Colorado State University Modeling Potential Distributions of Species at Large Spatial Extents Jim Graham Greg Newman, Nick Young, Catherine Jarnevich Natural Resource Ecology Laboratory Oregon State University / Colorado State University

Colorado State University Jim Graham BS CS and Math from California State University at Chico 15 Years Image Processing at HP 3 Years a CEO for GIS web corp. PhD in GIS from Colorado State University, Fort Collins 4 Years as Research Scientist at the Natural Resource Ecology Laboratory Visiting Professor at OSU

Colorado State University Jim’s Research Engaging citizen scientists Web-based GIS Integrating eco-informatics databases Global species occurrence databases Optimal data access for large spatial databases Habitat suitability modeling at large extents Risks and impacts of energy production

Colorado State University Oregon State University / Invasive Species Rat attacking New Zealand fantail Photo: David Mudge Mnemiopsis leidyi (comb jelly)

Colorado State University Oregon State University / Zebra Mussel Distribution Non-indigenous Aquatic Species Database (USGS), Oct 4 th, 2011

Colorado State University Predicting Distributions Predicting potential species distributions at large spatial and temporal extents Given: Limited data Most have unknown uncertainty Most biased/not randomly sampled >90% just “occurrences” or “observations” Lots of species Climate change and other scenarios

Colorado State University Oregon State University / Occurrences of Polar Bears From The Global Biodiversity Information Facility ( 2011)

Colorado State University Uncertainty in Data Experts more accurate in correctly identifying species than volunteers 88% vs. 72% Volunteers: 28% false negative identifications and 1% false positive identifications Experts: 12% false negative identifications and <1% false positive identifications Conspicuous vs. Inconspicuous Volunteers correctly identified “easy” species 82% of the time vs. 65% for “difficult” species 62% of false ids for GB were CB

Colorado State University GISIN Organizations

Colorado State University Tree Sparrow Occurrences Graham, J., C. Jarnevich, N. Young, G. Newman, T. Stohlgren, How will climate change affect the potential distribution of Eurasian Tree Sparrows (Passer montanus)? Current Zoology, House Sparrows Eurasian Tree Sparrows

Colorado State University Oregon State University / Modeling Process LatLonTempPrecip Occurrences Precipitation Temperature Modeling Algorithm VariableCoefficientP-Value Intercept Annual Precip Annual Temp Map Generation Model Parameters Environmental Layers Spreadsheets Habitat Suitability Map

Colorado State University Oregon State University / Tree Sparrow Model Graham, J., C. Jarnevich, N. Young, G. Newman, T. Stohlgren, How will climate change affect the potential distribution of Eurasian Tree Sparrows (Passer montanus)? Current Zoology, 2011

Colorado State University Spatial Modeling Concerns Over fitting the data Are we modeling biological/ecological theory? What does the model look like? In environmental space vs. geographic space Absence points? What do they mean? Analysis and representation of uncertainty? Can we really model the potential distribution of a species from a sub-sample?

Colorado State University Oregon State University / Two Approaches Occurrences “Greenhouse” experiments Occurrence/Presence Mechanistic Environmental Layers Model Map Correlate Generate Design Generate

Colorado State University Tamarisk Data

Colorado State University Adapted from Richard Pearson, Center for Biodiversity and Conservation at the American Museum of Natural History Observed Occurrences Realized Niche/Distribution Fundamental Niche/Distribution Model Fitted to Occurrences Geographical Space Environmental Space

Colorado State University Niche Temperature Salinity Population Growth Temperature Salinity From the Theory of Biogeography Brown, J.H., Lomolino, M.V. 1998, Biogeography: Second Edition. Sinauer Associates, Sinauer Massachusetts Environmental Space

Colorado State University Oregon State University / Germination Percentage Shrestha, A., E. S. Roman, A. G. Thomas, and C. J. Swanton Modeling germination and shoot-radicle elongation of Ambrosia artemisiifolia. Weed Science 47:

Colorado State University Maraghni, M., M. Gorai, and M. Neffati Seed germination at different temperatures and water stress levels, and seedling emergence from different depths of Ziziphus lotus. South African Journal of Botany 76: Over-fitting The Data? What should the model look like? Maxent model for Tamarix in the US: response to temperature when modeled with temperature and precipitation

Colorado State University Maxent Model Parameters bio12_annual_percip_CONUS, , 52.0, bio1_annual_mean_temp_CONUS, 0.0, -27.0, bio1_annual_mean_temp_CONUS^2, , 0.0, bio12_annual_percip_CONUS*bio1_annual_mean_temp_CONUS, , , (681.5<bio12_annual_percip_CONUS), , 0.0, 1.0 (760.5<bio12_annual_percip_CONUS), , 0.0, 1.0 (764.5<bio12_annual_percip_CONUS), , 0.0, 1.0 (663.5<bio12_annual_percip_CONUS), , 0.0, 1.0 (654.5<bio12_annual_percip_CONUS), , 0.0, 1.0 (789.5<bio12_annual_percip_CONUS), , 0.0, 1.0 (415.5<bio12_annual_percip_CONUS), , 0.0, 1.0 (811.5<bio12_annual_percip_CONUS), , 0.0, 1.0 (69.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 (433.5<bio12_annual_percip_CONUS), , 0.0, 1.0 (933.5<bio12_annual_percip_CONUS), , 0.0, 1.0 (87.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 (41.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 (177.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 (91.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 (1034.5<bio12_annual_percip_CONUS), , 0.0, 1.0 (319.5<bio12_annual_percip_CONUS), , 0.0, 1.0 (175.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 (233.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 (37.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 (103.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 (301.5<bio12_annual_percip_CONUS), , 0.0, 1.0 (173.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 (866.5<bio12_annual_percip_CONUS), , 0.0, 1.0 (180.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 (393.5<bio12_annual_percip_CONUS), , 0.0, 1.0 (159.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 (36.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 (188.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 (105.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 (153.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 (1001.5<bio12_annual_percip_CONUS), , 0.0, 1.0 (74.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 (109.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 (105.0<bio12_annual_percip_CONUS), , 0.0, 1.0 (25.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 (60.5<bio12_annual_percip_CONUS), , 0.0, 1.0 (231.5<bio12_annual_percip_CONUS), , 0.0, 1.0 (58.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 (49.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 (845.5<bio12_annual_percip_CONUS), , 0.0, 1.0 'bio1_annual_mean_temp_CONUS, , 232.5, (320.5<bio12_annual_percip_CONUS), , 0.0, 1.0 (121.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 (643.5<bio12_annual_percip_CONUS), , 0.0, 1.0 (232.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 (77.5<bio12_annual_percip_CONUS), , 0.0, 1.0 (130.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 (981.5<bio12_annual_percip_CONUS), , 0.0, 1.0 `bio12_annual_percip_CONUS, , 52.0, 'bio1_annual_mean_temp_CONUS, , 216.5, `bio1_annual_mean_temp_CONUS, , -27.0, 16.5 (397.5<bio12_annual_percip_CONUS), , 0.0, 1.0 `bio12_annual_percip_CONUS, , 52.0, (174.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 'bio1_annual_mean_temp_CONUS, , 150.5, `bio12_annual_percip_CONUS, , 52.0, 'bio1_annual_mean_temp_CONUS, , 119.5, 'bio1_annual_mean_temp_CONUS, , 219.5, (90.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 `bio12_annual_percip_CONUS, , 52.0, `bio12_annual_percip_CONUS, , 52.0, `bio12_annual_percip_CONUS, , 52.0, 59.0 (385.5<bio12_annual_percip_CONUS), , 0.0, 1.0 (645.5<bio12_annual_percip_CONUS), , 0.0, 1.0 'bio12_annual_percip_CONUS, , 532.5, `bio1_annual_mean_temp_CONUS, , -27.0, `bio1_annual_mean_temp_CONUS, , -27.0, `bio1_annual_mean_temp_CONUS, , -27.0, 18.5 (13.5<bio1_annual_mean_temp_CONUS), , 0.0, 1.0 `bio1_annual_mean_temp_CONUS, , -27.0, 19.5 linearPredictorNormalizer, densityNormalizer, numBackgroundPoints, entropy, Parameters Maxent model from Tamarix model of western US using precipitation and temperature.

Colorado State University Adapted from Richard Pearson, Center for Biodiversity and Conservation at the American Museum of Natural History Observed Occurrences Realized Niche/Distribution Fundamental Niche/Distribution Model Fitted to Occurrences Geographical Space Environmental Space

Colorado State University Oregon State University / Tamarisk Occurrences United States Environmental Space

Colorado State University Can we? Create a species distribution modeling method that: More closely matches ecological theory? Allows visualization of the model in environmental space? Does not use absence points? Allows integration of physiological knowledge and occurrence data? Allows editing the model to try scenarios? Represents uncertainty?

Colorado State University Some Caveats We are modeling “observations” A.Modeling occurrences with some uncertainty B.Modeling the realized niche if the data is a complete sample for the environmental space the species currently occupies C.Modeling the fundamental niche if B is true and the species is covering it’s full possible range of habitats Habitat Suitability Modeling Predicting the potential species distribution

Colorado State University Problems with Polynomials Lack flexibility Not well behaved at boundaries f(x) = a 0 + a 1 x + a 2 x a n x n, where a n ≠ 0 and n ≥ 2 Wikipedia.com

Colorado State University Oregon State University / Bezier and Spline Curves Wikiepedia.com P1P1 P0P0 P2P2 P3P3

Colorado State University Oregon State University / Modified Bezier Curves 08/10/2009International Biological Information Science P2P2 P3P3 P0P0 P1P1 S2S2 S1S1 S0S0 S3S3

Colorado State University

Oregon State University / HEMI Tamarix Model 08/10/2009International Biological Information Science Annual Precipitation (mm) Min Temp of coldest Month (°C*10) Potential Habitat Unsuitable Habitat 5 Points -> 10 Parameters AUC: 0.76

Colorado State University Area Under the Curve Metric Area Under the Curve (AUC) 1=Perfect 0=Worthless

Colorado State University 08/10/2009International Biological Information Science

Colorado State University Oregon State University / Potential Habitat 08/10/2009International Biological Information Science Potential Habitat Unsuitable Habitat

Colorado State University Oregon State University / Global Tamarisk Map? 08/10/2009International Biological Information Science 1.0 Habitat Suitability 2.0

Colorado State University Migration Animations Jim’s web site Gray whale model Barn swallows

Colorado State University Conclusions We can visualize models in environmental space At least in 2 dimensions We can build constrained models and edit the models for scenarios We can reduce the complexity of the models Absence points are not required Categorical variables have to be modeled separately for each category

Colorado State University Next Steps Characterize a number of species to determine how to constrain the envelopes Develop likelihood model to fit Bezier curves? Include uncertainty analysis and error surfaces Move HEMI to the web? More “good” data!

Colorado State University Modeling Online

Colorado State University Oregon State University / Acknowledgements Tom Stohlgren, Lane Carter, Abby Flory, Paul Evangelista, Sunil Kumar, Alycia Crall, Kirstin Holfelder, Sara Simonson, Lee Casuto, Annie Simpson, Jeff Morisette, and many more… Funding: USGS, NASA, NSF, GBIF, NBII NSF Grant #OCI

Colorado State University Deep Water: DOE Risks and Impacts of Exploration and Production of Fossil Fuels: Initially in Gulf of Mexico Expanding to the Arctic Scope: Sub-surface, flow modeling, environmental impacts, economic and social impacts Department of Energy Funding

Colorado State University

US IS Databases National: NIISS, EDDMaps, iMapInvasives, Discover Life, SERC, NAS GLIFWIC SWEMP IPANE ICE SEEPPC Texas Invaders NWIPC

Colorado State University 08/10/2009International Biological Information Science Mapping Species Distributions: Spatial Inference and Prediction (Ecology, Biodiversity and Conservation)

Colorado State University Two Approaches Mechanistic/Greenhouse: Limited genotypic variation Limited ecosystem complexity Widely used in agriculture Occurrence/Presence Based Do the models match ecological theory? Widely used with non-agricultural species Invasive species, T&E Even humans 08/10/2009International Biological Information Science

Colorado State University Oregon State University / Environmental Space: Earth 2010 Annual Precipitation Mean Annual Temperature (C * 10) Maximum Minimum Number of Samples

Colorado State University Oregon State University / System Architecture Database Browser HTML Pages Clients Images Web Server PHP Pages Raster Layers Images Spatial Library Job Controller Web Services Jobs InternetServerClient

Colorado State University Oregon State University / Models for Tamarisk in EGSNM

Colorado State University Impacts Shift management areas Agriculture/Aquaculture Wildlife reserves Natural areas Migration corridors Shift priorities Invasive species Threatened and endangered species Help make decisions for better management 08/10/2009International Biological Information Science

Colorado State University Data Issues All data has uncertainty Need to quantify and maintain uncertainty From field through to distribution maps! Don’t need every occurrence: Need environmental range covered Need to understand barriers to dispersal

Colorado State University Absence Points What do they mean? We did not find the species there at some point in time Was it there before? Was it removed? Has it been able to get there? We should plant it and see what happens! Pseudo-random points?

Colorado State University Database Where Who & When What Area (Name, Code) Visit (Date) Organism Data (TSN) Spatial Data (X, Y) TreatmentsAttributes ProjectsTaxon Units Organizations Pathways? Organism To Area

Colorado State University Structure Overview Database: SQL Server 2008 spatial Data in Geographic, GoogleMaps, others? Web pages and jobs: PHP High-performance and GIS Access: Java Statistics: R Modeling: Maxent 08/10/2009International Biological Information Science

Colorado State University New global model with: 5 points, 10 parameters 08/10/2009International Biological Information Science

Colorado State University Oregon State University / Extinct and Extirpated Passenger Pigeon Extinct Gray whales Recovered in Eastern Pacific Endangered in Western Pacific Extirpated from Atlantic American Chestnut Endangered

Colorado State University Introduction International Biological Information System (ibis.colostate.edu) Bioinformatics led by Jim Graham National Institute of Invasive Species Science ( Research led by Tom Stohlgren Global Invasive Species Information Network ( Data access led by Annie Simpson 08/10/2009International Biological Information Science

Colorado State University Features Add Data: Shapefiles, text files, KML files, PDA upload, enter coordinates, “click on map” Manage data online Map integrated data Download: Shapefiles, text files Model using Maxent 08/10/2009International Biological Information Science

Colorado State University Next Steps Mapping: KML Download GoogleEarth server? Integration with GISIN Modeling: SAHM? BRTs? Envelope Modeling? 08/10/2009International Biological Information Science

Colorado State University Challenges Dynamic ranges (annual migrations, range expansion/contraction) Sparse occurrence data Physiological data: limited Limited environmental data Modeling techniques from other disciplines Potential complex interactions 08/10/2009International Biological Information Science

Colorado State University Species Ranges are Changing Human population increasing until 2050 Increased demand on food, water, sanitation Climate is changing Shifts in species distributions Geologic resource scarcity (oil, P) Food, transportation more expensive Manmade and natural disasters Land use change, pollution, etc. = Increased pressure on key ecosystems