Access the full text.
Sign up today, get DeepDyve free for 14 days.
A. Raftery, G. Givens, J. Zeh (1995)
Inference from a Deterministic Population Dynamics Model for Bowhead WhalesJournal of the American Statistical Association, 90
C. Litton (1984)
Theory of Probability (3rd Edition)Journal of the Operational Research Society, 35
R. Steidl, J. Hayes, E. Schauber (1997)
Statistical power analysis in wildlife researchJournal of Wildlife Management, 61
Bayes Bayes (1763)
An essay towards solving a problem in the doctrine of chances.Philosophical Transactions, Royal Society of London, 53
Robert Kass, A. Raftery (1995)
Bayes factors
L. Wolfson, J. Kadane, M. Small (1996)
Bayesian Environmental Policy Decisions: Two Case StudiesEcological Applications, 6
C. Walters, D. Ludwig (1994)
Calculation of Bayes Posterior Probability Distributions for Key Population ParametersCanadian Journal of Fisheries and Aquatic Sciences, 51
G. Givens, A. Raftery, J. Zeh (1993)
Benefits of a Bayesian Approach for Synthesizing Multiple Sources of Evidence and Uncertainty Linked
A. Punt (1999)
On assessment of the Bering-Chukchi-Beaufort Seas stock of bowhead whales (Balaena mysticetus) using a Bayesian approachJ. Cetacean Res. Manage.
D. Ludwig (1996)
Uncertainty and the Assessment of Extinction ProbabilitiesEcological Applications, 6
B. Taylor, T. Gerrodette (1993)
The Uses of Statistical Power in Conservation Biology: The Vaquita and Northern Spotted OwlConservation Biology, 7
S. Innes (2000)
Marine Mammal Survey and Assessment Methods. G. W. Garner , S. C. Amstrup , J. L. Laake , B. F. J. Manly , L. L. McDonald , D. G. RobertsonThe Quarterly Review of Biology, 75
J. Hayes, R. Steidl (1997)
Statistical Power Analysis and Amphibian Population TrendsConservation Biology, 11
A. Birnbaum (1962)
On the Foundations of Statistical InferenceJournal of the American Statistical Association, 57
Wade Wade (2001)
A Bayesian stock assessment of the eastern Pacific gray whale using abundance and harvest data from 1967 to 1996.Journal of Cetacean Research and Management Special Volume:
V. Vieland, S. Hodge (1998)
Statistical Evidence: A Likelihood ParadigmAmerican Journal of Human Genetics, 63
David Poole, G. Givens, A. Raftery (1999)
A proposed stock assessment method and its application to bowhead whales, Balaena mysticetus
R. Peterman (1990)
Statistical Power Analysis can Improve Fisheries Research and ManagementCanadian Journal of Fisheries and Aquatic Sciences, 47
Punt Punt, Hilborn Hilborn (1997)
Fisheries stock assessment and decision analysis: the Bayesian approach.Reviews in Fish Biology and Fisheries, 7
B. Taylor, P. Wade, R. Stehn, J. Cochrane (1996)
A Bayesian Approach to Classification Criteria for Spectacled EidersEcological Applications, 6
D. Lindley (1986)
[Why Isn't Everyone a Bayesian?]: CommentThe American Statistician, 40
M. Mcallister, E. Pikitch, A. Punt, R. Hilborn (1994)
A Bayesian Approach to Stock Assessment and Harvest Decisions Using the Sampling/Importance Resampling AlgorithmCanadian Journal of Fisheries and Aquatic Sciences, 51
(1999)
A comparison of statistical methods for fitting population models to data. Pages 249-270 in
M. Tanner (1998)
Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions, 3rd EditionBiometrics, 54
(1994)
Abundance and population dynamics of two eastern Pacific dolphins, Stenella attenuata and Stenella longirostris orientalis
M. Omlin, P. Reichert (1999)
A comparison of techniques for the estimation of model prediction uncertaintyEcological Modelling, 115
P. Shaughnessy, J. Testa, R. Warneke (1995)
Abundance of Australian fur seal pups, Arctocephalus pusillus doriferus, at Seal Rocks, Victoria, in 1991-92 from Petersen and Bayesian estimatorsWildlife Research, 22
K. Reckhow (1990)
Bayesian inference in non-replicated ecological studiesEcology, 71
Lindley Lindley (1986)
Comment.The American Statistician, 40
M. Mcallister, J. Ianelli (1997)
Bayesian stock assessment using catch-age data and the sampling - importance resampling algorithmCanadian Journal of Fisheries and Aquatic Sciences, 54
V. Restrepo, J. Hoenig, J. Powers, S. Turner (1992)
A simple simulation approach to risk and cost analysis, with applications to swordfish and cod fisheries
J. Berger (1988)
Statistical Decision Theory and Bayesian Analysis
Punt Punt, Butterworth Butterworth (1997)
Assessments of the Bering‐Chukchi‐Beaufort Seas stock of bowhead whales ( Balaena mysticetus ) using maximum likelihood and Bayesian methods.Report of the International Whaling Commission, 47
(1986)
Memoir on the probability of the cause of events
Smith Smith, Gelfand Gelfand (1992)
Bayesian statistics without tears: a sampling‐resampling perspective.The American Statistician, 46
B. Efron (1986)
Why Isn't Everyone a Bayesian?The American Statistician, 40
Adrian Smith, A. Gelfand (1992)
Bayesian statistics without tears: A sampling-resampling perspectiveQuality Engineering, 37
J. Berger, D. Berry (1988)
Statistical Analysis and the Illusion of Objectivity, 76
F. Guess (1990)
Bayesian Statistics: Principles, Models, and ApplicationsTechnometrics, 32
Lawrence Joseph, P. Lee (1989)
Bayesian Statistics: An IntroductionThe American Statistician, 47
A. Ellison (1996)
AN INTRODUCTION TO BAYESIAN INFERENCE FOR ECOLOGICAL RESEARCH AND ENVIRONMENTAL
(1992)
A Bayesian approach to management advice when stock - recruitment parameters are uncertain
C. Geyer (1992)
Practical Markov Chain Monte CarloStatistical Science, 7
H. Jeffreys (1922)
The Theory of ProbabilityNature, 109
W. Gazey, M. Staley (1986)
Population Estimation from Mark‐Recapture Experiments Using a Sequential Bayes AlgorithmEcology, 67
C. Howson, P. Urbach (1989)
Scientific Reasoning: The Bayesian Approach
D. Rubin, D. Rubin (1988)
Using the SIR algorithm to simulate posterior distributions
M. Pascual, P. Kareiva (1996)
Predicting the outcome of competition using experimental data : Maximum likelihood and bayesian approachesEcology, 77
G. Givens, J. Zeh, A. Raftery (1995)
Assessment of the Bering-Chukchi-Beaufort Seas stock of bowhead whales using the BALEEN II model in a Bayesian synthesis framework
Ellison Ellison (1996)
An introduction to Bayesian inference for ecological research and environmental decision making.Ecological Applications, 6
Givens Givens, Raftery Raftery, Zeh Zeh (1993)
Benefits of a Bayesian approach for synthesizing multiple sources of evidence and uncertainty linked by a deterministic model.Report of the International Whaling Commission, 43
B. Dennis (1996)
Discussion: Should Ecologists Become Bayesians?Ecological Applications, 6
H. Traut (1991)
Bayesian reasoning in scienceNature, 352
B. Parker (1983)
Quantitative Applications in the Social SciencesJournal of the Operational Research Society, 34
Abstract: Bayesian statistical inference provides an alternate way to analyze data that is likely to be more appropriate to conservation biology problems than traditional statistical methods. I contrast Bayesian techniques with traditional hypothesis‐testing techniques using examples applicable to conservation. I use a trend analysis of two hypothetical populations to illustrate how easy it is to understand Bayesian results, which are given in terms of probability. Bayesian trend analysis indicated that the two populations had very different chances of declining at biologically important rates. For example, the probability that the first population was declining faster than 5% per year was 0.00, compared to a probability of 0.86 for the second population. The Bayesian results appropriately identified which population was of greater conservation concern. The Bayesian results contrast with those obtained with traditional hypothesis testing. Hypothesis testing indicated that the first population, which the Bayesian analysis indicated had no chance of declining at >5% per year, was declining significantly because it was declining at a slow rate and the abundance estimates were precise. Despite the high probability that the second population was experiencing a serious decline, hypothesis testing failed to reject the null hypothesis of no decline because the abundance estimates were imprecise. Finally, I extended the trend analysis to illustrate Bayesian decision theory, which allows for choice between more than two decisions and allows explicit specification of the consequences of various errors. The Bayesian results again differed from the traditional results: the decision analysis led to the conclusion that the first population was declining slowly and the second population was declining rapidly.
Conservation Biology – Wiley
Published: Oct 18, 2000
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.