1. Ecological forecasting in the intertidal zone: from MODIS to mussels Brian Helmuth, David Wethey, Venkat Lakshmi, Jerry Hilbish, Allison Smith, Lauren Szathmary, Christel Purvis University of South Carolina, Columbia

2. Intertidal zone is an interface between marine and terrestrial ecosystems

3. Recent worldwide observations of intertidal mortality linked to climate New Zealand OregonWashington Dead mussels Necrotic tissue Bleached algae Photo: Laura Petes

4. Potential causes of mortality, range shifts, and loss of biodiversity Direct physiological effects (acute and chronic) –Changes in aerial body temperature –Changes in water temperature Indirect effects –Competition –Predation Factors not related to climate (e.g., anthropogenic influences)

5. Ecological niche modeling Fundamental vs. realized niche space –Not all species range edges are set by climate –Should not expect to see impacts of climate change everywhere –Climate change may impact organisms in the middle of their ranges Organism performance changes spatially and temporally  include physiological data in niche modeling

6. Climate and Remote Sensing Data Theoretical Models of Organism Body Temperature Make Hypotheses Experimentally Test Hypotheses in the Field and Laboratory Determine Realized Niche Space Physiological and Ecological Data

7. Goals How do we make predictions about geographic range boundaries? –Poleward migrations –Mosaic patterns Where and when do we look for the current and future effects of climate change on ecological patterns? –Biodiversity –Abundance –Mortality How do we mitigate these effects?

8. Two organisms exposed to identical microclimates can experience different body temperatures Seastar at ~12°C Mussel at ~21°C See details on Szathmary et al. poster…

9. Shady Cove Cattle Point Tatoosh Boiler Bay Strawberry Hill Monterey Piedras Blancas Cambria Lompoc Landing Jalama Alegria Boat House Coal Oil Pt Thermal mosaic over a large geographic range (Helmuth et al. 2006 Ecol Monogr)

10. Topex-Poseidon R/S Data for Tidal Height Complex patterns are likely to occur worldwide due to tidal regimes

11. How do we measure animal temperature over large scales in physiologically meaningful ways? Avg Max Mussel Temp Avg MODIS Temp Avg Mussel Temp Critical Physiological Temp See Purvis poster for more details…

12. Quantifying effects of climate over a cascade of scales Weather station data In situ animal temperature data Remote sensing data http://uae2.gsfc.nasa.gov/terra_sat.gif High spatial resolution More field intensive Directly relevant to animals Wide spatial coverage Broad temporal coverage Noninvasive

13. Q stored T body T ground T air Q solar Q rad, sky Q rad, ground Q conduction Q convection Q evaporation Wind Thermal engineering model of animal temp. (“Inside out/Outside in”) m, c p T water

14. Variable Name Data Source (Satellite Platform) [Agency] Time Period Spatial Resolution Temporal Resolution Air Temperature Reanalysis [NCEP/NARR] 1948-present 1979-present 200 km (global) 32 km (NARR) 6 hour avg 3 hour avg LST MODIS (Aqua, Terra) [NASA] Aqua: Aug 2002-present Terra: Feb 2000-present 1 km, 5 km Daily, 8-day average SST MODIS (Aqua, Terra) [NASA] Aqua: Aug 2002-present Terra: Feb 2000-present 4.89 km, 9 kmDaily Solar RadiationPinker (GOES)1996-present0.5 deghourly Solar RadiationReanalysis [NCEP/NARR] 1948-present 1979-present 200 km (global) 32 km (NARR) 6 hour avg 3 hour avg Wave Height, Wind Speed TOPEX/POSEIDON (Jason-1) [NASA] 1992-present 6 km, 0.5 deg, 1 deg 5-day, 10-day Wind SpeedReanalysis [NCEP/NARR] 1948-present 1979-present 200 km (global) 32 km (NARR) 6 hour avg 3 hour avg Relative Humidity Reanalysis [NCEP/NARR] 1948-present 1979-present 200 km (global) 32 km (NARR) 6 hour avg 3 hour avg NASA/NOAA data used as inputs to thermal engineering models NCEP= National Centers for Environmental Prediction; NARR = North American Regional Reanalysis; incorporates NASA R/S Data and NOAA ground-based data as part of reanalysis

15. Model Performance vs. Field Data -5 -4 -3 -2 -1 0 +1 +2 +3 +4 +5 Difference in Monthly Average Maximum too cold too hot (Gilman et al., PNAS 2006)

16. Model Performance vs. Field Data -5 -4 -3 -2 -1 0 +1 +2 +3 +4 +5 Difference in Monthly Average Maximum too cold too hot (Gilman et al., PNAS 2006)

17. Air Temperature vs. Field Data too cold too hot Difference in Monthly Average Maximum -5 -4 -3 -2 -1 0 +1 +2 +3 +4 +5 (Gilman et al., PNAS 2006)

18. Hypothesis testing Produce short-range (8-day) forecasts and test using physiological measurements of stress (hsps, etc.) Generate hindcasts of body temperature back to 1950’s using historical data: compare against biogeographic data Make long-range predictions using GCM models: predict shifts in biodiversity/ ranges

19. Ecological forecasting http://tbone.biol.sc.edu/forecasting 8-day forecasted mussel temperatures in upper intertidal from July 23, 2006

20. Patterns of mortality match forecasting predictions??? Photo: Laura Petes

21. Physiological Information: Semibalanus balanoides have reproductive failure if SST > 10°C in winter Prediction: S. balanoides southern biogeographic limit moved north to the winter 10°C isotherm Remote Sensing Information: 10°C winter isotherm moved north between 1984 and 1998 Biogeography of Barnacles in Europe

22. Sea Surface Temperature (AVHRR 36km) February 1984February 1998 Arrows indicate the southern limit of S. balanoides based on field surveys from Southern Portugal to Denmark in 1984 and 2005

23. MODIS, ASTER Land Surface Temperature MODIS Sea Surface Temperature Climate Measurements Predator/Prey Studies Population Genetics Physiological Tolerances Thermal Engineering Models Wave Run-up Models Tide Cycles In Situ & Laboratory Research Remote Sensing Ecological Modeling Skin vs. Body Temp GPS Positioning Skin vs. Modeled Temp R/Sing Climatic Inputs In situ vs. Modeled Temp

24. Woody Turner and the NASA Ecological Forecasting Team NASA grant NNG04GE43G Nova Mieszkowska, Sierra Jones, Karly Jones, Sarah Gilman, Srinivas Chintala Bernardo Broitman, Carol Blanchette and Packard-PISCO (U.S. West Coast) Steve Hawkins, Alan Southward and MARCLIM (Marine Biological Association, Plymouth, U.K.) Cliff Cunningham and CORONA Acknowledgments