Research Unpacked

1. Linking long-term patterns of landscape heterogeneity to changing ecosystem processes in the Kruger National Park, South Africa Sandra MacFadyen11 PhD student and GeoSpatial Analyst, South African National Parks (sandra.macfadyen@sanparks.org) Hui C 2 and Verburg P 3 2 Supervisor, Stellenbosch University, Department of Botany & Zoology (chui@sun.ac.za)3 Co-supervisor, Vrije University, Amsterdam, Environmental Studies (peter.verburg@ivm.vu.nl)

2. Research Unpacked • Linking long-term patterns of landscape heterogeneity to changing ecosystem processes in the Kruger National Park, South Africa

3. Landscape Heterogeneity • Landscape heterogeneity is the cause and consequence of interactions between spatial patterns and ecological processes (Turner et al 2001). • Heterogeneity is the measure of the degree of difference between different landscape elements.

4. composition1 (type); structure2 (pattern) and function3 (process) MacFadyen 2010

5. Functional Importance • Spatial heterogeneity at a variety of scales is functionally important (Pickett et al 1999) • Without an adequate understanding of natural pattern and process, protected area managers are flying blind (Olson 2010)

6. ….Pattern = Process = Pattern…. • With the understanding that spatial patterns affect ecological processes, which in response affects spatial patterns, the natural spatial patterns of the heterogeneity should guide management decisions in protected areas rather than unnatural administrative boundaries (Leitão et al 2006) • Use pattern to decipher process Bailey 2009

7. Research Objectives • ID patterns of heterogeneity at different scales. • ID processes responsible for these patterns. • Investigate dynamics of pattern and process. • Management implications.

8. OBJECTIVE 2 ID processes <=> Patterns OBJECTIVE 1 ID landscape heterogeneity patterns ∆ scales OBJECTIVE 3 Dynamics of Pattern & process OBJECTIVE 4 Management Implications 1972 2010

9. INTRODUCTION South African National Parks Mabunda et al. 2003

10. INTRODUCTION Kruger National Park

11. INTRODUCTION History of Change

12. CHAPTER 1 Chapter 1 • research questions • Can Landsat-MSS -TM and -ETM+ data be satisfactorily geometrically and radiometricallyintercalibrated for standardized comparison? • Does auxiliary data influence pattern detection [physical landscape e.g. topographic elements (elevation, aspect, slope), geology and climate]? • What are the underlying patterns of landscape heterogeneity? • What contribution do landscape metrics make to the pattern and process question? • What is the influence of scale on the detection of landscape heterogeneity? • variables or indices • Landscape structural (spectral) heterogeneity • Landscape functional (metrics) heterogeneity • data requirements • x2 (summer and winter) to x4 (summer, autumn, winter, spring) images per year between 1972 and 2010 = 76-152 images • DEM, geology, rainfall • basic methodology • Real world = raster image (landsat) • geometric & radiometric correction = standardize Landsat MSS-TM-ETM (38yrs) • Band spatial autocorrelation = degree of spatial dependence = appropriate band combinations • Classify (unsupervised, object-orientated, conditional entropy) @ varied scales = test sensitivity of classifications • Integrate auxiliary data (elevation; slope; aspect; geology and rainfall)? – test SAC • Choose best fit (how?) • Calculate structural heterogeneity using moving window or multi-scale heterogeneity maps • Calculate landscape metrics (number of patches, average patch size, total edge density, double-logged fractal, contagion, aggregation index, interspersion/juxtaposition, patch shape variability, entropy, proximity and nearest neighbour distances) • Calculate functional heterogeneity using moving window or multi-scale heterogeneity maps • expected results

13. CHAPTER 1 What constitutes a Landscape

14. What constitutes a Landscape Landform (geology + topographic elements) +> climate <=> ecological processes <=> vegetation and animal response <=+> disturbance Wiens (1999)

15. Landscape Schematic HABITAT SOIL MOVEMENT OF WATER elevation regime slope + CLIMATE local weather LANDFORM aspect microclimate geology HABITAT

16. FLORA FAUNA HABITAT HABITAT SOIL MOVEMENT OF WATER elevation regime slope + CLIMATE local weather LANDFORM aspect microclimate geology HABITAT HABITAT

17. DISTURBANCES FLORA FAUNA HABITAT HABITAT HABITAT SOIL MOVEMENT OF WATER elevation regime slope + CLIMATE local weather LANDFORM aspect microclimate geology HABITAT HABITAT

18. CHAPTER 1 • Topography • Geology • Soil • Rainfall • Temperature • Flora • Fauna

19. LANDSAT ETM+ 10 May 2000 False-color composite

20. LANDSAT ETM+ 10 May 2000 False-color composite

21. LANDSAT ETM+ 10 May 2000 False-color composite

22. LANDSAT ETM+ 10 May 2000 True-color composite

23. LANDSAT ETM+ 10 May 2000 Panchromatic

24. CHAPTER 1 Limitations of Data • Scale: Extent and Resolution • Horizontal and Vertical structure

29. CHAPTER 1 Difference of Scale Elephant Elephant Shrew VS.

30. CHAPTER 1 Horizontal and Vertical

31. CHAPTER 2 Chapter 2 • research questions • ID relationship between selected processes and the structural patterns of landscape heterogeneity. • ID relationship between selected processes and the functional patterns of landscape heterogeneity. • What are the primary processes of landscape change in the KNP? • variables or indices • Correlation coefficients for derived landscape structural heterogeneity and ecological processes. • Correlation coefficients for derived landscape functional heterogeneity and ecological processes. • data requirements • Ch1 derived Landscape structural (spectral) heterogeneity • Ch1 derived Landscape functional (metrics) heterogeneity • Selected physical (fire), chemical (nutrients) and biological (animal movement) ecological processes • basic methodology • Identify processes (drivers of or responders to) of landscape change by exploring the relationships between landscape heterogeneity patterns and -herbivore response; -fire and -rainfall patterns. • Using General Linear and General Additive Models and test Neutral Landscape model and Geographically Weighted Regressions. • Test spatial auto-correlation • expected results

32. CHAPTER 2 Exclusion Experiments • Inside vs. Outside: What is different/missing?

33. CHAPTER 3 Chapter 3 • research questions • Are KNP landscapes homogenising or diversifying over the last 38 years? • What are the spatial and temporal patterns of heterogeneity change? • variables or indices • data requirements • Ch1 derived Landscape structural (spectral) heterogeneity • Ch1 derived Landscape functional (metrics) heterogeneity • basic methodology • Automate processing of imagery according to results of ch1 • Quantify differences between seasons, years, decades using Renyi’s generalized parametric diversity function - landscape spatial dynamics and/or Object-oriented and a chi-square transformation change detection algorithms - assess spatial changes in heterogeneity at different scales over time and/or R and the BFAST library – characterize change by both magnitude and direction. • Landscape trend analysis? • expected results • Change is either directional

34. LANDSAT ETM+ False-color composite 2000

35. LANDSAT TM False-color composite 1984

36. CHAPTER 4 Chapter 4 • research questions • management implications • How will the identification of drivers of landscape change, influence protected area management and decision making. • How can this help global conservation efforts? • Where is the most change occurring? • variables or indices • data requirements • basic methodology • expected results

37. CHAPTER 4 Application of Results • Philosophically • Theoretically • Practically •  KNP management plan

38. Schedule / Timeline

39. Thank you Questions?

40. Notes to myself • Be clear about what elements of landscape heterogeneity are being measured • What metrics and why. How will I decide what indices prove useful and how will I know if a changed index is important to ecosystem functioning. • Develop causal diagram to explain how factors interact, how will I investigate relationships and what data to use • Be clear about auto-correlation and spatial variability (e.g. within satellite image) • Be more specific about scale (explain extent vs. grain) • Stress natural systems when talking about ecological importance of heterogeneity (e.g. fragmentation=bad) • Be clear about what aspects of function will be addressed • NB to explain and defend image classification technique and add sensitivity tests • Can I test the validity of the statement, “ greater landscape heterogeneity provides increased ecosystem resilience and higher species richness”? • Add general explanation of landscape trend analysis • NB to explain why each time I describe how i.e why a certain technique/statistic