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 youngoftheyear 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 > > > > > > > > > > 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 habitattypes) 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 ttest. > > > >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 ttest 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 multistage 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 41:15751591) 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 45:834844). > > > >So, take your two sets of five estimates and do a ttest. 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 > >RandolphMacon College > >Ashland, VA 23005 > >8047527293 > >FAX: 8047524724 > >email: [log in to unmask] > >web: http://www.rmc.edu/~cgowan > >========================================================= > > > > >
