All for one or One for All? Mapping many species individually vs. simultaneously with random forest. Emilie Henderson, Janet Ohmann, Matthew Gregory, Heather Roberts and Harold Zald August 10, 2012 Ecological Society of America Annual Meeting Portland, Oregon
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All for one or One for All? Mapping many species individually vs. simultaneously with random forest. Emilie Henderson, Janet Ohmann, Matthew Gregory, Heather.
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All for one or One for All?
Mapping many species individually vs. simultaneously with random forest.
Emilie Henderson, Janet Ohmann, Matthew Gregory, Heather Roberts and Harold Zald
August 10, 2012Ecological Society of America Annual Meeting
Portland, Oregon
Species Distribution Modeling
• Been around for a long time, and has exploded over the last decade.
With the rise of new powerful statistical techniques and GIS tools, the development of predictive habitat distribution models has rapidly increased in ecology.
• Web of Knowledge: ‘species distribution’– 2000 - 2001: 556 articles– 2011 – 2012: 1,389 articles
SDM Uses
From Giusan and Thuiller 2005
Strategies for community-level modeling
• ‘assemble first, predict later’
• ‘predict first, assemble later’
• ‘assemble and predict together’
--Ferrier & Guisan 2006
Objective: Compare two strategies for community-level predictive mapping.
You Are Here
Pacific silver fir Abies amabilisGrand fir/ White fir Abies grandis / concolorSubalpine fir Abies lasiocarpaNoble fir / Shasta red fir Abies procera/shastensisBigleaf maple Acer macrophyllusRed alder Alnus rubraMadrone Arbutus menzieziiIncense cedar Calocedrus decurrensMountain mahogany Cercocarpus ledifoliusGiant chinkapin Chrysolepis chrysophyllaPacific Dogwood Cornus nutalliiOregon ash Fraxinus latifoliaWestern Juniper Juniperus occidentalisNo Trees PresentLodgepole pine Pinus contortaEngelman spruce Picea engelmaniiJeffrey Pine Pinus jeffreyiiSugar pine Pinus lambertianaWestern white pine Pinus monticolaPonderosa pine Pinus ponderosaBlack cottonwood Populus balsamifera ssp trichocarpaBitter cherry Prunus emarginataDouglas-fir Pseudotsuga menzieziiOregon white oak Quercus garryanaCalifornia black oak Quercus kelloggiiPacific yew Taxus brevifoliaWestern red cedar Thuja plicataWestern hemlock Tsuga heterophyllaMountain hemlock Tsuga mertensiana
Plot Data
Forest Inventory and Analysis Annual Plots: 1948 plots
Techniques – Random Forest Based (Breiman 2001, Cutler et al. 2007)
Binary prediction (R package: randomForest, Liaw & Wiener 2002)
• Community assembly rules can be used to help refine mapped species lists. (e.g., Guisan and Rahbek, 2011)
• But… imputation avoids the pitfalls & complications of re-assembling communities after mapping because they are never taken apart.
Conclusions• Practical Considerations:
– Models of individual species may be • Strongest in one dimension• Useful for understanding species’ ecology• The best option for some types of available data (e.g.,
presence-only data from museum specimens)
– Nearest Neighbor mapping is a useful tool for building multipurpose maps.
• Ranges and abundances• Composition• Diversity
Acknowledgements
• Nationwide Forest Imputation Study
• Landscape Ecology Modeling Mapping and Analysis team in Corvallis.