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  #1  
Old 06-20-2012, 09:52 PM
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Default mathgeek..Big Lake or Grand Isle

Which has the healthier looking fish
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Old 06-20-2012, 10:08 PM
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Which has the healthier looking fish
Last year's study answered that question between Big Lake and the oil spill affected area (Barataria and Terrebonne Bays), and the answer was that the oil spill impacted area had fish with higher condition factors than the Calcasieu Estuary. This year, we decided to focus our data collection on the Calcasieu Estuary, and we are not weighing and measuring nearly enough fish in Grand Isle to draw any comparisons like we did last year. We'll measure a few fish to see if there is anything of interest happening, but our numbers will only be sufficient to form a hypothesis for future work, not to compare with Calcasieu this year. Other than the recreational aspects, the goal of the Grand Isle trip was more to scope things out for future work than collect enough data for a valid regional assessment in 2012.
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Old 06-20-2012, 10:16 PM
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MathGeek...Lake Pontchartrain or Big Lake. Which has the healthier-looking fish?

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Old 06-20-2012, 10:24 PM
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MathGeek...Lake Pontchartrain or Big Lake. Which has the healthier-looking fish?

Lake P has the healthiest trout n the state!!
Tons of bait all year!!
Great flow of water; no serious issues since Katrina.
Them big football trout are proof Lake P has the healthy trout!!
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Old 06-21-2012, 12:19 AM
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Lake P has the healthiest trout n the state!!
Tons of bait all year!!
Great flow of water; no serious issues since Katrina.
Them big football trout are proof Lake P has the healthy trout!!
Thanks, MathGeek.
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Old 06-21-2012, 01:00 AM
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Lmao
Ur welcome
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Old 06-21-2012, 07:34 AM
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The likely sample size gather by a few humans is hardly big enough to compare differences in fish condition from two different "estuaries" with any confidence. Also, it is nearly impossible to hold "oil spill impacted area" as the single and only tested variable. JMO

-cheers
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Old 06-21-2012, 08:02 AM
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The likely sample size gather by a few humans is hardly big enough to compare differences in fish condition from two different "estuaries" with any confidence. Also, it is nearly impossible to hold "oil spill impacted area" as the single and only tested variable. JMO

-cheers
Actually, the USGS study by Jill Jenkins in 2004 compared contaminated and non-contaminated sites in the Calcasieu estuary and yielded statistically significant results with numbers of fish much smaller than our studies.

"Confidence" can be quantified with standard statistical techniques. The Jenkins study yielded a p = 0.0032. Comparing our 2011 data with the long term LA average for expectation yielded the observation that spotted seatrout were smaller than the LA average with p = 0.003 in the Calcasieu estuary, that black drum were smaller than the LA average with p < 0.001, and that red drum were smaller than the LA average with p = 0.012. It is well known and widely accepted that using the relative condition factor can produce statistically significant stock assessments without weighing and measuring thousands of fish, and the practice is common and accepted in fisheries management.
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Old 06-22-2012, 08:05 AM
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Originally Posted by MathGeek View Post
Actually, the USGS study by Jill Jenkins in 2004 compared contaminated and non-contaminated sites in the Calcasieu estuary and yielded statistically significant results with numbers of fish much smaller than our studies.

"Confidence" can be quantified with standard statistical techniques. The Jenkins study yielded a p = 0.0032. Comparing our 2011 data with the long term LA average for expectation yielded the observation that spotted seatrout were smaller than the LA average with p = 0.003 in the Calcasieu estuary, that black drum were smaller than the LA average with p < 0.001, and that red drum were smaller than the LA average with p = 0.012. It is well known and widely accepted that using the relative condition factor can produce statistically significant stock assessments without weighing and measuring thousands of fish, and the practice is common and accepted in fisheries management.

Thank you for your words Mathgeek. I'm sorry if it came across wrong...I wasn't intending to doubt any differences in sizes or health, but rather what causes any differences in sizes or health. What were the constituents involved in the "contaminated" areas of Calcasieu?

Having done some research work in this area, my personal opinion is that it's really hard to nail down any true cause/effect "ideas" because of the thousands and thousands and thousands of variables that affect the health of an estuary and its biological population.
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Old 06-22-2012, 08:53 AM
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Originally Posted by Coppertone View Post
Thank you for your words Mathgeek. I'm sorry if it came across wrong...I wasn't intending to doubt any differences in sizes or health, but rather what causes any differences in sizes or health. What were the constituents involved in the "contaminated" areas of Calcasieu?

Having done some research work in this area, my personal opinion is that it's really hard to nail down any true cause/effect "ideas" because of the thousands and thousands and thousands of variables that affect the health of an estuary and its biological population.
I agree, ascribing causality for observations (why fish are thinner or fatter in a given area or year) is harder to do with cetainty than observing what is happening (fish as one location or year are fatter than at another location or year).

However, with sufficient data one can say whether the data tends to support or fails to support a given hypothesis. Suppose one has a hypothesis that the oil spill negatively impacted important food sources in a specified area. If the fish were thinner in that area (compared with pervious years or the long term LA average), then the hypothesis would be supported (but not proven). If the fish were fatter or the same in the area, then the hypothesis would be disproven.

There are a number of hypotheses regarding the declining fish condition in the Calcasieu estuary. These hypotheses are not mutually exclusive, but if one is hypothesized to be the primary or most important cause, then it is possible that data exists or could be gathered to support or refute it.

For example, consider the hypothesis that the overharvesting of oysters is the primary cause of the decline of fish condition. This hypothesis would be supported by the following observations:

1. Species more heavily dependent on benthic food sources (bottom feeders) would likely be more impacted than species less dependent on benthic food sources.
2. Species that feed directly on oysters would be strongly impacted.
3. The decline of fish condition would be correlated in time (coincident or shortly after) a significant increase in oyster harvesting.
4. Other data or bioindicators showing that the oyster population is decreasing.

This hypothesis would be refuted by the following observations:

1. The species showing the greatest loss of condition factor are not heavily dependent on benthic food sources.
2. The greatest decline in fish condition occurred before significant increases in oyster harvesting.
3. Other data or bioindicators showing that the oyster population is actually increasing.

This kind of logic cannot really prove which of the competing hypothesis is the primary cause, but applied to all of the hypotheses, it might be possible to suggest which hypothesis (or hypotheses) are more likely to identify the causal factor(s).
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