L
Len Thomas
Researcher at University of St Andrews
Publications - 212
Citations - 15574
Len Thomas is an academic researcher from University of St Andrews. The author has contributed to research in topics: Population & Distance sampling. The author has an hindex of 51, co-authored 199 publications receiving 13485 citations. Previous affiliations of Len Thomas include University of British Columbia.
Papers
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Book
Introduction to Distance Sampling: Estimating Abundance of Biological Populations
Stephen T. Buckland,David E. Anderson,Kenneth P. Burnham,Jeffrey L. Laake,David L. Borchers,Len Thomas +5 more
TL;DR: In this article, the authors introduce the concepts of line transects, point tranchs, and related methods for study design and field methods, as well as illustrative examples.
Journal ArticleDOI
Distance software: design and analysis of distance sampling surveys for estimating population size.
Len Thomas,Stephen T. Buckland,Eric A. Rexstad,Jeffrey L. Laake,Samantha Strindberg,Sharon L. Hedley,Jon Bishop,Tiago Reis Marques,Kenneth P. Burnham +8 more
TL;DR: Advanced analysis topics covered include the use of multipliers to allow analysis of indirect surveys, the density surface modelling analysis engine for spatial and habitat modelling, and information about accessing the analysis engines directly from other software.
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State-space models of individual animal movement.
Toby A. Patterson,Toby A. Patterson,Len Thomas,Chris Wilcox,Otso Ovaskainen,Jason Matthiopoulos +5 more
TL;DR: The statistical robustness and predictive ability of state-space models make them the most promising avenue towards a new type of movement ecology that fuses insights from the study of animal behaviour, biogeography and spatial population dynamics.
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Estimating animal population density using passive acoustics
Tiago Reis Marques,Tiago Reis Marques,Len Thomas,Stephen W. Martin,David K. Mellinger,Jessica Ward,David Moretti,Danielle Harris,Peter L. Tyack +8 more
TL;DR: In this paper, the authors present an overview of animal density estimation using passive acoustic data, a relatively new and fast-developing field, and provide a framework for acoustics-based density estimation, illustrated with real-world case studies.
Journal ArticleDOI
Retrospective Power Analysis
TL;DR: The pdf contains the article; the ASCII file contains SAS code to calculate power and confidence limits for simple linear regression, as detailed in the article appendix.