Preservation of the Northern Spooky Tooky

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Preservation of the Northern Spooky Tooky

• Blue Team
• Lucas Annala-Kinne
• Dylan Esmonde
• Emma McArdle
• Rylee Sweeney

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Spooky Tooky

• Three Differing Sets of Data to determine the
  Spooky Tookys Fate
• Set 1 (University Biologists)
• Set 2 (Government Study)
• Set 3 (Sierra Club)

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Data Set 1University Biologists

• Growth Rate Equation
• ??(1 s/?) l?b
• a 3 years
• s 0.95
• so 0.15
• s1 0.72
• b 0.24
• ?3(1 - 0.95/?) (0.950.150.72)(0.24)
• ?3(1 - 0.95/?) 0.024624

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Data Set 1(University Biologists)

• Populations Crash Projections
• - ln ? / ln ?
• - ln(12000)/ln(0.97585)
• ? 384.21 Years
•  

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Data Set 2 (Government Study)

• Growth Rate Equation
• ??(1 s/?) l?b
• a 4 years
• s 0.5
• so 0.10
• s1 0.50
• b 0.28
• ?4(1 - 0.85/?) (0.850.100.50)(0.28)
•  
• ?4 (1 0.85/?) 0.0119

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Data Set 2(Government Study)

• Populations Crash Projections
• - ln ? / ln ?
• ? - ln(12000)/ln(0.8683)
•  
• ? 66.5 Years

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Data Set 3(Seirra Club)

• Growth Rate Equation
• ??(1 s/?) l?b
• a 2 years
• s 0.65
• so 0.05
• s1 0.35
• b 0.20
• ?2(1 - 0.65/?) (0.650.050.35)(0.20)
•  
• ?2 (0.65)?2/? 0.002275

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Data Set 3(Sierra Club)

• Population Crash Projection
• ? - ln ? / ln ?
•  
• ? - ln(12000)/ln(0.65348)
•  
• ? 22 Years

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Three Different Population Crash Projections

• Data Set 1
• (University Biologists)
• Crash after
• 384 years
• Data Set 2 (Government Study)
• Crash after
• 66 ½ years
• Data Set 3
• (Sierra Club)
• Crash after
• 22 years

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Which parameter most severely affects growth rate
(?)?
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Methodology

• First, I averaged the data sets to create a
  control.
• ??(1 s/?) l?b
• ? 3 years
• s .82
• so .1
• s1 .52
• b .24
• l?b.01023
• ? 0.835
• Second, I began testing values that lay below or
  above the average data set. In doing so, I made
  the assumption that the data sets were biased.

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age at which the species starts breeding
Changing ? - When ? is 4, ? 0.8375 When ? is 2,
? 0.8325 Change in ? 0.0050
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adult annual survival probability

• Changing s
• When s 0.95, l?b 0.0119, ? 0.962
• When s 0.65, l?b 0.008112, ? 0.668
• Change in ? 0.294

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survival probability at birth and in fledgling
stage of life

• Changing so
• Changing so to 0.05, l?b 0.00512,
• ? 0.8275
• Changing so to 0.15, l?b 0.01535,
• 0.841
• Change in ? 0.0123

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sub-adult annual survival probability

• Changing s1
• Changing s1 to 0.72, l?b 0.01417,
• ? 0.84
• Changing s1 to 0.35, l?b 0.00689,
• 0.83
• Change in ? 0.00728

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average reproductive rate of female offspring per
adult female in the overall population per year.

• Changing b
• Changing b to 0.28, l?b 0.01194,
• ? 0.837
• Changing b to 0.20, l?b 0.008528,
• ? 0.832
• Change in ? 0.005

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s, the adult annual survival probabilityaffects
? the most

• ? - Change in ? 0.005
• s1- Change in ? 0.00728
• so- Change in ? 0.0123
• s- Change in ? 0.294
• b- Change in ? 0.005
• the owls arent
  dying!I

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Introducing a New Variable (W)

• New Growth Equation
• ??(1 s/?) l?b
• 1 (s/?) w-?1
• Conflicting W-values
• Republican view w 10
• Democratic view w 15

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Data Set 1(University Biologists)

• Republican Projection (w 10)
• ? 0.771
• ? 36 Years
• Democrat Projection (w 15)
• ? 0.858
• ? 61 Years
• Original Projection (w 0)
• ? 0.9759
• ? 384.21 Years

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Data Set 2(Government Study)

• Republican Projection (w 10)
• ? 0.665
• ? 23 Years
• Democrat Projection (w 15)
• ? 0.749
• ? 32 ½ Years
• Original Projection (w 0)
• ? 0.8682
• ? 66 ½ Years

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Data Set 3(Sierra Club)

• Republican Projection (w 10)
• ? 0.4281
• ? 11 Years
• Democrat Projection (w 15)
• ? 0.5062
• ? 14 Years
• Original Projection (w 0)
• ? 0.65348
• ? 22 Years

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Why is ? sensitive to W?

• ??(1 s/?) l?b ??(1 s/?) l?b
• 1 (s/?) w-?1
• ??(1 s/?) ??(1 s/?)
• 1 (s/?) w-?1
• As W increases so will ?
• W ? W ?

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What do you mean theres an error?
Crook
Scientist
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(No Transcript)
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Mind your Ps and Hs
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Time for more strip malls
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Pick your Destiny