Biol 404: Ecological methods

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Biol 404 Ecological methods
http//www.zoology.ubc.ca/srivast/biol404
Instructor Dr. Diane Srivastava TA Spencer Wood
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What you will learn How to design
experiments How to estimate population size
growth How to quantify species interactions How
to compare ecological communities How to control
for noise How to analyse data correctly How to
present and interpret results How to critically
evaluate published research
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• My goals
• Encourage deep thinking about the scientific
  process
• Provide a toolkit of practical ecological AND
  analytical skills
• Explore both the power and limitations of
  experiments

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Course format Mondays 1-5pm lecture and
lab Wednesdays 1-2 pm lecture 4 lab reports
48 1 peer review 15 1 research proposal 10 2
statistics assignments 20 In-class exercises
2 Participation 5
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• My expectations
• You will do the readings in advance of both
  lectures and labs
• You will submit class data on time !
• You will pass in assignments on time (5 off per
  day, unacceptable after 1 week)

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Class participation! Essential. Participation
graded. Collaborations encouragedto a
limit. Midterm feedback.
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Line transect lecture
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Vegetation transects (Offwell, UK)
Coastal fauna Transects (West-central Florida)
Duck transects along roads (N. Dakota)
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Example 1 UK Butterfly monitoring scheme

                                                
            
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Example 2 Raptor Census - Kyle Elliott (2002)
and the Vancouver Natural History Society
Bald eagles
Short-eared owls
Red-tailed hawks
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Why transects, not always quadrats?
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Animals (in particular) detection bias
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Animals (in particular) detection bias
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Example VNHS Raptor census (Elliott, 2002)
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• Two general methods (see Krebs)
• Distance from random point to organism.
• 2. Distance from randomly selected organism to
  neighbouring organism.

2
1
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• Two general methods (see Krebs)
• Distance from random point to organism.

Area of circle (p r 2) contains one individual
Inverse of Density individuals per unit area
r
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• Two general methods (see Krebs)
• Distance from random point to organism.

All methods calculate area per individual for
each circle, invert, calculate mean density
n p sum (r2)
r
byth-ripley
r
r
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• Two general methods (see Krebs)
• Distance from random point to organism.

If look at third closest organism, we are
calculating area per three organisms, or if
divide by three, mean area per organism (n 3).
3n - 1 p sum (r2)
r
ordered distance
r
r
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• Two general methods (see Krebs)
• Distance from random point to organism.
• 2. Distance from randomly selected organism to
  neighbouring organism.

2
1
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• Two general methods (see Krebs)
• 2. Distance from randomly selected organism to
  neighbouring organism.

Area per two individuals, but two circles
cancels out to same p r 2 formula as before
r
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• Two general methods (see Krebs)
• 2. Distance from randomly selected organism to
  neighbouring organism.

Area per two individuals, but two circles
cancels out to same p r 2 formula as before
r
n p sum (r2)
byth-ripley
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• Two general methods (see Krebs)
• 2. Distance from randomly selected organism to
  neighbouring organism.

Problem how to randomly select first
individual? Nearest organism to a random point
BIASED
Never selected
Frequently selected
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• WAYS TO RESOLVE PROBLEM
• Mark all organisms with a number, and then
  randomly select a few.

BUT if we could count all organisms, we wouldnt
need a census!
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• WAYS TO RESOLVE PROBLEM
• Mark all organisms with a number, and then
  randomly select a few.
• Use a random subset of the area (mark organisms
  in random quadrats).

Byth and Ripley
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• WAYS TO RESOLVE PROBLEM
• Mark all organisms with a number, and then
  randomly select a few.
• Use a random subset of the area (mark organisms
  in random quadrats).
• Use a random point to locate organisms, but then
  ignore area between it and quadrat (biased to
  emptiness).

T-square
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Dartboard analogy for precision/ accuracy
True value
Accurate but not precise
Precise but not accurate
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Spatial pattern
More uniform
More aggregated
Random
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• Line transect lab next Monday
• 15 minute discussion in class beforehand.
• Expect cold weather and rain. Dress
  appropriately.
• How will you record your data? In the rain?