Modeling Habitat Relationships
using Point Counts
Tim Jones
Atlantic Coast Joint Venture
Use of Point Counts
• Investigate responses of avian
populations to management
treatments or to environmental
disturbances
• Estimate spatial distribution of
species
• Model bird-habitat relationships
• Monitor population trends
Study Design Considerations
• Pure trend estimation
– Systematic sampling
• Habitat-specific population estimate
– Stratified by habitat type
• Bird-habitat modeling
– Stratify by habitat type
– Avoid edges/boundaries
Numerous good
sources of
information for
technique
Minnesota’s Forest Bird
Diversity Initiative
What’s the Problem?
• Timber harvesting in Minnesota began to
significantly increase
• Forest songbirds have received little
management attention
Objectives
• Monitor relative abundance of common bird
species to assess annual changes,
• Define avian habitat relationships,
• Determine how forest management activities
influence breeding bird abundance and
distribution, and
• Provide a product that a regional wildlife
biologist could use to plan forest management
activities to accommodate a variety of bird
species, especially those with specific habitat
needs or declining populations in a region.
Monitoring Program Design
• Integrate with each National Forest's
method of describing vegetation cover
types
• forest stand that was > 40 acres, the
minimum size needed for three point
counts
• Fixed radius counts (100m) - although all
birds detected noted
• 10-minute counts (3, 3-5, 5+)
Study Area
12-year Data Summary
1991 - 2002
• > 250,000 individuals observed
• 182 species detected (note about 150
forest-dependent bird species in region)
Trend Analysis
• Statistical analysis
– Non-parametric route regression (James et al.
1996)
– Uses untransformed counts
– Does not assume functional form
– Data for each stand smoothed (LOESS)
– Fitted values averaged across stands for
each year
– Bootstrap 95% confidence interval (1,000
reps)
Disclaimer
• Counts not corrected for detectability
• Assumed all birds within 100m were
always detected
– Based on previous work in Upper Midwest
• Cost of double observer would have
resulted in effort costing > $90,000 (>
$120,000 in 2006)
Forest
Number of Species Number of stands
Tested
Chequamegon NF
50
133
Chippewa NF
49
135
Superior NF
41
168
St Croix
39
171
Southeast
40
211
Regional
35
436
Ovenbird
3.5
Mean
3.0
2.5
2.0
Regional
1.5
1990
1992
1994
1996
Year
1998
2000
White-throated Sparrow
2.0
Mean
1.5
1.0
Regional
0.5
1990
1992
1994
1996
Year
1998
2000
Superior NF
•
•
•
•
•
•
•
•
•
•
•
Decreasing
Eastern Wood-Pewee
•
Winter Wren
•
Ruby-crowned Kinglet
•
Golden-winged Warbler
•
Black-throated Green Warbler •
Black-and-white Warbler
•
Common Yellowthroat
Canada Warbler
Chipping Sparrow
White-throated Sparrow
Rose-breasted Grosbeak
Increasing
Black-capped Chickadee
Red-breasted Nuthatch
Northern Parula
Magnolia Warbler
Pine Warbler
Swamp Sparrow
Regional Summary
Decreasing
•
•
•
•
•
•
•
•
•
•
•
Eastern Wood-Pewee
Brown Creeper
Winter Wren
Hermit Thrush
Black-and-white Warbler
Ovenbird
Common Yellowthroat
Canada Warbler
Scarlet Tanager
Song Sparrow
White-throated Sparrow
Increasing
•
•
•
•
Yellow-bellied Flycatcher
Red-breasted Nuthatch
Northern Parula
American Redstart
Bird-Habitat Relationship
Modeling
Developing Models to Describe How Birds
Respond to Forest Habitat
Habitat Characteristics
• Local site variables
– dominant tree species, relative density
estimates, foliage height diversity (fhd),
percent canopy closure
• Landscape variables
– derived from Landsat TM satellite imagery
– metrics computed using FRAGSTATS
– patch size, cv patch size, patch richness,
Simpson’s diversity index, contagion, edge
density
100m
Habitat Relationship Models
• Statistical Models
– Forest composition
– Landscape pattern
– 82 species
• Probabilistic approach
– Empirical relationship to specific habitat
types
– Allow unified approach for all 129 species
Statistical Methods
• Multiple Linear Regression
– Widely used, assumes normal distribution
• Logistic Regression
– generalized linear model (GLIM), widely used,
assumes binomial distribution, loss of
information
• Classification & Regression Trees
– adaptive, but data intensive
• Poisson Regression
– GLIM, assumes Poisson distribution,
predicts either probability of occurrence or
count
Common Issues in Analyzing
Survey Data
• Small sample size
• Counts do not meet underlying assumptions
of multiple linear regression (e.g., large
spike of zero counts)
• Predictions not constrained by zero (i.e.,
negative abundance)
• Loss of information by converting counts to
presence/absence
Blackburnian Warbler
1
2
0
0
Count
8
0
0
4
0
0
0
0
1
2
3
4
5
6
7
8
9
1
0
N
u
m
b
e
r
o
f
I
n
d
i
v
i
d
u
a
l
s
Poisson Regression
• Poisson regression generally performed
well as compared to logistic regression
– except when the density is high (i.e., small
territory size); underlying data approximates
normal distribution
– At small means (i.e., low density) Poisson
regression performed as well as logistic
regression without loss of abundance
information
Lack of Fit and Poisson
Regression
• Often attributed to overdisperson, which
indicates that the variance and mean are
not equal
• Or because the rate of the count variable
varies between individuals (i.e.,
heterogeneity)
Nashville
Warbler
Node 1
Class = 1
MALANDB1 <=
Class Cases
0
257
1
626
N = 883
5.485
%
29.1
70.9
Node 2
Class = 1
CWPDB5 <= 2.375
Class Cases %
0
130 19.9
1
523 80.1
N = 653
Node 3
Class = 1
ODLANDB1 <= 54.170
Class Cases %
0
119 33.2
1
239 66.8
N = 358
Node 4
Class = 1
DELANDB4 <=
Class Cases
0
74
1
212
N = 286
Terminal
Node 1
Class = 1
Class Cases %
0
46 18.6
1
201 81.4
N = 247
0.725
%
25.9
74.1
Terminal
Node 2
Class = 0
Class Cases %
0
28 71.8
1
11 28.2
N = 39
Terminal
Node 3
Class = 0
Class Cases %
0
45 62.5
1
27 37.5
N = 72
Node 5
Class = 0
MFEDB1 <= 18.720
Class Cases %
0
127 55.2
1
103 44.8
N = 230
Terminal
Node 4
Class = 1
Class Cases %
0
11
3.7
1
284 96.3
N = 295
Node 6
Class = 0
CWEDB4 <= 10.640
Class Cases %
0
110 65.5
1
58 34.5
N = 168
Terminal
Node 5
Class = 0
Class Cases %
0
56 90.3
1
6
9.7
N = 62
% Correctly
Classified = 0.762
Terminal
Node 8
Class = 1
Class Cases %
0
17 27.4
1
45 72.6
N = 62
Node 7
Class = 0
MWPDB3 <= 0.835
Class Cases %
0
54 50.9
1
52 49.1
N = 106
Terminal
Node 6
Class = 0
Class Cases %
0
36 70.6
1
15 29.4
N = 51
Terminal
Node 7
Class = 1
Class Cases %
0
18 32.7
1
37 67.3
N = 55
Summary of Explanatory
Variables
#
100
500
1000
2000
5000
Composition
27
14
5
3
5
6
Patch
27
2
6
7
8
9
Climate
4
Landscape
1
Geographic
2
1
For more
information on
wide array of
statistical
approaches to
modeling species
occurrence and/or
abundance:
Practical Considerations
• Only 30 – 45% of deviance explained
• Difficult to implement for:
– Multiple species (with different responses)
– Multiple management scenarios
– Within a Monte Carlo framework - typically
run 1,000 simulations to bootstrap confidence
intervals
Optimal Solution
• Uniform approach for all 129 species of
interest
• Easily updated with new information (i.e.,
new years of data collectoin)
• Easily linked to predictions of future
habitat conditions
• Directly related to forest management
practices
Probabilistic Modeling Concept
• Use 10 years of field data to generate
probabilities of observing X number of
individuals in sampled area (6.4ha)
• Probabilities are cover type specific
• Updated annually to reflect additional data
• Avoid issue of how to scale density to a
given area
Sample Design
• Sampling unit = 6.4 ha
• Proportional allocation based on amount
of each USFS forest type
• Subsample - 2 points per stand, 10 minute
point count
Land Cover Classification
•
•
•
•
•
•
•
•
not used
jack pine
red pine
white pine
upland mixed
lowland conifer
oak
lowland decid
•
•
•
•
•
•
•
•
aspen/birch
northern hardwoods
regen conifer
regen decid
non-forested wetland
non-forested upland
developed
water
Observed Probability Matrix
Patch
Species
Type
p(0)
p(1)
p(2)
p(3)
p(4)
p(5)
p(6)
p(8)
p(11)
American Robin
1 0.772 0.170 0.039 0.015 0.000 0.000 0.005 0.000 0.000
American Robin
2 0.612 0.235 0.107 0.033 0.003 0.000 0.011 0.000 0.000
American Robin
3 0.818 0.152 0.010 0.020 0.000 0.000 0.000 0.000 0.000
American Robin
4 0.787 0.171 0.029 0.013 0.000 0.000 0.000 0.000 0.000
American Robin
5 0.739 0.198 0.055 0.008 0.000 0.000 0.000 0.000 0.000
American Robin
6 0.813 0.104 0.042 0.035 0.000 0.007 0.000 0.000 0.000
American Robin
7 0.724 0.209 0.049 0.018 0.000 0.000 0.000 0.000 0.000
American Robin
8 0.758 0.183 0.054 0.002 0.000 0.002 0.000 0.000 0.000
American Robin
9 0.706 0.202 0.064 0.020 0.003 0.005 0.000 0.000 0.000
American Robin
10 0.571 0.264 0.121 0.044 0.000 0.000 0.000 0.000 0.000
Simulation Methods
Step 1: Subdivide Patches
Step 2: Populate Subdivisions
• Draw number from random number
generator
• Compare to cumulative probability from
field data
• Determine number of individuals
“observed” for each “sample” area
Step 3: Patch Estimate
• For subdivisions that are not completely
contained in patch, proportionally reduce
estimated number of individuals
• Sum number of individuals across all
subdivisions of a patch
PatchTot 
n
 ind
i 1
i
Evaluation of Modeling
Approach
20
potl
140
band
r = 0.77
bland
r = 0.81
r = 0.77
Predicted Number of Individuals
140
20
boise
bould
r = 0.81
clov
r = 0.80
r = 0.69
140
20
erin
pine
r = 0.55
wolf
r = 0.77
r = 0.60
140
20
20
140
20
Observed Number of Individuals
140
Bandana
Ovenbird
Actual = 87.33
Est = 112.00
Predicted Number of Individuals
100
80
60
40
20
0
0
20
40
60
Observed Number of Individuals
80
100
Correlation between Observed and
Predicted Species Abundance
Plot
Bandana
Blandin
Boise
Boulder Lake
Clover
Erin
Pine
Potlatch
Wolf Ridge
Spearman’s rho
0.81
0.77
0.81
0.80
0.69
0.55
0.77
0.77
0.60
Conclusions
•
•
•
•
•
Model approximates reality
Incorporates observed variability
Appears to have no systematic bias
Easily implemented
Easily updated as additional data become
available
• Does not violate statistical assumptions
Summary
• Point counts are applicable to questions
at a variety of spatial scales and
geographic extents
• Point counts can relate habitat quantity to
a measure of species’ density or relative
abundance
• Point counts do not necessarily relate
density estimates to habitat quality
Summary (cont)
• Point counts good for assessing
adequacy of bird-habitat modeling
• Require long-term commitment of
resources to realize adequate sample size
• If designed correctly allow use to assess
cause of trend
Acknowledgements
Gerald J. Niemi, JoAnn Hanowski,
Nick Danz and Jim Lind
Natural Resources Research Institute,
University of Minnesota Duluth
Funded By
Legislative Commission for
Minnesota’s
Natural Resources
Cooperators
Blandin, Boise Cascade, Potlatch
Minnesota Ornithologists’ Union
University of Minnesota
Chippewa and Superior National Forests Minnesota Power
Dept of Fisheries and Wildlife
Deephaven Elementary School
National Fish & Wildlife Foundation
Natural Resources Research
James F. Bell Foundation
North Central Forest Experiment Station
Institute
Minnesota Audubon Council and Chapters Private Individuals
US EPA
Minnesota DNR
Rajala Lumber Company
US Fish & Wildlife Service
Minnesota Forest Industries (MFI)
Rasmussen Millwork Inc.
US Geological Survey
Minnesota Forest Stewardship Program
St. Louis County
Wolf Ridge Learning Center
Minnesota FRC Research Committee
The Nature Conservancy
Wood Promotion Council