Colorado State University: NAISN Hub

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1 Colorado State University: NAISN Hub

2 Researchers Colorado State University Tom Stohlgren Jim Graham Paul Evangelista Sunil Kumar Greg Newman Alycia Crall Sara Simonson Nick Young USGS Fort Collins Science Center Jeff Morisette Catherine Jarnevich Tracy Holcombe

3 Primary Research Areas 25 publications over past year Habitat Distribution Modeling Cyberinfrastructure Development and Support Citizen Science

4 Predicting Species in Space and Time: Risk Maps Response variable Predictor variables Model algorithm Model predictions -Presence only -Presence with pseudo-absence -Presence-absence -Count/abundance + Different environmental variables such as: -Topographic -Climatic -Soil -Geology -Disturbance Different modeling methods such as: -Maxent -GARP -Multiple regression -Logistic regression -CART Model evaluation Model validation -Map of probability of occurrence -Map of predicted count/abundance Example: Potential habitat distribution of invasive plant Dalmatian toadflax (Linaria dalmatica) in Colorado, USA Presence data Environmental layers Predicted probability + Temperature Precipitation Slope Elevation Maxent Maxent Model see Phillips et al. 2006

5 Potential habitat distribution for White Pine Blister Rust (Cronartium ribicola) in the Western United States Relative habitat suitability High Medium Low Percent contribution of predictors -Growing degree days (47.7%) -No. frost days (25.3%) -Elevation (5.3%) -Max. temperature (4.4%) -Radiation (3.8%) AUC = % data Training 25% data Testing (Maxent model; based on 918 presence records from multiple sources)

6 Predicting Suitable Habitat for the Freshwater Diatom Didymosphenia geminata in the Continental US Maxent model provided the most accurate predictions, followed by logistic regression, CART and GARP. The most suitable habitats were predicted to occur in the western US, in relatively cool sites, and at high elevations with a high baseflow index. The results provide insights into the factors that affect the distribution of D. geminata and a spatial basis for the prediction of nuisance blooms. This is the first attempt to model diatom habitat distribution at continental scale Kumar, S., Spaulding, S. A., Stohlgren, T. J., Hermann, K. A., Schmidt, T. S., and Bahls, L. L. (2009). Predicting habitat distribution for the freshwater diatom Didymosphenia geminata in the continental US. Frontiers in Ecology and the Environment 7, doi: /

7 Invasive Species Examples: Potential Python molurus Range in the United States Don t set your pet free here! Rodda et al What parts of the US mainland are climatically suitable for invasive alien pythons spreading from Everglades National Park? Biological Invasions 11: Current Distribution

8 Distributional changes and range predictions of downy brome (cheat grass) in Rocky Mountain National Park. Distributions of cheat grass are highly predictable in space and time with Maxent, a speciesenvironmental matching model. New surveys found cheat grass in areas identified by the model. Bromberg. JE, S. Kumar, C.S Brown, and TJ Stohlgren Distributional changes and range predictions of downy brome in Rocky Mountain National Park. Invasive Plant Science and Management (online)

9 Risk analysis for northern expansion of Africanized Honey Bee populations Working with researchers at NASA, we are examining habitat suitability for Africanized Honey Bees. Our county level models produced such good results apiarist were convinced to share point locations data to make more refined models. Phenology was important in the southeast, while climate drove models in the southwest. Jarnevich, CS, WE Esais, PLA Ma, TR Holcombe, JT Morrisette, JM Nightingale, JE Nickeson, TJ Stohglren, B Tan, & R Wolfe. In Review. Africanized Honey Bees Expanding North.

10 Risk analysis under changing climate at national scales Presence Locations Modeled Distributions: Future Distributions: Lepidium latifolium (Perennial pepperweed) Changes in habitat suitability Red = Increasing Yellow = Stable Blue Decreasing Grey = Unsuitable Black = Clamping (need data) Tracy R. Holcombe, Thomas J. Stohlgren and Catherine S. Jarnevich From Points to Forecasts: Predicting Invasive Species Habitat Suitability in the Near Term. Diversity 2010, 2: Information on seed-source locations and changing climates is the key to accurate risk analysis of invasion potential.

11 Current Distribution African Fountain Grass (Pennisetum setaceum) 2040 Legend Habitat Suitability

12 Modeling the Human Invader We looked at changes in urban 30m cells from LANDSAT from 1992 to AUC = Urbanization increased 7.5% (18,112 km2) in the nine year period--an area the size of Massachusetts. The spread of humans is easily predicted from growing degree days, elevation, humidity, and slope. Stohlgren, TJ, C.S. Jarnevich, and C. Giri Modeling the spread of the human invader in the United States using remote sensing time series. J. Applied Remote Sensing. 4(1)1-12.

13 Cyberinfrastructure International Biological Information System (IBIS) Cyberinfrastructure dedicated to providing information technology tools to help collect, manage, map, and model the world's biological field data Supports 17 websites, 8 invasive species specific Africanized Honey Bees CitSci.org A website in support of citizen science Global Invasive Species Information Network ID Source tools that focus on pest identification National Institute of Invasive Species Science Southern Arizona Buffelgrass Coordinating Committee Buffelgrass data portal Tamarix map Great Lakes Early Detection Network

14 Citizen Science Trainings Introduction to invasives Global positioning systems Monitoring protocols Website Online Tutorials Data Quality Citizen Scientist vs. Professional

15 Evaluation

16 Project Page

17 Manage Project Members

18 Create Data Entry Forms

19 Great Lakes Early Detection Network Internal Verification Stakeholders Natural Resource Managers Data Providers Cattails EDDMaps Researchers Tribal Governments Private Sector Regular Volunteers Registration GLIFWC IBIS imapinvasives MISIN New Invaders SWIMS GISIN GREAT LAKES EARLY DETECTION NETWORK WEBSITE Register For Alerts Customized Alert Casual Observers Others Report A Sighting Verify A Species Verification Acknowledgement

20 What s Next Data Management Survey with Canadian Partners Additional Development, Outreach, and Training for GISIN Great Lakes Early Detection Network Assessment of data contributions Partnership with Yukon River Intertribal Watershed Council Represents 70 First Nations and Tribes in Alaska and Yukon Territory Developing citizen science monitoring program for invasive plant species

21 What s Next