The correct equation is D = AO + AB exp(βB,0+βB,SSi,j+βB,TTi,j) + AC exp (βC,0+βC,SSi,j+βC,TTi,j) The publisher apologizes for these errors. 1. DelGiudice GD, Fieberg JR, Sampson BA (2013) A ...Long-Term Assessment of the Variability in Winter Use of Dense Conifer Cover by Female White-Tailed Deer.
Inbreeding depression is of major concern for the conservation of threatened species, and inbreeding avoidance is thought to be a key driver in the evolution of mating systems. However, the ...estimation of individual inbreeding coefficients in natural populations has been challenging, and, consequently, the full effect of inbreeding on fitness remains unclear. Genomic inbreeding coefficients may resolve the long-standing paucity of data on inbreeding depression in adult traits and total fitness. Here we investigate inbreeding depression in a range of life history traits and fitness in a wild population of red deer (Cervus elaphus) in Scotland using individual inbreeding coefficients derived from dense Single-Nucleotide Polymorphism (SNP) data (F
grm). We find associations between F
grm and annual breeding success in both sexes, and between maternal inbreeding coefficient and offspring survival. We also confirm previous findings of inbreeding depression in birth weight and juvenile survival. In contrast, inbreeding coefficients calculated from a deep and comparatively complete pedigree detected inbreeding depression in juvenile survival, but not in any adult fitness component. The total effect of inbreeding on lifetime breeding success (LBS) was substantial in both sexes: for F
grm = 0.125, a value resulting from a half-sib mating, LBS declined by 72% for females and 95% for males. Our results demonstrate that SNP-based estimates of inbreeding provide a powerful tool for evaluating inbreeding depression in natural populations, and suggest that, to date, the prevalence of inbreeding depression in adult traits may have been underestimated.
To determine animal hepatitis E virus (HEV) reservoirs, we analyzed serologic and molecular markers of HEV infection among wild animals in Germany. We detected HEV genotype 3 strains in inner organs ...and muscle tissues of a high percentage of wild boars and a lower percentage of deer, indicating a risk for foodborne infection of humans.
Deer (Cervidae) are key components of many ecosystems and estimating deer abundance or density is important to understanding these roles. Many field methods have been used to estimate deer abundance ...and density, but the factors determining where, when, and why a method was used, and its usefulness, have not been investigated. We systematically reviewed journal articles published during 2004–2018 to evaluate spatio‐temporal trends in study objectives, methodologies, and deer abundance and density estimates, and determine how they varied with biophysical and anthropogenic attributes. We also reviewed the precision and bias of deer abundance estimation methods. We found 3,870 deer abundance and density estimates. Most estimates (58%) were for white‐tailed deer (Odocoileus virginianus), red deer (Cervus elaphus), and roe deer (Capreolus capreolus). The 6 key methods used to estimate abundance and density were pedestrian sign (track or fecal) counts, pedestrian direct counts, vehicular direct counts, aerial direct counts, motion‐sensitive cameras, and harvest data. There were regional differences in the use of these methods, but a general pattern was a temporal shift from using harvest data, pedestrian direct counts, and aerial direct counts to using pedestrian sign counts and motion‐sensitive cameras. Only 32% of estimates were accompanied by a measure of precision. The most precise estimates were from vehicular spotlight counts and from capture–recapture analysis of images from motion‐sensitive cameras. For aerial direct counts, capture–recapture methods provided the most precise estimates. Bias was robustly assessed in only 16 studies. Most abundance estimates were negatively biased, but capture–recapture methods were the least biased. The usefulness of deer abundance and density estimates would be substantially improved by 1) reporting key methodological details, 2) robustly assessing bias, 3) reporting the precision of estimates, 4) using methods that increase and estimate detection probability, and 5) staying up to date on new methods. The automation of image analysis using machine learning should increase the accuracy and precision of abundance estimates from direct aerial counts (visible and thermal infrared, including from unmanned aerial vehicles drones) and motion‐sensitive cameras, and substantially reduce the time and cost burdens of manual image analysis.
A minority of deer abundance and density estimates were accompanied by a measure of precision, and bias was seldom evaluated. The usefulness of deer abundance and density estimates would be substantially improved by reporting key methodological details, robustly assessing bias, using methods that increase detection probability, and reporting the precision of estimates.
Associated with the spreading in (north)western direction of
Fascioloides magna
from its historic endemic area in Bohemia with its cervid hosts, unusual noticeable hepatic lesions (black-colored ...tissue, hemorrhage) were observed in deer harvested in hunting grounds and one deer farm located in the Upper Palatinate Forest close to the border to the Czech Republic, initially in the years of 2007 and 2009, respectively. Confirmation of the suspected diagnosis of
F. magna
infection in October 2011 prompted investigations on the occurrence of “fascioloidosis” among wild ungulates in that locality. From October 2011 to January 2014, livers from 89 cervids and two wild boars were examined for flukes. Thirty-seven livers (40.6%) harbored
F. magna
: 17 of 21 red deer, nine of 24 sika deer, six of eight fallow deer, four of 36 roe deer, one of two wild boars. Fluke burdens ranged from 2 up to 151 in red deer, from 2 up to 37 in fallow deer, and from 1 up to 7 in sika deer and in roe deer; one fluke was recovered from the liver of one wild boar. No other parasites were recovered from the livers. The rate of recovery of
F. magna
differed significantly (
p
< 0.001) among the species of deer (red deer, 81.0%; sika deer, 37.5%; fallow deer, 75.0%; roe deer, 11.1%) and between the age groups (< 1 year: 22.2%, 1 to 2 years: 26.0%, and > 2 years: 70.0%, respectively). There was no association (
p
> 0.1) between the rate of recovery of
F. magna
and the sex of the combined 80 deer of ≥ 1 year of age (male: 41.8% and female: 31.4%). The occurrence of
F. magna
in the wild ungulates in the Upper Palatinate Forest area in northeastern Bavaria is of epidemiological importance for the further spreading of the parasite into Germany with migrating deer.
Globally, many wild deer populations are actively studied or managed for conservation, hunting, or damage mitigation purposes. These studies require reliable estimates of population state parameters, ...such as density or abundance, with a level of precision that is fit for purpose. Such estimates can be difficult to attain for many populations that occur in situations that are poorly suited to common survey methods. We evaluated the utility of combining camera trap survey data, in which a small proportion of the sample is individually recognizable using natural markings, with spatial mark–resight (SMR) models to estimate deer density in a variety of situations. We surveyed 13 deer populations comprising four deer species (Cervus unicolor, C. timorensis, C. elaphus, Dama dama) at nine widely separated sites, and used Bayesian SMR models to estimate population densities and abundances. Twelve surveys provided sufficient data for analysis and seven produced density estimates with coefficients of variation (CVs) ≤ 0.25. Estimated densities ranged from 0.3 to 24.6 deer km–2. Camera trap surveys and SMR models provided a powerful and flexible approach for estimating deer densities in populations in which many detections were not individually identifiable, and they should provide useful density estimates under a wide range of conditions that are not amenable to more widely used methods. In the absence of specific local information on deer detectability and movement patterns, we recommend that at least 30 cameras be spaced at 500–1,000 m and set for 90 days. This approach could also be applied to large mammals other than deer.
For species of conservation concern and human–wildlife conflict, it is imperative that spatial population data be available to design adaptive‐management strategies and be prepared to meet challenges ...such as land use and climate change, disease outbreaks, and invasive species spread. This can be difficult, perhaps impossible, if spatially explicit wildlife data are not available. Low‐resolution areal counts, however, are common in wildlife monitoring, that is, the number of animals reported for a region, usually corresponding to administrative subdivisions, for example, region, province, county, departments, or cantons. Bayesian areal disaggregation regression is a solution to exploit areal counts and provide conservation biologists with high‐resolution species distribution predictive models. This method originated in epidemiology but lacks experimentation in ecology. It provides a plethora of applications to change the way we collect and analyze data for wildlife populations. Based on high‐resolution environmental rasters, the disaggregation method disaggregates the number of individuals observed in a region and distributes them at the pixel level (e.g., 5 × 5 km or finer resolution), thereby converting low‐resolution data into a high‐resolution distribution and indices of relative density. In our demonstrative study, we disaggregated areal count data from hunting bag returns to disentangle the changing distribution and population dynamics of three deer species (red, sika, and fallow) in Ireland from 2000 to 2018. We show an application of the Bayesian areal disaggregation regression method and document marked increases in relative population density and extensive range expansion for each of the three deer species across Ireland. We challenged our disaggregated model predictions by correlating them with independent deer surveys carried out in field sites and alternative deer distribution models built using presence‐only and presence–absence data. Finding a high correlation with both independent data sets, we highlighted the ability of Bayesian areal disaggregation regression to accurately capture fine‐scale spatial patterns of animal distribution. This study uncovers new scenarios for wildlife managers and conservation biologists to reliably use regional count data disregarded so far in species distribution modeling. Thus, it represents a step forward in our ability to monitor wildlife population and meet challenges in our changing world.
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