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Modeling Range Distributions of Terrestrial Vertebrates from Species Occurrences and Landscape Variables

Modeling Range Distributions of Terrestrial Vertebrates from Species Occurrences and Landscape Variables. Geoffrey M. Henebry Brian C. Putz Milda R. Vaitkus Amanda K. Holland and James W. Merchant. The Challenge of Habitat Modeling & Range Forecasting.

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Modeling Range Distributions of Terrestrial Vertebrates from Species Occurrences and Landscape Variables

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  1. Modeling Range Distributions of Terrestrial Vertebrates from Species Occurrences and Landscape Variables Geoffrey M. Henebry Brian C. Putz Milda R. Vaitkus Amanda K. Holland and James W. Merchant

  2. The Challenge of Habitat Modeling & Range Forecasting • Recent national efforts to regionalize species models by mosaicking range distributions of adjacent states have revealed significant differences in predicted species distributions both within and across state borders1. • A primary reason for this lack of concordance is variation in modeling methodologies. • To generate seamless regional and national range distribution, unified and generalizable approaches to modeling are required. 1Brannon, R. 2000. An exploratory look at combining vertebrate models from several states: An overview of vertebrate modeling in the western states. GAP Analysis Program Bulletin 9:21-24.

  3. Recursive Partitioning Algorithms Grow Statistical Trees from Multivariate Data • QUEST (Quick, Unbiased and Efficient Statistical Trees)2,3 • Similar to CART (Classification and Regression Trees) algorithm • QUEST has several advantages for habitat modeling: (1) much faster than CART; (2) unbiased variable selection; (3) handles missing values robustly; (4) handles categorical predictor variables with many categories; and (5) automated cross-validation. 2Shih, Y.-S. 1999. Families of splitting criteria for classification trees. Statistics and Computing 9:309-315. 3Lim, T.-S., Loh, W.-Y., and Shih, Y.-S. 2000. A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning Journal 40:203-228.

  4. Animal Modeling Using Statistical Trees Species Occurrence Data Soils Data Terrain Data Temperature Data Rescale Data to 40 km2 Hexagonal Coverage Variable Coverage Precipitation Data Land Cover Classification Wildlife-Habitat Relationship Model QUEST & Tree Pruning Model Inversion Expert Review Range Distribution Map

  5. Nebraska’s Approaches to Habitat Modeling

  6. Amphibian and Reptile Voucher Specimens in Nebraska State Museum Collected from 1970-1999 and georeferenced in 2000 46 species with >10 specimens totaling 12,497 occurrences

  7. Breeding Bird Survey Routes and Christmas Bird Count Circles in Nebraska

  8. Types of Occurrence Patterns • Distribution • Statewide • Delimited • Latitudinal • Longitudinal • Elevational • Patchy • Riparian • Peripheral • Regular • Erratic • Density • Common • Sporadic • Rare • Absent X

  9. Habitat Modeling Using Regional Contrasts Hard cases make for ugly or complicated models

  10. Occurrence data for Mourning Dove (Zenaida macroura ) n = 70,143 Common statewide occurrence

  11. Occurrence data for Ring Necked Pheasant (Phasianus colchicus) n = 32,628 Common statewide occurrence

  12. Occurrence data for Baltimore Oriole (Icterus galbula) n = 5,649 Sporadic statewide occurrence

  13. Occurrence data for Woodhouse’s Toad (Bufo woodhousii ) n=579 Common statewide occurrence

  14. Occurrence data for Gopher Snake (Pituophis catenifer) n=109 Sporadic statewide occurrence

  15. Occurrence data for Milk Snake (Lampropeltis triangulum) n=49 Rare statewide occurrence

  16. Occurrence data & model for Willet (Catoptrophorus semipalmatus) n = 156 Rare delimited occurrence

  17. Occurrence data & model for Short Horned Lizard(Phrynosoma douglasii ) n=21 escapee! Rare delimited/peripheral occurrence

  18. Occurrence data & model for Sharp Tailed Grouse(Tympanuchus phasianellus) n=557 Sporadic delimited occurrence

  19. Occurrence data & model for Tree Swallow (Tachycineta bicolor) n=120 Rare peripheral occurrence

  20. Occurrence data & model for Northern Cricket Frog (Acris crepitans) n=396 Sporadic delimited occurrence

  21. Occurrence data & model for Willow Flycatcher (Empidonax traillii) n=72 Rare peripheral occurrence

  22. Occurrence data & model for Many-lined Skink (Eumeces multivirgatus) n=55 Rare delimited occurrence

  23. Occurrence data & model for Prairie Skink (Eumeces septentrionalis) n=67 Sporadic delimited occurrence

  24. Occurrence data & model for Great Plains Skink (Eumeces obsoletus) n=28 Rare peripheral occurrence

  25. Concluding Thoughts • Transparency and repeatability is better than modeling via literature gestalt. • Inverting the habitat model to forecast range distribution predicts only the occurrence of modeled habitat, not species presence/absence or abundance. Implications for model accuracy assessment (potential vs. realized vs. realizable habitat), especially the interpretation of commission error. • Habitat modeling from statistical trees lend a greater degree of objectivity to data analysis but there is still substantial subjectivity/judgment in the pruning (generalization) phase of the modeling process. • Ability to incorporate museum voucher specimens and curated surveys into the habitat modeling strengthens the base for biodiversity planning.

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