Modelling community structure and species co-occurrence using fishery observer data

Pulver, Jeffrey Robert; Liu, Hui; Scott-Denton, Elizabeth

doi:10.1093/icesjms/fsw033

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The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.

i

Modelling community structure and species co-occurrence using fishery observer data

2016
By Pulver, Jeffrey Robert ; Liu, Hui ; Scott-Denton, Elizabeth
Source: ICES Journal of Marine Science, 73(7), 1750-1763

[PDF-1.21 MB]

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Journal Title:

ICES Journal of Marine Science
Personal Author:

Pulver, Jeffrey Robert ; Liu, Hui ; Scott-Denton, Elizabeth

Pulver, Jeffrey Robert ; Liu, Hui ; Scott-Denton, Elizabeth Less -
NOAA Program & Office:

NMFS (National Marine Fisheries Service) ; SEFSC (Southeast Fisheries Science Center)

NMFS (National Marine Fisheries Service) ; SEFSC (Southeast Fisheries Science Center) Less -
Description:

In this study, we modelled fishery observer data to compare methods of identifying community structure using cluster analyses to determine stratifications and probabilistic models for examining species co-occurrence in the Gulf of Mexico deepwater reef fish fishery. Comparing cluster analysis methods, the correlation measure of dissimilarity in combination with average agglomerative linkage was the most efficient method for determining species relationships using simulated random species as a comparison tool. Cluster analysis revealed distinct species stratifications and in combination with multiscale bootstrapping generated probabilities indicating the strength of stratifications in the fishery. A more parsimonious approach with probabilistic models was also developed to quantify pairwise species co-occurrence as random, positive, or negative based on the observed vs. expected fishing sets with co-occurrence. For the most common species captured, the probabilistic models predicted positive or negative co-occurrence between 84.2% of the pairwise combinations examined. These methods provide fishery managers tools for determining multispecies quota allocations and offer insights into other bycatch species of interest.
Keywords:

[+]

Aquatic Science Ecology Ecology, Evolution, Behavior And Systematics Oceanography
Source:

ICES Journal of Marine Science, 73(7), 1750-1763
DOI:

https://doi.org/10.1093/icesjms/fsw033
ISSN:

1095-9289;1054-3139;
Format:

PDF
Publisher:

Oxford University Press (OUP)
Document Type:

Journal Article
License:

https://creativecommons.org/publicdomain/zero/1.0/
Rights Information:

CC0 Public Domain
Compliance:

Submitted
Main Document Checksum:

[+]

urn:sha256:fcef200f1c9425f1d652cd9d450f1ef27b968ff58666a62e3c9b4871b6968fc1
Download URL:

https://repository.library.noaa.gov/view/noaa/51787/noaa_51787_DS1.pdf
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Modelling community structure and species co-occurrence using fishery observer data

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