Leelanau_Co._Supplimental_check_station_data 532.2 KB

THE CONCERNED SPORTSMEN OF MICHIGAN
MICHIGAN
Dedicated to protecting and promoting Michigan's Whitetail Deer resource, through the promotion of
responsible, science based policies and regulations.
www.concerned-sportsmen.net
Supplemental Check Station data For some time, as part of the ongoing debate over APR’s in Michigan, there has been controversy surrounding the
Leelanau Co. check station data, over whether or not it was supplemented with data gathered by either the sponsoring
organization of that APR stakeholder initiative or with data from area taxidermists, or both. Supporters of APR’s have
used charts generated from that check station data, to promote both the NW12 APR Initiative and the LPDMI APR
initiative. The published Leelanau Co. check station data has also been used in disease risk modeling, using that data to
simulate the changes to buck age structure that could occur under APR’s. After recently seeing debates over the
accuracy of that check station data resurface, I decided that it was time to get to the bottom of the issue and find out
once and for all what the deal was with the use of supplemental check station data in DMU 045. So I took a trip out to
the Traverse City Field Office, which is the local check station for Leelanau Co. and talked with the biologist and one of
the technicians there. I was able to confirm that supplemental data from sources other than DNR check stations was
combined with the harvest data obtained from DNR check stations. The published annual check station report does not
identify the source of the data for each DMU, it only identifies the total number of deer from each DMU that make up
the sample. In order to be able to identify the source of the data (where it came from), I obtained the raw data files for
all of the years during the baseline period (2000 – 2002) and the APR period (2003 – 2013). I was able to do this through
a FOIA request. After obtaining the data, I was able to sort it by year and by data source, in order to get an accurate
picture of where the data came from and how much came from each source. By separating the data by source, it was
possible to measure the impact of how the addition of supplemental data influenced the published results on an annual
basis. It was also then possible to filter out the supplemental data, to provide a sample composed solely of data
gathered from DNR or other governmental sources.
The results are somewhat predictable but nonetheless interesting. Before I get into the details, let me preface this by
saying, before any conspiracy theorists start to postulate why the supplemental data was added and whether there was
a conscious attempt to create an inaccurate picture of the impact of APR’s, let me clearly and unequivocally state that I
don’t think that to be the case. In my opinion, the supplemental data was added because check station participation has
been dropping every year and the biologists involved wanted to gather as large a sample as possible, to try and identify
any biometric changes that might have occurred (high grading). Folks have to keep in mind that the DNR does not use
check station data the same way that it’s been used by people on both sides of the APR debate. From a biometric
standpoint, they don’t care what the source of the data is, just that the deer were harvested in Leelanau Co., the bigger
the sample the better. So please leave the tinfoil hats on the nightstand and please don’t use the information that I’m
providing to launch some vast conspiracy theory condemning the DNR, that is not my intention for sharing this
information.
Ok, here is what I have found out regarding the supplemental data. Prior to the implementation of APR’s in 2003, all of
the data included in the annual check station totals came from a government check station source. These include the
Traverse City and Platte River DNR field offices, the National Park Service and a variety of other DNR check stations and
field offices around the state. Some later years also included some tribal harvest data gathered by tribal CO’s. In 2003,
the first year of APR’s in Leelanau Co., no supplemental data was added, the published data for that year only included
deer checked at government check stations.
Starting in 2004, Leelanau Whitetails, the sponsoring organization of the Leelanau APR initiative, gathered supplemental
data, which was submitted to the DNR and combined with the data gathered from government sources. The specific
source of the LW data is not identified. According to the local biologist and DNR technician, some of the LW data came
from two area processors but whether that was the only source or whether the club gathered data from other places, is
not indicated, it’s simply identified as Leelanau Whitetails data in the DNR records. LW gathered supplemental data
from 2004 – 2008. The amounts of the data varied but it was substantial and exceeded the amount of data gathered
from government sources during several of those years. The average over the five years that LW contributed data was
that it comprised 51% of the total published check station data gathered for Leelanau Co. during those years.
In 2009, after the APR renewal took place, Leelanau Whitetails stopped gathering supplemental data. Beginning that
year, supplemental data was gathered from several additional sources. Again, the records don’t indicate the specific
sources of the supplemental data, its lumped together under the designation “QDM”. In talking to the local biologist, it
was confirmed that two Leelanau Co. taxidermists contribute to the QDM data but whether they are the only source for
QDM data or whether data obtained from other sources is also combined with the taxidermist data, is unknown, just
that the QDM designated data does not come from a government check station source. It’s also not known whether
those taxidermists also provided some of the Leelanau Whitetails data, prior to 2009.
So after sorting all of the data by year and source and then breaking it down by gender and age class, the first thing I
wanted to look at was whether data from different sources during the same years was uniform or whether it varied by
data source. If it’s uniform, then the fact that supplemental data was included is no big deal, as it would not have any
impact on changing the results, it would simply result in a larger sample size. If there is variance between the data
gathered from different sources, mixing it would have an impact on changing the results and would invalidate
comparisons to previous years in which supplemental data was not added to the data obtained by government check
stations. After analyzing the data by source, it’s clear that the supplemental data does not show uniform results with
the government check station data and in addition, there is variance between the two different sources for
supplemental data.
This graphic shows the comparison between the two data sources, DNR and Leelanau Whitetails, during the years 2004
– 2008.
Figure 1
As you can see, the Leelanau Whitetails data significantly under-represents the yearling buck harvest and overrepresents the harvest of older age classes, when compared with the DNR data. Again, during this time period, the LW
data comprised 51% of the combined data that was published in the annual check station report for Leelanau Co.
The next graphic shows the comparison between the government check station data and the data designated QDM,
during the time period 2009 – 2013.
Figure 2
As you can see from this graphic, the variance is even more extreme, with the QDM data substantially underrepresenting the yearling age class harvest, while significantly over-representing the harvest in the older age classes.
During this time period, the QDM data comprised 38% of the combined data that was published in the annual check
station report for Leelanau Co. Since taxidermist data makes up at least part, if not all, of the QDM data, it’s not
surprising that the older age classes would have significantly greater numbers.
This next graphic is a comparison of all three data sources employed during the APR period. Note, that the time periods
for the supplemental sources are different then the DNR data, which covers the whole APR period.
Figure 3
Again, there is an obvious difference depending on what the source of the data is, which highlights the impact that
mixing data from separate sources may have on presenting an accurate picture of what the potential impact of APR’s
may be.
One of the graphics used by the LPDMI and posted frequently by LPDMI supporters utilizes the Leelanau Co. check
station data and displayed a comparison of yearling bucks checked and 3.5 and older bucks checked before and during
the APR period there. The two lines intersected, the yearling percentage continuing to decline and the line representing
older bucks continuing to rise. This chart was used to support the premise that mandatory APR’s spur the adoption of
even stricter voluntary restrictions, as an explanation for why yearling harvest rates continued to fall under APR’s.
The following graphic portrays those age classes during the same time period but with the supplemental data, removed.
Figure 4
While the initial reduction in yearling harvest and increase in the harvest of older bucks is similar to the original graphic,
the period from 2006 – 2013 takes on a different look with the supplemental data removed. Instead of a steady ongoing
decrease in yearling harvest rates and a steady, ongoing increase in the harvest percentage of older bucks, instead we
see the percentages of both age classes fluctuating up and down, from year to year. That would call into question the
premise that mandatory APR’s bring about some fundamental change in mind set, which translates to sustained and
increasing levels of voluntary restraint. Instead it appears that mandatory APR’s reduce yearling harvest levels to a
certain point and then other factors that vary from year to year, play a role in influencing yearling harvest.
As previously mentioned, one of the things I’ve used Leelanau Co. check station data for is for disease risk modeling, to
predict the potential impact that an increasing buck age structure resulting from APR’s may have on increasing herd
prevalence rates for frequency dependent communicable diseases like bovine TB and CWD. I was concerned that the
inclusion of supplemental data might affect the measurable impact that APR’s may have on buck age structure. After
filtering out the supplemental data and using just the DNR check station, this turned out to be the case, to some extent.
This graphic shows the progression in the average age of the male cohort of the herd under APR’s. Because the
supplemental data tended to understate the yearling portion of the harvest and overstate the harvest of older bucks, its
addition resulted in an accelerated increase in average age. Using the published check station with the supplemental
data included, the average buck age was approx. 4 months older then when just the DNR check station data is used.
Figure 5
Despite the slightly slower increase in average buck age, the results indicated in this graph still show a steady increase in
the average age of bucks resulting from APR’s. Whether that increase will continue will have to be seen but concerns
about the potential impact that APR’s may have on increasing prevalence rates of communicable disease remain valid.
Conclusion
So what should we make of all of this? There is no question that APR’s in Leelanau Co. have had an impact on reducing
yearling buck harvest percentages and that they also have resulted in advancing the buck age structure. That is not
particularly surprising, if you protect 70% of the yearling age class from harvest, barring any other sources of significant
mortality such as high winter kill, then naturally there will be increased numbers of older bucks the following year. This
graphic demonstrates the impact that APR’s have had on reducing yearling percentage of the total harvest in the NWLP,
comparing Leelanau Co. (using only DNR check station data) to the NW12 counties during the same time period (1999 –
2012).
Figure 6
It’s also obvious that the addition of supplemental data, gathered from sources other than government check stations,
changes the make-up of the resulting data and the resulting combination is probably not a very accurate representation
of the actual harvest. For example, the supplemental data labeled as QDM did not include any fawns, of either gender,
during the entire 5 year period that such data was gathered. It’s not credible to believe that no fawns were harvested in
Leelanau Co. during that 5 year period, which highlights the likelihood that supplemental data gathered from
taxidermists is unlikely to accurately reflect actual harvest patterns.
The small relative size of the samples gathered also needs to be noted. In 2012, approx. 2,200 deer were harvested,
roughly 1,500 of them antlered bucks. The 2012 DNR check station sample was composed of 54 antlered bucks and 25
antlerless deer. So reading too much into a non-random sample based on less than 4% of the total harvest, should be
avoided. The limited value that check station provides is the ability to compare it from year to year, to be able to
identify any trends and changes that occur. The inclusion of supplemental data, derived from sources that are even less
“random” than the sample gathered at government check stations, frustrates that comparison and renders such
comparisons largely invalid. While there may be some benefit in increasing the overall sample with supplemental data
to make it as large as possible for purposes of measuring biometric changes, my opinion is that supplemental data
should not be included in the published check station data, due to the complications that result when using that data for
comparison purposes with DMU’s where supplemental data is not added to the check station sample.

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