Detecting Unusual Temporal Patterns in Fisheries Time Series Data

Tyler Wagner, Stephen R. Midway, Tiffany Vidal, Brian J. Irwin, James R. Jackson

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Abstract: Long-term sampling of fisheries data is an important source of information for making inferences about the temporal dynamics of populations that support ecologically and economically important fisheries. For example, time series of catch-per-effort data are often examined for the presence of long-term trends. However, it is also of interest to know whether certain sampled locations are exhibiting temporal patterns that deviate from the overall pattern exhibited across all sampled locations. Patterns at these “unusual” sites may be the result of site-specific abiotic (e.g., habitat) or biotic (e.g., the presence of an invasive species) factors that cause these sites to respond differently to natural or anthropogenic drivers of population dynamics or to management actions. We present a Bayesian model selection approach that allows for detection of unique sites—locations that display temporal patterns with documentable inconsistencies relative to the overall global average temporal pattern. We applied this modeling approach to long-term gill-net data collected from a fixed-site, standardized sampling program for Yellow Perch Perca flavescens in Oneida Lake, New York, but the approach is also relevant to shorter time series data. We used this approach to identify six sites with distinct temporal patterns that differed from the lakewide trend, and we describe the magnitude of the difference between these patterns and the lakewide average trend. Detection of unique sites may be informative for management decisions related to prioritizing rehabilitation or restoration efforts, stocking, or determining fishable areas and for further understanding changes in ecosystem dynamics. Received October 13, 2015; accepted February 1, 2016Published online June 22, 2016

Original languageEnglish (US)
Pages (from-to)786-794
Number of pages9
JournalTransactions of the American Fisheries Society
Volume145
Issue number4
DOIs
StatePublished - Jul 3 2016

Fingerprint

Perca flavescens
time series analysis
population dynamics
fishery
fisheries
time series
gillnets
information sources
invasive species
sampling
lakes
ecosystem dynamics
ecosystems
habitats
lake
habitat
modeling
detection
trend

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Aquatic Science

Cite this

Wagner, Tyler ; Midway, Stephen R. ; Vidal, Tiffany ; Irwin, Brian J. ; Jackson, James R. / Detecting Unusual Temporal Patterns in Fisheries Time Series Data. In: Transactions of the American Fisheries Society. 2016 ; Vol. 145, No. 4. pp. 786-794.
@article{34793b83c1a74541a86da49a06ad5341,
title = "Detecting Unusual Temporal Patterns in Fisheries Time Series Data",
abstract = "Abstract: Long-term sampling of fisheries data is an important source of information for making inferences about the temporal dynamics of populations that support ecologically and economically important fisheries. For example, time series of catch-per-effort data are often examined for the presence of long-term trends. However, it is also of interest to know whether certain sampled locations are exhibiting temporal patterns that deviate from the overall pattern exhibited across all sampled locations. Patterns at these “unusual” sites may be the result of site-specific abiotic (e.g., habitat) or biotic (e.g., the presence of an invasive species) factors that cause these sites to respond differently to natural or anthropogenic drivers of population dynamics or to management actions. We present a Bayesian model selection approach that allows for detection of unique sites—locations that display temporal patterns with documentable inconsistencies relative to the overall global average temporal pattern. We applied this modeling approach to long-term gill-net data collected from a fixed-site, standardized sampling program for Yellow Perch Perca flavescens in Oneida Lake, New York, but the approach is also relevant to shorter time series data. We used this approach to identify six sites with distinct temporal patterns that differed from the lakewide trend, and we describe the magnitude of the difference between these patterns and the lakewide average trend. Detection of unique sites may be informative for management decisions related to prioritizing rehabilitation or restoration efforts, stocking, or determining fishable areas and for further understanding changes in ecosystem dynamics. Received October 13, 2015; accepted February 1, 2016Published online June 22, 2016",
author = "Tyler Wagner and Midway, {Stephen R.} and Tiffany Vidal and Irwin, {Brian J.} and Jackson, {James R.}",
year = "2016",
month = "7",
day = "3",
doi = "10.1080/00028487.2016.1150879",
language = "English (US)",
volume = "145",
pages = "786--794",
journal = "Transactions of the American Fisheries Society",
issn = "0002-8487",
publisher = "American Fisheries Society",
number = "4",

}

Detecting Unusual Temporal Patterns in Fisheries Time Series Data. / Wagner, Tyler; Midway, Stephen R.; Vidal, Tiffany; Irwin, Brian J.; Jackson, James R.

In: Transactions of the American Fisheries Society, Vol. 145, No. 4, 03.07.2016, p. 786-794.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Detecting Unusual Temporal Patterns in Fisheries Time Series Data

AU - Wagner, Tyler

AU - Midway, Stephen R.

AU - Vidal, Tiffany

AU - Irwin, Brian J.

AU - Jackson, James R.

PY - 2016/7/3

Y1 - 2016/7/3

N2 - Abstract: Long-term sampling of fisheries data is an important source of information for making inferences about the temporal dynamics of populations that support ecologically and economically important fisheries. For example, time series of catch-per-effort data are often examined for the presence of long-term trends. However, it is also of interest to know whether certain sampled locations are exhibiting temporal patterns that deviate from the overall pattern exhibited across all sampled locations. Patterns at these “unusual” sites may be the result of site-specific abiotic (e.g., habitat) or biotic (e.g., the presence of an invasive species) factors that cause these sites to respond differently to natural or anthropogenic drivers of population dynamics or to management actions. We present a Bayesian model selection approach that allows for detection of unique sites—locations that display temporal patterns with documentable inconsistencies relative to the overall global average temporal pattern. We applied this modeling approach to long-term gill-net data collected from a fixed-site, standardized sampling program for Yellow Perch Perca flavescens in Oneida Lake, New York, but the approach is also relevant to shorter time series data. We used this approach to identify six sites with distinct temporal patterns that differed from the lakewide trend, and we describe the magnitude of the difference between these patterns and the lakewide average trend. Detection of unique sites may be informative for management decisions related to prioritizing rehabilitation or restoration efforts, stocking, or determining fishable areas and for further understanding changes in ecosystem dynamics. Received October 13, 2015; accepted February 1, 2016Published online June 22, 2016

AB - Abstract: Long-term sampling of fisheries data is an important source of information for making inferences about the temporal dynamics of populations that support ecologically and economically important fisheries. For example, time series of catch-per-effort data are often examined for the presence of long-term trends. However, it is also of interest to know whether certain sampled locations are exhibiting temporal patterns that deviate from the overall pattern exhibited across all sampled locations. Patterns at these “unusual” sites may be the result of site-specific abiotic (e.g., habitat) or biotic (e.g., the presence of an invasive species) factors that cause these sites to respond differently to natural or anthropogenic drivers of population dynamics or to management actions. We present a Bayesian model selection approach that allows for detection of unique sites—locations that display temporal patterns with documentable inconsistencies relative to the overall global average temporal pattern. We applied this modeling approach to long-term gill-net data collected from a fixed-site, standardized sampling program for Yellow Perch Perca flavescens in Oneida Lake, New York, but the approach is also relevant to shorter time series data. We used this approach to identify six sites with distinct temporal patterns that differed from the lakewide trend, and we describe the magnitude of the difference between these patterns and the lakewide average trend. Detection of unique sites may be informative for management decisions related to prioritizing rehabilitation or restoration efforts, stocking, or determining fishable areas and for further understanding changes in ecosystem dynamics. Received October 13, 2015; accepted February 1, 2016Published online June 22, 2016

UR - http://www.scopus.com/inward/record.url?scp=84978208819&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84978208819&partnerID=8YFLogxK

U2 - 10.1080/00028487.2016.1150879

DO - 10.1080/00028487.2016.1150879

M3 - Article

VL - 145

SP - 786

EP - 794

JO - Transactions of the American Fisheries Society

JF - Transactions of the American Fisheries Society

SN - 0002-8487

IS - 4

ER -