11 research outputs found

    Resource metrics used to assess resource use of white-tailed deer (<i>Odocoileus virginianus</i>) fawns, Upper Peninsula of Michigan, USA, 2009–2011.

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    <p>Resource metrics used to assess resource use of white-tailed deer (<i>Odocoileus virginianus</i>) fawns, Upper Peninsula of Michigan, USA, 2009–2011.</p

    Generalized linear mixed-effect models assessing second order resource use of neonatal white-tailed deer (≤ 14 weeks of age; <i>n</i> = 129) during the post-partum period (14 May–31Aug), southcentral Upper Peninsula of Michigan, USA, 2009–2011.

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    <p>Models used radiolocations (1; <i>n</i> = 2713) and random points (0) as the binomial response variable and individual resources were used as a fixed effect with individual fawn and year as random effects on the intercept. Model accuracy was estimated using the area under a receiver operating characteristic curve (AUC).</p

    Predictions used to assess daily or seasonal survival of neonate white-tailed deer (≤ 14 weeks of age) relative to resource use, predation risk, birth body mass, winter severity, and vegetation hiding cover at the landscape scale in the southcentral Upper Peninsula of Michigan, USA, 2009–2011.

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    <p>Predictions used to assess daily or seasonal survival of neonate white-tailed deer (≤ 14 weeks of age) relative to resource use, predation risk, birth body mass, winter severity, and vegetation hiding cover at the landscape scale in the southcentral Upper Peninsula of Michigan, USA, 2009–2011.</p

    Kaplan–Meier estimates of neonate white-tailed deer fawn (≤ 14 weeks of age; <i>Odocoileus virginianus</i>; <i>n</i> = 129) survival from 14 May–31 August 2009–2011 in the southcentral Upper Peninsula of Michigan, USA.

    No full text
    <p>Kaplan–Meier estimates of neonate white-tailed deer fawn (≤ 14 weeks of age; <i>Odocoileus virginianus</i>; <i>n</i> = 129) survival from 14 May–31 August 2009–2011 in the southcentral Upper Peninsula of Michigan, USA.</p

    Metrics used to assess resource use of neonatal white-tailed deer (≤ 14 weeks of age), southcentral Upper Peninsula of Michigan, USA, 2009–2011.

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    <p>Metrics used to assess resource use of neonatal white-tailed deer (≤ 14 weeks of age), southcentral Upper Peninsula of Michigan, USA, 2009–2011.</p

    Location (black polygon) of white-tailed deer (<i>Odocoileus virginianus</i>) resource use and predation risk study, Upper Peninsula of Michigan, USA, 2009–2011.

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    <p>Location (black polygon) of white-tailed deer (<i>Odocoileus virginianus</i>) resource use and predation risk study, Upper Peninsula of Michigan, USA, 2009–2011.</p

    Cox-proportional hazards mixed-effects models assessing the effects of resource use, predation risk, birth body mass, winter severity, and vegetation hiding cover on the daily survival of white-tailed deer fawns (≤14 weeks of age; <i>Odocoileus virginianus</i>; <i>n</i> = 129) during the post-partum period (14 May–31 Aug), Upper Peninsula of Michigan, USA, 2009–2011.

    No full text
    <p>Models included individual fawn and year as random effects on the intercept. Models presented with standardized parameter estimates, standard errors (SE), probability values, degrees of freedom (<i>df</i>), and estimated hazard ratio parameter probability values, and percent integrated deviance explained indicating the reduction in the log-likelihood from the null model. Percent deviance explained was used to rank models. Model fit was assessed using a Chi-square test of log-likelihood of a given model (Log-likelihood <i>X</i><sup>2</sup>) compared to the null model.</p

    Generalized linear mixed-effect models assessing third order resource selection of white-tailed deer fawns (≤14 weeks of age; <i>Odocoileus virginianus</i>; <i>n</i> = 129) during the post-partum period (14 May–31 Aug), Upper Peninsula of Michigan, USA, 2009–2011.

    No full text
    <p>Models used radiolocations (1; <i>n</i> = 2713) and random points (0) as the binomial response variable and individual resources were used as a fixed effect with individual fawn and year as random effects on the intercept. Model prediction error was estimated using <i>k</i>-fold cross validation using 5 folds.</p
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