1. Linking long-term patterns of landscape heterogeneity to changing ecosystem processes in the Kruger National Park, South Africa
Sandra MacFadyen 1 1 PhD student and GeoSpatial Analyst, South African National Parks
Hui C 2 and Verburg P 3 2 Supervisor, Stellenbosch University, Department of Botany & Zoology 3 Co-supervisor, Vrije University, Amsterdam, Environmental Studies

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)
Figure 1: Framework of ecosystem components: composition1 (type); structure2 (pattern) and function3 (process).
still-to-add: animate figure
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)
The interactions between organisms and their landscapes creates and maintains landscape heterogeneity.

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
Landscape structure influences ecological system functioning (at broad to finer scales) AND is itself influenced by the results of interactions among biological, physical and geochemical processes.
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 1 OBJECTIVE 2 OBJECTIVE 3 OBJECTIVE 4
ID landscape heterogeneity patterns ∆ scales
OBJECTIVE 2
ID processes <=> Patterns
1972
2010
OBJECTIVE 3
Dynamics of Pattern & process
OBJECTIVE 4
Management Implications
still-to-add: highlight the central goal of PhD study

9. South African National Parks
INTRODUCTION
South African National Parks
SANParks currently manages 22 National Parks, proclaimed under the NEM:PA Act, and is responsible for 53% of SA’s protected areas (Hall-Martin & Carruthers 2003) totalling 3.2% (~ ha) of SA’s land mass. The Kruger National Park makes up the bulk of this area (~ 2 million ha) and as such it is the country’s premier National Park entrusted with the protection of 50 fish; 505 bird; 35 amphibian; 119 reptile; 1990 plant, 148 mammal and thousands of invertebrate species (Mabunda et al. 2003).
Mabunda et al. 2003

10. INTRODUCTION
Kruger National Park

11. History of Change INTRODUCTION
Since the official proclamation of the KNP in 1926, the protectionist philosophy responsible for its birth has ebbed in response to political, socio-economic, intellectual and ethical changes in society (Carruthers 1995). The science underpinning its nature conservation strategies has evolved iteratively through changing ecological paradigms. From eras of hunter-gatherers (Pre 200 AD); to Iron Age farmers, metalworkers and traders ( AD); pioneers and hunters ( ); game preservationists ( ); the creation of a National Park ( ); management intervention ( ) and political changes ( ), humans have long been engineers of natural systems (Mabunda et al. 2003). The extent of mans’ influence on these natural systems is linked to issues of human population density and the evolution of natural science theory and knowledge. In response, associated management regimes have ranged from simple naturalist interest, basic observation of natural phenomenon, a priori management intervention, systematic monitoring; a posteriori management intervention and finally, researched based adaptive management. Today complex adaptive systems and heterogeneity theories steer research, monitoring and management on a strategic adaptive course (Biggs & Rogers 2003).
still-to-add: group years ( ) into management groups

12. Chapter 1 CHAPTER 1 research questions
Can Landsat-MSS -TM and -ETM+ data be satisfactorily geometrically and radiometrically intercalibrated 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 = 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 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
still-to-add: finish off and convert to framework diagram instead of text

13. What constitutes a Landscape
CHAPTER 1
What constitutes a Landscape

14. What constitutes a Landscape
Landform (geology + topographic elements)
+> climate
<=> ecological processes
<=> vegetation and animal response
<=+> disturbance
A physical template, formed by the abiotic environment (topography + geology + climate) upon which complex interactions between landscape pattern (structure) and ecological processes (function) occur.
Wiens (1999)

15. Landscape Schematic + HABITAT SOIL MOVEMENT OF WATER elevation slope
aspect
geology
regime
local weather
microclimate
CLIMATE +
LANDFORM
The primary construct of a landscape is landform (comprised of geology and topographic elements: elevation, slope, aspect) and climate (at varying scales: regime  weather  microclimate). These all affect the movement of water, which in turn influences landform and the formation of soil. All of this => habitat

16. + HABITAT FLORA FAUNA HABITAT SOIL MOVEMENT OF WATER elevation slope
aspect
geology
regime
local weather
microclimate
CLIMATE +
LANDFORM
With a soil substrate in place, vegetation and animals can coexist but not without also influencing their surrounding landscape => habitat change.
HABITAT

17. + HABITAT DISTURBANCES HABITAT FLORA FAUNA HABITAT SOIL
HABITAT
FLORA
FAUNA
HABITAT
SOIL
MOVEMENT OF WATER
elevation
slope
aspect
geology
regime
local weather
microclimate
CLIMATE +
LANDFORM
At any point disturbance may influence these processes and associated patterns => habitat change.

18. Topography Geology Soil Rainfall Temperature Flora Fauna CHAPTER 1
KNP falls within:
Savanna biome (Low & Rebelo 1996)  20 board-scale vegetation types (Mucina & Rutherford 2006)
35 landscapes (Gertenbach 1983)  56 landtypes (Venter 1990)

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. Limitations of Data Scale: Extent and Resolution
CHAPTER 1
Limitations of Data
Scale: Extent and Resolution
Horizontal and Vertical structure
still-to-add: more limitations

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

30. Horizontal and Vertical
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
still-to-add: finish off and convert to framework diagram instead of text
Ecosystem processes = The physical, chemical and biological actions or events that link organisms and their environment (e.g. decomposition, production [of plant matter], nutrient cycling, and fluxes of nutrients and energy). Use pattern to decipher process

32. Exclusion Experiments
CHAPTER 2
Exclusion Experiments
Inside vs. Outside: What is different/missing?
ID processes with the help of long-term exclusion experiments. In the above example, fire and elephants have been excluded from Nwasitshumbe enclosure for ? Years.

33. Chapter 3 research questions variables or indices data requirements
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
still-to-add: finish off and convert to framework diagram instead of text
Landscapes undergo constant temporal and spatial change expressed as patterning in the structural properties of landscapes i.e. The proportion and distribution of available habitats are therefore in continuous flux along with many other ecological phenomena.
Healthy ecosystems are resilient and have an adaptive capacity that can absorb change.
Describing and understanding how these patterns of landscape heterogeneity change over time, is important to understand the system.

34. LANDSAT ETM+
False-color composite
2000

35. LANDSAT TM
False-color composite
1984

36. Chapter 4 research questions variables or indices data requirements
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
still-to-add: finish off and convert to framework diagram instead of text

37. Application of Results
CHAPTER 4
Application of Results
Philosophically
Theoretically
Practically
 KNP management plan
still-to-add: finish off
Pattern–process analysis aims to understand complex relationships between ecological processes and landscape patterns (Schröder and Seppelt 2006). Similarly, I will investigate what natural (in the relative absence of human disturbance) mechanisms (processes) drive landscape structural (pattern) change. In order to continue to generate and sustain compositional, structural and functional dimensions of biodiversity, the relationship between landscape heterogeneity and ecological patterns and processes must be understood (Ricotta et al 2003; Schindler et al 2008; Cullum and Rogers 2011).

38. Schedule / Timeline Activity Target Comments Literature review
Continuous
Read Read Read
Data collection
Dec-11
SANSA
Data processing
May-12
Investigate different geometric and radiometric correction techniques
Data exploration
Jul-12
Explore different ideas of how to model heterogeneity
Data analyses: Objective 1
Oct-12
Identify structural patterns of landscape heterogeneity at different scales.
Journal paper 1
Jan-13
Draft paper for publication
Data analyses: Objective 2
Mar-13
Identify processes responsible for these patterns.
Journal paper 2
Jun-13
Data analyses: Objective 3
Investigate the dynamics (occurrence and interaction) of both pattern and process over the past 38 years.
Journal paper 3
Aug-13
Data analyses: Objective 4
Nov-13
Identify responders to landscape change (pattern + process).
Journal paper 4
Feb-14
Data analyses: Objective 5
Apr-14
Identify drivers of landscape change and translate results into possible management actions.
SANParks report
Jun-14
Final project report
PhD thesis
Dec-14
Compile papers into final thesis
still-to-do: reformat text into gantt chart

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
still-to-do: xref contents