A GIS Approach to Modeling Native American Influence on Camas Distribution:

Transcription

1 A GIS Approach to Modeling Native American Influence on Camas Distribution: Humans as Environmental Variable Braden Elliott Oregon State University 29 March 2013 Northwest Anthropological Conference Portland, Oregon

2 Overview 1. What is camas? 2. Measuring indigenous natural resource management 3. Habitat Distribution Modeling, a brief introduction 4. Modeling camas habitat 5. Cultural centrality: humans as environmental variable 6. Comparing models 7. Next steps

3 Overview 1. What is camas? 2. Measuring indigenous natural resource management 3. Habitat Distribution Modeling, a brief introduction 4. Modeling camas habitat 5. Cultural centrality: humans as environmental variable 6. Comparing models 7. Next steps

4 Camas: wildflowers in the the genus Camassia Perennial, traditionally considered in lily family (Lilieaceae) Recent genetic evidence reclassifies camas in the agave family (Agavaceae) Blue flowers in late spring Edible bulb Important food source 4 major species: C. quamash (common camas) C. leichtlinii (greater camas C. howellii C. cusickii Image Source: Boise Weekly (Nicholas Collias) Used with permission.

5 Overview 1. What is camas? 2. Measuring indigenous natural resource management 3. Habitat Distribution Modeling, a brief introduction 4. Modeling camas habitat 5. Cultural centrality: humans as environmental variable 6. Comparing models 7. Next steps

6 Previous Work Kramer (2000) MA thesis measuring bulb size Looking for increase in size over time Study area: Oregon Tomimatsu et al. (2009) Sequencing ribosomal chloroplast DNA Looking for homogenization within human boundaries Study area: Puget Trough

7 Overview 1. What is camas? 2. Measuring indigenous natural resource management 3. Habitat Distribution Modeling, a brief introduction 4. Modeling camas habitat 5. Cultural centrality: humans as environmental variable 6. Comparing models 7. Next steps

8 Maxent (Maximum Entropy) one flavor of Habitat Distribution Modeling Goal: create a map of where to find suitable habitat Data Needed Presence records Environmental variables Process Generate background sample Compare response of species to background sample Calculate suitability of each pixel

9 Maxent (Maximum Entropy) one flavor of Habitat Distribution Modeling Goal: create a map of where to find suitable habitat Data Needed Presence records Environmental variables Process Generate background sample Compare response of species to background sample Calculate suitability of each pixel

10 Maxent (Maximum Entropy) one flavor of Habitat Distribution Modeling Goal: create a map of where to find suitable habitat Data Needed Presence records Environmental variables Process Generate background sample Compare response of species to background sample Calculate suitability of each pixel

11 Maxent (Maximum Entropy) one flavor of Habitat Distribution Modeling Goal: create a map of where to find suitable habitat Data Needed Presence records Environmental variables Process Generate background sample Compare response of species to background sample Calculate suitability of each pixel

12 Overview 1. What is camas? 2. Measuring indigenous natural resource management 3. Habitat Distribution Modeling, a brief introduction 4. Modeling camas habitat 5. Cultural centrality: humans as environmental variable 6. Comparing models 7. Next steps

13 Data Sources Camas Locations Oregon Flora Project Precipitation, Temperature Max/Min PRISM Climate Group Elevation National Elevation Dataset Slope, Aspect Derived from Elevation

14 Model Parameters Low Resolution All variables downsampled to match coarsest variable Elevation (30m x 30m) vs. PRISM (~925m x ~463m) Train/test partition Some camas locations used to test model, not create it Fitting response curves Overfitting vs. filtering out a real pattern

15 Overview 1. What is camas? 2. Measuring indigenous natural resource management 3. Habitat Distribution Modeling, a brief introduction 4. Modeling camas habitat 5. Cultural centrality: humans as environmental variable 6. Comparing models 7. Next steps

16 Centrality as an Emergent Property All culture area maps are wrong in some way Some problems are unavoidable (projection, scale) Some problems are individual choice (cultural boundaries symbolized with crisp lines) By combining maps, we may measure their agreement Low scores for areas in which the maps disagree High scores for areas in which the maps agree

17 Culture Area Maps Used in Analysis Benniman (1983) Goddard (1997) Jackson (1996) Wilkes (1841)

18 Digitizing and Combining Maps

19 Centrality

20 Overview 1. What is camas? 2. Measuring indigenous natural resource management 3. Habitat Distribution Modeling, a brief introduction 4. Modeling camas habitat 5. Cultural centrality: humans as environmental variable 6. Comparing models 7. Next steps

21 Measuring Model Performance Gain How much better the model does for presence locations compared to everything else, on a natural log scale = e raised to the ((average presence) - (average background)) e is roughly 2.72 AUC Area Under the Curve on a ROC (Receiver Operator Characteristic) plot Gain and AUC may be measured for both training and testing

22 Ecological vs. Socioecological Performance Measurement Ecological Model Socioecological Model Training AUC Training Gain (regularized) Training Gain (unregularized) Test AUC Test Gain (unregularized)

23 Comparing Variable Performance The model runs hundreds of times, re-weighting variables until a stable solution is reached. Final percent contribution (weight) of variables: Variable Contribution (%) Precipitation 55.6 Maximum Temperature 29.6 Elevation 5.8 Centrality 5.4 Minimum Temperature 1.8 Slope 0.9 Aspect 0.9

24 Overview 1. What is camas? 2. Measuring indigenous natural resource management 3. Habitat Distribution Modeling, a brief introduction 4. Modeling camas habitat 5. Cultural centrality: humans as environmental variable 6. Comparing models 7. Next steps

25 Next Steps Include more maps in Centrality variable Create and measure two more cultural variables Tribal Association Categorical variable Derived from Centrality through thresholding Cost Distance to Nearest Oven Continuous variable Derived from archaeological records, using Slope as cost Run more robust model performance measurements Define statistical significance in model comparison

26 Benniman, Jay Works Cited 1983 Tribes and Bands of Traditional Oregon. In Maps in Oregon Indians Culture, History, and Current Affairs: An Atlas and Introduction by Jeff Zucker, Kay Hummel, and Bob Hogfoss.Western Imprints of the Oregon Historical Socity. Elith, Jane, Steven J. Phillips, Trevor Hastie, Miroslav Dudik, Yung En Chee, and Colin J. Yates 2011 A Statistical Explanation of Maxent for Ecologists. Diversity and Distributions. Vol 17: Goddard, Ives 1996 Languages. Handbook of North American Indians. Vol 17. Jackson, Mollie 1997 Oregon Languages. Web: < Kramer, Stephenie 2000 Camas Bulbs, the Kalapuya, and Gender: Exploring Evidence of Plant Food Intensification in the Willamette Valley of Oregon. Unpulished Masters thesis, University of Oregon. Phillips, Steven, Miroslav Dudik, and Robert Schapire 2004 A Maximum-Entropy Approach to Species Distribution Modeling. Proceedings of the Twenty-First International Conference on Machine Learning: Pojar, Jim, and Andy MacKinnon 1994 Plants of the Pacific Northwest Coast. Lone Pine: Vancouver. Tomimatsu, Hiroshi, Susan Kephart, and Mark Vellend Phylogeography of Camassia quamash in western North America: postglacial colonization and transport by indigenous peoples. Molecular Ecology. Vo 18: Wilkes, Charles 1841 Oregon Territory. Maps. Oregon Historical Society Research Library. OrHi