Survival,cause-specific mortality,and population growth of white-tailed deer in western Virginia

Understanding the role of recruitment in population dynamics of white-tailed deer (Odocoileus virginianus) is important for management. In the central Appalachian Mountains, deer are part of a largely forested ecosystem that supports 3 carnivore species thought to be capable of influencing white-tailed deer recruitment: black bears (Urus americanus), coyotes (Canis latrans), and bobcats (Lynx rufus). Yet little is known about predation, how other environmental factors influence recruitment, or the importance of neonate survival to white-tailed deer population performance in the region. Our objectives were to identify causes of mortality for neonates, analyze effects of landscape attributes on survival of neonates, estimate survival rates for neonates and adult female white-tailed deer, and to model population growth trends based on current vital rates and hypothetical harvest and neonate survival scenarios. During 2019–2020, we captured 57 neonate deer in Bath County, Virginia, USA, by monitoring 38 pregnant females equipped with global positioning system collars and vaginal implant transmitters and by conducting transect searches for recently born neonates. We observed 37 neonate mortalities and identified cause of death using field and genetic evidence. Mortalities included 28 predation events and 9 deaths from other causes (e.g., abandonment, malnutrition, disease). Black bears accounted for 48.6% of neonate mortalities, and 64.2% of predation events (n = 18), followed by bobcats (n = 5) and coyotes (n = 3). Annual survival for adult female deer was 0.871 and neonate survival to 12 weeks old was 0.310. Elevation was a significant predictor of neonate survival; mortality risk increased 20% for every 100-m increase in elevation. Models of annual population growth using observed vital rates predicted an increasing population (λ = 1.10). A 10% increase in female harvest would still result in a potential population increase of 2% (λ = 1.02), but a 20% increase in harvest rate would result in a potential 7% decline (λ = 0.93). Neonate survival was higher near fertile valley bottoms and lower along forested ridges characterized by shallow, infertile soils and limited edge or early successional forests. While predation, largely influenced by black bears, was the leading cause of neonate mortality and contributed to low neonate survival, we observed little evidence of population decline, and suggest there is opportunity for a modest increase in harvest of female deer.     

Estimating effective population size from samples of sequences: a bootstrap Monte Carlo integration method.

We would like to use maximum likelihood to estimate parameters such as the effective population size N(e) or, if we do not know mutation rates, the product 4N(e) mu of mutation rate per site and effective population size. To compute the likelihood for a sample of unrecombined nucleotide sequences taken from a random-mating population it is necessary to sum over all genealogies that could have led to the sequences, computing for each one the probability that it would have yielded the sequences, and weighting each one by its prior probability. The genealogies vary in tree topology and in branch lengths. Although the likelihood and the prior are straightforward to compute, the summation over all genealogies seems at first sight hopelessly difficult. This paper reports that it is possible to carry out a Monte Carlo integration to evaluate the likelihoods approximately. The method uses bootstrap sampling of sites to create data sets for each of which a maximum likelihood tree is estimated. The resulting trees are assumed to be sampled from a distribution whose height is proportional to the likelihood surface for the full data. That it will be so is dependent on a theorem which is not proven, but seems likely to be true if the sequences are not short. One can use the resulting estimated likelihood curve to make a maximum likelihood estimate of the parameter of interest, N(e) or of 4N(e) mu. The method requires at least 100 times the computational effort required for estimation of a phylogeny by maximum likelihood, but is practical on today's work stations. The method does not at present have any way of dealing with recombination.     

Estimating joint kinematics from skin motion observation: modelling and validation

Modelling of soft tissue motion is required in many areas, such as computer animation, surgical simulation, 3D motion analysis and gait analysis. In this paper, we will focus on the use of modelling of skin deformation during 3D motion analysis. The most frequently used method in 3D human motion analysis involves placing markers on the skin of the analysed segment which is composed of the rigid bone and the surrounding soft tissues. Skin and soft tissue deformations introduce a significant artefact which strongly influences the resulting bone position, orientation and joint kinematics. For this study, we used a statistical solid dynamics approach which is a combination of several previously reported tools: the point cluster technique (PCT) and a Kalman filter which was added to the PCT. The methods were tested and evaluated on controlled human-arm motions, using an optical motion capture system (Vicon(TM)). The addition of a Kalman filter to the PCT for rigid body motion estimation results in a smoother signal that better represents the joint motion. Calculations indicate less signal distortion than when using a digital low-pass filter. Furthermore, adding a Kalman filter to the PCT substantially reduces the dispersion of the maximal and minimal instantaneous frequencies. For controlled human movements, the result indicated that adding a Kalman filter to the PCT produced a more accurate signal. However, it could not be concluded that the proposed Kalman filter is better than a low-pass filter for estimation of the motion. We suggest that implementation of a Kalman filter with a better biomechanical motion model will be more likely to improve the results.     

Estimating joint kinematics from skin motion observation: modelling and validation

Modelling of soft tissue motion is required in many areas, such as computer animation, surgical simulation, 3D motion analysis and gait analysis. In this paper, we will focus on the use of modelling of skin deformation during 3D motion analysis. The most frequently used method in 3D human motion analysis involves placing markers on the skin of the analysed segment which is composed of the rigid bone and the surrounding soft tissues. Skin and soft tissue deformations introduce a significant artefact which strongly influences the resulting bone position, orientation and joint kinematics. For this study, we used a statistical solid dynamics approach which is a combination of several previously reported tools: the point cluster technique (PCT) and a Kalman filter which was added to the PCT. The methods were tested and evaluated on controlled human-arm motions, using an optical motion capture system (Vicon^TM).

The addition of a Kalman filter to the PCT for rigid body motion estimation results in a smoother signal that better represents the joint motion. Calculations indicate less signal distortion than when using a digital low-pass filter. Furthermore, adding a Kalman filter to the PCT substantially reduces the dispersion of the maximal and minimal instantaneous frequencies. For controlled human movements, the result indicated that adding a Kalman filter to the PCT produced a more accurate signal. However, it could not be concluded that the proposed Kalman filter is better than a low-pass filter for estimation of the motion. We suggest that implementation of a Kalman filter with a better biomechanical motion model will be more likely to improve the results.     

Survival and cause-specific mortality of moose calves in Northeastern Minnesota

Ungulate reproductive success (calf production and survival) influences population performance. The moose (Alces alces) population in northeastern Minnesota, USA, has declined 65% from 2006 to 2018 but has begun to stabilize. Because causes of this decline were largely unknown, we investigated production, survival, and cause-specific mortality of calves of the global positioning system (GPS)-collared females in this population. In 2013 and 2014, we GPS-collared 74 neonates and monitored them for survival. In 2015 and 2016, we monitored 50 and 35 calving females for signs of neonatal mortality using changes in adult female velocities and assessed seasonal calf survival by aerial surveys. In 2013 and 2014 (pooled), survival to 9 months was 0.34 (95% CI = 0.23–0.52) for collared calves, and in 2015 and 2016 (pooled) survival was 0.35 (95% CI = 0.26–0.48) for uncollared calves. Mortality in all 4 years was high during the first 50 days of life. In 2013 and 2014 (pooled), calving sites were relatively safe for collared neonates; predator-kills occurred a median 17.0 days after departure and a median 1,142 m from calving sites. Predation was the leading cause of death of collared calves (84% of mortalities), with wolves (Canis lupus) accounting for 77% of these. Other forms of mortality for collared and uncollared calves included drowning, infection, vehicle collision, and natural abandonment. We documented higher wolf predation than other recent studies with similar predator communities. Identifying specific causes of calf mortality and understanding their relations to various landscape characteristics and other extrinsic factors should yield insight into mechanisms contributing to the declining moose population in northeastern Minnesota and serve as a basis for ecologically sound management responses. © 2019 The Wildlife Society.     

Estimating incidence from prevalence in generalised HIV epidemics: methods and validation

Background

HIV surveillance of generalised epidemics in Africa primarily relies on prevalence at antenatal clinics, but estimates of incidence in the general population would be more useful. Repeated cross-sectional measures of HIV prevalence are now becoming available for general populations in many countries, and we aim to develop and validate methods that use these data to estimate HIV incidence.

Methods and Findings

Two methods were developed that decompose observed changes in prevalence between two serosurveys into the contributions of new infections and mortality. Method 1 uses cohort mortality rates, and method 2 uses information on survival after infection. The performance of these two methods was assessed using simulated data from a mathematical model and actual data from three community-based cohort studies in Africa. Comparison with simulated data indicated that these methods can accurately estimates incidence rates and changes in incidence in a variety of epidemic conditions. Method 1 is simple to implement but relies on locally appropriate mortality data, whilst method 2 can make use of the same survival distribution in a wide range of scenarios. The estimates from both methods are within the 95% confidence intervals of almost all actual measurements of HIV incidence in adults and young people, and the patterns of incidence over age are correctly captured.

Conclusions

It is possible to estimate incidence from cross-sectional prevalence data with sufficient accuracy to monitor the HIV epidemic. Although these methods will theoretically work in any context, we have able to test them only in southern and eastern Africa, where HIV epidemics are mature and generalised. The choice of method will depend on the local availability of HIV mortality data.     

Estimating growth and mortality rates from size data

Summary A method is presented for estimating rates of individual growth and population mortality utilizing average individual size at two times during a year. The model assumes a constant rate of mortality, Brody-Bertalanffy growth, a stationary age distribution, and recruitment confined to one month each year. A hypothetical example is presented to show the interrelationships of the growth and mortality constants, size at recruitment, asymptotic size and average individual size. Three examples are presented using data from the literature: Flathead sole (Hippoglossoides elassodon), a sea urchin(Echinus esculentus), and the crown-of-thorns starfish(Acanthaster planci). The method appears to be a means of obtaining reasonable approximations of growth and mortality rates for a variety of organisms.     

LDJump: Estimating variable recombination rates from population genetic data

As recombination plays an important role in evolution, its estimation and the identification of hotspot positions is of considerable interest. We propose a novel approach for estimating population recombination rates based on genotyping or sequence data that involves a sequential multiscale change point estimator. Our method also permits demography to be taken into account. It uses several summary statistics within a regression model fitted on suitable scenarios. Our proposed method is accurate, computationally fast, and provides a parsimonious solution by ensuring a type I error control against too many changes in the recombination rate. An application to human genome data suggests a good congruence between our estimated and experimentally identified hotspots. Our method is implemented in the R ‐package LDJump, which is freely available at https://github.com/PhHermann/LDJump .     

Estimating incident population distribution from prevalent data

A prevalent sample consists of individuals who have experienced disease incidence but not failure event at the sampling time. We discuss methods for estimating the distribution function of a random vector defined at baseline for an incident disease population when data are collected by prevalent sampling. Prevalent sampling design is often more focused and economical than incident study design for studying the survival distribution of a diseased population, but prevalent samples are biased by design. Subjects with longer survival time are more likely to be included in a prevalent cohort, and other baseline variables of interests that are correlated with survival time are also subject to sampling bias induced by the prevalent sampling scheme. Without recognition of the bias, applying empirical distribution function to estimate the population distribution of baseline variables can lead to serious bias. In this article, nonparametric and semiparametric methods are developed for distribution estimation of baseline variables using prevalent data.     

Estimating population structure from AFLP amplification intensity

In the last decade, amplified fragment length polymorphisms (AFLPs) have become one of the most widely used molecular markers to study the genetic structure of natural populations. Most of the statistical methods available to study the genetic structure of populations using AFLPs consider these markers as dominant and are thus unable to distinguish between individuals being heterozygous or homozygous for the dominant allele. Some attempts have been made to treat AFLPs as codominant markers by using AFLP band intensities to infer the most likely genotype of each individual. These two approaches have some drawbacks, the former discarding potentially valuable information and the latter being sometimes unable to correctly assign genotypes to individuals. In this study, we propose an alternative likelihood‐based approach, which does not attempt at inferring the genotype of each individual, but rather incorporate the uncertainty about genotypes into a Bayesian framework leading to the estimation of population‐specific F_IS and F_ST coefficients. We show with simulations that the accuracy of our method is much higher than one using AFLP as dominant markers and is generally close to what would be obtained by using the same number of Single‐Nucleotide Polymorphism (SNP) markers. The method is applied to a data set of four populations of the common vole (Microtus arvalis) from Grisons in Switzerland, for which we obtained 562 polymorphic AFLP markers. Our approach is very general and has the potential to make AFLP markers as useful as SNP data for nonmodel species.     

Estimating the size of a transient population

Migratory populations often stop over for short periods of time at predictable sites along their migration routes. These staging areas can be heavily used and are potentially critical to the survival of the migrants. This paper presents a method for estimating the number of individuals using such an area and their average residence time. The estimator is based on daily population estimates and records of repeat sightings of identifiable individuals. Its application is illustrated with observations on a population of migrating birds, some of which could be identified from bands that were readable from a distance.     

A new strategy for estimating recombination fractions between dominant markers from an F2 population

Although most high-density linkage maps have been constructed from codominant markers such as single-nucleotide polymorphisms (SNPs) and microsatellites due to their high linkage information, dominant markers can be expected to be even more significant as proteomic technique becomes widely applicable to generate protein polymorphism data from large samples. However, for dominant markers, two possible linkage phases between a pair of markers complicate the estimation of recombination fractions between markers and consequently the construction of linkage maps. The low linkage information of the repulsion phase and high linkage information of coupling phase have led geneticists to construct two separate but related linkage maps. To circumvent this problem, we proposed a new method for estimating the recombination fraction between markers, which greatly improves the accuracy of estimation through distinction between the coupling phase and the repulsion phase of the linked loci. The results obtained from both real and simulated F2 dominant marker data indicate that the recombination fractions estimated by the new method contain a large amount of linkage information for constructing a complete linkage map. In addition, the new method is also applicable to data with mixed types of markers (dominant and codominant) with unknown linkage phase.     

A new statistical method for haplotype reconstruction from population data

Current routine genotyping methods typically do not provide haplotype information, which is essential for many analyses of fine-scale molecular-genetics data. Haplotypes can be obtained, at considerable cost, experimentally or (partially) through genotyping of additional family members. Alternatively, a statistical method can be used to infer phase and to reconstruct haplotypes. We present a new statistical method, applicable to genotype data at linked loci from a population sample, that improves substantially on current algorithms; often, error rates are reduced by > 50%, relative to its nearest competitor. Furthermore, our algorithm performs well in absolute terms, suggesting that reconstructing haplotypes experimentally or by genotyping additional family members may be an inefficient use of resources.     

Estimating population growth rates from stochastic Leslie matrices

Stochastic versions of exponential growth models predict that even when r or λ values calculated from mean vital statistics indicate exponential growth, most of the individual populations may become extinct. Several recent papers have considered this problem and some misunderstanding has arisen due to the difference between mathematical expectation of population size and most likely course of population growth. We replicated Boyce's (1977, 1979) simulations of population growth with age structure and a single randomly varying vital statistic, and reconciled some of these differences. Mean number can be projected using the dominant eigenvalue of the mean Leslie matrix, but the modal number may be considerably lower. We compared several measures of the rate of growth of the geometric mean or median of numbers and conclude that Tuljapurkar's α is an acceptable measure.     

Estimating recombination rates from population genetic data.

We introduce a new method for estimating recombination rates from population genetic data. The method uses a computationally intensive statistical procedure (importance sampling) to calculate the likelihood under a coalescent-based model. Detailed comparisons of the new algorithm with two existing methods (the importance sampling method of Griffiths and Marjoram and the MCMC method of Kuhner and colleagues) show it to be substantially more efficient. (The improvement over the existing importance sampling scheme is typically by four orders of magnitude.) The existing approaches not infrequently led to misleading results on the problems we investigated. We also performed a simulation study to look at the properties of the maximum-likelihood estimator of the recombination rate and its robustness to misspecification of the demographic model.     

A new method for estimating effective population sizes from a single sample of multilocus genotypes

Equations for the effective size ( N_e ) of a population were derived in terms of the frequencies of a pair of offspring taken at random from the population being sibs sharing the same one or two parents. Based on these equations, a novel method (called sibship assignment method) was proposed to infer N_e from the sibship frequencies estimated from a sibship assignment analysis, using the multilocus genotypes of a sample of offspring taken at random from a single cohort in a population. Comparative analyses of extensive simulated data and some empirical data clearly demonstrated that the sibship assignment method is much more accurate [measured by the root mean squared error, RMSE, of 1/(2 N_e )] than other methods such as the heterozygote excess method, the linkage disequilibrium method, and the temporal method. The RMSE of 1/(2 N_e ) from the sibship assignment method is typically a small fraction of that from other methods. The new method is also more general and flexible than other methods. It can be applied to populations with nonoverlapping generations of both diploid and haplodiploid species under random or nonrandom mating, using either codominant or dominant markers. It can also be applied to the estimation of N_e for a subpopulation with immigration. With some modification, it could be applied to monoecious diploid populations with self-fertilization, and to populations with overlapping generations.