6 research outputs found

    Mood, mileage and the menstrual cycle

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    Forty women took part in a study to determine the effects of high-intensity training and the menstrual cycle on mood states. Half of the sample were competitive distance runners following a training load of between 50 km and 130 km running per week. Seven athletes were amenorrhoeic and 13 either eumenorrhoeic or oligomenorrhoeic. The remaining 20 subjects were inactive women who menstruated regularly. The mean age of all 40 subjects was 29 years. Each subject completed two identical Profile of Mood States (POMS) questionnaires. The 33 menstruating subjects completed both a premenstrual and a midcycle form and the amenorrhoeic athletes completed the questionnaires at a 3-week interval, which acted as a control for the potential effects of premenstrual syndrome (PMS) among the menstruating females. Results showed highly significant differences in mood profiles among amenorrhoeic athletes, non-amenorrhoeic athletes and inactive women. The greatest difference was between premenstrual and midcycle measures for the inactive group. PMS appears to cause marked negative mood swings among menstruating women which the POMS inventory is sensitive in detecting. While the lowerintensity- training runners appeared to benefit psychologically from a training distance of approximately 50km week-', high-intensity training had an adverse effect on mood

    Reconstructing Hominin Interactions with Mammalian Carnivores (6.0–1.8 Ma)

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    Data of "Evaluating expert-based habitat suitability information of terrestrial mammals with GPS-tracking data"

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    In our paper "Evaluating expert-based habitat suitability information of terrestrial mammals with GPS-tracking data" (Global Ecology and Biogeography) we use GPS tracking data from 1,498 from 49 different species to evaluate the expert-based habitat suitability data from the International Union for Conservation of Nature (IUCN). Therefore, we used the GPS tracking data to estimate two measures of habitat suitability for each individual animal and habitat type: proportional habitat use (proportion of GPS locations within a habitat type), and selection ratio (habitat use relative to its availability). For each individual we then evaluated whether the GPS-based habitat suitability measures were in agreement with the IUCN data. To that end, we calculated the probability that the ranking of empirical habitat suitability measures was in agreement with IUCN’s classification into suitable, marginal and unsuitable habitat types. Our results showed that IUCN habitat suitability data were in accordance with the GPS data (>95% probability of agreement) for 33 out of 49 species based on proportional habitat use estimates and for 25 out of 49 species based on selection ratios. In addition, 37 and 34 species had a >50% probability of agreement based on proportional habitat use and selection ratios, respectively. These findings indicate that for the majority of species included in this study, it is appropriate to use IUCN habitat suitability data in macroecological studies. Furthermore, our study shows that GPS tracking data can be used to identify and prioritize species and habitat types for re-evaluation of IUCN habitat suitability data. In this dataset we provide the measures of habitat suitability for each individual and each habitat type, calculated using different methods. In addition, we provide data on the body mass and IUCN Red List category of the species, as well as whether the species can be considered a habitat specialist or habitat generalist

    Evaluating expert-based habitat suitability information of terrestrial mammals with GPS-tracking data

    No full text
    In our paper "Evaluating expert-based habitat suitability information of terrestrial mammals with GPS-tracking data" (Global Ecology and Biogeography) we use GPS tracking data from 1,498 from 49 different species to evaluate the expert-based habitat suitability data from the International Union for Conservation of Nature (IUCN). Therefore, we used the GPS tracking data to estimate two measures of habitat suitability for each individual animal and habitat type: proportional habitat use (proportion of GPS locations within a habitat type), and selection ratio (habitat use relative to its availability). For each individual we then evaluated whether the GPS-based habitat suitability measures were in agreement with the IUCN data. To that end, we calculated the probability that the ranking of empirical habitat suitability measures was in agreement with IUCN’s classification into suitable, marginal and unsuitable habitat types. Our results showed that IUCN habitat suitability data were in accordance with the GPS data (>95% probability of agreement) for 33 out of 49 species based on proportional habitat use estimates and for 25 out of 49 species based on selection ratios. In addition, 37 and 34 species had a >50% probability of agreement based on proportional habitat use and selection ratios, respectively. These findings indicate that for the majority of species included in this study, it is appropriate to use IUCN habitat suitability data in macroecological studies. Furthermore, our study shows that GPS tracking data can be used to identify and prioritize species and habitat types for re-evaluation of IUCN habitat suitability data. In this dataset we provide the measures of habitat suitability for each individual and each habitat type, calculated using different methods. In addition, we provide data on the body mass and IUCN Red List category of the species, as well as whether the species can be considered a habitat specialist or habitat generalist
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