Modeling habitat relationships using point counts - Partners in Flight

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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
Tested

Number of stands

Chequamegon NF

50

133

Chippewa NF

49

135

Superior NF

41

168

St Croix

39

171

Southeast

40

211

Regional

35

436

1990
1992
1994
1996
1998
2000
Year
1.5
2.0
2.5
3.0
3.5
Mean
Ovenbird

Regional

White
-
throated Sparrow

Regional

1990
1992
1994
1996
1998
2000
Year
0.5
1.0
1.5
2.0
Mean
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


Yellow
-
bellied Flycatcher


Red
-
breasted Nuthatch


Northern Parula


American Redstart


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

Decreasing

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

0
1
2
3
4
5
6
7
8
9
10
Number of Individuals
0
400
800
1200
Count
Blackburnian Warbler

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)

Terminal
Node 1
Class = 1
Class
Cases
%
0
46
18.6
1
201
81.4
N = 247
Terminal
Node 2
Class = 0
Class
Cases
%
0
28
71.8
1
11
28.2
N = 39
Node 4
Class = 1
DELANDB4 <= 0.725
Class
Cases
%
0
74
25.9
1
212
74.1
N = 286
Terminal
Node 3
Class = 0
Class
Cases
%
0
45
62.5
1
27
37.5
N = 72
Node 3
Class = 1
ODLANDB1 <= 54.170
Class
Cases
%
0
119
33.2
1
239
66.8
N = 358
Terminal
Node 4
Class = 1
Class
Cases
%
0
11
3.7
1
284
96.3
N = 295
Node 2
Class = 1
CWPDB5 <= 2.375
Class
Cases
%
0
130
19.9
1
523
80.1
N = 653
Terminal
Node 5
Class = 0
Class
Cases
%
0
56
90.3
1
6
9.7
N = 62
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
Node 7
Class = 0
MWPDB3 <= 0.835
Class
Cases
%
0
54
50.9
1
52
49.1
N = 106
Node 6
Class = 0
CWEDB4 <= 10.640
Class
Cases
%
0
110
65.5
1
58
34.5
N = 168
Terminal
Node 8
Class = 1
Class
Cases
%
0
17
27.4
1
45
72.6
N = 62
Node 5
Class = 0
MFEDB1 <= 18.720
Class
Cases
%
0
127
55.2
1
103
44.8
N = 230
Node 1
Class = 1
MALANDB1 <= 5.485
Class
Cases
%
0
257
29.1
1
626
70.9
N = 883
Nashville
Warbler

% Correctly

Classified = 0.762

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

1

Geographic

2

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

Species
Patch
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


Draw number from random number
generator


Compare to cumulative probability from
field data


Determine number of individuals
“observed” for each “sample” area

Step 2: Populate Subdivisions

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





n
i
i
Tot
ind
Patch
1
Evaluation of Modeling

Approach

20
140
20
140
20
140
Observed Number of Individuals
20
140
20
140
20
140
Predicted Number of Individuals
potl
band
bland
boise
bould
clov
erin
pine
wolf
r = 0.77
r = 0.81
r = 0.77
r = 0.81
r = 0.80
r = 0.69
r = 0.55
r = 0.77
r = 0.60
0
20
40
60
80
100
Observed Number of Individuals
0
20
40
60
80
100
Predicted Number of Individuals
Bandana
Ovenbird
Actual = 87.33
Est = 112.00
Plot

Spearman’s rho

Bandana

0.81

Blandin

0.77

Boise

0.81

Boulder Lake

0.80

Clover

0.69

Erin

0.55

Pine

0.77

Potlatch

0.77

Wolf Ridge

0.60

Correlation between Observed and
Predicted Species Abundance

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

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
Funded By

Legislative Commission for
Minnesota’s

Natural Resources