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)
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)
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 Chequamegon NF Chippewa NF Superior NF St Croix Southeast Regional Number of Species Tested 50 49 41 39 40 35 Number of stands 133 135 168 171 211 436
Ovenbird 3.5 3.0 Mean 2.5 2.0 Regional 1.5 1990 1992 1994 1996 1998 2000 Year
White-throated Sparrow 2.0 Mean 1.5 1.0 Regional 0.5 1990 1992 1994 1996 1998 2000 Year
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
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
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
Blackburnian Warbler 1200 Count 800 400 0 0 1 2 3 4 5 6 7 8 9 10 Number of Individuals
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 Node 1 Class = 1 MALANDB1 <= 5.485 Class Cases % 0 257 29.1 1 626 70.9 N = 883 Node 2 Class = 1 CWPDB5 <= 2.375 Class Cases % 0 130 19.9 1 523 80.1 N = 653 Node 5 Class = 0 MFEDB1 <= 18.720 Class Cases % 0 127 55.2 1 103 44.8 N = 230 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 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 1 45 N = 62 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 Terminal Node 5 Class = 0 Class Cases % 0 56 90.3 1 6 9.7 N = 62 Node 7 Class = 0 MWPDB3 <= 0.835 Class Cases % 0 54 50.9 1 52 49.1 N = 106 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 % Correctly Classified = 0.762 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
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
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
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
Evaluation of Modeling Approach
20 140 potl band bland r = 0.77 r = 0.81 r = 0.77 140 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 = 0.60 140 20 140 20 20 140 20 140 Observed Number of Individuals
Bandana Ovenbird Actual = 87.33 Est = 112.00 100 Predicted Number of Individuals 80 60 40 20 0 0 20 40 60 80 100 Observed Number of Individuals
Correlation between Observed and Predicted Species Abundance Plot Bandana Blandin Boise Boulder Lake Clover Erin Pine Potlatch Wolf Ridge Spearman s rho 0.81 0.77 0.81 0.80 0.69 0.55 0.77 0.77 0.60
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
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