The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Comparison of methods to account for autocorrelation in correlation analyses of fish data
Abstract
Autocorrelation in fish recruitment and environmental data can complicate statistical inference in correlation analyses. To address this problem, researchers often either adjust hypothesis testing procedures (e.g., adjust degrees of freedom) to account for autocorrelation or remove the autocorrelation using prewhitening or first-differencing before analysis. However, the effectiveness of methods that adjust hypothesis testing procedures has not yet been fully explored quantitatively. We therefore compared several adjustment methods via Monte Carlo simulation and found that a modified version of these methods kept Type I error rates near . In contrast, methods that remove autocorrelation control Type I error rates well but may in some circumstances increase Type II error rates (probability of failing to detect some environmental effect) and hence reduce statistical power, in comparison with adjusting the test procedure. Specifically, our Monte Carlo simulations show that prewhitening and especially first-differencing decrease power in the common situations where low-frequency (slowly changing) processes are important sources of covariation in fish recruitment or in environmental variables. Conversely, removing autocorrelation can increase power when low-frequency processes account for only some of the covariation. We therefore recommend that researchers carefully consider the importance of different time scales of variability when analyzing autocorrelated data.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Time Series Analysis: Forecasting and Control
Geurts, M., Box, G. E. P., & Jenkins, G. M. (1977) · Journal of Marketing Research · doi:10.2307/3150485 · Cited by 19,186
Pseudoreplication and the Design of Ecological Field Experiments
Hurlbert, S. H. (1984) · Ecological Monographs · doi:10.2307/1942661 · Cited by 8,377
Spectral Analysis and its Applications.
Freiberger, W. F., Jenkins, G. M., & Watts, D. G. (1969) · Journal of the American Statistical Association · doi:10.2307/2286100 · Cited by 4,981
The Analysis of Time Series: An Introduction
Dickey, D. A., & Chatfield, C. (1991) · Technometrics · doi:10.2307/1268794 · Cited by 3,525
On the Theoretical Specification and Sampling Properties of Autocorrelated Time-Series
Bartlett, M. S. (1946) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.2307/2983611 · Cited by 908
BIAS IN THE ESTIMATION OF AUTOCORRELATIONS
MARRIOTT, F. H. C., & POPE, J. A. (1954) · Biometrika · doi:10.1093/biomet/41.3-4.390 · Cited by 364
The “Effective” Number of Independent Observations in an Autocorrelated Time Series
Bayley, G. V., & Hammersley, J. M. (1946) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.2307/2983560 · Cited by 252
Money, income, and sunspots: Measuring economic relationships and the effects of differencing
Plosser, C. I., & Schwert, G. W. (1978) · Journal of Monetary Economics · doi:10.1016/0304-3932(78)90021-1 · Cited by 219
Analysis of Short Time Series: Correcting for Autocorrelation
Bence, J. R. (1995) · Ecology · doi:10.2307/1941218 · Cited by 199
The Analysis of Time Series: An Introduction (4th ed.)
Dickey, D. A. (1991) · Technometrics · doi:10.1080/00401706.1991.10484848 · Cited by 167
Cited by
Population diversity and the portfolio effect in an exploited species
Schindler, D. E., Hilborn, R., Chasco, B., Boatright, C. P., Quinn, T. P., Rogers, L. A., & Webster, M. S. (2010) · Nature · doi:10.1038/nature09060 · Cited by 1,605
Reorganization of North Atlantic Marine Copepod Biodiversity and Climate
Beaugrand, G., Reid, P. C., Ibañez, F., Lindley, J. A., & Edwards, M. (2002) · Science · doi:10.1126/science.1071329 · Cited by 1,213
Slow adaptation in the face of rapid warming leads to collapse of the Gulf of Maine cod fishery
Pershing, A. J., Alexander, M. A., Hernandez, C. M., Kerr, L. A., Le Bris, A., Mills, K. E., Nye, J. A., Record, N. R., Scannell, H. A., Scott, J. D., et al. (2015) · Science · doi:10.1126/science.aac9819 · Cited by 954
META-ANALYSIS OF COD–SHRIMP INTERACTIONS REVEALS TOP-DOWN CONTROL IN OCEANIC FOOD WEBS
Worm, B., & Myers, R. A. (2003) · Ecology · doi:10.1890/0012-9658(2003)084[0162:maocsi]2.0.co;2 · Cited by 482
Global climate change amplifies the entry of tropical species into the eastern Mediterranean Sea
Raitsos, D. E., Beaugrand, G., Georgopoulos, D., Zenetos, A., Pancucci-Papadopoulou, A. M., Theocharis, A., & Papathanassiou, E. (2010) · Limnology and Oceanography · doi:10.4319/lo.2010.55.4.1478 · Cited by 279
Metadata
- Authors
- Pyper, B. J., & Peterman, R. M.
- Venue
- Canadian Journal of Fisheries and Aquatic Sciences
- Publisher
- Canadian Science Publishing
- Published
- 1998
- Volume
- 55
- Issue
- 9
- Pages
- 2127-2140
- ISSN
- 0706-652X (ISSN-L)
- 1205-7533
- OpenAlex
- W2073289890
- Cited by
- 835
- References
- 16
- FWCI
- 20.01
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Comparison of methods to account for autocorrelation in correlation analyses of fish data. https://app.cochat.ai/papers/10.1139/cjfas-55-9-2127
- The paper
- Pyper, B. J., & Peterman, R. M. (1998). Comparison of methods to account for autocorrelation in correlation analyses of fish data. Canadian Journal of Fisheries and Aquatic Sciences. https://doi.org/10.1139/cjfas-55-9-2127
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
