Characterization of Scale in Commercial Fisheries Data

• Main Author: Sanderson, Melissa A.
• Other Authors: Halpin, Patrick N
• Format: Master Thesis
• Language: English
• Published: 2004
• Subjects: Fisheries; National Marine Fisheries Service Northeast; Atlantic Cod; Witch Flounder; atlantic cod; Northeast Atlantic
• Online Access: https://hdl.handle.net/10161/248

As management of commercial fisheries becomes more spatially oriented, collection of commercial fisheries data must adapt to accurately reflect location. An important component of accurate spatial data is scale. In an attempt to characterize patterns of scale in fisheries data, I tested the National Marine Fisheries Service Northeast bottom trawl survey data for spatial dependency using semivariogram analysis. Specifically, and more importantly for management, I wanted to determine if the distance between sample locations is a good predictor variable for how much fish will be caught. Focusing on 1996-2002 catch data for Atlantic cod and witch flounder, I found that for current data collection techniques, the variance of catch weight is spatially independent from distance between observations. Thus, the scale and spatial pattern of the data can not be characterized based on distance for the range of space and time analyzed. This finding does not rule out the possibility that spatial dependence may be observed in these fisheries if we were to examine data sets with finer spatial distances and finer time intervals. Because ocean processes vary significantly across time, the effect of aggregating the spatial data across time may have acted to conceal some of the potential trends in the data set. Determining the spatial patterns in the data is part of a sequential approach to understanding ecological processes. Alternative hypotheses that may possibly explain the spatial pattern of the data need to be tested and include spatial patterns being dependent upon bottom habitat complexity, water temperature, and/or prey availability. The goal is to find a variable that explains fish biomass patterns, allowing managers and scientists to begin to understand what proxy data they really need to collect and map, and at what scale, in order to predict patterns of fishes for effective and sustainable fisheries management.