https://cpw.cvlcollections.org/files/original/e81e19bd9473ce123290a1da6400c5a3.pdf d3ee00e4a2669be88995b59701af160e PDF Text Text The research in this publication was partially or fully funded by Colorado Parks and Wildlife. Dan Prenzlow, Director, Colorado Parks and Wildlife • Parks and Wildlife Commission: Marvin McDaniel, Chair • Carrie Besnette Hauser, Vice-Chair Marie Haskett, Secretary • Taishya Adams • Betsy Blecha • Charles Garcia • Dallas May • Duke Phillips, IV • Luke B. Schafer • James Jay Tutchton • Eden Vardy �Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore Author(s): Kate R. Searle, Mindy B. Rice, Charles R. Anderson, Chad Bishop and N. T. Hobbs Source: Oecologia , October 2015, Vol. 179, No. 2 (October 2015), pp. 377-391 Published by: Springer in cooperation with International Association for Ecology Stable URL: https://www.jstor.org/stable/43671670 JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at https://about.jstor.org/terms Springer and are collaborating with JSTOR to digitize, preserve and extend access to Oecologia This content downloaded from 45.130.83.192 on Fri, 02 Jul 2021 16:24:26 UTC All use subject to https://about.jstor.org/terms �Oecologia (2015) 179:377-391 DOI 10. 1 007/s00442-01 5-3348-9 IQI CrossMark BEHAVIORAL ECOLOGY - ORIGINAL RESEARCH Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore Kate R. Searle1 ■ Mindy B. Rice2 * Charles R. Anderson2 ■ Chad Bishop2 ■ N. T. Hobbs3 Received: 27 May 2014 / Accepted: 3 May 2015 / Published online: 26 May 2015 © Springer- Verlag Berlin Heidelberg 2015 Abstract Understanding how spatial and temporal hetAdditionally, the influence of vegetation phenology on erogeneity influence ecological processes forms central body a fat was much stronger than that of overall vegetation challenge in ecology. Individual responses toproductivity. heterogeneity In summary, changing annual weather patshape population dynamics, therefore understanding these in relation to seasonal precipitation, have terns, particularly responses is central to sustainable population management. the potential to alter body condition of this important unguEmerging evidence has shown that herbivores track heterolate species during the critical winter period. This finding geneity in nutritional quality of vegetation by responding to highlights the importance of maintaining large contigu- phenological differences in plants. We quantified the ous areas of benspatially and temporally variable resources to efits mule deer ( Odocoileus hemionus ) accrue from accessallow animals to compensate behaviourally for changing ing habitats with asynchronous plant phenology climate-driven in northresource patterns. west Colorado over 3 years. Our analysis examined both the direct physiological and indirect environmental effects Keywords Mule deer • Spatial heterogeneity • Temporal of weather and vegetation phenology on mule deer winter heterogeneity • Western Colorado • Climate change body condition. We identified several important effects of annual weather patterns and topographical variables on vegetation phenology in the home rangesIntroduction of mule deer. Crucially, temporal patterns of vegetation phenology were linked with differences in body condition, with deer climatic tend- patterns are altering the phenology Changing ing to show poorer body condition in areas of with less taxa asyndiverse and the resources on which they depend chronous vegetation green-up and later vegetation onset. around the globe (Parmesan 2007; Thackeray et al. 2010; The direct physiological effect of previous winter Haenel precipitaand Tielboerger 2015). For large-bodied herbi- tion on mule deer body condition was muchvores, less important the phenology of vegetation is a critical determinant than the indirect effect mediated by vegetation of diet phenology. quality (Van Soest 1994; Crawley 1983) that has been linked to diet choice, individual movement and per- formance (Hjeljord et al. 1990; Albon and Langvatn 1992; Herfindal et al. 2006; Hebblewhite et al. 2008; Mysterud Communicated by Göran C. Ericsson. et al. 2008; Hamel et al. 2009; Martinez-Jauregui et al. I SI Kate R. Searle 2009; Bischof et al. 2012; Nielsen et al. 2012; Singh katrle@ceh.ac.uk et al. 2012; Giroux et al. 2014; Monteith et al. 2014), as well as population processes such as survival, reproduc1 NERC Centre for Ecology and Hydrology, Bush Estate, Penicuik EH26 0QB, UK tion and density dependence (Mysterud et al. 2002; Wang 2 Colorado Parks and Wildlife, 3 17 W. Prospect Road, Fort et al. 2006; Pettorelli et al. 2007; Wittemyer et al. 2007; Collins, CO 80526, USA Middleton et al. 2013). The ability of landscapes to sup3 Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins 80524, CO, USA port herbivores is ultimately limited by the total amount of aboveground net primary production (ANPP) available for Ô Springer This content downloaded from 45.130.83.192 on Fri, 02 Jul 2021 16:24:26 UTC All use subject to https://about.jstor.org/terms �378 Oecologia consumption tigue ture, 2004). (2015) (McNaughton et landscapes with the winter body condition of adult al. female However, when spatial mule deer to facilitate understanding of how changing nutrients, pulses of 179:377-391 or moisture results weather patterns may affect this important species. Weather 198 var in s plant growth (Kudo he has important direct and indirect effects on1991), ungulate conperiod they h dition. during Harsh winters with largewhich snowpacks exert a direct prolong the of nutritional value. ev physiological effect on ungulateEmerging body condition (Catchpole et al. 2000; Pettorelli modified et al. 2005a, c), and also an indirect by ANPP are by the peak limits set timing of plant growth. In and particular, effect via vegetation phenology abundance (Albon and t in Langvatn plant ex 1992; Cote communities and Festa-Bianchet 2001; Pettorelli heterogeneity that et al. 2003, 2005a, c, 2006, 2007; Mysterud et induces al. 2008; heterogeneity particularly influencing tant plant Hamel phenology, offers et al. 2009; Nielsen et al. 2012; Mysterud and Aus- fu benefits foraging trheim 2014). We exploredto these direct and indirect effects he nutritional the performance using of individuals and th hierarchical Bayesian structural equation modelling torelli et al. 2007; Hebblewhite et to link variation in weather and vegetation phenology withal. 20 2008; Searle et al. 2010; Middleton et al. 2013; Giroux winter condition of adult mule deer in Northwest Colorado, et al. 2014; Hurley 2014; Iversen 2014). For instance, fine- USA. Using this framework, we developed hypotheses that scale dynamics of vegetation green-up across landscapes first related annual weather conditions and topography with may determine the length of time during spring when high- vegetation phenology in the annual home ranges of mule quality forage is available for ungulates. This means that deer, linking the onset and rate of vegetation green-up in access to heterogeneity can be a critically important feature of habitats for mobile herbivores (Owen-Smith 2004; the spring with seasonal temperature, rainfall and elevation. Fryxell et al. 2005; Hobbs et al. 2008; Searle et al. 2010). If warmer spring temperatures would have earlier onset to We expected that home ranges at lower elevations and with access to heterogeneity is limited by habitat fragmentation, vegetation green-up. Additionally, we expected that home mobile herbivores can suffer a reduction in diet quality and ranges with higher winter and spring precipitation would food availability leading to deleterious changes to popula- have later vegetation onset in the spring as well as steeper tion dynamics and abundance (Hobbs et al. 2008; Hobbs slopes of vegetation green-up. We did not look for direct and Gordon 2010; Searle et al. 2010; Blackburn et al. 201 1; effects of spring weather on subsequent body condition Herbener et al. 2012). These interactions between access during winter because previous work has shown for a range to spatial and temporal heterogeneity and ungulate perfor- of ungulates that effects of weather during spring and sum- mance will mediate the response of ungulate populations to mer are more likely to be mediated through their effects on vegetation (e.g. Pettorelli et al. 2005a, c; Mysterud et al. ing oil and gas development) and climate change. There- 2008). As such, we felt that these additional pathways environmental change, such as changing land use (includ- fore, understanding the underlying mechanisms and drivers would have over-parameterised the model. of these interactions is of great importance for informed We then considered hypotheses pertaining to how win- management of ungulate populations and the changing eco- ter body condition of individual deer is influenced by the systems in which they reside. temporal pattern of vegetation onset and green-up during Large herbivores such as mule deer ( Odocoileus hemionus ) have profound impacts on ecosystem structure and function (Hobbs 1996; Manier and Hobbs 2007; Fornara and Du Toit 2008; Beschta and Ripple 2009; Goheen et al. 2010; Allred et al. 2011; Gass and Binkley 2011; Nkwabi et al. 2011; Bai et al. 2012), and understanding the ways spring and early summer (4 April-25 June). More specifically, we predicted that individuals inhabiting home ranges with shallower vegetation green-up slopes (slower aggregate rate of vegetation green-up) would experience elongated periods when the vegetation is at peak quality, and that this benefit would accrue during the spring and carry in which their behaviour, individual performance and popu- over to winter months resulting in better subsequent winter lation dynamics are likely to change under future climate body condition for these individuals in comparison to those scenarios is crucial for effective management of ecosysinhabiting home ranges with steeper vegetation greentems. The condition of ungulates in winter is an important up slopes (Pettorelli et al. 2005c; Lendrum 2013; Hurley determinant of future fitness because of its impact on indi- 2014). We also predicted that individuals inhabiting home vidual body condition at the start of the breeding season ranges with an earlier vegetation onset [i.e. a higher value (Forchhammer et al. 2001; Steinheim et al. 2002; Solberg of the normalised difference vegetation index (NDVI) in et al. 2007; Rodriguez-Hidalgo et al. 2010; Taillon et al. early spring] would have higher winter body condition than 2012; Hurley 2014). In this paper, we mechanistically link individuals occupying home ranges with a later vegetation changes in annual weather patterns and variation in the onset (Pettorelli et al. 2005c). This is because individuspatial and temporal patterns of plant phenology across als in home ranges with earlier vegetation onset will have Ö Springer This content downloaded from 45.130.83.192 on Fri, 02 Jul 2021 16:24:26 UTC All use subject to https://about.jstor.org/terms �Oecologia (2015) 179:377-391 379 a prolonged period of access to fora of independent variables (weather, topography, vegetation value (Pettorelli eton dependent al. 2007), phenology) variables (winter body there condition) accrue benefits (Shipley over afour longer time 2002). The mule deer population segments body condition in the this study following occupy adjacent winter range areaswinte (Fig. 1), terms, with would pying We tion we expected that but each segment has distinct individuals seasonal movement patterns. higher forage productivity The North Ridge and North Magnolia population segments (in better winter con migrate east to west, while the Ryanbody Gulch and South Maghome ranges lower forag nolia groupswith migrate north to south, averaging approxihave formalised predictions matelythese 40 km between seasonal ranges. Winter ranges are phenology and mule deer wint primarily distinct, but there is some overlap within eastern three hypotheses, each represente and southern summer ranges. This population does, thereconfronted with fore, provide 3 an years opportunity to examine of the effects data of seat evidence: sonal variation in vegetation phenology on winter body condition of ungulates. The adjacencies of each population (a) Individuals inhabiting home ranges with earlier onset segment's winter range means that individuals experience of vegetation emergence have higher winter body con-comparatively similar winter conditions (Fig. 1); however, dition than those individuals inhabiting home ranges the migratory behaviour of each segment results in geowith later vegetation onset. graphically distinct seasonal ranges in the spring and sum- (b) Individuals occupying home ranges with more asynmer. As such, the distinct seasonal migratory patterns and chronous vegetation green-up have better winter body convergent winter ranges for individuals within this popu- condition than individuals occupying home ranges lation allow us to look for an effect of the temporal patwith faster, more synchronous vegetation green-up. tern of vegetation phenology on body condition in a large, (c) Individuals inhabiting home ranges with greater vege-mobile herbivore. tation productivity will have higher winter body condi- tion than individuals inhabiting homes ranges of lowerStudy area productivity. This study took place in the Piceance Basin located in More locally, numbers of mule deer in western Coloradonorthwest Colorado, USA, during 2009, 2010 and 2011 declined in the late 1980s up until the early 2000s (White (Fig. 1). The Piceance Basin supports one of the larg- and Lubow 2002; Bergman et al. 2011). Whilst population est migratory populations of mule deer in North America (White and Lubow 2002), and contains a diverse mix of numbers have since begun to increase in the region, populations remain low such that the Piceance regional popula-pinion pine ( Pinus edulis)-Utah juniper (Juniperus oste tion currently represents about half of historic highs from osperma ) woodland, sagebrush ( Artemesia spp)-steppe the late 1970s-early 1980s [from Colorado Parks and Wild-community, Gambel's oak ( Quercus gùmòe///)-mountain life (CPW) quadrat mark-resight survey data; C. Anderson, shrub complex, quaking aspen ( Populus tremuloides)unpublished data]. Therefore, better understanding of theDouglas fir (. Pseudotsuga menziesii) forest, and Englelong-term climatic, annual weather patterns and resourcemann spruce (Picea engelmannii)-s'jibd'pmQ fir (Abies drivers of these population trends is critical for conserving lasiocarpa) forest (Lendrum et al. 2014). The region typithis important species. cally experiences warm, dry summers (28 °C mean high) and cold winters (-12 °C mean low), with most of the annual moisture deriving from spring snowmelt (Western Materials and methods Regional Climate Center, 1983-2010). We observed four wintering mule deer population segments in the Piceance To explore the direct and indirect effects of weatherBasin: and the North Ridge segment (53 km2), just north of the vegetation phenology on mule deer winter body condition Dry Fork of Piceance Creek including the White River in we implemented hierarchical Bayesian structural equathe northeastern portion of the basin; the Ryan Gulch segtion modelling using data from 153 adult female mule ment (141 km2), between Ryan Gulch and Dry Gulch in the deer across four segments of a single winter range popusouthwestern portion of the basin; the North Magnolia seglation over the period 2009-201 1 in Northwest Colorado. ment (79 km2), between the Dry Fork of Piceance Creek Structural equation modelling is well suited to this analyand Lee Gulch in the north-central portion of the basin; and sis because it allows a series of hypothesized cause theand South Magnolia segment (83 km2), between Lee Gulch effect relationships to be captured within a single model, and Piceance Creek in the south-central portion of the basin which estimates the magnitude of direct and indirect effects (Fig. 1). Springer This content downloaded from 45.130.83.192 on Fri, 02 Jul 2021 16:24:26 UTC All use subject to https://about.jstor.org/terms �380 Fig. the Oecologia 1 Study four (2015) area containing population segments the Colorado Piceance mule deer population Deer capture 179:377-391 of region Table each 1 of Number the 3 of years de of o We employed helicopter net-gunnin Year Population segm et al. 1982; van Reenen 1982) to cap between early North Magnolia December and Sou early 2009 9 9 16 15 study areas over 3 consecutive ye 2010 17 19 18 19 Capture and handling procedures 2011 and 10 15 6 0 CPW Animal Care Use Comm tity 15-2008). In each year of stu individuals were ture myopathies (includes any mo vation, therefore capture) were two (December 2009 analysis one (December 2010), andrepresen zero (Ma Ô Springer This content downloaded from 45.130.83.192 on Fri, 02 Jul 2021 16:24:26 UTC All use subject to https://about.jstor.org/terms �Oecologia (2015) 179:377-391 381 deer were hobbled and blindfolded. Adult females were Assessing each individual deer's body condition, movetransported to localized handling sites for recording body ments, and MCP with the previous year's weather varimeasurements and fitted with global positioning system ables is supported by the high fidelity of deer to seasonal (GPS) collars (five or 24 fixes/day; G2110D, Advanced ranges (Kufeld et al. 1989; Brown 1992; Nicholson et al. Telemetry Systems, Isanti, MN) and released. GPS collars 1997). Females in this population of mule deer have occuwere supplied with timed drop-off mechanisms scheduled pied identical seasonal ranges over multiple years (Garrott to release early in April of the year following deployment. et al. 1987). This fidelity to home ranges allows us to relate All radio-collars were equipped with mortality-sensing annual weather patterns and forage conditions derived options (i.e. increased pulse rate following 4-8 h of inacfrom movement patterns observed subsequent to the meastivity). All captures were made during the winter for urement each of winter body condition with a fair degree of year of study, with capture dates ranging from December certainty. to March. Plant phenology Body condition and age We applied ultrasonography techniques described by Stephenson et al. (1998, 2002) and Cook et al. (2001) to meas- We used the NDVI as a proxy for vegetation phenology (greenness), which has been used extensively as a surrogate for vegetation productivity and dynamics (Pettorelli ure maximum subcutaneous rump fat (millimetres), loin et al. 2005b; Boone et al. 2006; Morisette et al. 2006; Petdepth (longissimus dorsi muscle, millimetres), and to esti- torelli et al. 2006; Bellis et al. 2008; Pettorelli et al. 2011). mate percent body fat for each individual. We estimated a Data were collected from the Global Land Cover Facility body condition score (BCS) for each deer by palpating the Moderate Resolution Imaging Spectroradiometer (MODIS) rump (Cook et al. 2001, 2007, 2010). Percent body fat was 16-day composite imagery (NASA 2000-2011). MODIS then estimated using a regression equation that incorporated rump fat measurements and BCS (Cook et al. 2007, 2010). Age of individuals was estimated based on tooth replacement and wear (Severinghaus 1949; Hamlin et al. 2000). uses NASA's Terra and Aqua satellites with 16-day orbits, a 2330-km swath, and a 250-m resolution. The NDVI is a ratio of red and near infrared reflectance using bands 1 and 2 of the MODIS sensors [NDVI = (NIR - RED)/ (NIR + RED) where NIR is the near-infrared light reflected by vegetation, and RED is the red visible light reflected by Home range calculations vegetation]. NDVI values range from -0.25 to 1 where negative values indicate sparse green vegetation. For each We downloaded and summarized data from GPS collars year of observation, we created several different indices for the buffers of individual deer from the satellite-derived deployed following collar drop and retrieval in early April NDVI of the next year. GPS collars deployed maintained themeasurements: same fix schedule of attempting fixes every 5 h. For each individual deer, a minimum convex polygon (MCP)Onset was of vegetation emergence placed around their GPS locations for each year using the Geospatial Modelling Environment (version 0.5.3 Beta; The mean value of NDVI for each individual's buffer per Hawthorne L. Beyer 2009-2011 http://www.spatialecolyear is on 4 April. This date was determined by visual ogy.com). Movement paths were then created betweeninspection each of mean NDVI curves for all individuals in each subsequent location for each deer and the average moveyear to capture the start of the green-up period (sensu Pettorelli et al. 2007; Mysterud et al. 2008). Home ranges with this distance was applied to each deer's MCP and all spahigher NDVI values on this date are expected to have expe- ment distance was calculated for that deer. A buffer of tial data described were extracted for each deer's buffered rienced earlier onset to vegetation growth. MCP, hereafter called 'buffer'. We did not separate GPS locations by season because transition from winter to sumSlope of NDVI during vegetation green-up mer range typically occurs rapidly in this population (C. Anderson, unpublished data). We therefore felt that using The slope between the mean NDVI values (termed 'slope') was measured at defined dates for each individual's buffer. all GPS locations for the entire year (December/March to April) resulted in the best representation of the yearly The dates defining the start and end of the green-up period home range use of each individual, capturing both winter were determined visually from plots of mean NDVI curves and summer ranges. We did not attempt to separate winter for all individuals in each year. After visual inspection of or summer ranges because we were interested in the comNDVI curves, we defined the green-up period as occurbined effect of weather and vegetation over the entire year. ring between 4 April and 25 June, and this date range was Ô Springer This content downloaded from 45.130.83.192 on Fri, 02 Jul 2021 16:24:26 UTC All use subject to https://about.jstor.org/terms �382 Oecologia (2015) 179:377-391 Analysis used for all individuals in the ana the speed of vegetation green-up To examine evidence for our hypotheses we used structural elongated or compressed is the phen equation modelling to examine links between variation in of plants in each individual's home body condition fat) of adult female mule 2007; Mysterud winter et al.(percent 2008)? We also deer in slope northwestern Colorado, plant phenology indices mum increase in during the g and weather. We used hierarchical structural equation modture any deviations from a linear in els within the Bayesian framework because this approachshort green-up such as very rapid, f can be used to examine both the direct (physiological) and torelli et al. 2007). However, resul indirect (via plant phenology) effects of weather on ungu- the were similar to those from sl cussed further. late body condition, whilst considering predictor variables of from multiple scales (yearly measurements of weather and Integrated The ing of ric of tions for likelihoods. the sum of the values Based onbuffer our understanding of the per system we surmised individual's 15 im INDVI an vegetation phenology, and individual variation between NDVI animals), and allowing flexibility in the choice of distribuis a mechanistic model for how weather and plant phenolpreceding body fat m ogy used directly and indirectly affect individual winter body commonly to estimate pr observation is grazing Weather condition of mule deer. The model quantified the direct ecosystems (Pettorelli effects of weather and topography on vegetation phenol- and et ogy (with units of years), the direct effects ofvariable plant phenoltopographic ogy (defined by the NDVI indices outlined above) on mule Weather data were collected from the PRISM climate deer winter body condition (with units of individuals), the direct group using their parameter-elevation relationships on effects of annual weather variation on mule deer winter body condition, and the indirect effects of weather, independent slopes model (PRISM) data sets of precipi- via plant phenology, on mule deer winter body condition tation, minimum temperature, and maximum temperature (Fig. 2). These relationships were structured to account for layers (Daly 1997). The resolution for all weather varivariation ables was 4 km. Both precipitation and temperature data at the individual level due to animal characteristics (age at capture, population segment). We then fit the were obtained from the year prior to when deer body condition data were collected (e.g. if an individual wasmodel cap- to observations spread over 3 years (2009, 2010, tured and measured in December 2010, then climate data 2011) from the four distinct population segments (Table 1; from 2009 was used). Fig. 1). Linear relationships were used throughout. We used fixed effects to account for variation amongst the 3 years Using this data we calculated the sum of precipitation of study, the age of the deer, the period of capture (early over the green-up period (beginning of April to end of or the late winter), and the population segment to which each June, hereafter referred to as 'spring precipitation'), and individual belonged. Capture dates in February and March sum of precipitation over the previous winter (beginning of January to end of March, hereafter referred to as were 'win-classed as a single 'late winter' category because captures generally occurred in late February or early March. ter precipitation'). We also calculated the mean maximum Captures in December are classified as 'early winter'. temperature over the green-up period (beginning of April to Structural equation modelling requires hypothesizend of June, hereafter referred to as 'spring temperature'). causal inferential paths and testing the significance of Elevation and aspect were collected from the US ing Geo- logical Survey digital elevation model with a 30-m these reso-paths both directly and indirectly through a mediating lution. Elevation units were in metres, and aspect variable used (vegetation phenology; Fig. 2). To implement this model within a hierarchical Bayesian framework, we specidegree categories based on the following: north (0-22.5), fied three separate model parts: data models for percent fat northeast (22.5-67.5), east (67.5-112.5), southeast (112.5and NDVI metrics, process models for linking weather, 157.5), south (157.5-202.5), southwest (202.5-247.5), west (247.5-292.5), northwest (292.5-337.5), and north topography, vegetation phenology and body condition, and prior distributions of parameters. (337.5-360). All data were resampled in ArcGIS to the broadest reso- The data model is the likelihood linking the data to the lution corresponding to the weather variables at 4 km. Tem- model parameters. We have two data models, one link- perature, precipitation, elevation, slope, and NDVI were ing observations of NDVI metrics to weather and topoextracted using spatial analyst in Arc GIS for each individ- graphic variables, and one linking observations of percent ual deer's buffer. body fat to plant phenology (NDVI metrics) and animal Ô Springer This content downloaded from 45.130.83.192 on Fri, 02 Jul 2021 16:24:26 UTC All use subject to https://about.jstor.org/terms �Oecologia Fig. 2 (2015) 179:377-391 383 equation Spring I Elevation | | Aspect I Winter | Spring Structural model diagram for how condiprecipitation! temperature tion [percent body fat (PF) during winter] of mule deer is affected by directly weather and and indirectly plant phenology in northwest Colorado. NDVI ifiyjj indices r / Normalised difference vegetation index I Age ^ condition HB characteristics (age at capture, capture period, population). site, is a fixed effect accounting for variation derived from NDVI metrics were logit transformed and percent body fat the four distinct mule deer population segments used in the were entered as percentages, such that: log it (NDVI/¿) ~ normal (/zNDVI¿¿, <xndvi) and PF itt ~ normal (/xPF¿¿, app) analysis. The second part of the process model gives the probability of the model prediction for percent fat for the ith deer in the rth year, /¿PF,p given the respective process model parameters, and the residual variance estimate for unaccounted variation in the mod- where NDVI/ ř is the observation for the NDVI metric for elled percent fat process and measurement error, app : the ith deer in the tih year, /iNDVli t is the model predic- P(/¿PF¿¿|c, dk, age¿, year,, capture_month;, crpp) , where c is the regression intercept, dk are the kth regression coefficients for the effects of phenology and weather on body tion for the NDVI metric for the ith deer in the rth year, and orNDVI is the residual variance across all observations of the NDVI metric not explained by weather or topography. Observations for the body condition (percent fat) for the ith fat, and age,, year, and capture_monthř are fixed effects accounting for variation due to age of individual deer, the deer in the rth year are denoted by PF, ř, residual variance year of observation and the period in which individuals not explained by the model is denoted as aPF, and /¿PF it is were caught for body fat measurements. These probabili- the model prediction for the percent fat of the ith deer in the ties are defined by two structural path equations: firstly for ith year. Observations of percent fat for individual deer are each NDVI metric (Eq. 1) and secondly for each percent fat measurement (Eq. 2): assumed to be independent in this analysis. If captures of individuals tended to target persistent social groups of deer this assumption may be invalid; however, previous work in this area has shown that statistical dependence between survival rates of sibling neonates using the same landscape was minor (C. Bishop, unpublished data). This suggests the /¿NDVIi>r = a + &ispringppt¿ , + i^winterppt,- 1 + &3springtempíř + &4elev/¿ -h fcsaspect,-, + site[populationř] (l) assumption of independence is warranted for individuals in /¿PF ¡J = c + difjiNDWlij + ¿2 winterppt; ř + d^gtļ our study area. The process component of the model relates the model predictions for NDVI metrics and percent fat to the param- + yr[year,] + cm[capturemonthř] (2) eters of the model. As such, it derives the probability of the Because our analysis is fully Bayesian, we specify model prediction for each NDVI metric for the ith deer in prior distributions for all model parameters in the hierar- the rth year, /¿NDVI, ř, given the respective process model parameters, and the residual variance estimate for unac- were standardised, therefore all prior distributions were counted variation in the modelled NDVI process or measurement error, ok dví : P(/iNDVI,-t/|fl, bk, site/, otndvi)» where a is the regression intercept, bk are the kth regression coefficients for weather and topographical effects, and chy. All input variables (weather, topography and age) assumed to be normally distributed and uninformative [all parameters ~ normal (0, 1.0E- 6)]. Because the data for NDVI indices were logit transformed, these were also assumed to be normally distributed, and uninformative Springer This content downloaded from 45.130.83.192 on Fri, 02 Jul 2021 16:24:26 UTC All use subject to https://about.jstor.org/terms �384 Fig. Oecologia 3 Mean (2015) 179:377-391 NDVI 4curves forthe each stud April) and end of Magnolia, b North of Ridge, c Ryan Gulch, the green-up period line represents NDVI for an individual d each individual's annua as the sum from of the mean NDVI values over 5March March to 31 NDVI values were calculated used 5 to of observation (2009, 2011), tick October for2010 each individual's and annual home range intervals, and arrows show vegetation 'on diffuse gamma priors were used for the inverse of the interval < 1.05). All model residuals were normally distribresidual variances of the NDVI model and percent fat uted (Pearson x2 normality test, onset P = 19.45, p = 0.08; slope P = 16.51, p = 0.17; INDVI P = 19.06, p = 0.09). model, ctndvi, aPF ~ gamma (0.001, 0.001). All models were fit using WinBUGS (Spiegelhalter 1999) softwareWinter body fat measurements varied between 3.0 and and a Markov chain Monte-Carlo (MCMC) procedure for 18.3 % across all individuals and years of study. Mean pereach model run for 10,000 iterations after an initial burncent fat measurements were approximately similar across in of 10,000 iterations to ensure convergence of all model the 3 years of observation (2009, 7.0 %, SD 1.5; 2010, parameters. Convergence diagnostics and autocorrelation 6.9 %, SD 1.5; 2011, 9.1 %, SD 4.0). Body fat measurestatistics were used to assess the mixing of three MCMC ments across the four study areas were also similar; highest chains per model, and to assess the MCMC sampling qualbody fat measurements were recorded in North Magnolia ity for each parameter. Prior to running each model(mean on 8.0 %, SD 2.6), followed by South Magnolia (mean actual data, models were tested on realistically simulated 7.7, SD 3.0), North Ridge (mean 7.3, SD 2.0), and Ryan data to test their ability to converge on reasonable paramGulch (mean 6.6, SD 1.4). Vegetation phenology followed eter estimates. All models performed well in simulations, similar patterns across the four study areas (Fig. 3), with converging on known parameter estimates such that 95 the % green-up period commencing around 4 April and reachcredible intervals for each parameter contained the true, ing a plateau around 25 June. As expected, deer caught in known value. late winter had significantly lower body fat estimates than those caught in early winter (Table 2). In all analyses, age of deer tended to be negatively correlated with body condiResults tion (greater than 75-85 % of the posterior density mass was negative; Table 2). All models converged satisfactorily on posterior distribuOnset tions for model parameters. All posterior distributions were approximately normal, and autocorrelation in the MCMC chains was not a factor after the initial burn-in period The onset model explained approximately 36 % of the vari(Gelman diagnostics for MCMC chains upper credibleation in body condition measurements and approximately <£) Springer This content downloaded from 45.130.83.192 on Fri, 02 Jul 2021 16:24:26 UTC All use subject to https://about.jstor.org/terms �Oecologia Table 2 etation (2015) 179:377-391 Posterior density index (. INDVI) Slope 385 estimates Onset for para INDVI a (intercept) -1.01 (-1.08, -0.94) -0.94 (-0.98, -0.91) 5.88 (5.79, 5.97) bx (spring ppt.) 0.12 (0.015, 0.22)* 0.073 (0.022, 0.13)* -0.1 1 (-0.25, 0.030) b2 (winter ppt.) 0.21 (0.13, 0.29)* 0.037 (-0.0040, 0.076) 0.22 (0.1 1, 0.33)* £3 (spring temp.) -0.26 (-0.36,-0.16)* -0.13 (-0.18, -0.081)* -0.10 (-0.24, 0.033) bA (elevation) 0.21 (0.13, 0.30)* 0.072 (0.030, 0.1 1)* 0.15 (0.044, 0.27)* b5 (aspect) -0.072 (-0.14, 0.0014) -0.068 (-0.1 1, -0.031)* 0.10 (0.0041, 0.20)* c (intercept) 11.94 (10.09, 13.78) 12.77 (10.75, 14.8) 10.62 (-1.17, 21.48) dx (phenology) -0.82 (-2.25, 0.54) 0.67 (-0.99, 2.27) 0.28 (- 1 .53, 2.24) d2 (winter ppt.) 0.38 (-0.23, 1.01) 0.0083 (-0.38, 0.39) -0.026 (-0.67, 0.63) d3 (age) -0.18 (-0.50, 0.14) -0.19 (-0.52, 0.14) -0.19 (-0.52, 0.14) Residual variance (percent fat model) 3.90 (3.14, 4.97) 3.92 (3.16, 5.00) 3.93 (3.16, 5.01) Residual variance (phenology model) 0.19(0.15,0.24) 0.048(0.039,0.060) 0.33(0.0.26,0.41) Year fixed effect on percent fat [2] -0.69 (-1.90, 0.51) [2] 0.18 (-0.90, 0.1.24) [2] -0.15 (-0.89, 0.60) [3] -1.29 (-2.69, 0.10) [3] -0.86 (-2.25, 0.51) [3] -1.04 (-2.36, 0.28) Population segment fixed effect on percent fat [2] -0.49 (-1 .42, 0.0.46) [2] -0.42 (- 1 .37, 0.55) [2] -0.42 (-1 .38, 0.58) [3] -0.93 (-0. 1 .96, 0.084) [3] -0.77 (- 1 .75, 0. 19) [3] -0.63 (- 1 .73, 0.43) [4] -0.40 (-1.31, 0.50) [4] -0.27 (-1.17, 0.62) [4] -0.26 (-1.17, 0.62) Capture period fixed effect -4.93 (-6.37, -3.53)* -4.89 (-6.34, -3.48)* -4.85 (-6.32, -3.43)* Estimates are mean posterior densities with 95 % credible intervals. Fixed effects for different years of study are c and 3 = 201 1), and for population segment are compared to North Magnolia, with 2 = North Ridge, 3 = Ryan Gulch ppt. Precipitation, temp, temperature * P < 0.05 63 % of the variation in NDVI metrics. Precipitation over (greater than 75 % of the posterior density mass was positive, Table 2; Fig. 4a), suggesting that deer inhabiting home the green-up period (spring precipitation) had a strong positive relationship with the mean NDVI value dur- ranges with earlier vegetation onset tended to have better body condition. ing the initial phase of vegetation green-up (4 April), as did elevation (Table 2; greater than 95 % of the posterior density was less than zero). Therefore, home ranges with Slope higher spring precipitation, or those at higher elevations, The slope model explained approximately 36 % of the varihad significantly earlier onset to vegetation growth. There was also an indication that precipitation during the previ-ation in body condition measurements and approximately 67 % of the variation in NDVI metrics. Precipitation durous winter (winter precipitation) resulted in earlier onset of vegetation (Table 2; more than 75 % of the posterior den- ing the previous winter (winter precipitation) and over the sity mass was greater than zero). Mean temperature dur- green-up period (spring precipitation) had a strong positive relationship with the mean slope of vegetation green-up, ing green-up (spring temperature) had a strong negative relationship with mean NDVI values on 4 April (Table 2;as did elevation (Table 2; more than 95 % of the posterior more than 95 % of the posterior density mass was greaterdensity mass was greater than zero). Mean temperature than zero) meaning that annual home ranges experiencingduring green-up (spring temperature) had a strong negawarmer spring temperatures had significantly later onset totive relationship with the mean slope of vegetation greenup (Table 2; greater than 95 % of the posterior density vegetation growth. Aspect also had a strongly negative relamass was less than zero). Aspect tended to be negatively tionship with mean NDVI values on 4 April, indicating that home ranges with more southerly or westerly aspects hadrelated to mean slope of vegetation green-up (greater than 75 % of the posterior density mass was negative; Table 2), later vegetation onset values than those with more northerly or eastern aspects (Table 2; more than 95 % of the posteriorindicating that home ranges with more southerly or westerly aspects tended to have slower rates of vegetation density mass was greater than zero). green-up in the spring. Previous winter precipitation tended There was weak evidence for a positive relationship between onset of vegetation green-up and body condition to be positively related to body condition (greater than Springer This content downloaded from 45.130.83.192 on Fri, 02 Jul 2021 16:24:26 UTC All use subject to https://about.jstor.org/terms �386 Fig. Oecologia 4 (2015) Structural a 179:377-391 equation model diagram for how condition (percent body fat during winter) of adult, female mule Spring precipitation 1 V deer is affected directly and indirectly by weather and veg- etation phenology (ow'°"S in northwest / Colorado in 2008, 2009 and 2010. Indirect linkages are +0.07^^^ ļ ^^^ (0.022,0.13) X / ļ manifested through a measure of vegetation greenness in the spring derived from NDVI measurements (a onset of / / T Onset jfJ vegetation, green-up, b c mean slope +0.67 INDVI). /of +0.0083 Thick solid (-0.99,2.27) j (-0.38,0.39) / lines represent strong evidence for an effect (95 % credible interval does not overlap zero), ____ Mule deer body while thin solid Age lines represent I weak evidence for an effect (-0.52,0.14) (percent fat) (95 % credible interval marginally includes zero). Dotted lines represent no clear effect. b Regression coefficient estimates are given with 95 % credible intervals. Plus sign Predicted precipitation V positive relationship, minus sign predicted negative relationship Spring Elevation | Aspect Winter Spring ^ +0.2l' (-0.14,0.0014) +0*2Ī // - - (0.13,0.30)' (0.13,0.29^y ^^)26 / / ^^M-0.36,-0.16) (0.015,0.22^^^ X / ^ļMean slop e|*y -0.82 / +0.38 (-2.25,0.54) / (-0.23,1.01) Age I (-0.50,0.14) (percent fat) c Spring | Elevation | | Aspect | Winter Spring precipitation V ' X (0.0041,0.20) +0 22 / ! / ' {0.iïÙK X ' (0.0041,0.20) ^/¡ +0 22 / / X" / ' / / (-0.24,0.033) INDVI Yļ ' ! +0.28 I / -0.026 (-1.53,2.24)1 J (-0.67,0.63) I / , Age I (-0.52,0.14) (percent fat) 40 Springer This content downloaded from 45.130.83.192 on Fri, 02 Jul 2021 16:24:26 UTC All use subject to https://about.jstor.org/terms �Oecologia 75 % of There 179:377-391 387 of plant growth in the spring through enhanced soil water posterior density mass was availability. Whilst there were indications of a similar relaa strong indication of a ne the was between tion (2015) tionship between the onset of vegetation emergence and slope of vegetation green-u winter precipitation, the strength of thisposterio relationship was than 87 % of the as clear. This is probablyindicating due to the contrasting actions th Table 2; not Fig. 4b), the (greater negative; home ranges poorer with increased soil moisture availability acting to encourwith offaster vegetation gree age vegetation growth, whilst remaining winterinhabi snowpack condition than deer would act to inhibit earlier growth (e.g. Christianson etgree al. asynchronous vegetation body more 2013). The speed of vegetation green-up increased in home Integrated ranges experiencing higher winter and spring precipitaNDVI tion, and in those located at higher elevations. This effect is consistent with results from other rangeland systems, The INDVI model explained approxim and iscondition likely due to a greater flush of measurem available moisture for variation in body mately during 30 % the plant growth in the spring, thereby accelerating green-up the variation in NDVI m Christianson et al. 2013). Home ranges with higher (Table2007; 2; more than 95 % of precipitation had significantly greater productivity greater winter than zero), and both s (INDVI), probably because the greater moisture availabilspring temperature during the gree with INDVI mass was and of of vegetation (Walker et al.had 1993, 1995;a Bjork and Molau previous winter strong enhances vegetation growth throughout the spring, and to be negatively ityrelated to INDVI (gr potentially the summer if snowmeltwas continues into the midthe posterior density mass negat dle part ofhad the year (Walker et al. 1993, 1995; Bjork and tion and aspect both strong positiv Molau 2007). than 95 % of th INDVI (Table 2; more mass was greater Home ranges with higher mean temperature during spring than zero), indicating had significantly later onset of vegetation growth, inaspect contrast with more southerly or westerly results from other regions (e.g. Pettorelli et al. 2005c). values than those towith more northerly Higher temperatures during the spring period may actof to (Table 2). We found no clear effect w or INDVI on or reduce onset of vegetation (Table growth if they reduce the Fi bodydelay condition 2; moisture available for plant growth, particularly in moun- tainous semi-arid regions such as the Piceance Basin. Home Discussion ranges with higher mean temperatures during spring had shallower slopes of vegetation green-up, which is also in contrast to previous studies (e.g. Pettorelli et al. 2007; CampIn this study, we demonstrate a tangible link between bell et al. 2013; Middleton et al. 2013). It is possible that the temporal pattern of vegetation phenology and unguwarmer spring temperatures resulted in later fulfillment of late body condition during the critical winter period for chilling requirements for some plant species (Yu et al. 2010; a migratory mule deer population. Moreover, we identify Paudel key variation in annual weather patterns that determine the and Andersen 2013). However, the applicability of this mechanism to our study system has yet to be evaluated onset and synchrony of vegetation green-up in this region. in the context of warming spring temperatures. Importantly, we demonstrate that the migratory strategies Home ranges with more southerly or westerly aspects displayed by different segments of a contiguous overwinhad greater INDVI values than those with more northerly tering population appear to influence the resulting perforeasterly aspects, again probably because of the ameliomance of individuals, with variation in the synchrony,or and rated microclimatic conditions for plant growth afforded to a lesser extent, the onset of vegetation within seasonal these slopes. The significant positive effect of elevation home ranges contributing to subsequent winter body by con- on INDVI is expected in our study area because areas of dition. Moreover, we show that this variation in vegetation higher elevation utilized in the summer have greater vegphenology is a more important driver of subsequent winter etation productivity over the year than those occupying body condition than total forage availability, when measured using remotely sensed data. Weather, topography and vegetation phenology lower elevations. Vegetation phenology and ungulate winter body condition Precipitation in the spring resulted in significantly earlier Our findings indicate that deer inhabiting home ranges with onset to vegetation growth, presumably because greater earlier vegetation onset tended to have better winter body precipitation during this period facilitates the initiation <0 Springer This content downloaded from 45.130.83.192 on Fri, 02 Jul 2021 16:24:26 UTC All use subject to https://about.jstor.org/terms �388 Oecologia (2015) 179:377-391 that inhabiting of overall hom vege those total m This is productivity because a high grassland systems to the start of the green-up period is t of individuals and t with an earlier start to the growing condition etation time is in period line mule al. components which forage appear is at pe tio-temporal previous work pattern on the Characteristics of individual animals werewhich found to be this region, d at with deer in relatively important'surfed' determinants of winter bodythe condition. than gr Across all analyses, on the age of deer tended to summer be negatively arriving the correlated of with bodyforage condition indicating that,occurs as expected, productivity 'jumped' et than onset. rather 2012), peak As such, older animals tended to have poorer body condition than individuals utilising summ animals. However, we detected little support for lier vegetation younger onset would experi a direct effect of previous winter precipitation on percentthe match between their arrival and body fat in any of thestudies analyses, indicating that during the quality forage. Other have d study period the direct,(May) physiological impact NDVI of previous effect of early season o winter precipitation was less important than the indirect (domestic sheep) in autumn acros weather effects on et body condition mediated through plant in Norway (Nielsen al. 2012), as phenology. It may be that mule deer inreindeer this region were free-ranging able to compensate for declines in et body condition result(Ballesteros al. 2013 body mass in etation onset et al. cies is a ing from previous winter positive precipitation over the course of found no eff (2007) onset the subsequent spring and summer; without juvenile growth orhowever, survival repeated northern body condition measurements of individuals over Canada and Italy, an on in greater of access of the to multiple years we are unable the to test this assumption. In influence of average addition, winter conditions during this study period were high-quality forage, rat average relatively mild, and in severe winters we would expect this timing of vegetation direct physiological effectthat to be much stronger. Studies ineff findings suggest the boreal forests with strong seasonality at northern latitudes differs between ungulate life have found summer fattening of ungulates linked to plant Importantly, we found evidence these onset home phenology to be a more important factor for body condiwith steeper, and there tion in autumn than winter body mass loss to due to harsh green-up tended have conditions (snow homes depth and temperature) ranges (Mysterud et al. occupying w ranges vegetation than deer vegetation 2008). Although body mass of yearling red deer ( Cervus green-up. Rapid changes elaphus) in Norway was linked to winter snow and to temcould translate gr found that the magnitude of these effects pointperature, init was time across a lan was much smaller than the indirect effects of climate oper-com rapid changes may also serve to ating through plants (Mysterud et al. 2008). Similarly, in over which high-quality forage is tation at a green-up given over a our study area, we detected no clearsuch effect of previousas winspatial scale, th ter precipitation on percent body fat of mule deer, whilst depressing diet quality over tially torelli of large et al. effects of previous spring precipitation and temperature on 2007; Middleton et vegetation body fat, mediated through plant phenology, has were much during spring with growth clearer.survival and sis growth of ), mountain of al. bee bighorn goat kids Conclusion in Canada, survival of Alpine ibex northern Italy (Pettorelli et al. 2 findings warrant increased attention to the com-am recruitment andOurpregnancy rates plexities arising from changing climatic patterns in this the USA (Middleton et al. 2013). En region. Although increased spring andshown winter precipitanology rates have also been t tion resulted in earlier onset of vegetation growth, they (C body mass in Eurasian beavers also acted to simultaneously compress vegetation green-up There is mounting evidence that t thereby potentially negatively phenolo affecting the body nism whereby periods vegetation of mule deer through a compression of the time many importantcondition life history aspects period over which high-quality forage isof available.both Howthe influence ever, winter precipitation also had a strong positive impact green-up on body fat w Interestingly, the slope of Springer This content downloaded from 45.130.83.192 on Fri, 02 Jul 2021 16:24:26 UTC All use subject to https://about.jstor.org/terms �Oecologia on (2015) overall efit large 2003). ture 179:377-391 389 Bureau productivity, of Land Managem vegetation their cooperation. Finally (Owen-Smith 1990 on statistical analysis. herbivores The interplay between precipit will ultimately determine vegetati References dynamics in this system. This highlight ter understanding of the complexity an weather of and future Albon SD, Langvatn R (1992) Plant phenology and the benefits of vegetation dynamics for climate pr migration in a temperate ungulate. Oikos 65:502-513 change, particularly Allred BW, Fuhlendorf SD, Engle DM, Elmore RD (2011) Ungulate regions with highly seasonal environme preference for burned patches reveals strength of fire-grazing ies such as this, which facilitate mechan interaction. Ecol Evol 1:132-144 annual mate ing We variation change, this Bai Y, Wu J, Clark CM, Pan Q, Zhang patterns L, Chen S, Wang Q, Han X in weather will an (2012) Grazing alters ecosystem functioning and C:N:P stoichibe an important com ometry of grasslands along a regional precipitation gradient. J knowledge gap. 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Ecology 87:1987-1994 contribution statement framework. conducted the and K. R. Brown CG (1992) Movement and migration patterns of mule deer in concéived, southeastern designed and Idaho. J Wildl Manage 56:246-253 conduc the C. B. and R. conce Campbell RD, NewmanC. C, Macdonald DW,A. Roseli F (2013) Proximateexperiments. weather patterns and spring green-up phenology effect Eurfield All aut interpretation manuscript asian beaver ( Castor fiber ) body mass and reproductive success: of data and analysis, the implications of climate change and topography. Glob Chang preparation. Biol 19:1311-1324 Catchpole EA, Morgan BJT, Coulson TN, Freeman SN, Albon SD (2000) Factors influencing Soay sheep survival. J R Stat Soc Ser C Appiwish Stat 49:453-472to thank D. Bil Acknowledgments We Cebrian J, Lartigue J (2004) Patterns of herbivory and decomposition Bergman for insightful comments on this m in aquatic and terrestrial Ecol Monogr 74:237-259 was funded and supported by ecosystems. CPW. This pro Christiansonresearch D, Klaver RW, Middleton AD, Kauffman MJ (2013) both mammal and avian sections of winterwho and spring phenoclimatology on large herbiM. Michaels fromConfounded CPW administered ranges. Landsc Ecol 28:427-437 were supported vore in part by an award from Cook RC, Cook JG, Murray Zager P, Johnson BK, Gratson MW Foundation (DEB 0444711) to DL, Colorado State (2001) Development of predictive models of for nutritional field condially, we thank numerous technicians tion for rocky Air mountain elk. J Wildl Manage 65:973-987 tion. We thank Quicksilver for assistance i and Hobbs Cook RC, Stephenson TR, Myers Cook JG, Shipleyfor LA (2007) f helicopters and L. Gepfert and L. WL, Coulter Validating predictive models of nutritional condition for sect mule port. Both the mammal and avian research J Wildl Manage 71:1934-1943 D. Finley of CPW are deer. thanked for crucial ass Cook RC, Cook JG, Stephenson TR, Meyers WL, McCorquodale SM, efforts. 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Ecol Appi 22(7):2007-2020 Springer This content downloaded from 45.130.83.192 on Fri, 02 Jul 2021 16:24:26 UTC All use subject to https://about.jstor.org/terms � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Journal Articles Description An account of the resource CPW peer-reviewed journal publications Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore Rights Information about rights held in and over the resource <a href="http://rightsstatements.org/vocab/InC-NC/1.0/" target="_blank" rel="noreferrer noopener">In Copyright - Non-Commercial Use Permitted</a> Date Created Date of creation of the resource. 2015-05-26 Subject The topic of the resource Mule deer Spatial heterogeneity Temporal heterogeneity Western Colorado Climate change Description An account of the resource Understanding how spatial and temporal heterogeneity influence ecological processes forms a central challenge in ecology. Individual responses to heterogeneity shape population dynamics, therefore understanding these responses is central to sustainable population management. Emerging evidence has shown that herbivores track heterogeneity in nutritional quality of vegetation by responding to phenological differences in plants. We quantified the benefits mule deer (<em>Odocoileus hemionus</em>) accrue from accessing habitats with asynchronous plant phenology in northwest Colorado over 3 years. Our analysis examined both the direct physiological and indirect environmental effects of weather and vegetation phenology on mule deer winter body condition. We identified several important effects of annual weather patterns and topographical variables on vegetation phenology in the home ranges of mule deer. Crucially, temporal patterns of vegetation phenology were linked with differences in body condition, with deer tending to show poorer body condition in areas with less asynchronous vegetation green-up and later vegetation onset. The direct physiological effect of previous winter precipitation on mule deer body condition was much less important than the indirect effect mediated by vegetation phenology. Additionally, the influence of vegetation phenology on body fat was much stronger than that of overall vegetation productivity. In summary, changing annual weather patterns, particularly in relation to seasonal precipitation, have the potential to alter body condition of this important ungulate species during the critical winter period. This finding highlights the importance of maintaining large contiguous areas of spatially and temporally variable resources to allow animals to compensate behaviourally for changing climate-driven resource patterns. Creator An entity primarily responsible for making the resource Searle, Kate R. Rice, Mindy B. Anderson Jr, Charles R. Bishop, Chad J. Hobbs, N. T. Language A language of the resource English Is Part Of A related resource in which the described resource is physically or logically included. Oecologia Extent The size or duration of the resource. 15 pages Bibliographic Citation A bibliographic reference for the resource. Recommended practice is to include sufficient bibliographic detail to identify the resource as unambiguously as possible. Searle, K. R., M. B. Rice, C. R. Anderson Jr, C. Bishop and N. T. Hobbs. 2015. Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore. Oecologia 179:377–391. <a href="https://doi.org/10.1007/s00442-015-3348-9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s00442–015–3348–9</a> Format The file format, physical medium, or dimensions of the resource application/pdf Type The nature or genre of the resource Article