Explore the vast range of titles available.

Florida Key deer (Odocoileus virginianus clavium) are an endangered subspecies of white-tailed deer endemic to the Lower Florida Keys. The New World screwworm (Cochliomyia hominivorax) infestation in July 2016 and Hurricane Irma on 10 September 2017 both caused the Key deer population to decline. Our objective was to estimate current Key deer population abundance using traditional distance sampling and a mark-resight methodology applicable for citizen scientist participation. For mark-resight efforts, deer were marked with hand sprayers using water-based livestock dye on Big Pine (BPK) and No Name keys (NNK). Biologists conducted road surveys between 9–13 March 2020 on BPK and NNK and collected data for mark retention, mark-resight, and distance calculations concurrently. Our mark-resight estimate (n = 748) was nearly 300 deer lower than the traditional distance estimate likely because of distance sampling's sensitivity to increased deer visibility along survey routes. Compared to historic data, our mark-resight population estimate indicated increased deer abundance compared to post-Hurricane Irma estimates (n = 573), but slightly below post-screwworm outbreak estimates (n = 860). Based on mark-retention data, we recommend all resight surveys be completed within 5 days of the first mark placement for the most dependable mark detection. We recommend our mark-resight method be used in future Key deer surveys as it is simple, efficient, and can bereliably completed with the assistance of volunteers therefore allowing for more regular monitoring.

1. Distance sampling is a widely used technique for estimating the size or density of biological populations. Many distance sampling designs and most analyses use the software Distance. 2. We briefly review distance sampling and its assumptions, outline the history, structure and capabilities of Distance, and provide hints on its use. 3. Good survey design is a crucial prerequisite for obtaining reliable results. Distance has a survey design engine, with a built-in geographic information system, that allows properties of different proposed designs to be examined via simulation, and survey plans to be generated. 4. A first step in analysis of distance sampling data is modelling the probability of detection. Distance contains three increasingly sophisticated analysis engines for this: conventional distance sampling, which models detection probability as a function of distance from the transect and assumes all objects at zero distance are detected; multiple-covariate distance sampling, which allows covariates in addition to distance; and mark-recapture distance sampling, which relaxes the assumption of certain detection at zero distance. 5. All three engines allow estimation of density or abundance, stratified if required, with associated measures of precision calculated either analytically or via the bootstrap. 6. Advanced analysis topics covered include the use of multipliers to allow analysis of indirect surveys (such as dung or nest surveys), the density surface modelling analysis engine for spatial and habitat modelling, and information about accessing the analysis engines directly from other software. 7.Synthesis and applications. Distance sampling is a key method for producing abundance and density estimates in challenging field conditions. The theory underlying the methods continues to expand to cope with realistic estimation situations. In step with theoretical developments, state-of-the-art software that implements these methods is described that makes the methods accessible to practising ecologists.

Various approaches have been developed to evaluate the consequences of spatial structure on evolution in subdivided populations. This book is both a review and new synthesis of several of these approaches, based on the theory of spatial genetic structure. François Rousset examines Sewall Wright's methods of analysis based on F-statistics, effective size, and diffusion approximation; coalescent arguments; William Hamilton's inclusive fitness theory; and approaches rooted in game theory and adaptive dynamics. Setting these in a framework that reveals their common features, he demonstrates how efficient tools developed within one approach can be applied to the others. Rousset not only revisits classical models but also presents new analyses of more recent topics, such as effective size in metapopulations. The book, most of which does not require fluency in advanced mathematics, includes a self-contained exposition of less easily accessible results. It is intended for advanced graduate students and researchers in evolutionary ecology and population genetics, and will also interest applied mathematicians working in probability theory as well as statisticians.

Microsatellite null alleles are commonly encountered in population genetics studies, yet little is known about their impact on the estimation of population differentiation. Computer simulations based on the coalescent were used to investigate the evolutionary dynamics of null alleles, their impact on FST and genetic distances, and the efficiency of estimators of null allele frequency. Further, we explored how the existing method for correcting genotype data for null alleles performed in estimating FST and genetic distances, and we compared this method with a new method proposed here (for FST only). Null alleles were likely to be encountered in populations with a large effective size, with an unusually high mutation rate in the flanking regions, and that have diverged from the population from which the cloned allele state was drawn and the primers designed. When populations were significantly differentiated, FST and genetic distances were overestimated in the presence of null alleles. Frequency of null alleles was estimated precisely with the algorithm presented in Dempster et al. (1977). The conventional method for correcting genotype data for null alleles did not provide an accurate estimate of FST and genetic distances. However, the use of the genetic distance of Cavalli-Sforza and Edwards (1967) corrected by the conventional method gave better estimates than those obtained without correction. FST estimation from corrected genotype frequencies performed well when restricted to visible allele sizes. Both the proposed method and the traditional correction method have been implemented in a program that is available free of charge at http://www.montpellier.inra.fr/URLB/. We used 2 published microsatellite data sets based on original and redesigned pairs of primers to empirically confirm our simulation results. [PUBLICATION ABSTRACT]

The empirical literature on economic growth and development has moved from the study of proximate determinants to the analysis of ever deeper, more fundamental factors, rooted in long-term history. A growing body of new empirical work focuses on the measurement and estimation of the effects of historical variables on contemporary income by explicitly taking into account the ancestral composition of current populations. The evidence suggests that economic development is affected by traits that have been transmitted across generations over the very long run. This article surveys this new literature and provides a framework to discuss different channels through which intergenerationally transmitted characteristics may impact economic development, biologically (via genetic or epigenetic transmission) and culturally (via behavioral or symbolic transmission). An important issue is whether historically transmitted traits have affected development through their direct impact on productivity, or have operated indirectly as barriers to the diffusion of productivityenhancing innovations across populations.

Imperfect detection during animal surveys biases estimates of abundance and can lead to improper conclusions regarding distribution and population trends. Farnsworth et al. (2005) developed a combined distance-sampling and time-removal model for point-transect surveys that addresses both availability (the probability that an animal is available for detection; e.g., that a bird sings) and perceptibility (the probability that an observer detects an animal, given that it is available for detection). We developed a hierarchical extension of the combined model that provides an integrated analysis framework for a collection of survey points at which both distance from the observer and time of initial detection are recorded. Implemented in a Bayesian framework, this extension facilitates evaluating covariates on abundance and detection probability, incorporating excess zero counts (i.e. zero-inflation), accounting for spatial autocorrelation, and estimating population density. Species-specific characteristics, such as behavioral displays and territorial dispersion, may lead to different patterns of availability and perceptibility, which may, in turn, influence the performance of such hierarchical models. Therefore, we first test our proposed model using simulated data under different scenarios of availability and perceptibility. We then illustrate its performance with empirical point-transect data for a songbird that consistently produces loud, frequent, primarily auditory signals, the Golden-crowned Sparrow (Zonotrichia atricapilla); and for 2 ptarmigan species (Lagopus spp.) that produce more intermittent, subtle, and primarily visual cues. Data were collected by multiple observers along point transects across a broad landscape in southwest Alaska, so we evaluated point-level covariates on perceptibility (observer and habitat), availability (date within season and time of day), and abundance (habitat, elevation, and slope), and included a nested point-within-transect and park-level effect. Our results suggest that this model can provide insight into the detection process during avian surveys and reduce bias in estimates of relative abundance but is best applied to surveys of species with greater availability (e.g., breeding songbirds).

1. Efficient and reliable methods for estimating animal density are essential to wildlife conservation and management. Camera trapping is an increasingly popular tool in this area of wildlife research, with further potential arising from technological improvements, such as video-recording functions that allow for behavioural observation of animals. This information may be useful in the estimation of animal density, even without individual recognition. Although several models applicable to species lacking individual markings (i.e. unmarked populations) have been developed, a methodology incorporating behavioural information from videos has not yet been established. 2. We developed a likelihood-based model: the random encounter and staying time (REST) model. It is an extension of the random encounter model by Rowcliffe et al. (J Appl Ecol 45:1228, 2008). The REST model describes the relationship among staying time, trapping rate, and density, which is estimable using a frequentist or Bayesian approach. We tested the reliability and feasibility of the REST model using Monte Carlo simulations. We also applied the approach in the African rainforest and compared the results with those of a line-transect survey. 3. The simulations showed that the REST model provided unbiased estimates of animal density. Even when animal movement speeds varied among individuals, and when animals travelled in pairs, the model provided unbiased density estimates. However, the REST model was vulnerable to unsynchronized activity patterns among individuals. Moreover, it is necessary to use a camera model with a fast and reliable infrared sensor and to set the camera trap's parameters appropriately (i.e. video length, delay period). The field survey showed that the staying time of two ungulate species in the African rainforest exhibited good fit with a temporal parametric distribution, and the REST model provided density estimates consistent with those of a line-transect survey. 4. Synthesis and applications. The random encounter and staying time model provides better efficiency and higher feasibility than the random encounter model in estimating animal density without individual recognition. Careful application of the random encounter and staying time model provides the potential to estimate density of many ground-dwelling vertebrates lacking individually recognizable markings, and thus should be an effective method for population monitoring.

Indices to population size have come under increasing criticism in recent years, on the grounds that indices might not faithfully represent the entire population. Most criticisms involve surveys of birds, particularly those based on point counts, which is my focus here. A variety of quantitative methods have been developed to reduce the bias of point counts, such as distance sampling, multiple-observer surveys, and time-of-detection methods. I argue that these developments are valuable, in that they enhance understanding of the detection process, but that their practical application may well be limited, likely to intensive studies focusing on a small number of species. These quantitative methods are not generally applicable to extensive, multiple-species surveys. Although criticism of the thoughtless use of indices is welcome, their wholesale rejection is not.

Species conservation and management require reliable information about animal distribution and population size. Better management actions within a species’ range can be achieved by identifying the location and timing of population changes. In the Greater Mahale Ecosystem (GME), western Tanzania, deforestation due to the expansion of human settlements and agriculture, annual burning, and logging are known threats to wildlife. For one of the most charismatic species, the endangered eastern chimpanzee (Pan troglodytes schweinfurthii), approximately 75% of the individuals are distributed outside national park boundaries, requiring monitoring and protection efforts over a vast landscape of various protection statuses. These efforts are especially challenging when we lack data on trends in density and population size. To predict spatio-temporal chimpanzee density and abundance across the GME, we used density surface modeling, fitting a generalized additive model to a 10-year time-series data set of nest counts based on line-transect surveys. The chimpanzee population declined at an annual rate of 2.41%, including declines of 1.72% in riparian forests (from this point forward, forests), 2.05% in miombo woodlands (from this point forward, woodlands) and 3.45% in nonforests. These population declines were accompanied by ecosystem-wide declines in vegetation types of 1.36% and 0.32% per year for forests and woodlands, respectively; we estimated an annual increase of 1.35% for nonforests. Our model predicted the highest chimpanzee density in forests (0.86 chimpanzees/km², 95% confidence intervals (CIs) 0.60–1.23; as of 2020), followed by woodlands (0.19, 95% CI 0.12–0.30) and nonforests (0.18, 95% CI 0.10–1.33). Although forests represent only 6% of the landscape, they support nearly one-quarter of the chimpanzee population (769 chimpanzees, 95% CI 536–1103). Woodlands dominate the landscape (71%) and therefore support more than a half of the chimpanzee population (2294; 95% CI 1420–3707). The remaining quarter of the landscape is represented by nonforests andsupports another quarter of the chimpanzee population (750; 95% CI 408–1381). Given the pressures on the remaining suitable habitat in Tanzania, and the need of chimpanzees to access both forest and woodland vegetation to survive, we urge future management actions to increase resources and expand the efforts to protect critical forest and woodland habitat and promote strategies and policies that more effectively prevent irreversible losses. We suggest that regular monitoring programs implement a systematic random design to effectively inform and allocate conservation actions and facilitate interannual comparisons for trend monitoring, measuring conservation success, and guiding adaptive management.

Population density is the most basic ecological parameter for understanding population dynamics and biological conservation. Distance-based methods (or plotless methods) are considered as a more efficient but less robust approach than quadrat-based counting methods in estimating plant population density. The low robustness of distance-based methods mainly arises from the oversimplistic assumption of completely spatially random (CSR) distribution of a population in the conventional distance-based methods for estimating density of non-CSR populations in natural communities. In this study we derived two methods to improve on density estimation for plant populations of non-CSR distribution. The first method modified an existing composite estimator to correct for the long-recognized bias associated with that estimator. The second method was derived from the negative binomial distribution (NBD) that directly deals with aggregation in the distribution of a species. The performance of these estimators was tested and compared against various distance-based estimators by both simulation and empirical data of three large-scale stem-mapped forests. Results showed that the NBD point-to-tree distance estimator has the best and most consistent performance across populations with vastly different spatial distributions. This estimator offers a simple, efficient and robust method for estimating density for empirical populations of plant species