Philip
Bulldust. You showed graphs in your book withourt any measure of
significance and now accuse me of incompetence! I do not normally
respond to your messages  and will not in future  since it is not in
the best interests of the GBS but this one is rude beyond belief.
Martin
On Sun, Mar 23, 2008 at 5:27 PM, Philip Veerman <> wrote:
>
> Further on this: Martin wrote:
>
> "The main thing that can be said about the long term trend for Pied
> Currawong abundance in the GBS results is that there is no trend. The
> values of the correlation coefficients for linear and polynomial trends for
> 21 and 26 year results for the species range from just over 0.1 to nearly
> 0.3. These are well below the values that would indicate a trend
> significantly different to zero.
>
> The facts are that the trends are obvious and strong. There are two trends
> occurring and they need to be teased apart and seen separately, especially
> because the biological impacts are different. We should be more concerned
> about the biological impacts than the statistics. It is during the summer
> that the PC is a major predator of other birds' nesting and during the
> winter that the PC spreads seeds of woody weeds. We need to see the GBS data
> in context. There are plenty on long standing bird surveys around the world
> that are not continuous and only comprise summer or winter counts. If we did
> that, then the results would be very different.
>
> I have just done a regression analysis for the first 21 years of GBS data.
> Bearing in mind that the analysis of the GBS does not exist beyond the first
> 21 years. Although I discounted the odd months out of year 1 and 21 because
> of the split, in that the survey started in July and ended in June). Winter
> includes June, July and August and Summer includes December, January and
> February. Therefore each analysis includes 63 data points. The analysis of
> variance from the regression is also presented (below).
>
> Just imagine that we did a GBS in only summer or winter. Two separate
> regression analyses for these data show: for winter, a probability value
> (significance of the ANOVA regression F test) of 2.0769E05 in other words,
> the chance that the observed decrease is due to chance factors is
> 0.000020769. Likewise for summer a probability value of 3.59055 E14 in
> other words, the chance that the observed increase is due to chance factors
> is 0.0000000000000359055. Normally biologists accept as reasonable evidence,
> anything less than 0.05 (meaning 95% probability). These two trends are way
> off the scale for any suggestion of no trend! Yet Martin says no trends,
> just on the simple basis of missing that the two trends are running in
> opposite ways (an increase and a decrease). The possibility of damage done
> to the GBS and COG's data by such incompetent analysis and comment is of
> great concern.
>
> It is simply the fact that summer abundance has increased to a similar
> margin as winter numbers have decreased that appear to confuse those not
> clear in the biological meaning of the results or capacity of the survey).
> It was not by accident that I chose this species as one of the three to
> present the full graph of abundance by month throughout the 21 years of the
> GBS (on page 90).
>
>
>
> SUMMARY OUTPUT
>
> Of regression analysis of Pied Currawong Winter values of A
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> Regression Statistics
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> Multiple R
>
> 0.508654885
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> R Square
>
> 0.258729792
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> Adjusted R Square
>
> 0.246577822
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> Standard Error
>
> 1.426770386
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> Observations
>
> 63
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> ANOVA
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> df
>
> SS
>
> MS
>
> F
>
> Significance F
>
>
>
>
>
>
>
> Regression
>
> 1
>
> 43.34189562
>
> 43.3419
>
> 21.29118
>
> 2.0769E05
>
>
>
>
>
>
>
> Residual
>
> 61
>
> 124.1760978
>
> 2.035674
>
>
>
>
>
>
>
>
>
>
>
> Total
>
> 62
>
> 167.5179934
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> Coefficients
>
> Standard Error
>
> t Stat
>
> Pvalue
>
> Lower 95%
>
> Upper 95%
>
> Lower 95.0%
>
> Upper 95.0%
>
>
> Intercept
>
> 7.934359474
>
> 0.363835294
>
> 21.80756
>
> 1.71E30
>
> 7.206825636
>
> 8.661893313
>
> 7.206825636
>
> 8.661893313
>
>
> X Variable 1
>
> 0.04561298
>
> 0.009885272
>
> 4.61424
>
> 2.08E05
>
> 0.06537981
>
> 0.025846155
>
> 0.06537981
>
> 0.025846155
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> SUMMARY OUTPUT
>
> Of regression analysis of Pied Currawong Summer values of A
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> Regression Statistics
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> Multiple R
>
> 0.782564696
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> R Square
>
> 0.612407504
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> Adjusted R Square
>
> 0.606053528
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> Standard Error
>
> 0.337899565
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> Observations
>
> 63
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> ANOVA
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> df
>
> SS
>
> MS
>
> F
>
> Significance F
>
>
>
>
>
>
>
> Regression
>
> 1
>
> 11.00449822
>
> 11.0045
>
> 96.38179
>
> 3.59055E14
>
>
>
>
>
>
>
> Residual
>
> 61
>
> 6.964743096
>
> 0.114176
>
>
>
>
>
>
>
>
>
>
>
> Total
>
> 62
>
> 17.96924131
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> Coefficients
>
> Standard Error
>
> t Stat
>
> Pvalue
>
> Lower 95%
>
> Upper 95%
>
> Lower 95.0%
>
> Upper 95.0%
>
>
> Intercept
>
> 1.479013121
>
> 0.086166484
>
> 17.1646
>
> 4.95E25
>
> 1.306712539
>
> 1.651313703
>
> 1.306712539
>
> 1.651313703
>
>
> X Variable 1
>
> 0.022983684
>
> 0.002341112
>
> 9.817423
>
> 3.59E14
>
> 0.01830234
>
> 0.027665027
>
> 0.01830234
>
> 0.027665027
>
> Also Martin wrote:
> I have attached a graph showing A values by week (months are difficult
> since the weeks don't all start and finish at the bginning/end of
> months) for 21 years and later years. This clearly shows a flattening
> of the seasonal distribution in the recent past with more birds in the
> warmer months and less in the colder months. There has been previous
> correspondence on this list (last year some time) about the reduced
> frequency of the huge flocks seen (particularly in the Weston area) in
> the early years of the Survey.
>
> As for "months are difficult since the weeks don't all start and finish at
> the beginning/end of months". What a non issue! Weeks and months are
> arbitrary human constructs and dividing lines. (Indeed none of the weeks and
> months start on the same day apart from on 1 January but it doesn't matter
> one iota). In contrast days and seasons are real. However collecting data by
> week happens to be convenient to our brains, our lifestyles and constraints
> of designing and printing a chart. The arrangement of the GBS Calendar was a
> brilliant way to approach the problem, invented by Henry Nix and it and the
> connection of weeks and months are fully explained in the GBS Report.
>
> Looking at the graph presented by weeks tells us almost nothing more that
> looking at the graph presented by months. It simply provides more data
> points. Each with a smaller sample size, thus increasing the importance of
> random sample error for the rarer species. Thus weeks makes it harder to
> provide the information in a graph and certainly much harder to describe in
> narrative. Believe me, having written all the text I did for the GBS Report
> it would have been much more difficult to describe every change in terms of
> week numbers rather than months, because everyone understands months.
>
> Lastly, not only has there "been previous correspondence on this list ...
> about the reduced frequency of the huge flocks seen ... in the early years
> of the Survey. True but I point out that this has been fully described in
> the GBS Report and the results Martin has now provided since Year 21 are
> simply a continuation of the same trend that I described therein (which is
> hardly surprising).
>
> Philip
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