On the Meaning and Interpretation of Predictive Future Models: An urgent example for the Yellow-billed Loon Andy Baltensperger, Falk Huettmann, Michael Lindgren EWHALE Lab University of Alaska Fairbanks

What is predictive modeling? Method to develop spatially continuous distribution maps based on environmental niche space Uses observed presence-only or presence/absence wildlife locations as inputs Locations intersected with spatially explicit environmental variables Machine learning software develops algorithm to describe environmental niche space of organism Algorithm projected across study extent to predict “relative indices of occurrence” that represent the geographic niche space of organism

Predictive Modeling Process Predictive Algorithm: Random Forests, Treenet, Maxent DEM Aspect Euclidean Distance Precipitation Temperature How many GBIF harvested points? 16200 lattice points, 5 km grid Regression tree software These models are not meant to be interpreted literally. Rather, they should be thought of as hypothesis aimed at describing general spatial trends in the data and we encourage people to evaluate their accuracy in the field. That said, these models are 90% accurate in predicting the occurrence points and as such represent the best available science for species distributions.

Model Performance Actual Class Total Class Percent Correct 0 N=243 250 96.80 242 8 1 65 98.46 64

Future Scenarios Modeling Utilizes IPCC climate projection scenario models as environmental variable inputs SNAP temperature and precipitation projections by decade until 2099 Currently no other future-projected variables to improve models (future infrastructure, corridors, urban expansion, etc.) No future data to evaluate model accuracy

Wildlife Climate Model warm a) Temp cool A Prediction of Cooling b) t-100 Time t t+100 warm Temp cool Wildlife Climate Model A Prediction of Cooling of Warming The accuracy of the Wildlife-Climate Niche Model remains ~ the same and accurate across predictions; but the specific Wildlife Model outcome is mostly driven by the provided and underlying Climate Models, and can even flip.

Hindcast Modeling Can also use predictive methods to project wildlife distributions in the past Stork nesting distribution for 1939 Prussia Stork nesting distribution from 1939 Assumes mechanisms remain the same. Some doubt that mechanisms will remain in the future too, but at least we are basing predictions on changing climatic variables, instead of just basing them off of the past. Wickert, Wallschlager and Huettmann, 2010

Yellow-billed Loon Model Harvested 65 breeding points from GBIF (Global Biodiversity Information Facility) portal Pseudo-absences randomly distributed outside of Breeding Range (NatureServe) Used SNAP temperature and precipitation projections as environmental variables Decadal averages for each month of the year for 2009, 2039, 2069 and 2099 Model algorithm created using RandomForestsTM (Salford Systems, Inc) and applied to Alaska extent Other data available from USFWS YBLO registry and AKGAP. Probably improve models, but not much. This is just to show what can be done with even relatively sparse data

Used GBIF datapoints (can add more data to make model more robust) Used GBIF datapoints (can add more data to make model more robust). Pseudoabsences randomly distributed outside of NatureServe breeding range.

Good job of predicting close to Nature Serve range Good job of predicting close to Nature Serve range. Even includes some areas not previously included, like eastern Arctic Refuge, St. Lawrence Island and Nunuvat Island. Whether they actually occur on Nunuvat is something that begs for field work. Any new data will aid in improving model accuracy and robustness.

Uses same locational inputs, but uses climate data from 2039 (based on IPP A1B scenario that uses a 7 degrees C increase for Alaska. More conservative than A2 scenario that has 10 degrees C increase.

Conclusions Futures predictions models can be very useful for determining spatially-explicit wildlife distribution trends Applicable to all locational datasets Geographic niche space for YBLO is shrinking with climate change Provide scientific, quantitative and robust insight into future, so that proactive management decisions can be initiated now based on environmental trajectories SNAP, in conjunction with our lab, recently released future predictions for caribou, Alaska marmots, trumpeter swans and reed canary grass, also working on gyrfalcons with Travis Booms. The trends are clear, no need to wait to “see” what will happen. We can predict it now