Habitat selection of white-tailed deer fawns and their dams in the Northern Great Plains

Abstract

Habitat availability can affect important life-history traits such as survival; however, little information exists on how microhabitat characteristics found at parturition sites selected by dams and bed sites selected by their offspring differ from the surrounding area and from each other. Therefore, we assessed how vegetation affected maternal parturition and offspring bed site selection for white-tailed deer (Odocoileus virginianus) in the Northern Great Plains. Dams selected for sites with decreased vegetation height, potentially improving their visibility, which may increase their ability to escape approaching predators. Conversely, there was no variation between vegetative characteristics at neonate bed sites and their associated random sites, indicating grasslands provide adequate concealment for neonates. Dams possess the ability to flee from approaching predators, thus increasing the importance of visibility while giving birth. Conversely, neonates depend on fear bradycardia as their main antipredator defense, so concealment is more important. Our results suggest that vegetation structure is an important characteristic to white-tailed deer as habitat needs vary between adults and neonates.

Non-preferred habitat increases the activity area of the endangered northern quoll (Dasyurus hallucatus) in a semi-arid landscape

“…Effective conservation requires knowledge of the spatial and temporal use of preferred habitats by target species (Dellinger et al 2020). Animal space use reflects resource availability in the landscape (Michel et al 2020;Wysong et al 2020). If landscapes are resource-rich, animals travel shorter distances (Doherty et al 2019;Martin and Martin 2007;Viana et al 2018).…”

Capture method affects survival estimates and subsequent interpretation of ecological covariates for a long‐lived cervid

“…Ecological covariates affecting survival may also vary throughout the first 90 days of a neonate's life. For example, birth mass (Cook et al, 2004;Lomas & Bender, 2007;Shuman et al, 2017), sex (Shuman et al, 2017;Warbington et al, 2017), birth date (Plard et al, 2015;Michel, Gullikson, et al, 2020), and maternal age (Dion et al, 2020) likely affect survival of ungulate neonates; however, results vary (Dion et al, 2020;Kautz et al, 2019;Post et al, 2003). Assessing how these ecological covariates may influence neonate survival at specific age intervals (e.g., <2-weeks, >2-weeks) will allow for a better understanding of what affects neonatal ungulate survival throughout early life.…”

“…Individual traits such as birth mass (Cook et al, 2004;Lomas & Bender, 2007;Shuman et al, 2017),capture age (Grovenburg et al, 2014), and birth date (Michel, Gullikson, et al, 2020;Plard et al, 2015), and environmental variables including weather (Ginnett & Young, 2000;Michel et al, 2018;Warbington et al, 2017), landscape composition and configuration (Gingery et al, 2018;Gulsby et al, 2017;Michel et al, 2018), and cohort effects (Douhard et al, 2013;Gaillard et al, 2003;Pigeon et al, 2017) affect ungulate offspring survival. Other factors such as maternal body condition may also affect offspring survival as ungulate mothers in better body condition are more likely to produce larger, healthier offspring with greater chances of survival than those in poor body condition (Carstensen et al, 2009;Duquette et al, 2015;Shallow et al, 2015).…”

Habitat selection of nesting and fledgling salt marsh songbirds in northeast Florida

“…This approach is analogous to discrete choice modeling of resource selection (Cooper and Millspaugh 1999), which compares each used point to multiple unused but available points, with availability defined at the level of an individual. Conditional logistic regression is frequently used in wildlife studies (Scharf et al 2018, Michel et al 2020, Barrile et al 2021) and differs from discrete choice by comparing each used point to a single rather than multiple available points. We chose this approach because measuring multiple random available points for each used point would be logistically challenging given the number of nests we monitored and because the small nesting territories would have resulted in highly clumped random points that were likely to overlap.…”