Density Dependent Habitat Selection by Michigan Wolves

Authors: Shawn T. O'Neil, Joseph K. Bump of Michigan Technological University and Dean E Beyer, Jr. of Michigan Department of Natural Resources. Presentation given at the 2015 Midwest Wolf Stewards Conference at Northland College. April 2015

Transcript

1. Density Dependent Habitat Selection by Michigan Wolves Modeling wildlife-habitat relationships over space and time Shawn T. O’Neil1, Joseph K. Bump1, and Dean E. Beyer, Jr2. 2015. 1Michigan Technological University 2Michigan Department of Natural Resources
2. Outline • Why study habitat selection? • Habitat selection by wolves: what we know • The importance of density dependence in habitat and niche modeling • The density dependent habitat selection model for Michigan wolves • Results • Conclusion & implications for management • Next steps
3. Why study habitat selection? • A link between the animal and its environment o Resource Selection Function o Species Distribution Model • Identifying habitat, or the ecological niche “the combination of resources and environmental conditions necessary to allow occupancy, survival, and reproduction of individuals (Morrison et al. 1992)” – Franklin et al. 2000 o Predicting species distributions o Protecting wildlife habitat o Conserving populations
4. Habitat selection by wolves: what we know • Common predictors (resources vs. risk) o Prey densities o Human densities o Roads o Agriculture • Context-dependent predictors o Elevation, slope, ruggedness o Streams o Land cover • Forested, open, edges o Snow Probability of wolf occurrence based on road and deer densities (upper, Potvin et al. 2005), and road densities (lower, Mladenoff et al. 1995)
5. • Newer developments • Context dependent selection of o Road networks o Logging roads o Human activity A) Selection during the day B) Selection at night Wolf pack selection of the Ya Ha Tinda Ranch near Banff National Park during day vs. night hours Adapted from Hebblewhite & Merrill (2008)
6. Habitat selection by wolves: newer developments • Importance of temporal variability o Interannual (Uboni et al. 2015a) • Pack differences (learned behavior) • Pack size • Precipitation • But not prey distribution! o Seasonal (Uboni et al. 2015b) • Roads • Elevation • Openness • Habitat type • A shifting habitat mosaic o Weather o Human land use (i.e. logging) o Prey distributions Uboni et al. 2015b
7. The importance of density dependence in habitat and niche modeling • Density dependence likely contributes to temporal variability in habitat selection o missing from most habitat selection models • May be a key variable when population is increasing (or decreasing) • Effect on habitat availability o Territorial species o Per capita availability of important habitat decreases as density increases o Competition for high quality sites • Losers forced into marginal habitat
8. Mean (A1 ) = 1.17 Available Mean (A1 )= 1.17 Mean (U1 ) = 0.93 U1 / A1 = 0.79 Mean (A2 )= 1.52 Mean (U1 ) = 0.93 U1 / A1 = 0.61 Mean (A1 ) = 1.17 Mean (U1 ) = 0.93 Used Available Mean (U1 ) = 0.93 Mean (A2 ) = 1.52 Available 2 Used The used habitat distribution is a weighted version of the available distribution The habitat selection function is proportional to the ratio between habitat use and availability (Aarts et al. 2013) Consider the habitat selection function: Any change in the availability distribution of a resource can cause changes in the habitat selection function! - The Generalized Functional Response (GFR) in resource selection (Matthiopoulos et al. 2011, Aarts et al. 2013)
9. • If density influences per-capita availability: o Availability declines as density increases o The use-availability ratio is unlikely to be static (i.e. selection or avoidance of a resource may change); we expect a “functional response” relating to density o Limiting features are likely to have greatest selection at low densities o Selection at high densities may not represent greatest habitat quality • Habitat selection is likely density dependent From McLoughlin et al. (2010)
10. Indications of density dependent MI population growth Number of wolves Estimated available habitat (km2) Home range size (km2) 2000 2005 2010
11. Mills 2008, Fig. 6.4, p. 123
12. The density dependent habitat selection model for Michigan wolves • We modeled habitat selection by Michigan wolves during recolonization (1994-2013) • Telemetry locations were used to generate seasonal home ranges o Each individual with ≥ 14 locations per summer/winter o 962 seasonal ranges • 258 individuals, 111 unique packs
13. Model covariates Prey indices Buck harvest % Deer wintering complex Distance deer wintering complex Mean annual snow depth Prey
14. Model covariates Natural features Elevation CV (Elevation) Forest-Open Edge Density Stream density % Open % Water/Wetlands Slope Prey Natural
15. Model covariates Minor road density Human Influence Prey Natural % Impervious surface % Agriculture Distance to major road Human
16. • Circular assessment window o ¼ of mean wolf home range area (50.75 km2) • Characterizing availability o Restricted to areas not already occupied by existing packs • Known pack territories from track surveys (Beyer et al., Michigan DNR) • Used vs. Available sampling design o For seasonal range within year: • 5 locations per seasonal range (Used distribution = ) • 50 locations per seasonal range (Available distribution = ) Prey Natural Human Cells included for processing Processing cell Focal statistics with moving neighborhood illustration http://resources.arcgis.com/en/help/main/10.1/index.html 1995 2010
17. • Habitat selection coefficients can be generated using logistic regression (McDonald 2013) • Additional structure was needed for longitudinal data (time series) o Repeated sampling of the same packs over time o Random spatial variation due to sampling design • Uneven trapping, monitoring effort • Generalized Linear Mixed Models (GLMMs) o Binomial distributed response (Used = 1, Available = 0) o Random effects: • PACK, BIOLOGICAL YEAR, SURVEY UNIT o R 3.1.1, Package ‘lme4’ o Habitat selection function = = exp() = exp(′ ) = exp( 1 1 +. . . + ) …2001 2002 2003 2004… ..Pack 1 Pack 1 Pack 1 Pack 2 Pack 2 Pack 3 Pack 3 Pack 3..
18. • Effect of wolf density o Wolf density per survey unit o MI DNR abundance estimation tracks wolf densities spatially and temporally (e.g. Potvin et al. 2005) o 3 classes: • Low (25th percentile) = < 5 wolves / 1000 km2 • Medium (25th – 75th percentile) = 5 – 10 wolves / 1000 km2 • High (75th – 100th percentile) = > 10 wolves / 1000 km2 o Interaction terms • Wolf Density * , where = 1 , … , (environmental covariates) • Model Selection o Full model sets for each category (prey, natural features, human influence) o Sequentially reduced each model by removing insignificant effects o Combined results into full model
19. Results • Final habitat selection model o Most of original predictors significantly influenced wolf selection o Many also varied significantly with wolf density • Cross Validation o Leave-one (year) – out, refit model to predict on the omitted data • Observed vs. expected, # locations per resource selection class o Spearman’s rank correlation ( ) = 0.94 (averaged for all years) o R2 = 0.92
20. Density- dependent predictors of habitat selection • Strength of selection/avoidance declines with increasing wolf density: • Bucks harvested (deer density) • % Impervious surface • Elevation • CV(elevation) • Stream density • Strength increases with increasing density • % Agriculture
21. Predicting over space and time • Accounting for • Changes in wolf density • Longitudinal data • Random process variation (sampling & monitoring design) • Results in • High predictive ability • A more informative model
22. Conclusion & Implications • Wolf habitat selection varies depending on density • Selection for some important predictors is strongest at low wolf densities • Selection for other (perhaps essential) predictors remains constant o Deer wintering complex • Density dependent habitat selection in territorial species suggests site-dependent regulation o Potential for source-sink dynamics (Mosser et al. 2009) o Need to consider interstate policies in management plans
23. Next steps • Does habitat selection correlate with fitness? o Population growth rate o Adult survival rate o Event-specific mortality rate • Does a source-sink dynamic exist between higher and lower quality habitats? • Does this regulate population growth? o Driven by resources vs. risk?
24. Acknowledgments: Dean Beyer, Jr., Erin Largent, Bob Doepker, Steve Carson, Brian Roell, Chris Webster, Michigan Department of Natural Resources • List of references: • Aarts, G., J. Fieberg, S. Brasseur, and J. Matthiopoulos. 2013. Quantifying the effect of habitat availability on species distributions. Journal of Animal Ecology 82:1135-1145. • Franklin, A. B., D. R. Anderson, R. J. Gutiérrez, and K. P. Burnham. 2000. Climate, habitat quality, and fitness in Northern Spotted Owl populations in northwestern California. Ecological Monographs 70:539-590. • Hebblewhite, M., and E. Merrill. 2008. Modelling wildlife-human relationships for social species with mixed-effects resource selection models. Journal of Applied Ecology 45:834-844. • Houle, M., D. Fortin, C. Dussault, R. Courtois, and J. P. Ouellet. 2010. Cumulative effects of forestry on habitat use by gray wolf (Canis lupus) in the boreal forest. Landscape Ecology 25:419-433. • Lesmerises, F., C. Dussault, and M. H. St-Laurent. 2012. Wolf habitat selection is shaped by human activities in a highly managed boreal forest. Forest Ecology and Management 276:125-131. • Matthiopoulos, J., M. Hebblewhite, G. Aarts, and J. Fieberg. 2011. Generalized functional responses for species distributions. Ecology 92:583-589. • McDonald, L., B. Manly, F. Huettmann, and W. Thogmartin. 2013. Location-only and use-availability data: analysis methods converge. Journal of Animal Ecology 82:1120-1124. • McLoughlin, P. D., D. W. Morris, D. Fortin, E. Vander Wal, and A. L. Contasti. 2010. Considering ecological dynamics in resource selection functions. Journal of Animal Ecology 79:4-12. • Mills, L. S. 2012. Conservation of wildlife populations: demography, genetics, and management. John Wiley & Sons. • Mladenoff, D. J., T. A. Sickley, R. G. Haight, and A. P. Wydeven. 1995. A regional landscape analysis and prediction of favorable gray wolf habitat in the Northern Great Lakes Region. Conservation Biology 9:279-294. • Mosser, A., J. M. Fryxell, L. Eberly, and C. Packer. 2009. Serengeti real estate: density vs. fitness-based indicators of lion habitat quality. Ecology Letters 12:1050-1060. • Potvin, M. J., T. D. Drummer, J. A. Vucetich, D. E. Beyer, R. O. Peterson, and J. H. Hammill. 2005. Monitoring and habitat analysis for wolves in upper Michigan. Journal of Wildlife Management 69:1660-1669. • Uboni, A., D. W. Smith, J. S. Mao, D. R. Stahler, and J. A. Vucetich. 2015a. Long- and short-term temporal variability in habitat selection of a top predator. Ecosphere 6:art51. • Uboni, A., J. A. Vucetich, D. R. Stahler, and D. W. Smith. 2015b. Interannual variability: a crucial component of space use at the territory level. Ecology 96:62-70.
25. Thank you! Questions?
26. Supplemental
27. Category Predictor (mean within moving window) Abbreviation Range (95% CI) Prey Availability Buck harvest (bucks / km2) BUCK 0.3 – 2.3 % Deer wintering complex PDWC 0.0 – 96.7 Distance to deer wintering complex (km) DDWC 0.0 – 11.8 Human % Agriculture AG 0.0 – 29.9 Distance to highway (km) HWY 1.1 – 20.1 % Developed impervious IMP 0.0 – 4.6 Minor road density (km / km2) RDENS 0.3 – 2.5 Natural Elevation (m) ELEV 187 – 521 Coefficient of variation of elevation CVEL 0.0 – 0.19 Slope (°) SLO 0.4 – 5.3 Open:Forested edge density (km / km2) EDGE 0.1 – 3.5 % Open OPEN 0.1 – 38.1 % Water and wetlands WET 0.0 – 83.6 Stream density (km / km2) SDENS 0.0 – 5.4 Interacting Variables Winter snow depth (cm) SNOW 83.7 – 502.2 Season (summer/winter) SEAS
28. Model Fixed Effects Structure BIC δBIC Prey (Full) + + × + + × + + × 22664.3 36.9 − : Prey (Reduced 1) + + × + × + ( + 22653.4 26.1 − : Prey (Reduced 2) + + × + × + × 22642.9 15.6 − : Prey (Reduced 3) + × + × + × + 22627.3 ℎ
29. Category Predictor SE ( ) Z P (Z) Prey Buck Harvest (BUCK) 5.051 0.518 9.755 < 0.001 % Deer Wintering Complex (PDWC) 0.891 0.106 8.422 < 0.001 Distance to DWC (DDWC) -3.188 0.288 -11.088 < 0.001 Human % Impervious Surface (IMP) -27.924 5.113 -5.462 < 0.001 % Agriculture (AG) 1.599 0.832 1.922 0.055 Distance to Major Highway (HWY) -1.037 0.375 -2.762 0.006 Natural Forest:Open Edge Density (EDGE) 3.818 0.196 19.479 < 0.001 Stream Density (SDENS) 3.248 0.547 5.939 < 0.001 % Water or Wetland (WET) -1.285 0.166 -7.730 < 0.001 Elevation (ELEV) 7.839 0.486 16.114 < 0.001 ELEV2 -3.943 0.957 -4.119 < 0.001 CV Of Elevation (CVEL) 4.475 0.457 9.794 < 0.001 Slope (SLO) -4.592 0.264 -17.405 < 0.001 Annual Snow Depth (SNOW) -1.289 0.256 -5.040 < 0.001 Seasonal Season (SEAS) -0.114 0.041 -2.799 0.005 PDWC × SEAS (winter) 0.891 0.106 8.422 < 0.001 PDWC × SNOW -2.185 0.375 -5.828 < 0.001 Density Dependent BUCK | Wolf Density (WOLF) = Medium -1.090 0.525 -2.076 0.038 IMP | WOLF = Medium 19.688 5.242 3.755 < 0.001 AG | WOLF = Medium -3.034 0.901 -3.366 0.001 HWY | WOLF = Medium -0.317 0.456 -0.696 0.487 ELEV | WOLF = Medium -3.570 0.377 -9.468 < 0.001 CVEL | WOLF = Medium -2.485 0.493 -5.040 < 0.001 SDENS | WOLF = Medium 0.339 0.604 0.562 0.574 WOLF = Medium 1.137 0.134 8.470 < 0.001 BUCK | WOLF = High -4.215 0.575 -7.324 < 0.001 IMP | WOLF = High 20.328 5.215 3.898 < 0.001 AG | WOLF = High -6.669 0.927 -7.192 < 0.001 HWY | WOLF = High 2.302 0.474 4.856 < 0.001 ELEV | WOLF = High -3.189 0.392 -8.135 < 0.001 CVEL | WOLF = High -3.545 0.458 -7.735 < 0.001 SDENS | WOLF = High -2.252 0.596 -3.777 < 0.001 WOLF = High 1.522 0.144 10.578 < 0.001
30. Year R2 R2 Marginal HSF Conditional HSF 1995-1998 0.91 0.42 0.94 0.65 1999 0.95 0.98 0.96 0.99 2000 0.96 0.8 0.92 0.92 2001 0.99 0.91 0.99 0.99 2002 0.92 0.82 0.98 0.99 2003 0.99 0.96 0.99 0.99 2004 0.99 0.94 0.97 0.96 2005 0.94 0.83 0.99 0.99 2006 0.99 0.91 0.99 0.98 2007 0.95 0.6 0.95 0.9 2008 0.94 0.5 0.98 0.93 2009 0.88 0.9 0.95 0.91 2010 0.93 0.83 0.82 0.98 2011 0.9 0.77 0.85 0.92 2012 0.98 0.89 0.92 0.8 2013 0.92 0.87 0.87 0.86 Yearly Avg. 0.95 0.81 0.94 0.92
31. • Consider the habitat selection function for a single variable, minor road density o = the resource selection function (RSF) = o = exp() = exp(′ ) = exp( 1 1 +. . . + ) • 1 , … , are environmental covariates • 1 , … , are coefficient estimates (+ = selection, - = avoidance) o Typically from logistic regression o = • is the used habitat distribution • is the available habitat distribution • Thus, the used habitat distribution is a weighted version of the available distribution • is proportional to the ratio between habitat use and availability (Aarts et al. 2013) • Use proportional to availability o Importance of the availability distribution in the use-availability ratio o Any change in can cause changes in , the estimated habitat selection function
32. Carrying capacity? • Mladenoff 1999: o 581 – 1,357 o But, • Potvin et al. model indicates less available habitat constrained by deer densities • Our results indicate habitat suitability is dynamic o K will vary from year to year o Good year for deer vs. bad year? • Bad year might mean more vulnerable prey • Good year might mean more deer… o Will wolves start preying on moose if deer stay low? o Habitat suitability should be measured by what promotes population growth • Reproduction & survival • Importance of getting good deer numbers