Stephen T. Buckland

Analysis of wildlife radio-tracking data

Preliminaries. Design of Radio-Tracking Studies. Effects of Tagging on the Animal. Estimating Animal Locations. Designing and Testing Triangulation Systems. Simple Movements. Home Range Estimation. Habitat Analysis. Survival Rate Estimation. Population Estimation. Data Analysis System. Appendices. Each chapter includes references. Index.

Model selection: An integral part of inference

We argue that model selection uncertainty should be fully incorporated into statistical inference whenever estimation is sensitive to model choice and that choice is made with reference to the data. We consider different philosophies for achieving this goal and suggest strategies for data analysis. We illustrate our methods through three examples. The first is a Poisson regression of bird counts in which a choice is to be made between inclusion of one or both of two covariates. The second is a line transect data set for which different models yield substantially different estimates of abundance. The third is a simulated example in which truth is known.

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.

Design and analysis methods for fish survival experiments based on release-recapture

Design and analysis methods for fish survival experiments based on release-recapture / , Design and analysis methods for fish survival experiments based on release-recapture / , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی

Long-term datasets in biodiversity research and monitoring: assessing change in ecological communities through time

The growing need for baseline data against which efforts to reduce the rate of biodiversity loss can be judged highlights the importance of long-term datasets, some of which are as old as ecology itself. We review methods of evaluating change in biodiversity at the community level using these datasets, and contrast whole-community approaches with those that combine information from different species and habitats. As all communities experience temporal turnover, one of the biggest challenges is distinguishing change that can be attributed to external factors, such as anthropogenic activities, from underlying natural change. We also discuss methodological issues, such as false alerts and modifications in design, of which users of these data sets need to be aware.

ANALYSIS OF POPULATION TRENDS FOR FARMLAND BIRDS USING GENERALIZED ADDITIVE MODELS

Knowledge of the direction, magnitude, and timing of changes in bird population abundance is essential to enable species of priority conservation concern to be identified, and reasons for the population changes to be understood. We give a brief review of previous techniques for the analysis of large-scale survey data and present a new approach based on generalized additive models (GAMs). GAMs are used to model trend as a smooth, nonlinear function of time, and they provide a framework for testing the statistical significance of changes in abundance. In addition, the second derivatives of the modeled trend curve may be used to identify key years in which the direction of the population trajectory was seen to change significantly. The inclusion of covariates into models for population abundance is also discussed and illustrated, and tests for the significance of covariate terms are given. We apply the methods to data from the Common Birds Census of the British Trust for Ornithology for 13 species of farmland birds. Seven of the species are shown to have experienced statistically significant declines since the mid-1960s. Two species exhibited a significant increase. The population trajectories of all but three species turned downward in the 1970s, although in most cases the 1980s brought either some recovery or a decrease in the rate of decline. The majority of populations have remained relatively stable in the 1990s. The results are comparable with those from other analysis techniques, although the new approach is shown to have advantages in generality and precision. We suggest extensions of the methods and make recommendations for the design of future surveys.

Monitoring change in biodiversity through composite indices

The need to monitor trends in biodiversity raises many technical issues. What are the features of a good biodiversity index? How should trends in abundance of individual species be estimated? How should composite indices, possibly spanning very diverse taxa, be formed? At what spatial scale should composite indices be applied? How might change-points—points at which the underlying trend changes—be identified? We address some of the technical issues underlying composite indices, including survey design, weighting of the constituent indices, identification of change-points and estimation of spatially varying time trends. We suggest some criteria that biodiversity measures for use in monitoring surveys should satisfy, and we discuss the problems of implementing rigorous methods. We illustrate the properties of different composite indices using UK farmland bird data. We conclude that no single index can capture all aspects of biodiversity change, but that a modified Shannon index and the geometric mean of relative abundance have useful properties.

Empirical models for the spatial distribution of wildlife

Empirical models for spatial distribution of wildlife, given data from a complete census or a random sample of sites, are reviewed briefly. 2. The use of covariates, recorded at different resolutions, for modelling spatial distribution is explored. Presentation of model predictions in map form is discussed. A framework of models for change in spatial distribution, given data from successive surveys, is developed. Methods for quantifying and presenting precision and bias are described. The methods are illustrated for two bird species (green woodpecker Picus viridis and redstart Phoenicurus phoenicurus) and for red deer Cervus elaphus, using data from north-east Scotland

POINT-TRANSECT SURVEYS FOR SONGBIRDS: ROBUST METHODOLOGIES

Abstract Point-transect sampling is widely used for monitoring trends in abundance of songbirds. It is conceptualized as a “snapshot” method in which birds are “frozen” at a single location. With conventional methods, an observer records birds detected from a point for several minutes, during which birds may move around. This generates upward bias in the density estimate. I compared this conventional approach with two other approaches: in one, the observer records locations of detected birds at a snapshot moment; in the other, distances to detected cues (songbursts), rather than birds, are recorded. I implemented all three approaches, together with line-transect sampling and territory mapping in a survey of four bird species. The conventional method gave a biased estimate of density for one species. The snapshot method was found to be the most efficient of the point-sampling methods. Line-transect sampling proved more efficient than the point-sampling methods for all four species. This is likely to be generally true, provided that terrain and habitat allow easy use of a design with random transect lines. I concluded that the snapshot method is more appropriate than the conventional timed-count method for surveying songbirds. Although precision was rather poor with the cue-based method (partly because too few resources were devoted to cue rate estimation), it may be particularly useful for some single-species surveys. In addition, it is the only valid method for estimating abundance from surveys in which acoustic equipment is used to detect birds. Muestreos en Transectos Puntuales para Aves Canoras: Metodologías Robustas

IMPROVING ESTIMATES OF BIRD DENSITY USING MULTIPLE- COVARIATE DISTANCE SAMPLING

Abstract Inferences based on counts adjusted for detectability represent a marked improvement over unadjusted counts, which provide no information about true population density and rely on untestable and unrealistic assumptions about constant detectability for inferring differences in density over time or space. Distance sampling is a widely used method to estimate detectability and therefore density. In the standard method, we model the probability of detecting a bird as a function of distance alone. Here, we describe methods that allow us to model probability of detection as a function of additional covariates—an approach available in DISTANCE, version 5.0 (Thomas et al. 2005) but still not widely applied. The main use of these methods is to increase the reliability of density estimates made on subsets of the whole data (e.g., estimates for different habitats, treatments, periods, or species), to increase precision of density estimates or to allow inferences about the covariates themselves. We present a case study of the use of multiple covariates in an analysis of a point-transect survey of Hawaii Amakihi (Hemignathus virens). Amélioration des estimations de densité d’oiseaux par l’utilisation de l’échantillonnage par la distance avec covariables multiples