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Tom,
Alcian blue with a jet gun seems to work well for some time, as long as
the fish aren't really small. What about fin clipping?
Sudhindra.
On Fri, 8 Aug 1997, Tom McMahon wrote:
> We are trapping young-of-the-year rainbow trout in some small tributaries,
> and want to estimate trap efficiency by use of short term dyeing. I have
> used Bismark Brown in the past, but I'm not sure it would work that
> effectively with young rainbow trout since they can be heavily pigmented.
> Any suggestions on other dyes that would be effective?
> Thank you.
> Tom McMahon
> Montana State University, Bozeman
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> and >>At 10:10 AM 5/30/97 -0400, you wrote:
> How does one calculate an error term for multiple population estimates? As
> >>an example, you want to test the hypothesis that fish abundance in two
> >>d>Tom McMahon wrote:
> ifferent habitat types was statistically different. You picked five
> >>samples of each habitat type and performed a depletion/removal estimate in
> >>each habitat. For each habitat sampled, you obtain a point population
> >>estimate with an associated variance. To compare population abundance
> >>among the two habitat types, it is straightforward to take the mean of the
> >>five population estimates, but my question is, how do you calculate the
> >>error term used in the statistical comparison? Using the variance of the
> >>five means would seemingly highly underestimate the true variance around
> >>each point estimate of abundance, but I'm unsure of the correct way to
> >>essentially take the 'mean' variance of the five individual estimates.
> >
> >
> >Tom,
> >It is OK to use the variance of the five means as the error term. Call this
> >total variance. This total variance is actually the sum of two variance
> >components:
> >
> >Var total = Var process + Var enumeration.
> >
> >The first component is due to process variance, which is true variation
> >among the sampled habitat units (i.e., each habitat unit, in truth,
> >contained a different number of animals). The second component of the
> >variance is enumeration variation, which is variation arising because you
> >did not count every animal in the sampled habitat units, you made an
> >estimate (i.e., you are not sure what the true population is in even one
> >sampled habitat unit).
> >
> >Think of it this way. If the habitat units you sampled all, in truth, had
> >exactly the same number of animals (process variation = 0), your five
> >estimates would probably still be different because each is an estimate with
> >associated enumeration variation. Thus, the total variance observed among
> >the five units would be due all to enumeration variation. On the other
> >hand, if the habitat units sampled did differ in the number of animals, but
> >you were able to do a complete count in each unit (enumeration variation =
> >0), then the total variance would all be due to process variation. In
> >truth, the situation is somewhere in between: you have both process
> >variation and enumeration variation included in the total variance you
> >observe among the five estimates.
> >
> >A conservative statistical test (i.e., one that would tend not to reject the
> >null hypothesis of no difference between habitat-types) would use the total
> >variance as the error term. If you do this test and it rejects, you can
> >make a good case that the two habitat types really do support different
> >numbers of fish. Try a t-test.
> >
> >If the test doesn't reject, it may be due to low power, and the low power
> >may be due to high enumeration variation. In this case you would like to
> >subtract out the enumeration variation because it is inflating your error
> >term. You could do this because you have estimates of enumeration variation
> >(the variances of each of the 5 population estimates; these variances are
> >based on the statistical model [the removal model] that you used to generate
> >the estimates). Details can be found in Skalski and Robson (1992.
> >Techniques for wildlife investigations - design and analysis of capture
> >data. Academic Press). However, in almost all cases, it turns out that
> >most of the total variance is due to process variation, not enumeration
> >variation, so you don't get much more power in your test anyway. If your
> >simple t-test using the total variance doesn't reject, the problem is
> >probably that your sample size of habitat units is too small, not that you
> >did a poor job estimating abundance within each sampled unit.
> >
> >In truth, what you have is a multi-stage sampling design. At the first
> >stage, you *randomly* selected habitat units from some larger population of
> >habitat units. At the second stage, you made an estimate of abundance in
> >each sampled habitat unit. Two stages, two sources of variation. Hankin
> >(1984. Multistage sampling designs in fisheries research: applications in
> >small streams. CJFAS 41:1575-1591) does a nice job of explaining all this.
> >
> >These ideas are most important when designing a sampling program. The basic
> >question is, given limited time and money, should you put your effort into
> >sampling more habitat units with less efficiency in each (i.e., reducing
> >process variance at the expense of increased enumeration variation), or
> >should you put your effort into sampling fewer habitat units with greater
> >efficiency in each (i.e., increased process variance but with reduced
> >enumeration variation). Almost always, the best choice is more units with
> >less effort in each (see Hankin and Reeves 1988, CJFAS 45:834-844).
> >
> >So, take your two sets of five estimates and do a t-test. If it rejects,
> >rejoice. If not, you probably will need to sample more habitat units.
> >
> >Chas.
> >
> >
> >
> >=========================================================
> >Charles Gowan
> >Department of Biology and Environmental Studies Program
> >Randolph-Macon College
> >Ashland, VA 23005
> >804-752-7293
> >FAX: 804-752-4724
> >email: [log in to unmask]
> >web: http://www.rmc.edu/~cgowan
> >=========================================================
> >
> >
>
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