1. 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

2. 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

3. 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.

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

5. 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

6. Wildlife Climate Model warm a) Temp cool A Prediction of Coolingb)
t 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.

7. 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

8. 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

9. 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.

10. 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.

11. 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.

14. 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