TEAM TERMINATOR

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TEAM TERMINATOR
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Data Set 1 University Biologists Population
Crash in 384 years, with a growth rate of 0.975,
just short of equilibrium replacement Realisticall
y likely no policy action for an event almost
400 years away Why?
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• 384 years ago? The first successful English
  colony in the West Indies was established, and
  the first calculator mechanism to add or subtract
  six-digit numbers was invented.
• Spooky Tooky Owl 400 years into the future?
  Insignificant at best.

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Data Set 2 Government Study

• Population Crash in 66 years
• Growth rate of 0.868
• Lower survival probabilities (S, S0 and S1)
  quickens population crash and lowers growth rate

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Policy Implications

• Population collapse time of 66 years allows for
  more than enough time to act in favor of the
  protection of the wondrous Spooky Tooky.
• However, the time scale of over 60 years lapses
  generations and political eras, possibly causing
  a lack of interest in an issue not critical for
  such a relatively long time.

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Data Set 3 Sierra Club

• Population Crash in 22 years
• Growth Rate of 0.653
• Significantly lower survival probabilities than
  the other two data sets, causing the relatively
  earlier crash
• Possibly an inherent bias in the Sierra Clubs
  findings to further their cause of environmental
  protection

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Policy Implications

• Of all the data sets, the Sierra Clubs findings
  allows for a time frame most applicable and
  appealing to policy work
• 22 years is a realistic time span for policy
  enactment and completion.
• Example 1985 Vienna Treaty on ozone depletion
  and CFC use (22 years ago)

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Comparative effects on Growth Rate

• Lambda lt1 (Population crash)
• Which factors are most sensitive?
• Average of 3 studies

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a (Reproductive age)

• Rising alpha? Rising Lambda
• (Childhood survival)
• Tooky Studies
• a 2,3,4
• ?(.8291 .8316 .8344)
• ?Variability .0053

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Beta (female fecundity)

• Not very impactful
• Tooky Studies
• B.20 .24 .28
• Lambda(.8291 .8316 .8339)
• ?Variability .0048

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LaProbability of surviving birth, childhood, and
1st adult year (s0s1s)

• Study
• La (10.26, 4.25, 1.14)
• Lambda (.8479 .8314 .8207)
• ?Variability .0272

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S adult survival probability

• Tooky Studies
• S 95, 85, 65
• ? 0.9629,
• 0.8635, 0.6519

• S cannot be less than Lamba
• (makes left side negative)
• Most important variable
• ?Variability.3
• Least well known! (30)

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Policy implications

• ?a .005, ?B .005, ?la .03, ?s .3
  
• S and Lalpha are the only things we affect
• More study focus on determining s
• Policy should focus on increasing s
• Protect Tookies

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The W Effect on Lambda
Data Set W 10 W 15 No W
1 (uni) 0.771 0.858 0.976
2 (govt) 0.665 0.749 0.868
3 (s.c.) 0.428 0.506 0.653

• Larger lambda value with no W
• Lambda value increases as W increases

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The W Effect On Crash Time
Data Set W 10 W 15 No W
1 (uni) 36 years 61 years 384 years
2 (govt) 23 years 32 years 66 years
3 (s.c.) 11 years 14 years 22 years

• Longer crash time with no W
• Crash time increases as W increases

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Why results are sensitive to W

• ??a (1-s/?) l?b
• 1- (s/ ?)w-a1
• As w ? ?, (s/ ?)w-a1 ? 0
• As w ? ?, (s/ ?)w-a1 ? (s/ ?), so
• ??a (1-s/?) l?b ? ?a l?b
• 1- (s/ ?)w-a1

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W Cont, Effect on Crash Time

• ? -ln (N)
• ln (?)
• ? ? as w ?
• As ? ?, ln (?) ?
• Implication Increasing crash time for
  increasing w.

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Policy ImplicationsCrash Time for W 10
1 (uni) 2 (govt) 3 (s.c.)
36 years 23 years 11 years
1 Problem may be ignored because 36 years is
outside the timeline of policy and political life
times. Slow movement toward policy may occur,
but this is unlikely. 2 Policy may be made to
protect Tookies because 23 years is a realistic
time period for policy making. 3 Problem will
either be ignored because it will be perceived as
being too late, or drastic action may be taken.
ESA provision, for example.
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Policy ImplicationsCrash Time for W 15
1 (uni) 2 (govt) 3 (s.c.)
61 years 32 years 14 years
1 Problem will definitely be ignored. Who
cares what happens in 60 years??? 2 Problem may
be ignored because 32 years is outside the
timeline of policy and political life times.
Slow movement toward policy may occur. 3
Problem will either be ignored because it is too
late, or drastic action will be taken.
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Determination of k
Data Set 1 Data Set 2 Data Set 3
p 0.4 0.7 0.2
h 0.38 0.5 0.25
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Uncertainty in k
Data Set 1 Data Set 2 Data Set 3
p 0.4 0.7 0.2
h 0.38 0.5 0.25
k 0.772 0.85 0.8
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Determination of Pcalculated

• H is estimated to be 0.65 before the strip mall
  was built

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The Effect of Uncertainty in k

• University Biologists

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The Effect of Uncertainty in k

• Government Study

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The Effect of Uncertainty in k

• Sierra Club Study

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• Good News for white guys with spare weekend time

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