Discussion
Our top model for 3-month neonate survival supported our prediction that survival would vary by capture method as neonates captured via VITs displayed up to a 26% lower survival rate than opportunistically captured neonates. This result supports previous studies assessing variation in survival rates related to capture methods where survival estimates were 7 to 25% lower for ungulate neonates captured via VITs compared to opportunistically captured ungulate neonates (Gilbert et al., 2014; Chitwood et al., 2017; Dion et al., 2020). We found opportunistically caught neonates were about 6 days-old, which supports Dion et al. (2020; 6-days); however, variation in age between capture methods may be as little as 3.5-days (Kautz et al., 2019). Gilbert et al. (2014) simulated left truncation by removing black-tailed deer neonates that died within 2 days of age from a known-age dataset, which still resulted in increased survival estimates compared to survival estimates derived from datasets that did not contain left-truncation data (i.e., VIT only data). Although variation in survival estimates related to the inability to capture neonates within the first seven days of life is intuitive (Chitwood et al., 2017; Dion et al., 2020), survival rates can vary even when failing to capture neonates <2 days old (Gilbert et al., 2014). Therefore, research designed to derive neonatal survival estimates should capture neonates via VITs or should acknowledge that derived survival estimates could be up to 26% lower than those derived from datasets including opportunistically captured neonates.
In addition to reporting variation in survival estimates related to capture method, Gilbert et al. (2014) reported variation in model selection and interpretation of ecological covariates related to grouping and subsequently analyzing neonate survival by capture method. Our results further support Gilbert et al. (2014) as we derived three different top models based on how we grouped and analyzed our data. For example, S(Int2) was our top model when only using neonates captured via VITs, S(Canopy + Precip1) was our top model when assessing survival for opportunistically captured neonates, and S(Canopy + Precip2) was our top model when assessing survival for all neonates regardless of capture type. Although we found variation in top models related to capture method, models including percent canopy cover and total precipitation during differing time intervals were competing in each candidate set albeit interpretation of total precipitation slightly varied among models (ranging from being unimportant to having a negative relationship with survival). Regardless, variation in our results did not differ as drastically as they did for Gilbert et al. (2014); yet, our results still supported their conclusions and emphasize the importance of accounting for capture method in survival analyses when interpreting model selection results and effects of ecological covariates on survival.
We assumed results from our VIT only analysis best represented truth due to lack of left truncation in the dataset (Gilbert et al., 2014; Chitwood et al., 2017; Dion et al., 2020) and therefore, only interpret those results relative to ungulate ecology. Our top model for 3-month survival from our VIT only data supported our prediction that survival would vary by age and indicated survival was lowest early in life and increased later in life. Additionally, survival varying by three age intervals supported findings of Grovenburg et al. (2011) and Rohm, Nielsen and Woolf (2007) who noted that white-tailed deer neonate survival varied by three age intervals with survival being lowest early in life and subsequently increasing with increased age. Our results only partially support Nelson and Woolf (1987) who found neonate survival varied by three age intervals; however, they reported survival was least during the second interval (i.e., 2 – 8 weeks of age). Nelson and Woolf (1987) attributed lower survival in the second interval to this age coinciding with white-tailed deer neonates being mobile but not yet able to evade predators. Although variation in the results reported by Grovenburg et al. (2011), Rohm, Nielsen and Woolf (2007) and Nelson and Woolf (1987) may be related to how opportunistically caught neonates were aged (Grovenburg et al., 2014), our results better serve as a base for comparison as neonates included in our VIT only analysis were closest to known age. 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 (Michel et al., 2020a ), and maternal age (Dion et al., 2020) likely affect survival of ungulate neonates; however, results vary (Post et al., 2003; Kautz et al., 2019; Dion et al., 2020). 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.
We also observed our S(Canopy+Precip1) survival model as competing for 3-month survival from our VIT only dataset. Total amount of precipitation from 0 – 2 weeks of a neonate’s life did not affect its survival. However, we identified a weak but positive relationship between neonate survival and percent canopy cover. Percent canopy cover may be an important feature on prairie landscapes due to its limited occurrence of forested cover, which comprised ≤9% of all cover types in our study. Additionally, although forested cover only comprised a small percentage of cover types in our study relative to grasslands and croplands, it may provide an important feature in helping neonates seek refuge from precipitation events, which can lead to hypothermia and subsequent death in neonates (Linnell, Aanes & Andersen, 1995; Grovenburg et al., 2010, 2012; Warbington et al., 2017). However, other cover types likely also provide cover from precipitation and other weather events as neonates tend to select bedsites with an increased understory in grassland landscapes (Grovenburg et al., 2010; Michel et al., 2020b ).
Left truncation affected derived survival estimates, model selection, and interpretation of ecological covariates for 3-month survival. However, capture method did not affect our interpretation of 6-month survival, as it was not the top nor a competing model for our 6-month survival candidate set. This further supported Gilbert et al. (2014) who found capture method no longer affected survival estimates beyond 30-days for black-tailed deer juveniles as well as Grovenburg et al. (2014) who found that age no longer affected 120-day survival estimates for white-tailed deer and mule deer (O. hemionus ) juveniles. This result is important to consider when designing studies assessing ungulate survival. For example, if research is designed to assess factors affecting survival early in life (<3 months) then a capture method that minimizes left truncation (VITs) should be used. However, if research is designed to estimate factors affecting survival later in life (>3 months) then opportunistic capture methods are suitable.
Our top model describing 6-month survival was S(Canopy+Precip2) which supported our prediction that percent canopy cover would positively affect survival while total precipitation would negatively affect juvenile survival. White-tailed deer juveniles can be susceptible to hypothermia (Linnell, Aanes & Andersen, 1995; Grovenburg et al., 2010, Grovenburg, Klaver & Jenks, 2012) and therefore, increased precipitation likely predisposes individuals to succumbing to hypothermia when adequate cover is not available (Warbington et al., 2017). Percent canopy cover likely provides the necessary cover to help juveniles thermoregulate during precipitation events. However, our results contradict those of Michel et al. (2018) who found that juvenile survival in the Northern Great Plains was related positively to total monthly precipitation. Differences in the effects of precipitation between our studies is likely related to scale as our study was comprised of 3 study areas in relatively close proximity whereas Michel et al. (2018) conducted a meta-analysis including 8 study sites across 3 states. Therefore, total precipitation during the parturition season likely has a negative impact on juvenile survival at local scales whereas it has a positive impact on survival at large scales, potentially because of the relationship among total precipitation, the quality of forage available to mothers, and maternal body condition (Michel et al., 2018). Consequently, understanding and interpreting variation in survival analyses relative to scale is important.