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1.
Dan J. Kaminski Tyler M. Harms Jim M. Coffey 《The Journal of wildlife management》2019,83(7):1565-1580
Spotlight surveys for white-tailed deer (Odocoileus virginianus) can yield large presence-only datasets applicable to a variety of resource selection modeling procedures. By understanding how populations distribute according to a given resource for a reference area, density and abundance can be predicted across new areas assuming the relationship between habitat quality (measured by an index of selection) and species distribution are equivalent. Habitat-based density estimators have been applied to wildlife species and are useful for addressing conservation and management concerns. Although achieving reliable population estimates is a primary goal for spotlighting studies, presence-only models have yet to be applied to spotlight data for estimating habitat selection and abundance for deer. From 2012 to 2017, we conducted spring spotlight surveys in each of 99 counties in Iowa, USA, and collected spatial locations for 20,149 groups of deer (n = 71,323 individuals). We used a resource selection function (RSF) based on deer locations to predict the relative probability of use for deer at the population level and to estimate statewide abundance. The number of deer observed statewide increased significantly with increasing RSF value for all years and the mean RSF value along survey transects explained 59% of the variability in county-level deer counts, indicating that a functional response between habitat quality and deer distribution existed at landscape scales. We applied our RSF to a habitat-based density estimator (extrapolation) and zero-inflated Poisson (ZIP) and negative binomial (ZINB) count models to predict statewide abundance from spotlight counts. Population estimates for 2012 were variable, indicating that atypical weather conditions may affect spotlight counts and population estimates in some years. For 2013–2017, we predicted a mean population of 439,129 (95% CI ∼ ± 55,926), 440,360 (∼ ± 43,676), and 465,959 (∼ ± 51,242) deer across years for extrapolation, ZIP, and ZINB models, respectively. Estimates from all models were not significantly different than estimates from an existing deer population accounting model in Iowa for 2013 and 2016, and differed by <76,000 deer for all models from 2013–2017. Extrapolation and ZIP models performed similarly and differed by <2,897 deer across all years, whereas ZINB models showed inconsistencies in model convergence and precision of estimates. Our results indicate that presence-only models are capable of producing reliable and precise estimates of resource selection and abundance for deer at broad landscape scales in Iowa and provide a tool for estimating deer abundance in a spatially explicit manner. © 2019 The Wildlife Society. 相似文献
2.
ABSTRACT Ecologists often develop complex regression models that include multiple categorical and continuous variables, interactions among predictors, and nonlinear relationships between the response and predictor variables. Nomograms, which are graphical devices for presenting mathematical functions and calculating output values, can aid biologists in interpreting and presenting these complex models. To illustrate benefits of nomograms, we developed a logistic regression model of elk (Cervus elaphus) resource selection. With this model, we demonstrated how a nomogram helps scientists and managers interpret interactions among variables, compare the relative biological importance of variables, and examine predicted shapes of relationships (e.g., linear vs. nonlinear) between response and predictor variables. Although our example focused on logistic regression, nomograms are equally useful for other linear and nonlinear models. Regardless of the approach used for model development, nomograms and other graphical summaries can help scientists and managers develop, interpret, and apply statistical models. 相似文献
3.
RYAN A. LONG JONATHAN D. MUIR JANET L. RACHLOW JOHN G. KIE 《The Journal of wildlife management》2009,73(2):294-302
ABSTRACT Studies of resource selection form the basis for much of our understanding of wildlife habitat requirements, and resource selection functions (RSFs), which predict relative probability of use, have been proposed as a unifying concept for analysis and interpretation of wildlife habitat data. Logistic regression that contrasts used and available or unused resource units is one of the most common analyses for developing RSFs. Recently, resource utilization functions (RUFs) have been developed, which also predict probability of use. Unlike RSFs, however, RUFs are based on a continuous metric of space use summarized by a utilization distribution. Although both RSFs and RUFs predict space use, a direct comparison of these 2 modeling approaches is lacking. We compared performance of RSFs and RUFs by applying both approaches to location data for 75 Rocky Mountain elk (Cervus elaphus) and 39 mule deer (Odocoileus hemionus) collected at the Starkey Experimental Forest and Range in northeastern Oregon, USA. We evaluated differences in maps of predicted probability of use, relative ranking of habitat variables, and predictive power between the 2 models. For elk, 3 habitat variables were statistically significant (P < 0.05) in the RSF, whereas 7 variables were significant in the RUF. Maps of predicted probability of use differed substantially between the 2 models for elk, as did the relative ranking of habitat variables. For mule deer, 4 variables were significant in the RSF, whereas 6 were significant in the RUF, and maps of predicted probability of use were similar between models. In addition, distance to water was the top-ranked variable in both models for mule deer. Although space use by both species was predicted most accurately by the RSF based on cross-validation, differences in predictive power between models were more substantial for elk than mule deer. To maximize accuracy and utility of predictive wildlife-habitat models, managers must be aware of the relative strengths and weaknesses of different modeling techniques. We conclude that although RUFs represent a substantial advance in resource selection theory, techniques available for generating RUFs remain underdeveloped and, as a result, RUFs sometimes predict less accurately than models derived using more conventional techniques. 相似文献
4.
Kevin T. Shoemaker Levi J. Heffelfinger Nathan J. Jackson Marcus E. Blum Tony Wasley Kelley M. Stewart 《Ecology and evolution》2018,8(6):3556-3569
Resource selection functions (RSFs) are tremendously valuable for ecologists and resource managers because they quantify spatial patterns in resource utilization by wildlife, thereby facilitating identification of critical habitat areas and characterizing specific habitat features that are selected or avoided. RSFs discriminate between known‐use resource units (e.g., telemetry locations) and available (or randomly selected) resource units based on an array of environmental features, and in their standard form are performed using logistic regression. As generalized linear models, standard RSFs have some notable limitations, such as difficulties in accommodating nonlinear (e.g., humped or threshold) relationships and complex interactions. Increasingly, ecologists are using flexible machine‐learning methods (e.g., random forests, neural networks) to overcome these limitations. Herein, we investigate the seasonal resource selection patterns of mule deer (Odocoileus hemionus) by comparing a logistic regression framework with random forest (RF), a popular machine‐learning algorithm. Random forest (RF) models detected nonlinear relationships (e.g., optimal ranges for slope and elevation) and complex interactions which would have been very challenging to discover and characterize using standard model‐based approaches. Compared with standard RSF models, RF models exhibited improved predictive skill, provided novel insights about resource selection patterns of mule deer, and, when projected across a relevant geographic space, manifested notable differences in predicted habitat suitability. We recommend that wildlife researchers harness the strengths of machine‐learning tools like RF in addition to “classical” tools (e.g., mixed‐effects logistic regression) for evaluating resource selection, especially in cases where extensive telemetry data sets are available. 相似文献
5.
1999年 6月 ,四川省德阳某珍稀动物养殖场梅花鹿Ｃｅｒｖｕｓｎｉｐｐｏｎ突然发病 ,常规抗菌治疗无效 ,先后死亡 4只 ,其死前症状和尸体剖解病变基本一致。取死亡梅花鹿病理材料送实验室作微生物培养 ,结果显示为耶尔森氏菌Ｙｅｒｓｉｎｉａ感染。并进行了动物毒性实验、药敏试验。由于近年来国内梅花鹿因各种感染引起急性死亡的报导较多 ,而各地所分离的病原菌结果存在较大的差异 ,加上本群梅花鹿发病急 ,死亡快 ,现将该病的临床症状、尸体解剖病变、实验室诊断、动物毒性试验、药敏试验和防治方法介绍如下 ,供同行参考。1 临床症状 死… 相似文献
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Jameson Mori;William Brown;Daniel Skinner;Peter Schlichting;Jan Novakofski;Nohra Mateus-Pinilla; 《Ecology and evolution》2024,14(11):e70487
White-tailed deer (Odocoileus virginianus) are a cervid species found mostly in the Americas. Managing white-tailed deer requires understanding their relationship with the environment, which was characterized by Roseberry and Woolf (Wildlife Society Bulletin 1, 1998, 252) for all counties in Illinois, USA, who incorporated habitat quantity and quality in a deer habitat suitability index. However, this index was based on satellite imagery from 1996 and did not explore the smaller spatial scales used by deer. Our study addressed these gaps by developing a deer land cover utility (LCU) score for each TRS (township, range, and section), township, and county in Illinois based on the methodology outlined in Roseberry and Woolf (Wildlife Society Bulletin 1, 1998, 252) but using data from the National Land Cover Database (2001–2021). These deer LCU scores were validated against minimum deer population data using Bayesian regression with additional covariates relevant to hunting and deer density. These models performed well with Bayesian R2 values of 0.501 (TRS), 0.5 (township), and 0.969 (county). The regression coefficients for the deer LCU scores were statistically significant (95% credibility interval not containing 0) and positive at the TRS, township, and county levels, reflecting the expected relationship between minimum deer density and deer LCU. Predictions made by these regression models on new data were accurate, with the median absolute difference between the true and predicted values being 0.398 deer/km2 for TRS', 0.085 deer/km2 for townships, and 0.066 deer/km2 for counties. This deer LCU could be used in other studies about deer in Illinois or studies in which deer are a relevant factor such as investigations about deer disease or tick distribution. This modeling approach could also be adapted to different wild species, locations, and/or time periods for which land cover data is available. 相似文献
7.
Daniel W. Sallee Brock R. McMillan Kent R. Hersey Steven L. Petersen Randy T. Larsen 《The Journal of wildlife management》2023,87(1):e22318
Ungulates often alter behavior and space use in response to interspecific competition. Despite observable changes in behavior caused by competitive interactions, research describing the effects of competition on survival or growth is lacking. We used spatial modeling to determine if habitat use by female mule deer (Odocoileus hemionus) was affected by other ungulate species prior to, during, and after parturition. We conducted our study in the Book Cliffs region of eastern Utah, USA, during 2019 and 2020. We used resource selection function (RSF) analysis to model space use of 4 ungulate species that potentially competed with mule deer: bison (Bos bison), cattle, elk (Cervus canadensis), and feral horses. We incorporated RSF models for competing species into a random forest analysis to determine if space use by mule deer was influenced by these other ungulate species. We used survival and growth data from neonate mule deer to directly assess potential negative effects of other ungulates. Habitat use by elk was an important variable in predicting use locations of mule deer during birthing and rearing. The relationship was positive, suggesting interference competition was not occurring. Survival of neonate mule deer increased as the probability of use by elk increased (hazard ratio = 0.185 ± 0.497 [SE]). Further, probability of use by elk in rearing habitat had no influence on growth of neonate mule deer from birth to 6 months of age, suggesting that exploitative competition was not occurring. 相似文献
8.
Habitat‐selection analysis lacks an appropriate measure of the ecological significance of the statistical estimates—a practical interpretation of the magnitude of the selection coefficients. There is a need for a standard approach that allows relating the strength of selection to a change in habitat conditions across space, a quantification of the estimated effect size that can be compared both within and across studies. We offer a solution, based on the epidemiological risk ratio, which we term the relative selection strength (RSS ). For a “used‐available” design with an exponential selection function, the RSS provides an appropriate interpretation of the magnitude of the estimated selection coefficients, conditional on all other covariates being fixed. This is similar to the interpretation of the regression coefficients in any multivariable regression analysis. Although technically correct, the conditional interpretation may be inappropriate when attempting to predict habitat use across a given landscape. Hence, we also provide a simple graphical tool that communicates both the conditional and average effect of the change in one covariate. The average‐effect plot answers the question: What is the average change in the space use probability as we change the covariate of interest, while averaging over possible values of other covariates? We illustrate an application of the average‐effect plot for the average effect of distance to road on space use for elk (Cervus elaphus ) during the hunting season. We provide a list of potentially useful RSS expressions and discuss the utility of the RSS in the context of common ecological applications. 相似文献
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家养有蹄类动物的反刍行为已有大量研究,而受限于野外条件,野生有蹄类动物反刍行为的研究有限。以往研究认为,体型可能是影响动物反刍行为的重要因素。本研究以日本奈良公园日本梅花鹿Cervus nippon nippon和中国江苏大丰麋鹿Elaphurus davidianus为研究对象,观察记录并比较分析了发情期的日本梅花鹿与发情期的麋鹿在卧息时反刍行为的种内及种间差异。按照年龄性别差异,分5种类型(成年雄性、成年雌性、亚成雄性、亚成雌性和幼鹿)研究两物种食团咀嚼时间、咀嚼频率和咀嚼速率的差异。结果表明,1)日本梅花鹿的咀嚼速率和食团咀嚼时间受性别-年龄的显著影响:咀嚼速率为幼体>亚成雌性>亚成雄性>成年雌性>成年雄性,而食团咀嚼时间为成年雄性>亚成雄性>成年雌性>亚成雌性>幼体;2)性别-年龄对麋鹿咀嚼频率有显著的影响,成年雄性<成年雌性<亚成雄性<亚成雌性<幼体;3)两物种在咀嚼频率、食团咀嚼时间和咀嚼速率上的差异均有统计学意义,体型更大的麋鹿咀嚼速率更慢,食团咀嚼时间更长。与体型相关的性别及年龄显著影响了日本梅花鹿和麋鹿的反刍行为。 相似文献
10.
MATTHEW W. WAGNER URS P. KREUTER RONALD A. KAISER R. NEAL WILKINS 《The Journal of wildlife management》2007,71(5):1729-1738
Abstract: In areas with dense landownership patterns, management of white-tailed deer (Odocoileus virginianus) depends upon collective decision making of landowners and hunters. To resolve conflicts associated with this commons dilemma, wildlife management associations (WMAs) have become a popular mechanism for coordinating wildlife management decisions in private land states, especially in Texas, USA. Social capital, represented by metrics such as trust, reciprocity, and community involvement, has been identified as an important determinant of the success of collaborative institutional arrangements. To determine the influence of social capital on the effectiveness of WMAs, we address 2 research questions: 1) do WMAs exhibit elements of social capital, and 2) what landowner characteristics affect elements of social capital within WMAs? We used a mail survey questionnaire to determine the effect of various factors on the activities and management practices in 4 WMAs in 2 regions in Texas: the Lower Post Oak Savannah (LPOS) and the Central Post Oak Savannah (CPOS). The LPOS landowners were members of larger associations, had generally acquired their land more recently, held more frequent meetings, and tended to have longer association membership than CPOS landowners, yet they exhibited lower social capital. The CPOS landowners owned significantly larger properties, and were predominantly absentee wealthy males that considered relaxation and hunting more important land uses than property ownership for a place to live. The smaller group size of the CPOS associations may be the most important factor in building and maintaining social capital. Intra-association trust, a primary measure of social capital, was positively influenced by the longevity of property ownership, the number of association meetings, the percentage of males in the association, and other factors. Conversely, negative influences on trust included absentee ownership and the proportion of woodland habitat present in each WMA. We suggest that deer are a common-pool resource whose populations are dependent upon collective action by stakeholders. Social capital building within landowner associations could facilitate the sustainable harvest of quality deer and possibly lead to cooperative management of other common-pool natural resources. 相似文献
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四川梅花鹿Cervus nippon sichuanicus为国家Ⅰ级重点保护动物,四川铁布梅花鹿自然保护区分布有我国现存最大的梅花鹿野生种群。2011年6—9月,采用样方法对保护区梅花鹿夏季栖息地选择进行了调查,共布设109个样方(利用样方61个,对照样方48个),测量并比较了海拔、坡度等20个生境因子。结果显示,梅花鹿偏好利用的植被类型为灌丛、草甸、针叶林,同时选择隐蔽度较高的生境;此外,距水源距离、灌木高度、灌木盖度、灌木密度、草本高度和草本盖度6个连续变量在利用样方和对照样方之间差异有统计学意义(F<0.05或U<0.05)。回归模型分析结果表明,梅花鹿夏季偏好选择草本盖度大、靠近水源和林缘的生境,回避远离水源和林缘的生境。 相似文献
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Melissa M. Turner Aimee P. Rockhill Christopher S. DePerno Jonathan A. Jenks Robert W. Klaver Angela R. Jarding Troy W. Grovenburg Kenneth H. Pollock 《The Journal of wildlife management》2011,75(4):905-912
Coyotes (Canis latrans) may affect adult and neonate white-tailed deer (Odocoileus virginianus) survival and have been implicated as a contributor to the decline of deer populations. Additionally, coyote diet composition is influenced by prey availability, season, and region. Because coyote movement and diet vary by region, local data are important to understand coyote population dynamics and their impact on prey species. In southeast Minnesota, we investigated the effect of coyotes on white-tailed deer populations by documenting movement rates, distances moved, and habitats searched by coyotes during fawning and nonfawning periods. Additionally, we determined survival, cause-specific mortality, and seasonal diet composition of coyotes. From 2001 to 2003, we captured and radiocollared 30 coyotes. Per-hour rate of movement averaged 0.87 km and was greater (P = 0.046) during the fawning (1.07 km) than the nonfawning period (0.80 km); areas searched were similar (P = 0.175) between seasons. Coyote habitat use differed during both seasons; habitats were not used in proportion to their availability (P < 0.001). Croplands were used more (P < 0.001) than their proportional availability during both seasons. Use of grasslands was greater during the fawning period (P = 0.030), whereas use of cropland was greater in the nonfawning period (P < 0.001). We collected 66 fecal samples during the nonfawning period; coyote diets were primarily composed of Microtus spp. (65.2%), and consumption of deer was 9.1%. During the study, 19 coyotes died; annual survival rate range was 0.33–0.41, which was low compared with other studies. Consumption of deer was low and coyotes searched open areas (i.e., cropland) more than fawning areas with dense cover. These factors in addition to high coyote mortality suggested that coyote predation was not likely limiting white-tailed deer populations in southeast Minnesota. © 2011 The Wildlife Society. 相似文献
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Stephen L. Webb Stephen Demarais Bronson K. Strickland Randy W. DeYoung Brian P. Kinghorn Kenneth L. Gee 《The Journal of wildlife management》2012,76(1):48-56
Selective harvesting in wild deer (Odocoileus spp.) populations is a common practice that may influence antler size. However, in free-ranging populations, response due to selection is unknown or difficult to quantify because antlers are influenced by nutrition and population demographics. We used quantitative genetic models to predict how white-tailed deer (O. virginianus) antlers would respond to selection and what variables (i.e., population size, age structure, mating ratio, and heritability) most affected antler size. We validated our quantitative genetics program by comparing model results with a population of deer used for controlled breeding experiments; modeled antler points (AP) and score increased (2.2–4.3 AP and 48.5–97.7 cm, respectively) after 8 years of selection, similar to observed increases in AP (3.2) and score (92.3 cm) from the controlled population. In modeled free-ranging populations, mating ratio, age structure, and heritability were more important in influencing antler size than size of the population. However, response to selection in free-ranging populations was lower (0.1–0.9 AP) than controlled breeding populations even after 20 years of selection. These results show that selective harvesting of free-ranging white-tailed deer may be inefficient to change population-level genetic characteristics related to antler size. Response of antlers in free-ranging deer will be less than controlled populations, and possibly modeled free-ranging simulations, because individual reproductive success of males is lower, breeding is done by a large group of males, and reproductive and survival rates are lower. These factors, and others, reduce the amount of improvement that can be made to antlers due to selection. Therefore, selective harvesting in free-ranging populations should be justified for managing population demographics and dynamics, but not for changing the genetic characteristics of populations. © 2011 The Wildlife Society. 相似文献
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于2012年10—12月在桃红岭国家级自然保护区采用随机样方法调查了梅花鹿和野猪的生境选择。结果表明:梅花鹿秋季频繁在高海拔(365.5 m±141.7 m)、坡度平缓(17.80°±12.68°)、阳坡、上坡位空间活动,选择高度为0.81 m±0.36 m的灌丛或草丛,灌木高度较高(4.70 m±1.68 m)、密度较小(7.56棵±4.55棵)的生境活动,对乔木直径、郁闭度和覆盖度3个生态因子表现为随机选择;野猪则选择灌林木生境,对其余的生态因子表现为随机选择。逐步判别分析结果显示,坡度、海拔和灌木密度3个变量导致了梅花鹿与野猪之间的生境分离(Wilk's λ=0.801,χ2=24.89,df=3,P＜0.001),判别函数方程:F=0.653×坡度-0.546×海拔+0.840×灌木密度+0.144,正确判别率达82.4%。 相似文献
15.
DUANE R. DIEFENBACH ERIC S. LONG CHRISTOPHER S. ROSENBERRY BRET D. WALLINGFORD DAVID R. SMITH 《The Journal of wildlife management》2008,72(6):1296-1303
Abstract: Dispersal distances and their distribution pattern are important to understanding such phenomena as disease spread and gene flow, but oftentimes dispersal characteristics are modeled as a fixed trait for a given species. We found that dispersal distributions differ for spring and autumn dispersals of yearling male white-tailed deer (Odocoileus virginianus) but that combined data can be adequately modeled based on a log-normal distribution. We modeled distribution of dispersal distances from 3 distinct populations in Pennsylvania and Maryland, USA, based on the relationship between percent forest cover and mean dispersal distance and the relationship between mean and variance of dispersal distances. Our results suggest distributions of distances for dispersing yearling male white-tailed deer can be modeled by simply measuring a readily obtained landscape metric, percent forest cover, which could be used to create generalized spatially explicit disease or gene flow models. 相似文献
16.
Priscilla K. Coe Bruce K. Johnson Michael J. Wisdom John G. Cook Martin Vavra Ryan M. Nielson 《The Journal of wildlife management》2011,75(1):159-170
Knowledge of how landscape features affect wildlife resource use is essential for informed management. Resource selection functions often are used to make and validate predictions about landscape use; however, resource selection functions are rarely validated with data from landscapes independent of those from which the models were built. This problem has severely limited the application of resource selection functions over larger geographic areas for widely distributed species. North American elk (Cervus elaphus) is an example of a widely-distributed species of keen interest to managers and for which validation of resource selection functions over large geographic areas is important. We evaluated the performance of resource selection functions developed for elk on one landscape in northeast Oregon with independent data from a different landscape in the same region. We compared predicted versus observed elk resource use for 9 monthly or seasonal periods across 3 yr. Results showed strong, positive agreement between predicted and observed use for 2 spring and 3 late summer-early fall models (3-yr r = 0.81–0.95). Predicted versus observed use was negatively or weakly positively correlated for 3 summer models and 1 mid-fall model (3-yr r = −0.57–0.14). Predicted and observed use correlated well when forage was limited (spring and late summer or early fall), corresponding to important biological stages for elk (parturition and breeding seasons). For these seasonal periods, model covariates such as rate of motorized traffic and canopy closure often were effective predictors of elk resource selection. The models we validated for spring and late summer-early fall may be used to evaluate management activities in areas with similar landscape characteristics. © 2010 The Wildlife Society. 相似文献
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Scale for resource selection functions 总被引：3，自引：0，他引：3
Mark S. Boyce 《Diversity & distributions》2006,12(3):269-276
Resource selection functions (RSFs) are statistical models defined to be proportional to the probability of use of a resource unit. My objective with this review is to identify how RSFs can be used to unravel the influence of scale in habitat selection. In wildlife habitat studies, including radiotelemetry, RSFs can be estimated using a variety of statistical methods, all of which can be used to explore the role of scale. All RSFs are bounded by the resolution of data and the spatial extent of the study area, but also allow predictor covariates to be measured at a variety of scales. Conditional logistic regression permits designs (e.g. matched case) that relate the process of habitat selection to a limited domain of resource units that might better characterize what is truly ‘available’ to the animal. Scale influences the process of habitat selection, e.g. food resources are often selected at fine spatial scales, whereas landscape patterns at much larger scales typically influence the location of home ranges. Scale also influences appropriate sampling in many ways: (1) heterogeneity might be obliterated (transmutation) if resolution or grain size is too large, (2) variance of habitat characteristics might be undersampled if extent or domain is too small, (3) timing and duration of observations can influence RSF models, and (d) both spatial and temporal autocorrelations can vary directly with the intensity of sampling. Using RSFs, researchers can examine habitat selection at multiple scales, and predictive models that bridge scales can be estimated. Using Geographical Information Systems, predictor covariates in RSF models can be measured at different scales easily so that the predictive ability of models at alternative spatial and temporal domains can be explored by the investigator. Identification of the scale that best explains the data can be evaluated by comparing alternative models using information‐theoretic metrics such as Akaike Information Criteria, and predictive capability of the models can be assessed using k‐fold cross validation. 相似文献
20.
The ability to predict energy and protein allocation to different body condition parameters according to environmental constraints is a key component in understanding the processes underlying population dynamics. We investigated the influence of a proxy of population density and environmental factors on individual body condition parameters of female white-tailed deer (Odocoileus virginianus) based on long-term monitoring (2002–2013) of autumn harvest on Anticosti Island, Québec, Canada. We used dressed body mass, peroneus muscle mass, and rump fat thickness to evaluate the nutritional status of 3,123 adult females. Density index and winter precipitation negatively affected fat reserves in autumn. We detected the negative effect of winter precipitation on fat reserves only at low density likely because individuals at high density were already in bad condition. High normalized difference vegetation index (NDVI) in spring (May–Jun) reduced body mass, and this influence was more pronounced under high population density, probably because individuals at high densities were less likely to be buffered against environmental fluctuations when resources were scarcer than resources at low population density. Using different body condition parameters, our results provide additional insights on how northern ungulates influenced by food limitation may respond to future environmental changes. We recommend managers to collect long-term data on multiple physiological indicators of body condition. These data could be used as an index of ecological changes and provide a quantitative basis to help setting harvest objectives or supporting adaptive management. © 2020 The Wildlife Society. 相似文献