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

Transcription

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

7

8

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

12

13

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

15 Statistical analysis Trend 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)

17 Forest Chequamegon NF Chippewa NF Superior NF St Croix Southeast Regional Number of Species Tested Number of stands

18 Ovenbird Mean Regional Year

19 White-throated Sparrow 2.0 Mean Regional Year

20 Superior NF Decreasing Increasing Eastern Wood-Pewee Black-capped Chickadee Winter Wren Red-breasted Nuthatch Ruby-crowned Kinglet Northern Parula Golden-winged Warbler Magnolia Warbler Black-throated Green Warbler Pine Warbler Black-and-white Warbler Swamp Sparrow Common Yellowthroat Canada Warbler Chipping Sparrow White-throated Sparrow Rose-breasted Grosbeak

21 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

22 Bird-Habitat Relationship Modeling

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 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 1200 Count Number of Individuals

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 Node 1 Class = 1 MALANDB1 <= Class Cases % N = 883 Node 2 Class = 1 CWPDB5 <= Class Cases % N = 653 Node 5 Class = 0 MFEDB1 <= Class Cases % N = 230 Node 3 Class = 1 ODLANDB1 <= Class Cases % N = 358 Terminal Node 4 Class = 1 Class Cases % N = 295 Node 6 Class = 0 CWEDB4 <= Class Cases % N = 168 Terminal Node 8 Class = 1 Class Cases N = 62 Node 4 Class = 1 DELANDB4 <= Class Cases % N = 286 Terminal Node 3 Class = 0 Class Cases % N = 72 Terminal Node 5 Class = 0 Class Cases % N = 62 Node 7 Class = 0 MWPDB3 <= Class Cases % N = 106 Terminal Node 1 Class = 1 Class Cases % N = 247 Terminal Node 2 Class = 0 Class Cases % N = 39 % Correctly Classified = Terminal Node 6 Class = 0 Class Cases % N = 51 Terminal Node 7 Class = 1 Class Cases % N = 55

33 Summary of Explanatory Variables # Composition Patch Climate 4 Landscape 1 1 Geographic 2

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 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

40 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 American Robin American Robin American Robin American Robin American Robin American Robin American Robin American Robin American Robin

41

42

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

46

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 n Patch = ind Tot i i = 1

48 Evaluation of Modeling Approach

49 potl band bland r = 0.77 r = 0.81 r = Predicted Number of Individuals 20 boise bould clov r = 0.81 r = 0.80 r = 0.69 erin pine wolf r = 0.55 r = 0.77 r = Observed Number of Individuals

50 Bandana Ovenbird Actual = Est = Predicted Number of Individuals Observed Number of Individuals

51 Correlation between Observed and Predicted Species Abundance Plot Bandana Blandin Boise Boulder Lake Clover Erin Pine Potlatch Wolf Ridge Spearman s rho

52 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

53 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

54 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

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

56 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