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| Landing distribution (dark grey points) of small pelagic fishery in the GC from 2002 to 2007 |
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| Finding fisheries action regions in the form of WRI, in a re-sampled 4 km grid. |
Those involved in the project analyzed a database from logbooks detailing fishing
activity from October 2002 to June 2007. They then transformed various fishing
data into a Weighted Region Index (WRI) through the use of fuzzy logic
operators. This ultimately allowed for a more complete understanding of
hierarchies between various fishing areas in the Gulf, as well more advanced
knowledge of species composition in different regions.
This data can then be utilized in a number of ways. It can
be used assist in conservation efforts as well as to improve the success rates
of commercial and recreational fishing efforts. In closing, this article
details a successful application of GIS in a very unique field.
Source:
Lanz, E., Nevárez-Martínez,
M. O., López-Martínez, J., & Dworak, J. A. (2008). Spatial distribution and
species composition of small pelagic fishes in the Gulf of California. Revista
De Biología Tropical, 56(2), 575-590.
I always find fishing articles such as this interesting because as knowledge gets out of good fishing areas, more people will fish there and eventually the fish will find another spot less populated with fishermen. GIS would simply need to be used often because the fishing holes would change over time. Very interesting.
ReplyDeleteIt would be interesting to model human interactions with fish populations to see how they can change the regression models. I wonder if fish populations would react to humans, or if the human forces would just reduce fish populations in area with over fishing.
DeleteThe biggest finding in the article is the use of the fuzzy logic operators to transform the Weighted Region Index. What is interesting about fuzzy operators in GIS, is that it allows to model the world as it is, "Gray", and not Black or White. Fuzzy set were expressed in this study as a continuous scale from 1 (full membership) to 0 (full non-membership), were full memberships reflected the relative importance of each individual cell value in the definition of fishing regions. Very cool modeling.
ReplyDeleteI thought the same thing after reading this blog as Seve. While I understand why it would be necessary to collect data on fish population in different areas it almost seems like this GIS data would telling fishermen "Hey look! There is a high volume of fish in this area...come fish over here!" In result I would imagine fishermen would run to that area and fish which would drastically lessen the amount of fish in that area. On a different note though this GIS data is able to visually make humans aware of areas where populations of fish may be endangered and should avoid fishing.
ReplyDeleteLast Spring semester my group in my ecology class studied the spatial distribution of invasive species of mollusks in southeastern Asia and South America. GIS made everything easier to see when working on the project, and we were able to find regions of Texas suseptible to invasion based off of their living requirements. This research is kind of similar in that it uses some of the same ideas we used.
ReplyDelete