%. At night, lynx selected open habitats, such as meadows, which are associated with high ungulate abundance. By contrast, during the day, lynx selected habitats offering dense understorey cover and rugged terrain away from human infrastructure. In summer, land-cover type greatly shaped lynx habitats, whereas in winter, lynx selected lower altitudes. We concluded that open habitats need to be considered for more realistic habitat models and contribute to future management and conservation (habitat suitability, carrying capacity) of Eurasian lynx in Central Europe.
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http://dx.doi.org/10.1002/ece3.3204 | DOI Listing |
Sensors (Basel)
December 2024
School of Biological and Environmental Sciences, Liverpool John Moores University, James Parsons Building, Byrom Street, Liverpool L3 3AF, UK.
Camera traps offer enormous new opportunities in ecological studies, but current automated image analysis methods often lack the contextual richness needed to support impactful conservation outcomes. Integrating vision-language models into these workflows could address this gap by providing enhanced contextual understanding and enabling advanced queries across temporal and spatial dimensions. Here, we present an integrated approach that combines deep learning-based vision and language models to improve ecological reporting using data from camera traps.
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December 2024
Department of Agricultural, Alimentary, Environmental and Forestry Sciences, Biosystem Engineering Division-DAGRI, University of Florence, Piazzale delle Cascine 15, 50144 Florence, Italy.
The present research aimed to evaluate whether two sensors, optical and laser, could highlight the change in olive trees' canopy structure due to pruning. Therefore, two proximal sensors were mounted on a ground vehicle (Kubota B2420 tractor): a multispectral sensor (OptRx ACS 430 AgLeader) and a 2D LiDAR sensor (Sick TIM 561). The multispectral sensor was used to evaluate the potential effect of biomass variability before pruning on sensor response.
View Article and Find Full Text PDFPlants (Basel)
December 2024
NBFC-National Biodiversity Future Center, 90133 Palermo, Italy.
This study investigates the diversity and distribution of intertidal () species across different protection zones within the "Capo Gallo-Isola delle Femmine" Marine Protected Area (MPA) in the central Mediterranean Sea. Five species ( and ) were observed on the intertidal rocky shores, with varied abundances across the MPA's protection zones. was the only species found in all zones, with a much higher cover percentage in the most protected area (zone A).
View Article and Find Full Text PDFPlants (Basel)
December 2024
National Research Council of Italy, Institute of Biomolecular Chemistry (CNR-ICB), Via P. Gaifami 18, 95126 Catania, Italy.
L. (Myrtaceae), widely valued for its aromatic leaves and essential oil, plays a significant role in traditional medicine and modern phytotherapy. The variability in its essential oil composition and bioactive compounds across different populations underscores its potential for novel therapeutic discoveries and agricultural utilization.
View Article and Find Full Text PDFNutrients
December 2024
Health Care & Nutritional Science Institute, R&D Division, Morinaga Milk Industry Co., Ltd., 5-1-83, Higashihara, Zama 252-8583, Kanagawa, Japan.
Background: Older people are more susceptible to deterioration of the gut microbiota. Prebiotics help improve the gut microbiota. Inulin, a major prebiotic, stimulates the growth of ; however, it produces a large amount of gas, which leads to abdominal symptoms.
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