1. Using historic data sources to calibrate and validate models of species’ range dynamics Giovanni Rapacciuolo University of California Berkeley giorapac@gmail.com

2. Distribution changes are amongst the most common responses to global change Rapacciuolo et al. (accepted) Global Change Biology e.g. 20 th century elevational range shifts in California

3. Predicting species’ range dynamics under global change is crucial A two-step process: Model calibration – estimation and adjustment of model parameters to maximize agreement between model output and input data Model validation – assessment of how well predictions from the calibrated model agree with data independent from input data

4. How can historical data sources be used to improve model calibration and validation?

5. Species distribution models (SDMs): an inadequate answer to our need for predictions Commonly-used SDMs are static, correlative models that rely on two unlikely assumptions: Observed species distributions are in equilibrium with environmental factors that limit those distributions Species location data used for modelling are representative of the species’ true distribution Model calibration

6. For better models of species’ range dynamics we need spatiotemporal data Unbiased estimation of range dynamics in non- equilibrium situations requires incorporating distribution data at more than a single point in time. We need to model more than just the probability of presence, but the probability of local colonization and persistence For limited species and areas, systematic surveys at multiple time periods are available, but what about the rest? Model calibration

7. Historic surveys Opportunistic, “messy” data are available for many species/regions in web repositories Photo: http://mvz.berkeley.edu Vegetation Type Mapping Kelly et al. 2005 Model calibration Biological collections Photographs Nature Reserve network data

8. More to messy data than meets the eye List length (n species observed) Replicate visit j within site i (i.e. different dates) Replicate visit j within cell i (i.e. different coordinates) = detection = nondetection Model calibration t t + n …

9. We have repeated visits: the basis for an occupancy model! Ecological process Observation process Following MacKenzie et al. (2006) Model calibration

10. Accounting for imperfect detection in messy data Observed occupancy Predicted occupancy Detection probability = 0.356 Detection probability = 0.092 Model calibration

11. Deriving ecological patterns from messy data Model calibration Latitude Probability of occupancy 1901 - 1950 1951-2000

12. Extending the model Check the effect of potentially violating assumptions (e.g. closure) Improving the observation process model with e.g. more realistic time of year model, spatial autocovariate, collection method term Making the model dynamic by adding colonization and persistence parameters Examining the effect of environmental covariates on colonization/extinction Model calibration

13. Calibrated models are used to generate predictions of likely future changes, but how do we validate predictions of events that are yet to happen?

14. British Plants British ButterfliesBritish Birds 1930-1969 1987-1999 1970-19821968-1972 1995-19991988-1991 Testing models using data on historic range changes Model validation Model calibration using data at t Model validation using data at t + 1 Projection

15. Considering the entire range, t + 1 data are accurately predicted from t data… Rapacciuolo et al. 2012 PLoS ONE Model validation

16. …but when focusing on changing parts of ranges, predictions are no better than random! % observed range change Correctly-predicted squares STABLE occupancyCHANGING occupancy Rapacciuolo et al. 2012 PLoS ONE Model validation

17. Rapacciuolo et al. 2014 Methods in Ecology & Evolution Change in predicted probability of presence We need a better measure of how well models predict change over time: TV plots! AUC = 0.934 Model validation Turtle Dove

18. Conclusions Correlative SDMs do not capture processes underlying many species’ range dynamics  ‘Messy’ historic data can help account for those processes during model calibration Existing approaches provide misleading estimates of how well models are likely to predict species’ future range changes  New methods that make direct use of historic data can provide a better assessment

19. Acknowledgements Andy Purvis (Imperial College London, Natural History Museum) David Roy (Biological Records Centre) Simon Gillings (British Trust for Ornithology) Kevin Walker (BSBI) Richard Fox (Butterfly Conservation) Charles Marshall Rosie Gillespie Andy Rominger Michelle Koo Karthik Ram Kevin Koy (University of California Berkeley)