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.