Spring spotlight counts provide reliable indices to track changes in population size of mountain‐dwelling red deer <i>Cervus elaphus</i>

Corlatti, Luca; Gugiatti, Alessandro; Pedrotti, Luca

doi:10.2981/wlb.00244

Cited by 33 publications

(36 citation statements)

References 114 publications

(174 reference statements)

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“…Our results are consistent with those of Corlatti et al () who concluded that spotlighting was an effective method in heterogeneous landscapes capable of producing reliable indices of population abundance over time for red deer. McShea et al () and Beaver et al (), however, noted that sampling along roads that follow landscape features that structure animal populations violates critical assumptions for experimental designs and may bias the detection rates and subsequent density estimates for populations (Ward et al ).…”

Section: Discussionsupporting

confidence: 93%

“…Past efforts have evaluated environmental factors (e.g., weather, seasonal, land cover types) influencing spotlight counts (Progulske and Duerre , Rybarczyk , McCullough ), the accuracy of counts, and the precision of population indices (McCullough , Fafarman and DeYoung , Cypher , Whipple et al ). Although spotlight methods are capable of producing reliable indices of relative abundance for ungulates in some landscapes (Focardi et al , Garel et al , McShea et al , Anderson et al , Corlatti et al ), the effectiveness of spotlight counts to produce reasonable and precise estimates of density has remained inconsistent because of bias associated with study design, environment, or animal behavior (Focardi et al , McShea et al , Anderson et al ).…”

mentioning

confidence: 99%

“…The use of non‐linear roads may violate assumptions for random or systematic sampling in distance sampling studies, and may further bias detection rates and density estimates if roads follow landscape features that structure animal populations (McShea et al , Beaver et al ) or animals select for or against features created by road openings (e.g., in highly forested landscapes; McCaffery and Creed , Ward et al ). Second, observation rates for deer across landscapes are rarely constant and may vary according to factors (e.g., observer, topography, land cover type; Corlatti et al ). Although distance sampling was developed to address limitations in traditional line‐transect surveys, Collier et al () argued that spotlight surveys were incapable of producing reliable estimates of relative abundance because of the high variability in the detection probability for white‐tailed deer.…”

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confidence: 99%

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Using spotlight observations to predict resource selection and abundance for white‐tailed deer

2019

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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.

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Section: Discussionsupporting

confidence: 93%

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confidence: 99%

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confidence: 99%

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Using spotlight observations to predict resource selection and abundance for white‐tailed deer

2019

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“…Corlatti et al. ). In 4 yr, from 2011 to 2016, within the study site a total of 358 female deer (0.5 yr: n = 81; 1.5 yr: n = 39; 2+ yr: n = 238) were culled by professional hunters, under the supervision of the Park Authority.…”

Section: Methodsmentioning

confidence: 99%

“…External variables included population size in the previous spring (based on mark–resight estimates: Corlatti et al. ) and meteorological variables. On the Alps, deer fertilization occurs at the beginning of October, and fat reserves are mainly deposited in spring–summer: Because in this environment warmer temperatures are linked to anticipated vegetation growth (Pettorelli et al.…”

Section: Methodsmentioning

confidence: 99%

The cooler the better? Indirect effect of spring–summer temperature on fecundity in a capital breeder

et al. 2018

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Female reproductive performance is a central component of ungulate population dynamics, and it can be influenced by individual, social, and environmental factors. Researchers have often assumed direct effects of different predictors on reproduction, yet more complex relationships should be considered when investigating temporal variations in life‐history traits within a broader eco‐evolutionary context. In this study, we explored direct effects of individual, social, and environmental predictors on female reproductive performance and investigated potential causal chains among variables. We analyzed the variation in fecundity, measured as the probability of being pregnant, in 215 adult female deer Cervus elaphus culled on the Italian Alps, with respect to age, body mass, kidney fat, jaw length, lactation status, population size, temperature and precipitation in spring–summer, temperature and snow depth in winter, and the delayed effect of spring–summer temperature. We used random forest and logistic regression models to select variables whose direct effects best explained variation in fecundity. Path analysis was used to test for alternative hypotheses of direct/indirect effects between pre‐selected weather (spring–summer temperature) and individual (age, kidney fat index [KFI]) predictors. The most important direct predictors of fecundity were age, kidney fat, and the interaction between kidney fat and spring–summer temperature. Path analysis supported the hypothesis that higher spring–summer temperature had negative, indirect effects on the probability of being pregnant, mediated by decreasing values of KFI. Our study revealed some complex, cause–effect relationships between weather stochasticity, body condition, and reproduction, possibly suggesting a conditional trade‐off between opportunity for reproduction and survival, and emphasizing how environmental variations and individual characteristics may interact to shape life‐history traits in ungulate populations.

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Disentangling demographic effects of red deer on chamois population dynamics

Donini

Pedrotti

Ferretti

et al. 2021

Ecology and Evolution

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Investigating the impact of ecological factors on sex‐ and age‐specific vital rates is essential to understand animal population dynamics and detect the potential for interactions between sympatric species. We used block count data and autoregressive linear models to investigate variation in birth rate, kid survival, female survival, and male survival in a population of Alpine chamois Rupicapra rupicapra rupicapra monitored over 27 years within the Stelvio National Park, Central Italian Alps, as function of climatic variables, density dependence, and interspecific competition with red deer Cervus elaphus. We also used path analysis to assess the indirect effect of deer abundance on chamois growth rate mediated by each demographic parameter. Based on previous findings, we predicted that birth rate at [t] would negatively relate to red deer abundance at year [t − 1]; survival rates between [t] and [t + 1] would negatively relate to red deer abundance at year [t − 1] and to the interactive effect of winter precipitation at [t + 1] and chamois density at [t]. Our results showed that birth rate was positively related to spring–summer precipitation in the previous year, but this effect was hampered by increasing red deer abundance. Kid and female survival rates were negatively related to the combined effect of chamois abundance and winter precipitation. Male and female survival rates were negatively related to lagged red deer abundance. The path analysis supported a negative indirect effect of red deer abundance on chamois growth rate mediated by birth rate and female survival. Our results suggest that chamois population dynamics was largely explained by the synergistic effect of density dependence and winter harshness, as well as by interspecific competition with red deer, whose effects were seemingly stronger on the kid–female segment of the population.

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Spring spotlight counts provide reliable indices to track changes in population size of mountain‐dwelling red deer Cervus elaphus

Cited by 33 publications

References 114 publications

Using spotlight observations to predict resource selection and abundance for white‐tailed deer

Using spotlight observations to predict resource selection and abundance for white‐tailed deer

The cooler the better? Indirect effect of spring–summer temperature on fecundity in a capital breeder

Disentangling demographic effects of red deer on chamois population dynamics

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