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

Habitat selection of white-tailed deer fawns and their dams in the Northern Great Plains 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 conceal- ment 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. . . . . Keywords Bed site selection Northern Great Plains Odocoileus virginianus Parturition site selection Vegetative structure Introduction grasslands (Hebblewhite et al. 2005) whereas resident elk de- creased wolf predation risk by consuming forage located near Habitat availability influences important life-history charac- human activity (Hebblewhite and Merrill 2009). Ciuti et al. teristics such as survival. For example, elk (Cervus (2014) reported mule deer (Odocoileus hemionus) neonate canadensis) experienced increased mortality from wolves survival decreased as habitat fragmentation increased in the (Canis lupus) when using pine forests compared with presence of high coyote (Canis latrans) populations. In con- trast, elk, moose (Alces americanus), and white-tailed deer (Odocoileus virginianus) avoided direct predation risk by Communicated by: Dries Kuijper not selecting resources in areas that posed greater predation risk (Kittle et al. 2008). Therefore, understanding how indi- Electronic supplementary material The online version of this article (https://doi.org/10.1007/s13364-020-00519-6) contains supplementary viduals use available habitat can potentially explain how pop- material, which is available to authorized users. ulations persist in dynamic environments. Although there are several predators of white-tailed deer, * Eric S. Michel coyotes are the main predator of adult white-tailed deer in the eric.michel@state.mn.us Northern Great Plains (Moratz et al. 2018) and are also an important predator of white-tailed deer neonates in the Department of Natural Resource Management, South Dakota State Northern Great Plains (Brinkman et al. 2004; Grovenburg University, Brookings, SD 57007, USA et al. 2011) and throughout their range (Gingery et al. 2018; Present address: Division of Fish and Wildlife, Minnesota Kautz et al. 2019;Warbington etal. 2017). Furthermore, coy- Department of Natural Resources, Farmland and Wildlife otes are reported to have substantial impacts on white-tailed Populations Research Group, 35365 800th Avenue, deer neonate populations at local scales (Chitwood et al. 2015; Madelia, MN 56062, USA Kilgo et al. 2012). However, results on effects of habitat com- North Dakota Game and Fish Department, Bismarck, ND 58501, position and structure on neonate survival is inconsistent USA 826 Mamm Res (2020) 65:825–833 (Chitwood et al. 2015; Gulsby et al. 2017; Kilgo et al. 2014; characteristics affect site-specific selection for offspring, di- Michel et al. 2018). rect comparisons regarding how these characteristics differ Although general cover types can affect survival, micro- between offspring and their dams are limited. habitat characteristics also can affect where individuals choose Our objective was to compare vegetative characteristics to seek cover. Moose (Bowyer et al. 1999), American bison found at white-tailed deer parturition sites and neonate bed (Bison bison;Kazeet al. 2016), and woodland caribou sites after assessing whether vegetative characteristics of both (Rangifer tarandus; Leclerc et al. 2012) dams selected partu- parturition and neonate bed sites differed from paired random rition sites at greater elevations, likely to increase visibility sites. Both dams and neonates likely select sites to reduce and avoid predation. Similarly, after the peak of parturition predation risk (Lehman et al. 2016; Pitman et al. 2014; (28 June to 9 July), pronghorn (Antilocapra americana)fe- Rearden et al. 2011) and increase thermoregulatory efficiency males selected areas with low vegetative biomass, whereas (Grovenburg et al. 2010; Kjellander et al. 2012; Linnell et al. neonate to female ratios were positively correlated with great- 1995). Therefore, we developed multiple hypotheses er vegetative biomass (Christie et al. 2017). This suggests (Tables 1 and 2) toassesswhat vegetativecharacteristics af- females select areas that maximize detection of approaching fected maternal parturition site and neonate bed site selection. predators (Yoakum 2004), while balancing the need for con- Additionally, given the prevalence of row crop agriculture in cealment of the neonate (Barrett 1984). the Northern Great Plains (Wright and Wimberly 2013), we Much research has focused on microhabitat characteristics examined if percent of various cover types found within par- associated with neonate bed site selection. Black-tailed deer turition and bed sites varied throughout the parturition season (Bowyer et al. 1998) and pronghorn (Lehman et al. 2009) to assess if use of row crops increased as crops matured and neonates selected bed sites with increased forb cover and subsequently provided increased cover. overstory canopy cover while white-tailed deer neonates se- lected bed sites with greater vertical structure (Grovenburg et al. 2010;Huegeletal. 1986). Conversely, although con- Materials and methods cealment was important for neonatal elk < 2 weeks old, neo- nates tended to select for cover that allowed for increased Study area visibility as they aged (Pitman et al. 2014). Roe deer (Capreolus capreolus) neonate bed site selection also varied We focused neonate capture in a 2652-km area in the central throughout the parturition season with late-born neonates in- portion of Burleigh County (47.0449° N, 100.5050° W), North creasing use of agricultural areas compared with their early- Dakota, in a 1492-km area in the southwestern portion of born counterparts (Linnell et al. 2004). Although microhabitat Dunn County (47.2122° N, 102.7260° W), North Dakota, in a 1865-km area in the southwestern portion of Grant County (46.3951° N, 101.5536° W), North Dakota, and in a 1492-km Table 1 List of variables included for each of 9 models describing area in the central portion of Perkins County (45.3888° N, various vegetation characteristics and structure found for 63 neonate bed sites located in Burleigh, Dunn, and Grant Counties, North Dakota, 102.3224° W; Fig. 1), South Dakota. Burleigh County, North and Perkins County, South Dakota, USA. We captured neonates in Dakota, was located within the Northwestern Glaciated Plains Burleigh County, North Dakota, from 20 May to 30 June 2011 and Level III Ecoregion while Grant and Dunn counties, North from 23 May to 23 June in Dunn and Grant Counties, North Dakota, Dakota, and Perkins County, South Dakota, were located in and in Perkins County, South Dakota, during 2014 and 2015 the Northwestern Great Plains Level III Ecoregion (Bryce Model name Variable description Grassland Percent grassland Table 2 List of variables included for each of 4 models describing Forested Percent forested various vegetation characteristics and structure found for 16 parturition sites located in Burleigh, Dunn, and Grant Counties, North Dakota, and Forb Percent forbs including alfalfa Perkins County, South Dakota, USA. We captured neonates in Burleigh Vegetative structure Understory vegetation height + percent County, North Dakota, from 20 May to 30 June 2011 and from 23 May to canopy cover 23 June in Dunn and Grant Counties, North Dakota, and in Perkins Vegetative overstory Overstory vegetation height County, South Dakota, during 2014 and 2015 Vegetative understory Understory vegetation height Model name Variable description Grassland + structure Percent grassland + understory vegetation height + percent canopy cover Grassland Percent grassland Forested + structure Percent forested + understory vegetation Forb Percent forb height + percent canopy cover Vegetative overstory Overstory vegetation height Forb + structure Percent cropland + understory vegetation height + percent canopy cover Vegetative understory Understory vegetation height Mamm Res (2020) 65:825–833 827 Fig. 1 Study area where vegetative characteristics for 53 parturition and 140 white-tailed deer bed sites were measured. a Burleigh County, North Dakota. b Dunn County, North Dakota. c Grant County, North Dakota. d Perkins County, South Dakota. We captured neonates in Burleigh County, North Dakota, from 20 May to30June2011and from 23 May to 23 June in Dunn and Grant Counties, North Dakota, and in Perkins County, South Dakota, during 2014 and 2015 et al. 1998). Grasslands and croplands were the dominant cover (Koeleria macrantha), and reed canarygrass (Phalaris types and ranged from 60 to 86% and 11 to 26%, respectively, arundinacea). Introduced grasses included smooth brome while forested cover types ranged from 0.01% in Perkins (Bromus inermis), orchardgrass (Dactylis glomerata), crested County to 9% in Dunn County (Cropland Data Layer, United wheatgrass (Agropyron sp.), timothy (Phleum pratense), and States Department of Agriculture (USDA) 2011). Wetlands Kentucky bluegrass (Poa pratensis). Primary harvested crops and water also were prevalent cover types (7%) in Burleigh includedcorn(Zea mays), wheat (Triticum aestivum), sunflowers County but were not prevalent in Dunn, Grant, or Perkins (Helianthus annuus), and alfalfa (Medicago sativa). Other crops Counties (United States Department of Agriculture (USDA) included flaxseed (Linum usitatissimum), canola (Brassica sp.), 2011). Thirty-year mean annual precipitation ranged from soybeans (Glycine max), barley (Hordeum vulgare), safflower 41.2 cm (Grant County) to 44.9 cm (Burleigh and Perkins (Carthamus tinctorius), oats (Avena sativa), and Sudangrass Counties) and variation in 30-year mean monthly temperature (Sorghum bicolor). was greatest in Perkins County ranging from − 12.1 to 30.3 °C (North Dakota State Climate Office 2016). Data collection Landscapes in this region were dominated by native mixed grassland prairie species comprised western wheatgrass We captured neonates in Burleigh County, North Dakota, (Pascopyrum smithii), needle-and-thread (Hesperostipa comata), from 20 May to 30 June 2011 and from 23 May to 23 June green needlegrass (Nassella viridula), little bluestem in Dunn and Grant Counties, North Dakota, and in Perkins (Schizachyrium scoparium), big bluestem (Andropogon County, South Dakota, during 2014 and 2015. We captured gerardii), Indiangrass (Sorghastrum nutans), prairie Junegrass adult female (≥ 1.5-year-old) white-tailed deer via helicopter 828 Mamm Res (2020) 65:825–833 net guns (Native Range Capture Services, Elko, NV, USA). Statistical analysis We then affixed very high frequency (VHF) radio-collars (model M2610B, Advanced Telemetry Systems, Isanti, MN) Due to logistical constraints that delayed us from measuring to individuals and inserted Vaginal Implant Transmitters vegetation at parturition sites, bed sites, and their respective (Advanced Telemetry Systems, Inc., Isanti, MN, USA; paired random sites up to 39 days later, we restricted our partu- Bowman and Jacobson 1998;Carstensenetal. 2003; rition and bed site selection analyses to sites where we collected Swanson et al. 2008). We also used reproductive female post- vegetation measurements within 14 days of locating sites. partum behavior as an indicator of presence of neonates Therefore, we assessed if vegetation characteristics varied be- (Downing and McGinnes 1969; Huegel et al. 1985; White tween parturition and bed sites and their paired random sites et al. 1972) and then captured neonates by hand or net. We using a conditional logistic model and estimated odds ratios wore latex gloves and stored all radio-collars and other equip- using the clogit function in the Survival package in Program R ment in natural vegetation to minimize scent transfer. We (R Core Team 2016 version 3.3.1; Therneau 2015). The clogit fitted neonates with expandable breakaway radio-collars and function allows for specific comparisons between capture and monitored individuals daily for the first 30 days using a truck- paired random sites. We developed nine models describing gen- mounted null-peak antenna system (Brinkman et al. 2002), eral cover type, vegetative structure, or a combination of cover hand-held Yagi antennas, aerial telemetry, and omnidirection- type and structure for bed sites (Table 1). We simplified our al whip antennas. We determined bed sites to be locations candidate set to four models describing vegetative structure and where we opportunistically captured neonates and parturition composition for parturition sites due to sample size (n = 16; sites to be locations where we found a VIT. We only captured Table 2). We then ranked each model using Akaike’s neonates once. All handling methods followed the American Information Criterion corrected for small sample size (AIC ) Society of Mammalogists guidelines for mammal care and use and considered models within 2ΔAIC as potentially competing (Sikes et al. 2016) and were approved by the South Dakota (Burnham and Anderson 2002). We derived AIC values, num- State University Institutional Animal Care and Use ber of parameters, and model weights using the AIC and weight Committee (Approval No. 10-006E and 13-091A). functions in the MuMIn package in Program R (Barton 2016). We completed vegetation assessments at parturition sites and We assessed correlation among explanatory variables using the neonate bed sites immediately if neonates flushed upon approach cor.test function and included multiple variables in a single mod- or collected measurements within 39 days if neonates did not el when |r| ≤ 0.50. We used the model.avg. function in the flush. We measured all vegetation heights using a modified MuMIn package in Program R when necessary to calculate Robel pole (Robel et al. 1970) with 10-cm increments. The ob- model-averaged coefficients. We considered variables important server was about 4 m from the Robel pole when collecting veg- when their 95% confidence intervals (95% CIs) excluded 0 etation data. Vegetation overstory height represented the tallest (Burnham and Anderson 2002;Arnold 2010). We considered vegetation marked on the Robel pole, whereas understory vege- odds ratios important when their 95% CIs excluded 1. We pres- tation height represented the tallest vegetation where the Robel ent all means ± 1 standard deviation. pole was completely obstructed. We recorded measurements Finally, we visually assessed if dam and neonate use of spe- from the center of the parturition or bed site in each cardinal cific cover types varied by quantifying the number of parturition direction and averaged them (by site) to determine height of the and bed sites that we found in each cover type on a weekly basis vegetation overstory and height of vegetation understory (Robel throughout the parturition season (day 1 representing the first et al. 1970). We recorded ocular estimations of percent cover parturition/bed site found followed by the subsequent 6 days). using 5% increments for bare ground, forbs (including alfalfa), Although vegetative measurements were delayed, the cover type grass, litter, row crop, shrub, and tree in 24, 1.0-m Daubenmire of each site would not have changed temporally. Therefore, we plots (Daubenmire 1959) spaced at 1-m intervals along four per- used our entire dataset for this assessment. pendicular transects originating at the center of parturition or bed sites and paired random sites. We estimated tree canopy cover at 6 m north, south, east, and west of parturition and bed sites or Results paired random sites using a spherical densiometer (Uresk et al. 1999). We followed the methods of Grovenburg et al. (2010)and We captured neonates from 20 May to 30 June and collected identified each paired random site within 250 m of its associated vegetation data at 34 parturition sites primarily located in parturition or bed site. Locating paired random sites within grassland (47%; n = 16), riparian (26%, n = 9), and wooded 250 m of its associated parturition or bed site allowed us to keep (18%, n = 6) cover types with all other cover types containing random sites within the same cover type. After locating paired ≤ 9% (n = 3) of parturition sites. We collected vegetation data random sites in similar cover types (grassland, forested, riparian), at 63 individual neonate bed sites primarily located in grass- we then collected data in the same manner as described above for lands (68%; n = 43), followed by riparian (17%; n =11), and parturition and bed sites (Grovenburg et al. 2010). wooded (11%; n = 7) cover types with all other cover types Mamm Res (2020) 65:825–833 829 containing ≤ 2% (n = 2) of bed sites. Explanatory variables vegetation height (β = 0.001; 95% CI, − 0.009–0.034, n=63) were not correlated (|r| ≤ 0.33). displaying a general positive impact on bed site selection. The Given we found most parturition sites in grasslands, we 95% CIs for all other model-averaged coefficients greatly over- reduced our analysis to include only those parturition sites lapped 0 (Table 5). Mean understory vegetation height was 36.2 found in grasslands. In doing so, we observed two competing ± 14.0 cm at bed sites and 32.4 ± 16.3 cm at random sites. Mean models that described vegetation characteristics at parturition overstory vegetation height was 72.8 ± 24.5 cm at bed sites and sites (Table 3). Our top supported model was our vegetative was 69.5 ± 24.6 cm at random sites. The likelihood ratio test understory model, which carried a majority of model weight indicated adequate model fit for our vegetative structure model (w = 0.64). Understory vegetation height at parturition sites (6.96, DF =2, P = 0.030) and for our forb + structure model differed from random sites and had a negative effect (β = − (8.26, DF =3, P = 0.040) but not for our vegetative understory 0.168; 95% CI, − 0.325–− 0.011, n = 16) on parturition site model (3.32, DF =1, P = 0.70). selection such that for every 1-cm decrease in understory veg- We did not detect any trends for variation in cover types used etation height, probability of a female selecting that site for for parturition sites (Fig. 2) and bed sites (Fig. 3) throughout the parturition increased 15.4% (odds ratio = 0.845; 95% CI, parturition season as we consistently found parturition and bed 0.722–0.989). Mean understory vegetation height was sites in grassland and riparian cover types. We did not find any 22.6 ± 12.7 cm at parturition sites and was 31.8 ± 13.0 cm at parturition sites in row crop or other cover types. random sites. Overstory vegetation height at parturition sites differed from random sites and had a negative effect (β = − 0.067; 95% CI, − 0.128–− 0.005, n = 16) on parturition site Discussion selection such that for every 1-cm decrease in overstory veg- etation height, probability of a female selecting that site for Our understory vegetation height model was our top supported parturition increased 6.5% (odds ratio = 0.935; 95% CI, model for parturition site selection with our overstory vegetation 0.880–0.994). Mean overstory vegetation height was height model competing with our top model. Adult female white- 52.9 ± 26.9 cm at parturition sites and was 71.2 ± 16.8 cm at tailed deer selected for shorter vegetation than random when random sites. The likelihood ratio test indicated adequate selecting parturition sites. Our results support Rearden et al. model fit (vegetative understory model, 9.26, DF =1, P = (2011) who found female elk selected for parturition sites with 0.002; vegetative overstory model, 8.01, DF =1, P =0.005). increased visibility (but see Alldredge et al. 1991,Barrett 1984, Although our most parsimonious model describing neonate Barbknecht et al. 2011, and Lehman et al. 2016 for cases where bed site selection was our vegetative structure model, it carried female ungulates selected parturition sites with increased cover). low model weight (w =0.31; Table 4). Our forb + structure Regardless, moose (Bowyer et al. 1999), American bison (Kaze model (w = 0.19) and vegetative understory model (w =0.14) i i et al. 2016), and woodland caribou (Leclerc et al. 2012) selected also appeared to be competing. The 95% CIs in our top for parturition sites at higher elevations, likely to increase their models overlapped 0 for all variables (S1). Therefore, we calculated the model-averaged coefficients due to model uncertainty (Burnham and Anderson 2002). After mod- Table 4 Model results for 9 models describing various vegetation el averaging, there was a trend of understory vegetation height characteristics and structure found for 63 bed sites of neonate white- tailed deer located in Burleigh, Dunn, and Grant Counties, North (β = 0.025; 95% CI, − 0.005–0.065, n = 63) and overstory Dakota, and Perkins County, South Dakota, USA. We captured neonates in Burleigh County, North Dakota, from 20 May to 30 June 2011 and from 23 May to 23 June in Dunn and Grant Counties, North Dakota, and in Perkins County, South Dakota, during 2014 and 2015 Table 3 Model results for 4 models describing various vegetation characteristics and structure found for 16 parturition sites of adult Model ΔAIC w K c i female white-tailed deer located in Burleigh, Dunn, and Grant Counties, North Dakota, and Perkins County, South Dakota, USA. We captured Vegetative structure 0.00 0.31 2 neonates in Burleigh County, North Dakota, from 20 May to 30 Forb + structure 0.91 0.19 3 June 2011 and from 23 May to 23 June in Dunn and Grant Counties, North Dakota, and in Perkins County, South Dakota, during 2014 and Vegetative understory 1.51 0.14 1 Grassland + structure 2.19 0.10 3 Forested + structure 2.25 0.10 4 Model ΔAIC w K c i Forb 3.56 0.05 1 Vegetative understory 0.00 0.64 1 Vegetative overstory 3.58 0.05 1 Vegetative overstory 1.25 0.34 1 Grassland 4.58 0.03 1 Forb 7.87 0.01 1 Forested 5.28 0.02 2 Grassland 9.23 0.01 1 830 Mamm Res (2020) 65:825–833 Table 5 Model-averaged beta coefficients and 95% confidence Although we report a general trend of increased vegetation intervals for 63 bed sites of white-tailed deer neonates collected through- height at neonate bed sites compared with random sites, weak out South Dakota and North Dakota, USA. We captured neonates in estimates and imprecise confidence intervals preclude us from Burleigh County, North Dakota, from 20 May to 30 June 2011 and from 23 May to 23 June in Dunn and Grant Counties, North Dakota, and in directly discussing this variation. Nevertheless, several studies Perkins County, South Dakota, during 2014 and 2015 report ungulate neonates such as mule deer (Gerlach and Vaughan 1991), elk (Pitman et al. 2014), bighorn sheep Variable Beta Lower 95% CI Upper 95% CI (Ovis canadensis; Smith et al. 2015), and pronghorn (Barrett Understory vegetation height 0.025 − 0.005 0.065 1984;Christieet al. 2017) selected for bed sites with increased Percent canopy cover 0.044 − 0.018 0.143 concealment. White-tailed deer neonates display fear brady- Percent forb 0.006 − 0.021 0.070 cardia and are relatively immobile within their first 30 days of life (Carl and Robbins 1988; Lent 1974). Therefore, increased Percent grass − 0.001 − 0.040 0.032 Percent forested − 0.023 − 0.569 0.189 understory and overstory vegetation height provides increased cover and visual obstruction from predators potentially de- Percent shrub − 0.005 − 0.159 0.080 creasing predation risk, though the effects of vegetation at Overstory vegetation height 0.001 − 0.009 0.034 bed sites on neonate survival are inconsistent (Canon and Bryant 1997; Chitwood et al. 2015). Increased vegetative height at neonate bed sites also could potentially help neonates visibility. Therefore, given the magnitude of difference between thermoregulate during inclement weather (precipitation vegetative heights recorded at parturition sites compared with events), potentially influencing survival (Grovenburg et al. random sites and the increased probability of a mother selecting 2010;Kjellander etal. 2012; Linnell et al. 1995). Grassland a site based on vegetative height, increasing visibility during a was the most common cover type in our study, comprised up to 86% of the landscape, and was the most common cover birthing event is seemingly an important antipredator defense strategy for white-tailed deer mothers using grassland cover types type in which neonate bed sites were located throughout the parturition season. Concomitantly, fawn survival is generally in the Northern Great Plains. Fig. 2 Number of parturition sites used by week throughout the parturition season in South Dakota and North Dakota, USA. We located parturition sites from 20 May to 30 June 2011 and from 23 May to 23 June in 2014 and 2015. Parturition sites were not found in either row crop or other cover types Mamm Res (2020) 65:825–833 831 Fig. 3 Number of bed sites used by week throughout the parturition season in South Dakota and North Dakota, USA. We located parturition sites from 20 May to 30 June 2011 and from 23 May to 23 June in 2014 and For example, previous research has shown that forested areas high in the Northern Great Plains (Michel et al. 2018). Given the lack of variation between vegetative structure at neonate provide important winter cover for adult white-tailed deer bed sites compared with random sites (understory vegetation (Grovenburg et al. 2011), whereas white-tailed deer neonate height = ~ 4-cm difference; overstory vegetation height = ~ 3- survival decreased with increasing forested cover; potentially cm difference), our results indicate grasslands likely provide because small linear tree plantings on the prairie may serve as the vegetative structure necessary for adequate concealment ecological traps due to coyote predation (Grovenburg et al. and thermoregulation that neonates require early in life in the 2012b). Nevertheless, understanding age-specific habitat re- Northern Great Plains. quirements allows for more specific habitat management, Neither maternal nor neonate use of cover types varied which ultimately encourages vegetative diversity on the land- throughout the parturition season. Although row crops such scape and could potentially impact life-history characteristics as corn and soybeans mature throughout the summer and, such as survival for several age-classes. therefore, provide increased hiding cover as neonates age We recommend maintaining a mosaic of grassland, ripari- (Grovenburg et al. 2012a), we found no evidence that white- an, and forested cover types in agriculturally dominated land- tailed deer use agricultural crops more than other cover types scapes as those cover types contained 92% of parturition sites later in the parturition season. This is likely because grass- and 86% of all bed site locations in our study. Furthermore, lands, riparian areas, and forested areas represent permanent maintaining an understory vegetation height of about 23 cm cover and provide vegetation with adequate height during the for mothers and about 36 cm for neonates while maintaining parturition season. Phenology of cool-season grasses also can an overstory vegetation height of about 53 cm for mothers and impact white-tailed deer selection given that 47% of parturi- about 73 cm for neonates should allow for adequate visibility tion sites and 68% of neonate bed sites were found in grass- for mothers and adequate concealment for neonates, particu- lands. For example, cool-season grasses grow mostly in early larly in grassland cover types. Maintaining this vegetative spring and generally complete flowering by 21 June (Leopold height will also likely allow mothers to detect predators during and Kriedemann 1975; Weier et al. 1974); we captured all parturition events (Rearden et al. 2011) and may assist neo- neonates by 30 June. Dams also selected understory vegeta- nates in avoiding detection by predators (Gerlach and tion that was about 23 cm tall while neonates selected vege- Vaughan 1991; Pitman et al. 2014; Smith et al. 2015). tation that was about 36 cm tall, a height most cool-season Finally, dam and neonate use of cover types did not vary plants reach before maturation. Therefore, between growth of throughout the parturition season, suggesting white-tailed cool-season grasses and residual dead plant material, dams deer likely do not increase use of agricultural crops throughout and neonates likely had enough permanent cover in grasslands the parturition season; however, deer do likely increase their throughout the parturition season while obtaining additional use of row crops later in summer as crops provide increased cover from riparian and forested areas. cover as they mature (Grovenburg et al. 2012a). Regardless, Our results emphasize the importance of understanding agricultural crops are probably not beneficial in providing whether habitat requirements vary by life-stage for a species. cover to white-tailed deer during the parturition season. 832 Mamm Res (2020) 65:825–833 Acknowledgments We thank the North Dakota Game and Fish Bowman JL, Jacobson HA (1998) An improved vaginal-implant trans- Department, South Dakota Department of Game, Fish and Parks, the mitter for locating white-tailed deer birth sites and fawns. Wildl Soc Minnesota Department of Natural Resources, the Department of Natural Bull 26:295–298 Resource Management at South Dakota State University, and numerous Bowyer RT, Kie JG, Van Ballenberghe V (1998) Habitat selection by private landowners, graduate students, and technicians for their help and neonatal black-tailed deer: climate, forage, or risk of predation? J cooperation with this project. We also thank D. Morina, C. Chitwood, M. 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Wildl Soc Bull 32:726–731. https://doi.org/10.2193/ 0091-7648(2004)032[0726:SOWDIA]2.0.CO;2 All handling methods followed the American Society of Mammalogists Bryce SJ, Omernik M, Pater DE, Ulmer M, Schaar J, Freeouf J, Johnson guidelines for mammal care and use (Sikes et al. 2016) and were ap- R, Kuck P, Azevedo SH (1998) Ecoregions of North Dakota and proved by the South Dakota State University Institutional Animal Care South Dakota. (Map poster). U.S. Geological Survey, Jamestown, and Use Committee (Approval No. 10-006E and 13-091A). ND Burnham KP, Anderson DR (2002) Model selection and multimodel Disclaimer Any mention of trade, product, or firm names is for descrip- inference: a practical information-theoretic approach. Springer- tive purposes only and does not imply endorsement by the US Verlag, New York Government. Canon SK, Bryant FC (1997) Bed-site characteristics of pronghorn fawns. 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The images or other third party material in this article DePerno CS (2015) Do biological and bedsite characteristics influ- are included in the article's Creative Commons licence, unless indicated ence survival of neonatal white-tailed deer? PLoS One 10: otherwise in a credit line to the material. If material is not included in the e0119070. https://doi.org/10.1371/journal.pone.0119070 article's Creative Commons licence and your intended use is not Christie KS, Jensen WF, Boyce MS (2017) Pronghorn resource selection permitted by statutory regulation or exceeds the permitted use, you will and habitat fragmentation in North Dakota. J Wildl Manag 81:154– need to obtain permission directly from the copyright holder. To view a 162. https://doi.org/10.1002/jwmg.21147 copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Ciuti S, Jensen WF, Neilsen SE, Johnson SK, Hosek BM, Boyce MS (2014) An evaluation of historical mule deer fawn recruitment in North Dakota. 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Acta Theriol 49:103–111. https://doi.org/10.1007/BF03192512 Michel ES, Jenks JA, Kaskie KD, Klaver RW, Jensen WF (2018) Publisher’snote Springer Nature remains neutral with regard to jurisdic- Weather and landscape factors affect white-tailed deer neonate tional claims in published maps and institutional affiliations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Acta Theriologica Springer Journals

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

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References (68)

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Springer Journals
Copyright
Copyright © The Author(s) 2020
ISSN
0001-7051
eISSN
2199-241X
DOI
10.1007/s13364-020-00519-6
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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 conceal- ment 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. . . . . Keywords Bed site selection Northern Great Plains Odocoileus virginianus Parturition site selection Vegetative structure Introduction grasslands (Hebblewhite et al. 2005) whereas resident elk de- creased wolf predation risk by consuming forage located near Habitat availability influences important life-history charac- human activity (Hebblewhite and Merrill 2009). Ciuti et al. teristics such as survival. For example, elk (Cervus (2014) reported mule deer (Odocoileus hemionus) neonate canadensis) experienced increased mortality from wolves survival decreased as habitat fragmentation increased in the (Canis lupus) when using pine forests compared with presence of high coyote (Canis latrans) populations. In con- trast, elk, moose (Alces americanus), and white-tailed deer (Odocoileus virginianus) avoided direct predation risk by Communicated by: Dries Kuijper not selecting resources in areas that posed greater predation risk (Kittle et al. 2008). Therefore, understanding how indi- Electronic supplementary material The online version of this article (https://doi.org/10.1007/s13364-020-00519-6) contains supplementary viduals use available habitat can potentially explain how pop- material, which is available to authorized users. ulations persist in dynamic environments. Although there are several predators of white-tailed deer, * Eric S. Michel coyotes are the main predator of adult white-tailed deer in the eric.michel@state.mn.us Northern Great Plains (Moratz et al. 2018) and are also an important predator of white-tailed deer neonates in the Department of Natural Resource Management, South Dakota State Northern Great Plains (Brinkman et al. 2004; Grovenburg University, Brookings, SD 57007, USA et al. 2011) and throughout their range (Gingery et al. 2018; Present address: Division of Fish and Wildlife, Minnesota Kautz et al. 2019;Warbington etal. 2017). Furthermore, coy- Department of Natural Resources, Farmland and Wildlife otes are reported to have substantial impacts on white-tailed Populations Research Group, 35365 800th Avenue, deer neonate populations at local scales (Chitwood et al. 2015; Madelia, MN 56062, USA Kilgo et al. 2012). However, results on effects of habitat com- North Dakota Game and Fish Department, Bismarck, ND 58501, position and structure on neonate survival is inconsistent USA 826 Mamm Res (2020) 65:825–833 (Chitwood et al. 2015; Gulsby et al. 2017; Kilgo et al. 2014; characteristics affect site-specific selection for offspring, di- Michel et al. 2018). rect comparisons regarding how these characteristics differ Although general cover types can affect survival, micro- between offspring and their dams are limited. habitat characteristics also can affect where individuals choose Our objective was to compare vegetative characteristics to seek cover. Moose (Bowyer et al. 1999), American bison found at white-tailed deer parturition sites and neonate bed (Bison bison;Kazeet al. 2016), and woodland caribou sites after assessing whether vegetative characteristics of both (Rangifer tarandus; Leclerc et al. 2012) dams selected partu- parturition and neonate bed sites differed from paired random rition sites at greater elevations, likely to increase visibility sites. Both dams and neonates likely select sites to reduce and avoid predation. Similarly, after the peak of parturition predation risk (Lehman et al. 2016; Pitman et al. 2014; (28 June to 9 July), pronghorn (Antilocapra americana)fe- Rearden et al. 2011) and increase thermoregulatory efficiency males selected areas with low vegetative biomass, whereas (Grovenburg et al. 2010; Kjellander et al. 2012; Linnell et al. neonate to female ratios were positively correlated with great- 1995). Therefore, we developed multiple hypotheses er vegetative biomass (Christie et al. 2017). This suggests (Tables 1 and 2) toassesswhat vegetativecharacteristics af- females select areas that maximize detection of approaching fected maternal parturition site and neonate bed site selection. predators (Yoakum 2004), while balancing the need for con- Additionally, given the prevalence of row crop agriculture in cealment of the neonate (Barrett 1984). the Northern Great Plains (Wright and Wimberly 2013), we Much research has focused on microhabitat characteristics examined if percent of various cover types found within par- associated with neonate bed site selection. Black-tailed deer turition and bed sites varied throughout the parturition season (Bowyer et al. 1998) and pronghorn (Lehman et al. 2009) to assess if use of row crops increased as crops matured and neonates selected bed sites with increased forb cover and subsequently provided increased cover. overstory canopy cover while white-tailed deer neonates se- lected bed sites with greater vertical structure (Grovenburg et al. 2010;Huegeletal. 1986). Conversely, although con- Materials and methods cealment was important for neonatal elk < 2 weeks old, neo- nates tended to select for cover that allowed for increased Study area visibility as they aged (Pitman et al. 2014). Roe deer (Capreolus capreolus) neonate bed site selection also varied We focused neonate capture in a 2652-km area in the central throughout the parturition season with late-born neonates in- portion of Burleigh County (47.0449° N, 100.5050° W), North creasing use of agricultural areas compared with their early- Dakota, in a 1492-km area in the southwestern portion of born counterparts (Linnell et al. 2004). Although microhabitat Dunn County (47.2122° N, 102.7260° W), North Dakota, in a 1865-km area in the southwestern portion of Grant County (46.3951° N, 101.5536° W), North Dakota, and in a 1492-km Table 1 List of variables included for each of 9 models describing area in the central portion of Perkins County (45.3888° N, various vegetation characteristics and structure found for 63 neonate bed sites located in Burleigh, Dunn, and Grant Counties, North Dakota, 102.3224° W; Fig. 1), South Dakota. Burleigh County, North and Perkins County, South Dakota, USA. We captured neonates in Dakota, was located within the Northwestern Glaciated Plains Burleigh County, North Dakota, from 20 May to 30 June 2011 and Level III Ecoregion while Grant and Dunn counties, North from 23 May to 23 June in Dunn and Grant Counties, North Dakota, Dakota, and Perkins County, South Dakota, were located in and in Perkins County, South Dakota, during 2014 and 2015 the Northwestern Great Plains Level III Ecoregion (Bryce Model name Variable description Grassland Percent grassland Table 2 List of variables included for each of 4 models describing Forested Percent forested various vegetation characteristics and structure found for 16 parturition sites located in Burleigh, Dunn, and Grant Counties, North Dakota, and Forb Percent forbs including alfalfa Perkins County, South Dakota, USA. We captured neonates in Burleigh Vegetative structure Understory vegetation height + percent County, North Dakota, from 20 May to 30 June 2011 and from 23 May to canopy cover 23 June in Dunn and Grant Counties, North Dakota, and in Perkins Vegetative overstory Overstory vegetation height County, South Dakota, during 2014 and 2015 Vegetative understory Understory vegetation height Model name Variable description Grassland + structure Percent grassland + understory vegetation height + percent canopy cover Grassland Percent grassland Forested + structure Percent forested + understory vegetation Forb Percent forb height + percent canopy cover Vegetative overstory Overstory vegetation height Forb + structure Percent cropland + understory vegetation height + percent canopy cover Vegetative understory Understory vegetation height Mamm Res (2020) 65:825–833 827 Fig. 1 Study area where vegetative characteristics for 53 parturition and 140 white-tailed deer bed sites were measured. a Burleigh County, North Dakota. b Dunn County, North Dakota. c Grant County, North Dakota. d Perkins County, South Dakota. We captured neonates in Burleigh County, North Dakota, from 20 May to30June2011and from 23 May to 23 June in Dunn and Grant Counties, North Dakota, and in Perkins County, South Dakota, during 2014 and 2015 et al. 1998). Grasslands and croplands were the dominant cover (Koeleria macrantha), and reed canarygrass (Phalaris types and ranged from 60 to 86% and 11 to 26%, respectively, arundinacea). Introduced grasses included smooth brome while forested cover types ranged from 0.01% in Perkins (Bromus inermis), orchardgrass (Dactylis glomerata), crested County to 9% in Dunn County (Cropland Data Layer, United wheatgrass (Agropyron sp.), timothy (Phleum pratense), and States Department of Agriculture (USDA) 2011). Wetlands Kentucky bluegrass (Poa pratensis). Primary harvested crops and water also were prevalent cover types (7%) in Burleigh includedcorn(Zea mays), wheat (Triticum aestivum), sunflowers County but were not prevalent in Dunn, Grant, or Perkins (Helianthus annuus), and alfalfa (Medicago sativa). Other crops Counties (United States Department of Agriculture (USDA) included flaxseed (Linum usitatissimum), canola (Brassica sp.), 2011). Thirty-year mean annual precipitation ranged from soybeans (Glycine max), barley (Hordeum vulgare), safflower 41.2 cm (Grant County) to 44.9 cm (Burleigh and Perkins (Carthamus tinctorius), oats (Avena sativa), and Sudangrass Counties) and variation in 30-year mean monthly temperature (Sorghum bicolor). was greatest in Perkins County ranging from − 12.1 to 30.3 °C (North Dakota State Climate Office 2016). Data collection Landscapes in this region were dominated by native mixed grassland prairie species comprised western wheatgrass We captured neonates in Burleigh County, North Dakota, (Pascopyrum smithii), needle-and-thread (Hesperostipa comata), from 20 May to 30 June 2011 and from 23 May to 23 June green needlegrass (Nassella viridula), little bluestem in Dunn and Grant Counties, North Dakota, and in Perkins (Schizachyrium scoparium), big bluestem (Andropogon County, South Dakota, during 2014 and 2015. We captured gerardii), Indiangrass (Sorghastrum nutans), prairie Junegrass adult female (≥ 1.5-year-old) white-tailed deer via helicopter 828 Mamm Res (2020) 65:825–833 net guns (Native Range Capture Services, Elko, NV, USA). Statistical analysis We then affixed very high frequency (VHF) radio-collars (model M2610B, Advanced Telemetry Systems, Isanti, MN) Due to logistical constraints that delayed us from measuring to individuals and inserted Vaginal Implant Transmitters vegetation at parturition sites, bed sites, and their respective (Advanced Telemetry Systems, Inc., Isanti, MN, USA; paired random sites up to 39 days later, we restricted our partu- Bowman and Jacobson 1998;Carstensenetal. 2003; rition and bed site selection analyses to sites where we collected Swanson et al. 2008). We also used reproductive female post- vegetation measurements within 14 days of locating sites. partum behavior as an indicator of presence of neonates Therefore, we assessed if vegetation characteristics varied be- (Downing and McGinnes 1969; Huegel et al. 1985; White tween parturition and bed sites and their paired random sites et al. 1972) and then captured neonates by hand or net. We using a conditional logistic model and estimated odds ratios wore latex gloves and stored all radio-collars and other equip- using the clogit function in the Survival package in Program R ment in natural vegetation to minimize scent transfer. We (R Core Team 2016 version 3.3.1; Therneau 2015). The clogit fitted neonates with expandable breakaway radio-collars and function allows for specific comparisons between capture and monitored individuals daily for the first 30 days using a truck- paired random sites. We developed nine models describing gen- mounted null-peak antenna system (Brinkman et al. 2002), eral cover type, vegetative structure, or a combination of cover hand-held Yagi antennas, aerial telemetry, and omnidirection- type and structure for bed sites (Table 1). We simplified our al whip antennas. We determined bed sites to be locations candidate set to four models describing vegetative structure and where we opportunistically captured neonates and parturition composition for parturition sites due to sample size (n = 16; sites to be locations where we found a VIT. We only captured Table 2). We then ranked each model using Akaike’s neonates once. All handling methods followed the American Information Criterion corrected for small sample size (AIC ) Society of Mammalogists guidelines for mammal care and use and considered models within 2ΔAIC as potentially competing (Sikes et al. 2016) and were approved by the South Dakota (Burnham and Anderson 2002). We derived AIC values, num- State University Institutional Animal Care and Use ber of parameters, and model weights using the AIC and weight Committee (Approval No. 10-006E and 13-091A). functions in the MuMIn package in Program R (Barton 2016). We completed vegetation assessments at parturition sites and We assessed correlation among explanatory variables using the neonate bed sites immediately if neonates flushed upon approach cor.test function and included multiple variables in a single mod- or collected measurements within 39 days if neonates did not el when |r| ≤ 0.50. We used the model.avg. function in the flush. We measured all vegetation heights using a modified MuMIn package in Program R when necessary to calculate Robel pole (Robel et al. 1970) with 10-cm increments. The ob- model-averaged coefficients. We considered variables important server was about 4 m from the Robel pole when collecting veg- when their 95% confidence intervals (95% CIs) excluded 0 etation data. Vegetation overstory height represented the tallest (Burnham and Anderson 2002;Arnold 2010). We considered vegetation marked on the Robel pole, whereas understory vege- odds ratios important when their 95% CIs excluded 1. We pres- tation height represented the tallest vegetation where the Robel ent all means ± 1 standard deviation. pole was completely obstructed. We recorded measurements Finally, we visually assessed if dam and neonate use of spe- from the center of the parturition or bed site in each cardinal cific cover types varied by quantifying the number of parturition direction and averaged them (by site) to determine height of the and bed sites that we found in each cover type on a weekly basis vegetation overstory and height of vegetation understory (Robel throughout the parturition season (day 1 representing the first et al. 1970). We recorded ocular estimations of percent cover parturition/bed site found followed by the subsequent 6 days). using 5% increments for bare ground, forbs (including alfalfa), Although vegetative measurements were delayed, the cover type grass, litter, row crop, shrub, and tree in 24, 1.0-m Daubenmire of each site would not have changed temporally. Therefore, we plots (Daubenmire 1959) spaced at 1-m intervals along four per- used our entire dataset for this assessment. pendicular transects originating at the center of parturition or bed sites and paired random sites. We estimated tree canopy cover at 6 m north, south, east, and west of parturition and bed sites or Results paired random sites using a spherical densiometer (Uresk et al. 1999). We followed the methods of Grovenburg et al. (2010)and We captured neonates from 20 May to 30 June and collected identified each paired random site within 250 m of its associated vegetation data at 34 parturition sites primarily located in parturition or bed site. Locating paired random sites within grassland (47%; n = 16), riparian (26%, n = 9), and wooded 250 m of its associated parturition or bed site allowed us to keep (18%, n = 6) cover types with all other cover types containing random sites within the same cover type. After locating paired ≤ 9% (n = 3) of parturition sites. We collected vegetation data random sites in similar cover types (grassland, forested, riparian), at 63 individual neonate bed sites primarily located in grass- we then collected data in the same manner as described above for lands (68%; n = 43), followed by riparian (17%; n =11), and parturition and bed sites (Grovenburg et al. 2010). wooded (11%; n = 7) cover types with all other cover types Mamm Res (2020) 65:825–833 829 containing ≤ 2% (n = 2) of bed sites. Explanatory variables vegetation height (β = 0.001; 95% CI, − 0.009–0.034, n=63) were not correlated (|r| ≤ 0.33). displaying a general positive impact on bed site selection. The Given we found most parturition sites in grasslands, we 95% CIs for all other model-averaged coefficients greatly over- reduced our analysis to include only those parturition sites lapped 0 (Table 5). Mean understory vegetation height was 36.2 found in grasslands. In doing so, we observed two competing ± 14.0 cm at bed sites and 32.4 ± 16.3 cm at random sites. Mean models that described vegetation characteristics at parturition overstory vegetation height was 72.8 ± 24.5 cm at bed sites and sites (Table 3). Our top supported model was our vegetative was 69.5 ± 24.6 cm at random sites. The likelihood ratio test understory model, which carried a majority of model weight indicated adequate model fit for our vegetative structure model (w = 0.64). Understory vegetation height at parturition sites (6.96, DF =2, P = 0.030) and for our forb + structure model differed from random sites and had a negative effect (β = − (8.26, DF =3, P = 0.040) but not for our vegetative understory 0.168; 95% CI, − 0.325–− 0.011, n = 16) on parturition site model (3.32, DF =1, P = 0.70). selection such that for every 1-cm decrease in understory veg- We did not detect any trends for variation in cover types used etation height, probability of a female selecting that site for for parturition sites (Fig. 2) and bed sites (Fig. 3) throughout the parturition increased 15.4% (odds ratio = 0.845; 95% CI, parturition season as we consistently found parturition and bed 0.722–0.989). Mean understory vegetation height was sites in grassland and riparian cover types. We did not find any 22.6 ± 12.7 cm at parturition sites and was 31.8 ± 13.0 cm at parturition sites in row crop or other cover types. random sites. Overstory vegetation height at parturition sites differed from random sites and had a negative effect (β = − 0.067; 95% CI, − 0.128–− 0.005, n = 16) on parturition site Discussion selection such that for every 1-cm decrease in overstory veg- etation height, probability of a female selecting that site for Our understory vegetation height model was our top supported parturition increased 6.5% (odds ratio = 0.935; 95% CI, model for parturition site selection with our overstory vegetation 0.880–0.994). Mean overstory vegetation height was height model competing with our top model. Adult female white- 52.9 ± 26.9 cm at parturition sites and was 71.2 ± 16.8 cm at tailed deer selected for shorter vegetation than random when random sites. The likelihood ratio test indicated adequate selecting parturition sites. Our results support Rearden et al. model fit (vegetative understory model, 9.26, DF =1, P = (2011) who found female elk selected for parturition sites with 0.002; vegetative overstory model, 8.01, DF =1, P =0.005). increased visibility (but see Alldredge et al. 1991,Barrett 1984, Although our most parsimonious model describing neonate Barbknecht et al. 2011, and Lehman et al. 2016 for cases where bed site selection was our vegetative structure model, it carried female ungulates selected parturition sites with increased cover). low model weight (w =0.31; Table 4). Our forb + structure Regardless, moose (Bowyer et al. 1999), American bison (Kaze model (w = 0.19) and vegetative understory model (w =0.14) i i et al. 2016), and woodland caribou (Leclerc et al. 2012) selected also appeared to be competing. The 95% CIs in our top for parturition sites at higher elevations, likely to increase their models overlapped 0 for all variables (S1). Therefore, we calculated the model-averaged coefficients due to model uncertainty (Burnham and Anderson 2002). After mod- Table 4 Model results for 9 models describing various vegetation el averaging, there was a trend of understory vegetation height characteristics and structure found for 63 bed sites of neonate white- tailed deer located in Burleigh, Dunn, and Grant Counties, North (β = 0.025; 95% CI, − 0.005–0.065, n = 63) and overstory Dakota, and Perkins County, South Dakota, USA. We captured neonates in Burleigh County, North Dakota, from 20 May to 30 June 2011 and from 23 May to 23 June in Dunn and Grant Counties, North Dakota, and in Perkins County, South Dakota, during 2014 and 2015 Table 3 Model results for 4 models describing various vegetation characteristics and structure found for 16 parturition sites of adult Model ΔAIC w K c i female white-tailed deer located in Burleigh, Dunn, and Grant Counties, North Dakota, and Perkins County, South Dakota, USA. We captured Vegetative structure 0.00 0.31 2 neonates in Burleigh County, North Dakota, from 20 May to 30 Forb + structure 0.91 0.19 3 June 2011 and from 23 May to 23 June in Dunn and Grant Counties, North Dakota, and in Perkins County, South Dakota, during 2014 and Vegetative understory 1.51 0.14 1 Grassland + structure 2.19 0.10 3 Forested + structure 2.25 0.10 4 Model ΔAIC w K c i Forb 3.56 0.05 1 Vegetative understory 0.00 0.64 1 Vegetative overstory 3.58 0.05 1 Vegetative overstory 1.25 0.34 1 Grassland 4.58 0.03 1 Forb 7.87 0.01 1 Forested 5.28 0.02 2 Grassland 9.23 0.01 1 830 Mamm Res (2020) 65:825–833 Table 5 Model-averaged beta coefficients and 95% confidence Although we report a general trend of increased vegetation intervals for 63 bed sites of white-tailed deer neonates collected through- height at neonate bed sites compared with random sites, weak out South Dakota and North Dakota, USA. We captured neonates in estimates and imprecise confidence intervals preclude us from Burleigh County, North Dakota, from 20 May to 30 June 2011 and from 23 May to 23 June in Dunn and Grant Counties, North Dakota, and in directly discussing this variation. Nevertheless, several studies Perkins County, South Dakota, during 2014 and 2015 report ungulate neonates such as mule deer (Gerlach and Vaughan 1991), elk (Pitman et al. 2014), bighorn sheep Variable Beta Lower 95% CI Upper 95% CI (Ovis canadensis; Smith et al. 2015), and pronghorn (Barrett Understory vegetation height 0.025 − 0.005 0.065 1984;Christieet al. 2017) selected for bed sites with increased Percent canopy cover 0.044 − 0.018 0.143 concealment. White-tailed deer neonates display fear brady- Percent forb 0.006 − 0.021 0.070 cardia and are relatively immobile within their first 30 days of life (Carl and Robbins 1988; Lent 1974). Therefore, increased Percent grass − 0.001 − 0.040 0.032 Percent forested − 0.023 − 0.569 0.189 understory and overstory vegetation height provides increased cover and visual obstruction from predators potentially de- Percent shrub − 0.005 − 0.159 0.080 creasing predation risk, though the effects of vegetation at Overstory vegetation height 0.001 − 0.009 0.034 bed sites on neonate survival are inconsistent (Canon and Bryant 1997; Chitwood et al. 2015). Increased vegetative height at neonate bed sites also could potentially help neonates visibility. Therefore, given the magnitude of difference between thermoregulate during inclement weather (precipitation vegetative heights recorded at parturition sites compared with events), potentially influencing survival (Grovenburg et al. random sites and the increased probability of a mother selecting 2010;Kjellander etal. 2012; Linnell et al. 1995). Grassland a site based on vegetative height, increasing visibility during a was the most common cover type in our study, comprised up to 86% of the landscape, and was the most common cover birthing event is seemingly an important antipredator defense strategy for white-tailed deer mothers using grassland cover types type in which neonate bed sites were located throughout the parturition season. Concomitantly, fawn survival is generally in the Northern Great Plains. Fig. 2 Number of parturition sites used by week throughout the parturition season in South Dakota and North Dakota, USA. We located parturition sites from 20 May to 30 June 2011 and from 23 May to 23 June in 2014 and 2015. Parturition sites were not found in either row crop or other cover types Mamm Res (2020) 65:825–833 831 Fig. 3 Number of bed sites used by week throughout the parturition season in South Dakota and North Dakota, USA. We located parturition sites from 20 May to 30 June 2011 and from 23 May to 23 June in 2014 and For example, previous research has shown that forested areas high in the Northern Great Plains (Michel et al. 2018). Given the lack of variation between vegetative structure at neonate provide important winter cover for adult white-tailed deer bed sites compared with random sites (understory vegetation (Grovenburg et al. 2011), whereas white-tailed deer neonate height = ~ 4-cm difference; overstory vegetation height = ~ 3- survival decreased with increasing forested cover; potentially cm difference), our results indicate grasslands likely provide because small linear tree plantings on the prairie may serve as the vegetative structure necessary for adequate concealment ecological traps due to coyote predation (Grovenburg et al. and thermoregulation that neonates require early in life in the 2012b). Nevertheless, understanding age-specific habitat re- Northern Great Plains. quirements allows for more specific habitat management, Neither maternal nor neonate use of cover types varied which ultimately encourages vegetative diversity on the land- throughout the parturition season. Although row crops such scape and could potentially impact life-history characteristics as corn and soybeans mature throughout the summer and, such as survival for several age-classes. therefore, provide increased hiding cover as neonates age We recommend maintaining a mosaic of grassland, ripari- (Grovenburg et al. 2012a), we found no evidence that white- an, and forested cover types in agriculturally dominated land- tailed deer use agricultural crops more than other cover types scapes as those cover types contained 92% of parturition sites later in the parturition season. This is likely because grass- and 86% of all bed site locations in our study. Furthermore, lands, riparian areas, and forested areas represent permanent maintaining an understory vegetation height of about 23 cm cover and provide vegetation with adequate height during the for mothers and about 36 cm for neonates while maintaining parturition season. Phenology of cool-season grasses also can an overstory vegetation height of about 53 cm for mothers and impact white-tailed deer selection given that 47% of parturi- about 73 cm for neonates should allow for adequate visibility tion sites and 68% of neonate bed sites were found in grass- for mothers and adequate concealment for neonates, particu- lands. For example, cool-season grasses grow mostly in early larly in grassland cover types. Maintaining this vegetative spring and generally complete flowering by 21 June (Leopold height will also likely allow mothers to detect predators during and Kriedemann 1975; Weier et al. 1974); we captured all parturition events (Rearden et al. 2011) and may assist neo- neonates by 30 June. Dams also selected understory vegeta- nates in avoiding detection by predators (Gerlach and tion that was about 23 cm tall while neonates selected vege- Vaughan 1991; Pitman et al. 2014; Smith et al. 2015). tation that was about 36 cm tall, a height most cool-season Finally, dam and neonate use of cover types did not vary plants reach before maturation. Therefore, between growth of throughout the parturition season, suggesting white-tailed cool-season grasses and residual dead plant material, dams deer likely do not increase use of agricultural crops throughout and neonates likely had enough permanent cover in grasslands the parturition season; however, deer do likely increase their throughout the parturition season while obtaining additional use of row crops later in summer as crops provide increased cover from riparian and forested areas. cover as they mature (Grovenburg et al. 2012a). Regardless, Our results emphasize the importance of understanding agricultural crops are probably not beneficial in providing whether habitat requirements vary by life-stage for a species. cover to white-tailed deer during the parturition season. 832 Mamm Res (2020) 65:825–833 Acknowledgments We thank the North Dakota Game and Fish Bowman JL, Jacobson HA (1998) An improved vaginal-implant trans- Department, South Dakota Department of Game, Fish and Parks, the mitter for locating white-tailed deer birth sites and fawns. Wildl Soc Minnesota Department of Natural Resources, the Department of Natural Bull 26:295–298 Resource Management at South Dakota State University, and numerous Bowyer RT, Kie JG, Van Ballenberghe V (1998) Habitat selection by private landowners, graduate students, and technicians for their help and neonatal black-tailed deer: climate, forage, or risk of predation? J cooperation with this project. We also thank D. Morina, C. Chitwood, M. Mammal 79:415–425. https://doi.org/10.2307/1382972 Festa-Bianchet, and two anonymous reviewers for their helpful Bowyer RT, Van Ballenberghe V, Kie JG, Maier JAK (1999) Birth-site comments. selection by Alaskan moose: maternal strategies for coping with a risky environment. J Mammal 80:1070–1083. https://doi.org/10. 2307/1383161 Funding information This work was supported by the Federal Aid to Brinkman TJ, DePerno CS, Jenks JA, Haroldson BS, Erb JD (2002) A Wildlife Restoration administered through North Dakota Game and vehicle-mounted radiotelemetry antenna system design. Wildl Soc Fish Department (Project W-67-R-57, Study No. C-VIII) and through South Dakota Game, Fish, and, Parks (Study No. 7555). Bull 30:256–258 Brinkman TJ, Jenks JA, DePerno CS, Haroldson BS, Osborn RG (2004) Survival of white-tailed deer in an intensively farmed region of Compliance with ethical standards Minnesota. Wildl Soc Bull 32:726–731. https://doi.org/10.2193/ 0091-7648(2004)032[0726:SOWDIA]2.0.CO;2 All handling methods followed the American Society of Mammalogists Bryce SJ, Omernik M, Pater DE, Ulmer M, Schaar J, Freeouf J, Johnson guidelines for mammal care and use (Sikes et al. 2016) and were ap- R, Kuck P, Azevedo SH (1998) Ecoregions of North Dakota and proved by the South Dakota State University Institutional Animal Care South Dakota. (Map poster). U.S. Geological Survey, Jamestown, and Use Committee (Approval No. 10-006E and 13-091A). ND Burnham KP, Anderson DR (2002) Model selection and multimodel Disclaimer Any mention of trade, product, or firm names is for descrip- inference: a practical information-theoretic approach. Springer- tive purposes only and does not imply endorsement by the US Verlag, New York Government. Canon SK, Bryant FC (1997) Bed-site characteristics of pronghorn fawns. J Wildl Manag 61:1134–1141. https://doi.org/10.2307/ Conflict of interest The authors declare that they have no conflict of interest Carl GR, Robbins CT (1988) The energetic cost of predator avoidance in neonatal ungulates: hiding versus following. Can J Zool 66:239– Open Access This article is licensed under a Creative Commons 246. https://doi.org/10.1139/z88-034 Attribution 4.0 International License, which permits use, sharing, Carstensen M, DelGiudice GD, Sampson BA (2003) Using doe behavior adaptation, distribution and reproduction in any medium or format, as and vaginal-implant transmitters to capture neonate white-tailed long as you give appropriate credit to the original author(s) and the deer in north-central Minnesota. Wildl Soc Bull 31:634–641 source, provide a link to the Creative Commons licence, and indicate if Chitwood MC, Lashley MA, Kilgo JC, Pollock KH, Moorman CE, changes were made. 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Acta Theriol 49:103–111. https://doi.org/10.1007/BF03192512 Michel ES, Jenks JA, Kaskie KD, Klaver RW, Jensen WF (2018) Publisher’snote Springer Nature remains neutral with regard to jurisdic- Weather and landscape factors affect white-tailed deer neonate tional claims in published maps and institutional affiliations.

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