Impacts and trends in climate change and other drivers in South African national parks

1. Impacts and trends in climate change and other drivers in South African national parks Nicola J. van Wilgen, Victoria Goodall, Stephen Holness, Steven L. Chown& Melodie A. McGeoch Ferreira S., Foxcroft L., Govender D., Hofmeyr M., Holness S., Roux D., Barendse J., Bezuidenhout H., Bradshaw P., Daemane E., de Klerk-Lorist L., Dopolo M., Freitag-Ronaldson S., Nel J., Russell I., Spear D., van Helden P., van Niekerk L., Vermeulen W., Zimmerman D., Bengis L., Cowell C., Ernst Y., Gaylard A., Greaver C., Herbst M., Lane E., Michel A., Oosthuizen A., Sink K., Solano-Fernandez S., van Niekerk L. & Wassenaar T.

2. Why is it important to assess GEC in PAs? Protected Areas (PAs) are the world’s premier conservation strategy “Moreover, protected areas are key to buffering unpredictable impacts of impending climate change.” – CBD BUT can we be sure that PAs will be immune to climate change and related environmental stressors? Biodiversity losses as a result of a several global change drivers has been observed within PAs (Gaston 2008) SANParks, with a core biodiversity protection mandate, needs to understand the threat posed by Global Environmental Change (GEC) drivers and the degree to which these drivers might compromise the ability to achieve this mandate

3. Global Environmental Change in SANParks Broad project objective: To conduct a quantitative assessment of the current and projected impacts of GEC on Parks, and to use this as a basis for providing policy directives and management recommendations Focus on six change drivers Alien species * Climate change * Disease Change in Freshwater Systems Habitat change Resource use (Overharvesting) *

4. Rationale for CC sub-project • Time and space required for species to respond to change (Parmesan and Yohe 2003, Parmesan 2006, Heard et al. 2012) • Placing PAs in the correct placesis essential to achieve habitat protection, biodiversity representation and provide climate change adaptation corridors and refuges • It’s really important to know what’s happening at local scales • Important to consider past/current trends because future predictions are broad in scale and vary quite dramatically depending on scenario • Local knowledge is needed to support management • Important for park expansion and zonation and decisions regarding species reintroduction or culling • Objective: assess evidence of existing climate change (magnitude, direction and spatial variability) in national parks

5. Assessment of past trends • Weather data obtained from SAWS for 64 stations across SA in / adjacent to national parks • Data checked and cleaned, longest and best series chosen per park • Calculated monthly and annual means, minima and maxima in R • Trend analysis: Linear and LOWESS • Repeated trend analysis using an R module RClimDex • Extreme events identified per month and annually • Trends in rainfall seasonality (not discussed)

6. Basic information per park (longest series)

7. Rainfall patterns

8. Rainfall patterns

9. Annual trends: Rainfall Trends influenced by years with above/ below average rainfall Length of available time series played a large role in whether significant differences were found or not van Wilgen et al. in preparation

10. Significant annual trends: Rainfall Rates of change Sig. decrease Sig. increase No data for Camdeboo & Karoo

11. Mokala rainfall van Wilgen et al. in preparation

12. Extreme Events: Rainfall Agulhas Table Mountain: Cape Point Number of extreme high rainfall months (1.5 x average wettest month) Kruger: Skukuza Photo: Monique McQuillan

13. Extreme Events: Rainfall AddoElephant: Alexanderbos Garden Route: Bloukrans Occurrence of extreme wet years (1.2 x average annual rainfall) Kruger: Skukuza Photo: Monique McQuillan

14. Rainfall variation van Wilgen et al. in preparation

15. Annual trends: Temperature van Wilgen et al. in preparation

16. Significant annual changes: Temperature Rates of temperature change where + and significant No data for Bontebok, Camdeboo & Marakele

17. LOWESS trends in annual temperature LOWESS = locally-weighted polynomial regression (reduces the influence of extreme events and allows the gradient of the trend line to vary over time (Cleveland 1979, Cleveland 1981)

18. Temperature trends by month: TMNP Mar Mar Significant changes in GREEN van Wilgen et al. in preparation

19. Extreme Events: Temperature Photo: Ruth-Mary Fisher Extremes calculated as top & bottom 2.5% of normalized values: Warm: top 2.5% of minimum temperatures Cold: Bottom 2.5% of minimum temperatures Hot: Top 2.5% of maximum temperatures Cool: Bottom 2.5% of maximum temperatures

20. Extreme Events: Temperature Photo: Ruth-Mary Fisher Most extreme increase: 36 days over 50 years!

21. Future climate change • Study conducted by: Stephen Holness, Peter Bradshaw, Guy Midgley, Emma Archer, Barend Erasmus, Danni Guo • Three future scenarios were developed based on current data (1960-1999) using the 15 best GCM models available • Predictions were made for the medium term (for 2050) under each of these scenarios

22. Driver impact per park

23. Acknowledgements • South African Weather Service for providing data • Ulrike Irlich for formatting original data • Andrew W. Mellon Foundation and the SANParks Park Development Fund for funding Thank you! Ferreira S., Foxcroft L., Govender D., Hofmeyr M., Holness S., Roux D., Barendse J., Bezuidenhout H., Bradshaw P., Daemane E., de Klerk-Lorist L., Dopolo M., Freitag-Ronaldson S., Nel J., Russell I., Spear D., van Helden P., van Niekerk L., Vermeulen W., Zimmerman D., Bengis L., Cowell C., Ernst Y., Gaylard A., Greaver C., Herbst M., Lane E., Michel A., Oosthuizen A., Sink K., Solano-Fernandez S., van Niekerk L. & Wassenaar T.