Modeling Habitat Relationships using Point Counts

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Modeling Habitat Relationships using Point Counts

• Tim Jones
• Atlantic Coast Joint Venture

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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

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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

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• Numerous good sources of information for technique

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Minnesotas Forest Bird Diversity Initiative
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Whats the Problem?

• Timber harvesting in Minnesota began to
  significantly increase
• Forest songbirds have received little management
  attention

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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.

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Monitoring Program Design

• Integrate with each National Forest's method of
  describing vegetation cover types
• forest stand that was gt 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)

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Study Area
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gt 500 Stands
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12-year Data Summary 1991 - 2002

• gt 250,000 individuals observed
• 182 species detected (note about 150
  forest-dependent bird species in region)

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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)

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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 gt 90,000 (gt 120,000 in 2006)

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Ovenbird
Regional
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White-throated Sparrow
Regional
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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

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Regional Summary
Increasing
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

• Yellow-bellied Flycatcher
• Red-breasted Nuthatch
• Northern Parula
• American Redstart

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Bird-Habitat RelationshipModeling
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Developing Models to Describe How Birds Respond
to Forest Habitat
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Species Habitat Models
Population Demographics
Range Limits
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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,
  Simpsons diversity index, contagion, edge density

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How species response to

• Landscape variables
• Land cover
• Age of forest stand
• Climatic factors

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5000m
2000m
1000m
500m
100m
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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

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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

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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

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Blackburnian Warbler
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Poisson Frequency Distributions
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Evaluating Poisson Regression

• Simulated bird counts on real 1.1 million ha
  landscape
• Randomly sampled conifer patches to obtain counts
• Used Poisson and logistic regression models to
  fit data
• Compared performance of the 2 regression
  techniques

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Simulation Details

• Artificial species which uses only conifer
  patches
• Assumed individuals were evenly distributed in
  each patch
• Simple functional relationship between number of
  individuals and patch size
• Did not explicitly model spatial autocorrelation
  between patches

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Model Performance
High
Low
Density
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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

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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)

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Nashville Warbler
Correctly Classified 0.762
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Summary of Explanatory Variables
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• For more information on wide array of statistical
  approaches to modeling species occurrence and/or
  abundance

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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

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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

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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

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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

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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

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Observed Probability Matrix
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Simulation Methods
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Step 1 Subdivide Patches
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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

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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

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Evaluation of ModelingApproach
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Correlation between Observed and Predicted
Species Abundance
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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

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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

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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

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Acknowledgements

• Gerald J. Niemi, JoAnn Hanowski,
• Nick Danz and Jim Lind
• Natural Resources Research Institute, University
  of Minnesota Duluth

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Cooperators
Funded By
Legislative Commission for Minnesotas Natural
Resources