# Presentation: 285430

## 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

**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**

## Slide 7

## Slide 8

## 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

## Slide 12

## > 500 Stands

**> 500 Stands**

## Slide 14

## 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)**

## Slide 18

| 
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 |

## Ovenbird

**Ovenbird**

**Regional**

## White-throated Sparrow

**Regional**

## 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

## Species Habitat Models

**Species Habitat Models**

**Range**
**Limits**

**Population**
**Demographics**

## 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**

## How species response to:

**How species response to:**

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

## 5000m

**100m**

**2000m**

**5000m**

**1000m**

**500m**

## 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

**Blackburnian Warbler**

## Poisson Frequency Distributions

**Poisson Frequency Distributions**

## 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**

## 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**

## Model Performance

**High**

**Low**

**Density**

## 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

**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

## Slide 48

## Slide 49

## 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

**Compare to cumulative probability from field data**
**Determine number of individuals “observed” for each “sample” area**

**Step 2: Populate Subdivisions**

## Slide 53

## 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 **

## Evaluation of ModelingApproach

## Slide 56

**Notes:**
Spearman Correlation coefficients

## Slide 57

## Correlation between Observed and Predicted Species Abundance

| 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

**Funded By**

**Legislative Commission for Minnesota’s **
**Natural Resources**