Modeling Habitat Relationships using Point Counts

1. Modeling Habitat Relationships using Point Counts Tim Jones Atlantic Coast Joint Venture

2. 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

3. 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

4. Numerous good sources of information for technique

5. Minnesota’s Forest Bird Diversity Initiative

6. What’s the Problem? • Timber harvesting in Minnesota began to significantly increase • Forest songbirds have received little management attention

9. 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.

10. 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+)

11. Study Area

14. 12-year Data Summary 1991 - 2002 • > 250,000 individuals observed • 182 species detected (note about 150 forest-dependent bird species in region)

15. 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)

16. 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)

18. Ovenbird Regional

19. White-throated Sparrow Regional

20. 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 Superior NF

21. 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 Regional Summary Decreasing Increasing

22. Bird-Habitat RelationshipModeling

23. Developing Models to Describe How Birds Respond to Forest Habitat

24. 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

25. 5000m 2000m 1000m 500m 100m

26. 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

27. 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

28. 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

29. Blackburnian Warbler

30. 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

31. 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)

32. Nashville Warbler % Correctly Classified = 0.762

33. Summary of Explanatory Variables

34. For more information on wide array of statistical approaches to modeling species occurrence and/or abundance:

35. 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

36. 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

37. 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

38. 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

39. 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 Land Cover Classification

40. Observed Probability Matrix

43. Simulation Methods

44. Step 1: Subdivide Patches

45. 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

47. 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

48. Evaluation of ModelingApproach