Identifying activity patterns from activity counters in ETHOSYS® collars on red deer |
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| Institution: | 1. Comportement et Ecologie de la Faune Sauvage, Institut National de la Recherche Agronomique, Boîte Postale 52627, 31326 Castanet-Tolosan, Cedex, France;2. Unité de Recherche sur les Herbivores, Institut National de la Recherche Agronomique, Site de Theix, 63122 Saint-Genès-Champanelle, France;1. Laboratório de Ecologia e Conservação de Biodiversidade, Programa de Pós-graduação em Ecologia de Ecossistemas, Universidade Vila Velha – UVV, Rua Comissário José Dantas de Melo, no 21, Bairro Boa Vista, Vila Velha, Espírito Santo, CEP 29.102-920, Brazil;2. Instituto SerraDiCal de Pesquisa e Conservação, Belo Horizonte, Minas Gerais, Brazil;2. The University of Texas Health Science Center at San Antonio, Department of Biochemistry, San Antonio, Texas;3. Texas A&M University, Department of Mechanical Engineering, College Station, Texas;4. University of North Carolina at Chapel Hill, Department of Pathology and Laboratory Medicine, Chapel Hill, North Carolina;1. Department of Anesthesiology, Hebei General Hospital, Shijiazhuang, Hebei province 050051, China;2. Department of Gynecology, Hebei General Hospital, Shijiazhuang, Hebei province 050051, China;3. Department of Pharmacy, Bethune International Peace Hospital of Chinese PLA, Shijiazhuang, Hebei province 050082, China;1. Toulouse School of Economics, University Toulouse 1 Capitole, France;2. University of Toulouse, Toulouse Business School and Toulouse School of Economics, France;2. Department of Physiology, Faculty of Medicine, University of Szeged, H-6720 Szeged, Dóm tér 10., Hungary |
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| Abstract: | Identifying activity patterns of animals from activity counts recorded by sensors in the storage-telemetry-system ETHOSYS® requires to calibrate activity counters to field activities. We continuously observed from dawn to dusk during days 1, 3 and 5 three collared red deer hinds introduced into a 1-ha enclosure for five consecutive days in February (winter), May (spring) and July (summer) 2001. On the basis of concurrently count data collected every 5 min for each hind and each season, we selected the threshold sensor values which best allowed separation of inactive (mainly in the lying position) and active bouts. In winter and spring, the threshold sensor values ranged from 5 to 20 counts per 5 min, which allowed to correctly classified 90.1–98.5% of diurnal sampling intervals. However, higher threshold values (from 55 to 110 counts per 5 min) were found in summer, because foraging and lying periods occur more frequently together within the 5-min interval counts. So, only 76.9–81.4% of sampling intervals were correctly discriminated. To compare the daily activity patterns of hinds in each season, the percentage of time spent active was calculated on a hourly basis. Synchronized periods of activity occur among the three hinds throughout a 24-h period both in winter and summer. However, especially during daytime, the activity patterns of hinds differed significantly in spring. This could affect interpretation of activity patterns of animals from activity counts averaged from several individuals, even if having similar status and environmental conditions or over periods longer than the interval count adopted in the present study. |
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