Eric Rexstad

Distance software: design and analysis of distance sampling surveys for estimating population size

Summary 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.

SMALL‐MAMMAL DENSITY ESTIMATION: A FIELD COMPARISON OF GRID‐BASED VS. WEB‐BASED DENSITY ESTIMATORS

Statistical models for estimating absolute densities of field populations of animals have been widely used over the last century in both scientific studies and wildlife management programs. To date, two general classes of density estimation models have been developed: models that use data sets from capture–recapture or removal sampling techniques (often derived from trapping grids) from which separate estimates of population size (N) and effective sampling area (Â) are used to calculate density (D = N/Â); and models applicable to sampling regimes using distance-sampling theory (typically transect lines or trapping webs) to estimate detection functions and densities directly from the distance data. However, few studies have evaluated these respective models for accuracy, precision, and bias on known field populations, and no studies have been conducted that compare the two approaches under controlled field conditions. In this study, we evaluated both classes of density estimators on known densities of e...

Design and analysis of line transect surveys for primates

Line transect surveys are widely used for estimating abundance of primate populations. The method relies on a small number of key assumptions, and if these are not met, substantial bias may occur. For a variety of reasons, primate surveys often do not follow what is generally considered to be best practice, either in survey design or in analysis. The design often comprises too few lines (sometimes just 1), subjectively placed or placed along trails, so lacks both randomization and adequate replication. Analysis often involves flawed or inefficient models, and often uses biased estimates of the locations of primate groups relative to the line. We outline the standard method, emphasizing the assumptions underlying the approach. We then consider options for when it is difficult or impossible to meet key assumptions. We explore the performance of these options by simulation, focusing particularly on the analysis of primate group sizes, where many of the variations in survey methods have been developed. We also discuss design issues, field methods, analysis, and potential alternative methodologies for when standard line transect sampling cannot deliver reliable abundance estimates.

Spatial models for distance sampling data : recent developments and future directions

Summary 1. Our understanding of a biological population can be greatly enhanced by modelling their distribution in space and as a function of environmental covariates. Such models can be used to investigate the relationships between distribution and environmental covariates as well as reliably estimate abundances and create maps of animal/ plant distribution. 2. Density surface models consist of a spatial model of the abundance of a biological population which has been corrected for uncertain detection via distance sampling methods. 3. We review recent developments in the field and consider the likely directions of future research before focussing on a popular approach based on generalized additive models. In particular, we consider spatial modelling techniques that may be advantageous to applied ecologists such as quantification of uncertainty in a two-stage model and smoothing in areas with complex boundaries. 4. The methods discussed are available in an R package developed by the authors (dsm) and are largely implemented in the popular Windows software Distance.

Questionable multivariate statistical inference in wildlife habitat and community studies (a reply)

We analyzed a data set constructed from functionally unrelated, easily collected observations (e.g., meat, stock, and liquor prices) around Fort Collins, Colorado, using principal components analysis (PCA), canonical correlation analysis (CC), and discriminant function analysis (DFA). Each produced seemingly significant results and suggested strong relationships between the variables measured. We suggest that multivariate techniques can provide invalid inferences when used with data containing no relationships. We question the use of these techniques in studies of wildlife habitat.

MODELING HETEROGENEITY IN SURVIVAL RATES OF BANDED WATERFOWL

A model is presented that incorporates time-specific recovery rates and heterogeneous survival rates. This model constitutes an extension of the family of models presented by Brownie et al. (1985, Statistical Inference from Band-Recovery Data-A Handbook, 2nd edition, Washington, D.C.: Fish and Wildlife Service, Department of the Interior). Numerical studies of the model under known amounts of heterogeneity investigate issues of identifiability, bias, and precision. Additional work under time-specific variation in survival rates investigates the properties of the estimation procedure. We provide an example and the theoretical foundation for extensions to other models in the Brownie et al. (1985) framework.

Line Transect Sampling of Primates: Can Animal-to-Observer Distance Methods Work?

Line transect sampling is widely used for estimating abundance of primate populations. Researchers commonly use animal-to-observer distances (AODs) in analysis, in preference to perpendicular distances from the line, which is in marked contrast with standard practice for other applications of line transect sampling. We formalize the mathematical shortcomings of approaches based on AODs, and show that they are likely to give strongly biased estimates of density. We review papers that claim good performance for the method, and explore this performance through simulations. These confirm strong bias in estimates of density using AODs. We conclude that AOD methods are conceptually flawed, and that they cannot in general provide valid estimates of density.Line transect sampling is widely used for estimating abundance of primate populations. Researchers commonly use animal-to-observer distances (AODs) in analysis, in preference to perpendicular distances from the line, which is in marked contrast with standard practice for other applications of line transect sampling. We formalize the mathematical shortcomings of approaches based on AODs, and show that they are likely to give strongly biased estimates of density. We review papers that claim good performance for the method, and explore this performance through simulations. These confirm strong bias in estimates of density using AODs. We conclude that AOD methods are conceptually flawed, and that they cannot in general provide valid estimates of density.

LARGE-SCALE MOVEMENTS AND HABITAT CHARACTERISTICS OF KING EIDERS THROUGHOUT THE NONBREEDING PERIOD

Abstract ABSTRACT King Eiders (Somateriaspectabilis) breeding inwestern Canada and Alaska molt wing feathers andspend the winter in remote areas of the Bering Sea,precluding direct observation. To characterizetiming of migration and habitat used by King Eidersduring the nonbreeding period, we collectedlocation data for 60 individuals (27 femalesand 33 males) over three years from satellitetelemetry and utilized oceanographic informationobtained by remote sensing. Male King Eidersdispersed from breeding areas, arrived at wing moltsites, and dispersed from wing molt sites earlierthan females in all years. Males arriving earlierat wing molt sites molted flight feathers at higherlatitudes. Distributions of molt and winterlocations did not differ by sex or among years. Ofthe variables considered for analysis, distance toshore, water depth, and salinity appeared to bestdescribe King Eider habitat throughout thenonbreeding period. King Eiders were located closerto shore, in shallower water with lower salinitythan random locations. During the winter, lower iceconcentrations were also associated with King Eiderlocations. This study provides some of the firstlarge-scale descriptions of King Eider migrationand habitat outside the breeding season.

Seasonal Changes in Body Mass, Composition, and Organs of Northern Red-Backed Voles in Interior Alaska

Northern red-backed voles ( Clethrionomys rutilus ) undergo a pronounced annual cycle in body mass and are heaviest in summer and lightest in winter. We trapped voles throughout 1994 to determine how changes in body composition and organ size contributed to this cycle. Body mass peaked in summer for females and spring for males. Seasonal changes in body mass were primarily due to changes in lean mass. Body mass was 30–50% lower in winter than summer, and water content of lean mass was lowest in winter. Total body fat was low throughout the year but peaked (as with body mass) in spring (males) or early summer (females). Energy reserves in the form of fat depots are apparently most crucial during the breeding season. A low relative ash content in early summer was possibly due to a cation imbalance in the diet. Absolute and relative sizes of different body components contributed to the annual cycle in total body mass. All body components (except brown adipose tissue) declined in absolute mass, dry mass, and percent water during autumn, with skeletomuscular components contributing most to loss of total body mass. Most body components declined in proportion to declines in total body mass. However, liver, reproductive tract, and muscle mass of males declined proportionally more than total body mass; heart, brain, and bone declined proportionally less. Whole body analyses suggest that the annual cycle of body mass in C. rutilus is driven by seasonal changes in optimal body size. Component analyses are consistent with the hypothesis that the primary selective force driving seasonal changes in body components is the enhanced overwinter survival of C. rutilus with relatively small body size.

Evaluation of wolf density estimation from radiotelemetry data

Abstract Density estimation of wolves (Canis lupus) requires a count of individuals and an estimate of the area those individuals inhabit. With radiomarked wolves, the count is straightforward but estimation of the area is more difficult and often given inadequate attention. The population area, based on the mosaic of pack territories, is influenced by sampling intensity similar to the estimation of individual home ranges. If sampling intensity is low, population area will be underestimated and wolf density will be inflated. Using data from studies in Denali National Park and Preserve, Alaska, we investigated these relationships using Monte Carlo simulation to evaluate effects of radiolocation effort and number of marked packs on density estimation. As the number of adjoining pack home ranges increased, fewer relocations were necessary to define a given percentage of population area. We present recommendations for monitoring wolves via radiotelemetry.