modeling ecological connectivity in a protected …world heritage site and 107 protected areas of...
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MODELING ECOLOGICAL CONNECTIVITY IN A PROTECTED AREA NETWORK IN SOUTHEAST TANZANIA
A THESIS PRESENTED TO THE DEPARTMENT OF HUMANITIES AND SOCIAL SCIENCES
IN CANDIDACY FOR THE DEGREE OF MASTER OF SCIENCE
By ADAM P. DIXON
NORTHWEST MISSOURI STATE UNIVERSITY MARYVILLE, MISSOURI
JUNE, 2012
MODELING ECOLOGICAL CONNECTIVITY IN SOUTHEAST TANZANIA USING A FLAGSHIP SPECIES
Modeling Elephant Movement
Adam P. Dixon
Northwest Missouri State University
THESIS APPROVED
Thesis Advisor, Dr. Ming-Chih Hung Date Dr. Patricia Drews Date Dr. Yi-Hwa Wu Date Dean of Graduate School, Dr. Gregory Haddock Date
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MODELING ECOLOGICAL CONNECTIVITY IN A PROTECTED AREA
NETWORK IN SOUTHEAST TANZANIA
Abstract
The increased fragmentation of habitat capable of sustaining the life cycle of paradigm
species, such as the African elephant, is occurring due to agricultural and urban expansion. In
addition to its intrinsic value as one of the largest animals on the planet, the elephant can serve
as an umbrella species, whose habitat also sustains a large number of other plant and animal
species. Predicting the migration patterns of elephants may offer clues to how to best develop
an effective use of resources in the development of protected and managed areas for the
conservation of nature. Using habitat preference values developed from a habitat selection
framework which uses a combination of elephant GPS point locations with a land cover dataset,
a least cost corridor can be developed. Conditional Minimum Transit Cost is used to develop a
more realistic passage space between protected areas in southeast Tanzania. The network of
protected areas contain some of the last vestiges of east African coastal forest, which contain
world class ecosystems of high endemism and species richness. Using the elephant as a flagship
species might improve the chances of conservation in this region which contains a UNESCO
World Heritage Site and 107 protected areas of various level of management.
Ecological connectivity between protected areas is modeled amongst a network of
protected areas. A new strategy - Conditional Minimum Transit Cost - is successfully employed
for modeling least cost movement across a vast protected area network across the southeast
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Tanzania landscape. Several bottlenecks, areas of concern and areas where increased
protection could increase the effectiveness of corridor space are identified. Using the protected
area as a surrogate for habitat patches may be problematic, since it does not accurately reflect
the landscape as an elephant experiences it. This is most reflected in the results as most
corridors with high resistance to movement are those with a greater distance between origin
and destination. An analysis that identifies habitat patches of adequate size and using those
results as origin and destination nodes is suggested in subsequent iterations of similar analyses.
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Table of Contents
List of Figures ............................................................................................. vii
List of Tables................................................................................................ ix
Acknowledgements ...................................................................................... x
List of Abbreviations ................................................................................... xi
Chapter 1: Introduction ............................................................................... 1
1.1 Research objective ...............................................................................................6
Chapter 2: Literature review ....................................................................... 7
2.1 Least cost modeling .............................................................................................9
2.2 Modeling habitat................................................................................................16
Chapter 3: GIS modeling of connectivity ................................................... 22
3.1 Study area ..........................................................................................................22
3.2 Methodology overview ......................................................................................25
3.3 Water .................................................................................................................28
3.4 Land cover .........................................................................................................31
3.5 Slope ..................................................................................................................34
3.6 Roads .................................................................................................................35
3.7 Human density ...................................................................................................36
3.8 Cost surface .......................................................................................................38
3.9 Corridor modeling with CMTC ............................................................................41
3.9.1 Modeling corridor width and alternate dispersal corridors ..............................45
3.10 Calculating CMTC .............................................................................................47
3.10.1 Finding five percent.......................................................................................48
3.10.2 Extracting five percent ..................................................................................49
Chapter 4: Results and discussion ............................................................. 51
4.1 Cost surface results and discussion ....................................................................52
4.2 Zonal characteristics discussion ..........................................................................60
4.3 CMTC results and discussion ..............................................................................62
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4.3.1 Regional zone results and discussion ...............................................................63
4.3.2 Results from a single corridor ..........................................................................71
4.3.3 Entire study area results and discussion ..........................................................74
Chapter 5: Conclusion ............................................................................... 78
5.1 Least cost modeling ...........................................................................................79
5.2 The use of data ..................................................................................................80
5.3 Model processing issues.....................................................................................83
5.4 The exclusion of scale sensitivity ........................................................................85
5.5 Final thoughts ....................................................................................................85
Appendix A: Africover land cover classes reclassified .............................. 87
Appendix B: Modeled PA connections ..................................................... 91
Appendix C: List of corridors between PAs modeled ................................ 95
References ............................................................................................... 101
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List of Figures Figure 1. Theoretical least cost movement across a cost surface ...................................10
Figure 2. Study area .......................................................................................................23
Figure 3. Flowchart of cost surface development ...........................................................27
Figure 4. Data used for water source .............................................................................29
Figure 5. Flowchart for modeling corridors ....................................................................42
Figure 6. The extent of each zone of analysis within study area. ....................................44
Figure 7. Results of the distance to water analysis .........................................................53
Figure 8. Land cover reclassification ..............................................................................54
Figure 9. Result of the slope analysis .............................................................................55
Figure 10. Result of the distance to roads analysis .........................................................56
Figure 11. Human population density reclassification ....................................................58
Figure 12. The final cost of movement surface...............................................................59
Figure 13. Total area in each zone .................................................................................61
Figure 14. Percent of land cover type for each zone of analysis ....................................62
Figure 15. Model result for Zone A ................................................................................65
Figure 16. Model Result for Zone B ...............................................................................66
Figure 17. Model result for Zone C .................................................................................67
Figure 18. Model result for Interzone A .........................................................................69
Figure 19. Results from Interzone B ...............................................................................70
Figure 20. Model result between Uluguru South and Kimboza in Zone A ......................72
Figure 21. Model result between Ntama and Lionja in Zone C ......................................73
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Figure 22. Model results for entire PA network .............................................................76
Figure 23. Model result classified for PA network .........................................................77
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List of Tables
Table 1. Values given to distance to water criterion .......................................................30
Table 2. Integrated Galanti Land Cover Classes into Preferential Scale...........................33
Table 3. Values used for slope criterion in model ...........................................................35
Table 4. Results of distance to roads analysis .................................................................36
Table 5. Values applied to weight human population density criterion in model............38
Table 6. Final amalgamation of movement cost surface values ......................................41
Table 7. Number of CMTC analyses completed ..............................................................47
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Acknowledgements
The author would like to thank Dr. Neil Burgess of WWF-US for providing the research
concept as well as my thesis advisor Dr. Ming Hung for providing feedback during the
nearly two year process which was required for this thesis. The other two members of
the thesis committee, Dr. Patty Drews and Dr. Yi Hwa Wu, improved the quality of the
thesis considerably through the review process. The author’s parents, grandparents,
siblings and friends were of immense value for their constant support and love.
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List of Abbreviations
AHP Analytical Hierarchy Process
CMTC Conditional Minimum Transit Cost
EROS Earth Resources Observation Satellite
ESA European Space Agency
ESRI Environmental Systems Research Institute, Inc.
FAO Food and Agriculture Organization
GIS Geographic Information Systems
IUCN International Union for the Conservation of Nature
NDVI Normalized Difference Vegetation Index
ORNL Oak Ridge National Laboratory
PAs Protected Areas
SRTM Shuttle Radar Topography Mission
TM Thematic Mapper
UNESCO United Nations Educational, Scientific and Cultural Organization
WDPA World Database of Protected Areas
WWF World Wildlife Fund for Nature
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Chapter 1: Introduction
Geographic Information Systems (GIS) have been employed in recent decades in
the natural sciences arena to model natural phenomena. The discipline of ecology has
been particularly enhanced due to its primary focus on the interactions of the biotic and
abiotic while occurring in some measure of space. The utility of GIS in this type of
research is vast. Ecosystems and individuals in those ecosystems can be modeled from
the global scale to the local. The two forms of visual geographic data, vector and raster,
are well suited for ecological analysis and visualization. Points, lines and polygons as
vector data bound to coordinate systems are powerful allies in the construction and
deconstruction of natural space. Raster surfaces as cell values help to effectively define
ecological characteristics in a simple or complex matrix framework. It is within this
nexus of ecology and GIS that this paper will demonstrate the utility of modeling
ecological connectivity across geographic space using a megafauna species in east Africa
as a surrogate for the connectivity.
Land use conversion is an increasing reality over much of the world’s terrestrial
surfaces. An expected 8.9 billion people are expected to occupy the earth by the year
2050 (United Nations 2005). Agricultural intensification and expansion will need to
increase substantially in order to provide nourishment for many of these people (Gibbs
et al. 2010). Urban areas are expected to triple in size by the year 2030 (Angel et al.
2005). African road networks are projected to increase substantially to support new
economies across the continent (Buys et al. 2006). In northern Tanzania, a road is
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planned to cross the ancient migration routes of the large mammals of the Serengeti
grasslands (Dobson et al. 2010).
The effective result of land use conversion is habitat fragmentation, which is
correlated with declining species abundance (Donovan and Flather 2002), and is an
increasing trend on a global scale (Fahrig 2003). Habitat fragmentation occurs when an
ecosystem goes through a disturbance, which can be natural or anthropogenic in origin.
Natural disasters may include fire, meteorological storms or other natural processes.
Anthropogenic sources are the origin of land use conversion. Agriculture, urban areas
or infrastructure all may be cited as reasons for land use conversion. Species dependent
on the ecological characteristics of fragmented areas are forced to move to a different
location and often experience early mortality. The reduction of habitat area also
contributes to the insularization of species populations (Newmark 1996), since the total
area of potential habitat space reduces in size resulting in an ecosystem unable to
sustain the same population as before.
The first proposed method to deal with land use conversion is to find the most
ecologically significant regions and propose areas for reservation from further human
activity. These reserves establish protected areas (PAs), which have various levels of
management and administration. Protected areas are managed at the international,
national and local levels. The enforcement of various restrictions on the use of lands
within PAs for human activities can be poorly managed in some countries. While an
imperfect solution to the problem of habitat loss, the creation of PAs have provided a
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high degree of successful species conservation which otherwise would have been lost
(Naugton-Treves et al. 2005).
As stated, the creation of PAs has not decreased the overall acceleration of
worldwide species extinction at rates which are estimated to be occurring 1000 times
faster than normal background evolutionary processes (Primm et al. 1995).
Explanations for this rapid loss of unique life forms include anthropogenic-caused land
use conversion, as well as associated vulnerabilities correlated to habitat degradation.
Therefore, the strategies for conservation of biodiversity employed by non-
governmental organizations (NGOs) and government institutions have not sufficiently
decreased the rate of extinctions. The traditional paradigm of discrete populations
within discrete geographic regions has proven inadequate to deal with species loss.
Isolation of wildlife populations within protected areas has been shown to reduce
genetic diversity and increase genetic drift (Frankam 2005), as well as increase the risk
of competitors, predators, parasites, diseases, and natural catastrophes (Shaffer 1981).
Thus, perceiving healthy wildlife populations as continuously dispersed across a
functional geographic space may more adequately address the concern of the species
loss.
The concept of modeling habitat connectivity has been proposed as a landscape
planning tool to avoid the negative effects of habitat insularization. Habitat connectivity
refers to the realization of natural pathways a species will take while moving across
geographic space. Habitat selection and the path of least resistance might help predict
the likelihood for an individual to take a certain route. The concept of random walking
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serves as a baseline for the concept of connectivity, which posits that an individual will
take the path of least resistance. The path is often characterized by slope, elevation,
foraging and mating potential amongst other parameters which may be used to predict
the most likely route traveled by an individual. The rationale of connectivity is that if
individuals can have some protected route to continue their life cycles and spread
genetic diversity amongst macropopulations, then perhaps the conservation objective
of stabilizing species loss can be achieved. By extension, a perfect place to find a route
is between protected areas which suffer from insularization and might have increased
capacity to conserve species if some degree of connectivity with the exterior landscape
is realized.
Large mammals work well as a surrogates for the overall success of conservation
efforts. It has frequently been demonstrated that the first observable species loss from
an area due to habitat disturbance are large mammals (Newmark 1993). This has been
observed for the American Bison on the North American prairie (Newmark 1993), as
well as the black panther, whose historic range covered Florida through to south central
South America (Rabinowitz and Zeller 2010). The African elephant Loxodonta africana
is a large mammal species whose ability to range across the landscape is limited by the
abundance of foraging material and other traditional habitat characteristics, as well as
the more recent human-elephant conflict events that have resulted in large numbers of
elephant mortality in recent times (Blanc et al. 2007). The population of African
elephants plummeted fifty years ago down to only 100,000 individuals from 2 million,
and then in recent decades has finally risen to somewhere around 500,000 (Blanc et al.
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2007). Perhaps the lower numbers of elephants from historical times indicates the
pressures facing many ecosystems across eastern Africa, and indeed across the globe.
The concept of flagship species is helpful when developing conservation
scenarios for an area of conservation concern. The term flagship species in this paper is
intended to have similar connotations to the terms surrogate species, umbrella species,
indicator species or focal species. The elephant is certainly a prime example of a species
with enough malleable marketing prowess and ecological significance to fit nicely into
any definition produced for any of the terms. It is a species that needs large tracts of
habitat that by conserving will automatically operate as effective conservation for a
number of other species (Simberloff 1998). To fully assess the effectiveness of a flagship
species, an ecological characterization of similar habitat can evaluate the true value of a
flagship species. Epps et al. (2011) found that elephant presence was positively
correlated with a number of other large mammals. Therefore, predicting the pathway
an elephant may take from one protected area to another might help to ensure the
continued existence of the species, as well as serve as a flagship species for a network of
interconnections between the biotic, abiotic and physiographic that serves to stabilize
ecosystems.
Developing predictive parameters for modeling ecological connectivity within a
GIS is a complex exercise. There are many decision points in designing a methodology
to execute a model. There is, however, a vast collection of literature that has been
developed over the last several decades that can serve as a guide to methodology
development.
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1.1 Research objective
The protected areas of southeast Tanzania currently lack a conservation plan
with an incorporated connectivity analysis. This has implications for coastal forest and
elephant populations in the region. The research objective is to offer a methodology to
model elephant corridors for ecological pattern and process within protected areas in
southeast Tanzania between Selous Game Reserve and the eastern coast.
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Chapter 2: Literature review
The definition of connectivity has been described as the degree to which the
landscape facilitates or impedes movement among resource patches (Taylor et al.
1993). Reviewing the topic of connectivity modeling yields a great deal of research
dealing with the GIS and mathematical basis for connectivity modeling and perhaps in
higher quantity the ecological application that serves as the major driver for inquiry into
the concept. Connectivity from a GIS perspective is perhaps as important as any other
subsection of research. Ensuring that nodes of connectivity are designed properly plays
a major role in the overall effectiveness of the effort. The concept of connectivity from
the ecological perspective can be developed once the GIS theory is developed.
Establishing of a focal species for the landscape being modeled is a critical component
to the effort since the behavioral characteristics of the species are what the model is
calculating. Evaluating the role of geographic information within the project is also of
great concern. The quality of the data governs to a large degree the outputs.
Addressing issues in corridor effectiveness ensures outputs are actually correlated with
the natural occurrence of phenomena.
Connectivity modeling may have originated with the concept of graph theory.
Two major concepts in graph theory are that of the node and the edge, which are both
not necessarily bound to geographic coordinates and can be employed outside of a GIS.
Perhaps most similar is the ESRI geometric network, which is used for routing network
problems like shortest path analysis and the traveling salesman problem. The
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Geometric Network borrows heavily from graph theory. An additional first concept that
emerged to make connectivity modeling possible is Dijkstra’s algorthim, which finds the
shortest path between two nodes in a network (Urban et al. 2009).
In ecological applications, a node represents a habitat patch, which may have a
greater or lesser value in habitat quality, and an edge may represent some structural
component to the landscape. Urban and Keitt (2001) described the graph theoretic
perspective in landscape applications as a way to evaluate functional connections
between habitat patches and dispersal corridors, and that the gain or loss of edges and
nodes is a way to evaluate different management strategies. They demonstrated how
Mexican Spotted Owl habitat in the American Southwest can be evaluated using patch
structure and edge length to determine impact of different habitat alteration scenarios.
Urban et al. (2009) argued that graph theory is perhaps the best suited device to model
landscape connectivity since it provides insights into conservation and ecology that no
other geostatistcal device can. This might be the evaluation of habitat patch resistance
to disturbance or how to optimize a habitat mosaic.
McRae et al. (2008) took a permutation of graph theory as it evolved in electrical
engineering theory and proposed that electrical circuit theory may best represent an
ecological network. Nodes were defined as resistors and edges as currents with a
positive or negative charge. They suggested that statistical software is able to compute
landscapes of much higher complexity with thousands of habitat patches in a
reasonable amount of time rather than some GIS applications and is therefore a more
apt tool in the understanding of landscape complexity. Shah and McRae (2008)
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developed a software package called CircuitScape that automatically converts
geographic vector and raster data into a circuit network and creates measures of
voltage and resistance across a landscape. They argued that CircuitScape will help to
analyze larger landscapes due to its ability to take large datasets and provide a
sophisticated analysis without the aid of large research-grade computers.
2.1 Least cost modeling
Also progressing along a parallel track throughout the last couple decades is the
use of GIS software to compute connectivity, such as ESRI’s ArcGIS. Graph theory and
GIS methods to measure landscape connectivity adapt Dijkstra’s 1959 algorithm which
computes least cost paths between edges and nodes. ESRI’s Spatial Analyst extension
contains a set of tools to find the least cost path between two points on a landscape
using a raster surface that represents cost of movement through each raster cell. This is
essentially modeled as a line the width of the raster lattice cell between the two points.
Most research is an applied use of connectivity measurement in the form of
least-cost analysis with the development of cost surfaces (Adriaensen et al. 2003;
Driezen et al. 2007; LaRue and Nielsen 2008; Pinto and Kiett 2008; Urban et al. 2009).
Cost surfaces measure the cost to traverse from one point on the landscape to another.
Least cost modeling begins from the notion that terrestrial habitat patches are often
surrounded by a complex mosaic of land cover types ranging from perfectly hospitable
to hostile to movement by wildlife (Ricketts 2001). It is then complemented by the
ideas of graph theory, which postulates that connectivity can be quantified though the
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measurement of edges and nodes along possible paths between two points (Pinto and
Keitt 2008). The general idea of connectivity is then applied to ecological space by
defining the ecological linkages across a landscape.
Landscape connectivity can be broken down into structural components, as well
as functional components. Structural connectivity refers to characteristics of the
landscape such as habitat suitability, while functional connectivity refers to the potential
mobility in a given landscape and takes into consideration elevation change across a
landscape, a waterway or human interference to movement (Adriaensen et al. 2003).
Least-cost modeling can be used to model landscape connectivity based on the patterns
and processes of a landscape based on the potential mobility of a species.
Calculating a least-cost path is based on a simple algorithm that calculates any
given movement from one cell to another and chooses the neighbor cell with the lesser
value. Each cell has eight neighbors and thus eight potential directions that may offer
the least cost of travel (Figure 1).
Figure 1. Theoretical least cost movement across a cost surface
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Adriaensen et al. (2003) described the algorithm as movement from cell Ni to
Ni+1, as shown in Equation 1 for least-cost movement. Ni is the accumulated cost of
movement along the path and Ni+1 is the final value of the neighbor cell. The cost to
reach cell Ni is calculated using resistance values (r) of each cell plus the average cost to
move through cell Ni and Ni+1.
푵풊 ퟏ = 푵풊 + 풓풊 풓풊 ퟏퟐ
Equation 1
Using an average cost make the relationship symmetrical since movement can occur in
either direction, as shown in Equation 2 for diagonal least-cost movement. When
moving between diagonal cells, the cost is multiplied by the square root of two to
compensate for the increased distance between the cells.
푵풊 ퟏ = 푵풊 + (풓풊 풓풊 ퟏퟐ
) × √ퟐ Equation 2
Cost is described as a friction or resistance value and is the first of two surface
layers used in the calculation in the GIS. The second surface layer is the source layer,
which represents the habitat patch or the source from where the connectivity will be
calculated. Several studies utilize the least-cost tool available within ESRI ArcGIS
Toolbox to derive a calculation (Adriaensen et al. 2003; Cushman et al. 2009; La Rue and
Nielsen 2007; Driezen et al. 2007; Rabinowitz and Zeller 2010).
Determining friction/resistance is an essential component to modeling
connectivity. Several studies have developed resistance surfaces derived from habitat
suitability models of one to several focal species (La Rue and Nielson 2007; Beier et al.
2007; Rouget et al. 2006). Chetkiewicz et al. (2006) had argued that resource selection
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functions, which are based on species occurrence data, provide a better framework for
modeling wildlife movement. Rouget et al. (2006) produced a least-cost model based
on an amalgam of environmental factors, including vegetation type, habitat suitability,
protected area location, and future land-use changes. Beier et al. (2007) suggested that
a resistance surface be developed by essentially creating a grid of values that are the
inverse of the habitat suitability model. Raster cells that have high suitability or
likelihood of species occurrence will score low on resistance to travel across the cell.
The least-cost resistance layer is formed by combining multiple GIS datasets that
represent the landscape matrix, and then reclassifying them to reflect either arbitrary,
field-based, or empirical values representing resistance. An example of field-based
values comes from Ricketts (2001) where butterflies were measured for resistance of
movement between two points between landscape matrices defined by the tree
species, which were either conifer forest or willow thicket. Individual resistance values
were obtained for several butterfly species by mark recapture and a maximum
likelihood equation, which produced index values ranging from 0.9 to 12.6. Another
example is Cushman et al. (2009) who attempted to measure resistance values for
movement of grizzly bears using molecular genetics to determine bear movement, and a
set of GIS datasets (land cover, roads, slope and elevation) within a statistical modeling
framework to determine the correlation between movement and matrix. The bear
movements that correlated with landscape matrix heterogeneity provided evidence for
the determination of resistance values. Another variation in determining resistance
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from empirical studies comes from Driezen et al. (2007) who assigned resistance values
based on hedgehog Erinaceus europaeus occurrence data points within land cover type.
Beier et al. (2007) reviewed two common algorithms to combine the relative
influence of multiple criteria. The weighted arithmetic mean, which is differentiated
from a normal mean by its exclusion of dividing the sum by the number of inputs,
assigns a percentage weight to each criterion, as shown in Equation 3 for the weighted
arithmetic mean. It is multiplied by the likelihood score
푺풖풊풕풂풃풊풍풊풕풚 = ∑(푺풏 × 푾풏) Equation 3
where S is the score for criterion n and W is the weight or percentage for criterion n.
The weighted geometric mean is an appealing algorithm to use because it is very
sensitive to each criterion contribution to overall likelihood. It is found by again
assigning a percentage weight to each criterion, but then exponentiating the percentage
with the suitability score, as shown in Equation 4 for weighted geometric mean, where
푺풖풊풕풂풃풊풍풊풕풚=∏(푺풏푾풏) Equation 4
S is the score for criterion n and W is the weight or percentage for criterion n; ∏ means
to multiply the n terms. For instance, if the species is highly sensitive to roads or urban
areas, but other criteria score high, the geometric mean will ensure that the final
suitability score is low. As is noted in Beier et al. (2007), this reflects Liebig’s law of the
minimum, where the potential of an individual is determined by the most limiting
factor.
When species occurrence data is not available, researchers have asked experts
of the species or ecosystem being modeled to score criteria relating to likelihood of
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occurrence. The criteria then incorporate expert-based opinion into a resistance layer
through the use of an analytical hierarchy process (AHP). La Rue and Nielsen (2007)
derived weights for five criteria (land cover, human density, distance to paved roads,
slope, and distance to water) into a habitat suitability model using an AHP derived from
the inputs of eleven experts. AHP is a two-step process that utilizes pair-wise
comparison matrices that clarify the relative weight of each attribute (e.g. grassland)
within a criterion (land cover), and then summing them to determine a weight for each
attribute. The attribute weights and then criterion weights from each expert are
averaged to derive the final weights to use within the suitability raster surface. They
found that through the use of their AHP, they were able to predict species occurrence
68% of the time.
Criticism of expert-based species prediction and the subsequent resistance
layers produced has been leveled on the basis of the “subjective translation” that such a
process incorporates into the scientific process (Beier et al. 2008). The overwhelming
tendency for researchers to incorporate subjective measurements into the design
process may originate from the availability of enough GIS datasets to form a prediction
hypothesis and a lack of field derived species occurrence and movement GIS datasets.
Moreover, with several studies of field-based occurrence data in which expert-based
models are validated, such as La Rue and Neilsen (2007), there is no empirical evidence
for eliminating the incorporation of expert-based opinion. However, if species
occurrence data does exist, a new suite of analysis tools may be employed, such as
15
resource selection framework, or habitat selection framework that can help provide
more meaningful evaluations of efficacy.
The criteria used to develop resistance surfaces within a GIS are often dependent
on available data resources. A model is therefore frequently constructed with GIS data
that is easily accessible and available for download via the World Wide Web. Land cover
is an often used data layer and is frequently the largest determinant of reclassified
resistance values. Other frequently used criteria include factors related to human
disturbance (roads, population density) and topographic factors such as hydrography,
elevation, slope, and aspect (Beier et al. 2008).
Determining the width of the wildlife corridor is the next major step in the
design process. Through a simple least-cost path analysis, a corridor is initially designed
to be one cell raster in width, which is not representative of the entire area needed to
assure representation of wildlife movement. Several methods have been explored in
the quest to obtain the most representative width needed to assure the patterns and
processes of ecological functions are attained. Rouget et al. (2006) used the concept of
irreplacability of biodiversity to determine the final corridor width. They used a
separate modeling process to quantify irreplacablity values of what they deemed
planning units. Planning units were used to measure the overall contribution of raster
cells to biodiversity, as well as to incorporate conservation management practices into
the overall design of the corridor. Beier et al. (2007) advised to widen corridors to
buffer against edge effects (e.g. invasive species, human disturbance) to conserve
ecosystem processes through the accommodation of multiple focal species’ minimum
16
area for viability, and to ensure the ability of wildlife to adapt to climate change. The
minimum area needed for viability of a species ensures that a focal species can carry out
many of its life functions, such as foraging, breeding and nesting within the space
provided by the corridor. Applying the least-cost algorithm in several iterations that
models the top tier of least-cost paths available may be a useful technique to determine
the biological usefulness of the modeled corridor (Adriaensen et al. 2003). Pinto and
Keitt (2008) employed this idea and used an iterative technique to model the top ten
percent of least-cost pathways that ultimately modeled a wide variety of possible
movement routes through the landscape. They used a modified least-cost algorithm
called Conditional Minimum Transit Cost (CMTC) to determine multiple least-cost
pathways between habitat patches, as shown in Equation 5. The raster cell that is used
as the origination point contains a vertex (V) at the center of the cell that contains a line,
or technically called an edge, extended to each of its eight neighbor cells. The nearest
habitat patch cells are considered source vertices (S). The target vertex (T), is the
destination habitat patch cell. CMTC is calculated as:
푪푴푻푪(푽, 푺,푻) = 푪풖풎풎풖풍풂풕풊풗풆 푪풐풔풕(푽,푺) + 푪풖풎풎풖풍풂풕풊풗풆 푪풐풔풕(푽,푻)
Equation 5
2.2 Modeling habitat
As previously mentioned, the concept of a focal species has emerged as an
effective conservation target when planning due to the simplicity in designing for only
one species. The idea is that a paradigm species, such as the African elephant, can serve
17
as an umbrella for a great number of species whose habitat will also be conserved as the
elephant’s habitat is conserved. The elephant serves as a habitat generalist that should
serve well as a surrogate for a great many other species. Caro and O’Doherty (1999)
suggested that effective protection of a viable population is assumed to protect
populations of other sympatric members of the same guild, biota at lower levels or
appreciable parts of the ecosystem. Landscapes are dynamic and species requirements
vary. While this project is only using one focal species, it should not be taken for
granted that one species be considered adequate for comprehensive conservation
planning (Beier et al. 2008).
The African elephant is a widely studied species. A great deal of literature has
been published that describes elephant habitat use, movement patterns and behavioral
choices around human populations. These papers form the basis for a cost surface since
a new set of field measurements is not a viable option for this Master’s thesis.
Furthermore, the models were developed within the limitations of a desktop analysis.
Galanti et al. (2006) investigated the space and habitat use of the elephant in an
area several hundred kilometers to the northwest of this research paper’s study area in
central Tanzania. They used GPS satellite radio-tracking to develop a spatial
representation of home range of several individuals in multiples herds of both male and
female elephants during the wet and dry season. Home range is a polygon of the
combined GPS point locations taken. The Africover Tanzania Multipurpose Landcover
Database (Di Gregario 2002) was used to determine land cover characteristics which are
essentially an explanation of habitat type. Habitat utilization was calculated by dividing
18
the area of habitat type by the total home range of the individual. This produced a total
percentage of habitat use by averaging each individual’s habitat utilization.
Harris et al. (2008) found that elephants in southern Africa have preferences for
proximity to water and available vegetation for forage, and a low tolerance for humans.
They put GPS collars on nine elephants in two locations, one in an elephant reserve in
Mozambique and another in a national park in Namibia. For vegetation data, they
created a land cover dataset developed from Landsat 7 ETM+ imagery classified into
nine classes. Water was also interpreted from satellite imagery for the wet and dry
seasons. They also mapped homesteads and village locations from regional maps. Then
they took their tracking data and calculated the probability of movement from one
location fix from the GPS to the next location fix. From this they concluded that
elephants traveled up to seven to nine kilometers away from water sources during the
dry season and ten to twenty-three kilometers away from the water during the wet
season. They also concluded that elephants prefer areas of high vegetation rather than
low. Finally, they found that elephants prefer distances from humans of at least five
kilometers. The final conclusion of the paper was that, “elephants prefer to move little,
eat well, drink easily and avoid people (Harris et al. 2008).”
Ngene et al. (2009) determined the percentage of positive correlation to habitat
factors that affect elephant distribution around a national park and reserve in north
central Kenya. They investigated proximity to water, shrubland, forest, distance to
settlements and distance to roads. Using elephant location data from GPS collars, they
developed point data following nine elephants over an eleven month period. They then
19
used the point data to extract distance values from each factor. They found that factors
explaining the presence of elephants in their study area are thirteen percent distance to
drinking water points, eleven percent distance to seasonal rivers, fifteen percent due to
elevation, ten percent due to presence of shrubland, nine percent presence of forest,
eight percent distance to human settlements, and seven percent distance to minor
roads (Ngene et al. 2009). They found that by using these factors they could predict
elephant distribution seventy-three percent of the time.
Of additional note in the evaluation of factors involved in the prediction of
elephant distribution is the total slope that an elephant is willing to tolerate as preferred
habitat. Wall et al. (2006) observed that elephants occur in a downward trend as the
elevation gradient increases and that a slope of thirty degrees is the maximum angle
they can tolerate. This corresponds with evidence suggested in Ngene et al. (2009)
where they found that elephants most prefer flatter areas and that they risk potential
injury when traversing along a high gradient.
Evaluating the final corridor design may be the final step in the ultimate design
process. All layers used are subject to inaccuracy (land cover, etc.), and a corridor
should be ultimately evaluated for contribution to overall conservation goals. However,
a universal theory on how to do this remains somewhat elusive since there are so many
variables within the design of a corridor to evaluate. Rouget et al. (2006) focused on
evaluating designed corridors through comparison with simple river buffers. They then
contrasted the extent of natural area, thicket representation (a major ecotype in their
study area), elephant suitability, conservation of vegetation targets, achievement of
20
process targets (along hydrologic gradients), avoidance of land-use pressures and
amount of protected areas found within the designed corridors and within the river
buffers. Beier et al. (2007) suggested evaluating a corridor on the basis of three tools:
frequency distribution of habitat quality for focal species, quantification of intensity and
length of bottlenecks within the corridor design, and a list of the longest distances focal
species would have to travel between habitat patches. A frequency distribution of
habitat suitability for focal species would provide a metric for comparing alternative
corridors. Bottlenecks occur within corridors when optimal cell rasters are limited, and
might create an impassable zone within the corridor. These should therefore be
identified and evaluated as potential disrupters to corridor functionality. Rabinowitz
and Zeller (2010) decided to label bottlenecks within their modeled corridor “corridors
of concern,” to be evaluated on a case by case basis. They feared the bottlenecks could
sever corridor functionality and prohibit the genetic exchange between species
populations. Similar to bottlenecks within modeled corridors is the concern of longer
distances needed to travel between habitat patches. Statistical analyses can be
performed based on the total length of least-cost pathways, which can provide a
measure of corridor effectiveness.
Lastly, it may be of use to discuss the importance of stakeholders for the design
and implementation of wildlife corridors. Beier et al. (2007) found that ignoring the
stakeholders who would ultimately implement the plan and simply focusing on the
design of a wildlife corridor was the first error made in their study. Involving
stakeholders at the beginning of the process engenders ownership of a wildlife corridor
21
implementation project, helps to eliminate “turf battles” between disparate agencies
working in the area and has shown to increase overall chances a project will be
successful (Beier et al. 2007). Since this project is located in Tanzania, some lessons may
be learned from previous conservation projects there. Harris and Hazen (2006)
described how the exclusion of Maasai knowledge in Northern Tanzania marginalized
local populations and discouraged flexible management strategies, to which local
knowledge could have been helpful. Understanding these flaws of corridor design from
a remote location may prove useful later, as concern for a rigid methodology may be
tempered with the notion that the design is perhaps flawed from the commencement of
any seriously designed conservation project without stakeholder involvement.
Conservation around the globe is at a critical moment due to explosive
population growth, agricultural intensification and land use conversion. The traditional
paradigm of islands of protected areas amongst an inhospitable matrix will lead to the
decline of ecosystem health. Douglas-Hamilton et al. (2005) described their study area
in southern Kenya and northern Tanzania as an archipelago of isolated protected
“islands” within a “sea” of pastoral areas. The protected areas of southeastern Tanzania
–which protect some of the last vestiges of east African coastal forests – are similarly
becoming “islands” in a sea of inhospitable matrix of human development. These
protected areas are of global biodiversity significance, and it may serve conservation
goals well to define their ecological connectivity using the elephant as a flagship species.
22
Chapter 3: GIS modeling of connectivity
The concept of a connectivity model is fairly simple. There is an origin, a
destination and the path of least resistance found in between them. However, applying
this method to a network of 107 protected areas over a 500,000 km2 area is rather
complicated. There are a multitude of decisions, tradeoffs and determinations of
whether the methodology is consistent with the ecological theory made throughout the
process. Additionally, the entirety of this research was completed through a desktop
analysis. There was no possibility of field work, nor is it within the scope of a master’s
thesis to complete such a large project. The overall goal is to create a foundational
prototype of ecological connectivity amongst a protected area network in the study
area. To actualize the research, field validation through the monitoring of actual
elephants, and perhaps a few other focal species, as well as an accuracy assessment of
geographic information would have to be completed.
3.1 Study area
The study area (Figure 2) contains 107 protected areas that extend to the
eastern coast of Tanzania. Selous Game Reserve, which is positioned just to the west of
the study area, has been designated as a United Nations Educational, Scientific and
Cultural Organization (UNESCO) World Heritage Site due to its globally significant
biodiversity (United Nations Environment Programme 2008).
23
Tanzanian national parks and reserves contain one of the world’s most
impressive assemblages of large mammals worldwide (Newmark 1993). These include
the critically endangered Black Rhinoceros Dicero bicornis, African Wild Dog Lycaon
pictus, and the African Elephant Loxodonta africana. Also within the study area, and
represented in many of the protected areas east of Selous Game Reserve, are coastal
forests and directly along the coast, mangrove forests. Coastal forests of eastern Africa
Figure 2. Study area
24
support a high number of endemic genera and species of plants and animals, including
six bird species, two mammals, six reptiles, five amphibians and at least 50 invertebrates
and 100 vascular plant species (Burgess et al. 1993). There are 92 endemic forest tree
species (White 1983). Conservation International - a US-based international
conservation organization - has rated the coastal forests of eastern Africa as a global
hotspot for biodiversity (Myers et al. 2000).
The network of protected areas was chosen due to their contribution to the
protection of the last vestiges of coastal forest in east Africa (Burgess and Clarke 2008),
and because population growth, infrastructure development, and agricultural expansion
are all threats to conservation in the region. The elephant was chosen as a focal species
due to the species’ tendency to be a habitat generalist and a roamer across wide areas
of land. The elephant is known to occur with high frequency across the south Tanzanian
landscape and has widespread recognition. The study area extent was drawn to include
the protected area network surrounding the final vestiges of coastal forest in the region.
A total of 107 protected areas are established in the study area. A buffer of ten
kilometers was created to prevent the constraining of the analytical window, so that
areas of high habitat quality that might facilitate movement were given enough area to
be identified within a reasonable distance (Beier et al. 2008). Providing an initial design
for the conservation of ecological connectivity could be helpful to the preservation of
ecological integrity of the protected areas within the study area. During the last 35 - 85
years, there have been six species of large mammals that have become locally extinct
within six Tanzanian parks (although not located in this study area). Habitat
25
insularization from habitat conversion outside of protected areas within the Tanzanian
protected area reserve system (Newmark 1996) highlight the urgency to adapt
management strategies.
3.2 Methodology overview
The first step is to determine where to place the origin and destination points.
Modeling connectivity is widely defined as occurring between source habitat patches.
Therefore choosing which habitat patches would represent a node within the
connectivity network represents a major design point since the inclusion of each habitat
patch in the study area would present a major computational challenge since there
would likely be thousands of points. If it was decided that each habitat patch needed to
be found, a minimum area would need to be established as a threshold for what is a
suitable and not a suitable patch size. This was not the chosen procedure due to the
theoretical and computational challenge of establishing criteria for suitable patches.
Considering the scale of the study area and the large protected area network, it was
decided that each protected area could be considered a kind of meta-habitat patch, and
that the centroid of each protected area polygon could be used as origin/destination
points. The centroid of each polygon was not always found to be well within the
boundary of the polygon. As can be seen in Figure 2, the shapes of the polygons were
often polymorphic and the centroid could be found even outside of the polygon
26
boundary. This is due to the GIS operation that determines the centroid by determining
the average geometric center of the polygon.
Using the protected area polygon centroid, rather than habitat patches,
represents a slight departure from traditional corridor design, but it presents a viable
option for the scale at which the model was run and is in line with the concept that
protected areas are the main nodes of analysis. A major assumption, with some
reasonable justification, is that protected areas in the study region always contain high
quality habitat for elephants. Douglas-Hamilton et al. (2005) found that elephants spent
an average fifty-five percent of their time within protected areas, with the remaining
time spent outside of protection. This indicates that elephants use protected areas at
least a majority of the time, but depend on the landscape outside of protected area
boundaries to complete the movement necessary to forage, breed and reproduce.
All geographic data was clipped to a drawn rectangle surrounding the study area.
A buffer of ten kilometers from the outlying PAs was ensured so that ecological
connectivity would have a reasonable space to be modeled. All geographic data was
then reprojected out of its native format into UTM Zone 37S, which is the UTM zone in
which the PA network in southern Tanzania is located.
The next major step is the development of a cost surface (Figure 3). A cost
surface is the raster grid that is used to calculate least-cost paths. Based on the
literature review and on available geographic datasets, it was determined that the
factors used to model connectivity would be proximity to water, land cover, slope,
proximity to roads, and human density. These factors are assumed to be fairly good
27
predictors of elephant habitat suitability according to Ngene et al. (2009). The cost
surface is essentially a habitat suitability grid representing the ease of movement across
each raster cell. If habitat suitability is high, the cost of movement will be low; and the
inverse for a cell with low suitability.
Figure 3. Flowchart of cost surface development
28
Elephants exhibit different behaviors in the wet and dry season primarily due to
forage and drinking water opportunities that are less frequently encountered during the
dry season. For the purpose of finding connectivity and thus finding areas of high
conservation value, it was decided that the model would be designed for the dry season.
During the dry season, intermittent streams are often not longer viable drinking water
sources, and an elephant’s range is reduced significantly due to their frequent need for
hydration (Ngene et al. 2009). The dry season is the most critical for the elephant’s
resource availability and a loss in critical dry season habitat would be detrimental. The
model was therefore developed to reflect this seasonal characteristic since potential
movement is most restricted during this time of the year.
3.3 Water
The water dataset (Figure 4) was obtained from two sources. Water is an
extremely important predictor of wildlife in Tanzania, and is the single biggest predictive
factor for elephant presence (Ngene et al. 2009). There were a few available water
datasets to choose from. World Wildlife Fund (WWF) Hydrosheds data provides global
drainage network data derived from Shuttle Radar Topography Mission (SRTM)
elevation data (Lehner et al. 2008). Hydrosheds was modeled by finding the elevation
gradients suitable for water drainage and does not verify the actual presence of water,
however when visually compared with satellite imagery on Google earth (date
unspecified) the drainage gradient is unmistakable across the landscape. A shortcoming
29
of Hydrosheds data is that it does not represent wetlands, lakes or ponds that might
also serve as potential drinking spots for the elephant. Therefore, polygon data
obtained from a website data warehouse of Tanzania GIS data called the Tanzania GIS
User’s Group was used in addition to the Hydrosheds data. The Tanzania GIS User’s
Group lakes dataset included mostly small lakes along drainage gradients. It is assumed
Figure 4. Data used for water source
30
that this combination of Hydrosheds and the Tanzaniza GIS User’s Group geographic
data would create the best option for using water within the model.
To process the water data, the polygon data was converted to polyline and
unioned with one another. The distance to the nearest polyline was then calculated
across the study area. Harris et al. (2008) found that during the dry season elephants in
a reserve in a fairly proximate region south of the study area typically stay within two to
three kilometers of water and are found with much less frequency seven kilometers
away. Ngene et al. (2009) observed that elephants require drinking water every one to
two days and that the average for an elephant point location was 4.3 km, with up to
seventy-five percent of locations being between 1.7 and 6.1 km from drinking water
points. Elephants were sometime found up to 25.6 km away from water sources. These
two papers were synthesized to a set of categorical distances to water (0-1 km, 1-3 km,
3-5 km, 5-26km, and >26 km). The categories were then ranked on a scale of 1 to 10 of
likelihood that the distance to water in the raster cell would facilitate elephant
movement (Table 1). The values were chosen based on imprecise selection of values
offered by Ngene et al. (2009) and Harris et al. (2008). Each paper contained graphs
with values derived from analyses of elephant location in relation to resource location.
These graphs were used to heuristically derive the selection of values of 0 to 10.
Table 1. Values given to distance to water criterion
Km Scale (0 -10) 0 - 1 10 1 - 3 6 3 - 5 4
5 - 26 2
>26 0
31
3.4 Land cover
Land cover is another critical component to model design. Perhaps the best
descriptor of landscape composition, land cover describes the types of habitat of an
elephant. Choosing a source for the land cover data was a rather difficult process, since
it was not clear which dataset would be most suitable. It was determined that there
were two appropriate choices in land cover datasets. Globcover is a global land cover
dataset produced by the European Space Agency (ESA). It contains twenty three land
cover classes, has a resolution of 300 meters, and was derived from the EROS satellite
imagery from the year 2008. An accuracy assessment provided by ESA found that
Globcover correctly identifies landscape characteristics fifty-eight percent of the time.
Africover is officially called the Tanzania Multipurpose Land Cover Database. The
Africover project has a set of land cover datasets available for several African countries.
The Tanzania land cover dataset will be referred to as Africover for simplicity. Africover
was developed through visual interpretation of Landsat 5 Thematic Mapper (TM)
imagery obtained between 1997 and 1998 (Di Gregario 2002). Since it is a visually
interpreted land cover dataset and is polygon data, there is no resolution given. They
do, however, measure the smallest land area as a minimum mapping unit of 25 hectares
(Di Gregario 2002). They do not provide an accuracy assessment, but provide some
guidelines to assess accuracy if needed.
Obtaining cost surface values for land cover was perhaps the most difficult part
of this research. There are a variety of methods used to determine elephant habitat
preferences in regards to vegetation and potential foraging opportunities. Each of the
32
papers reviewed a different measure of land cover to derive preference values. Many
used a dataset that was privately created and is publicly unavailable. It was considered
a possibility to create an original land cover dataset for this research paper, or perhaps
something as simple as NDVI or a greenness index, but it would have been difficult to
assess accuracy and to use literature to demonstrate corresponding elephant
preferences.
The only study that did use a publicly available dataset and happened to be in
close proximity to the study area of this research was Galanti et al. (2006), whom used
Africover. Their study area was Tarangire National Park, located about 250 km
northwest of the study area of this resarch. Due to the proximity and the likelihood that
the elephants in the study area exhibit similar habitat foraging characteristics, results
from their research were incorporated into the land cover evaluation. Additionally,
since they used the Africover dataset, there would perhaps be less propagation of error.
This is another unverified, but acknowledged assumption made in the design of the
model.
The Africover dataset was produced by the Food and Agriculture Administration
(FAO) of the United Nations and was obtained through direct contact with them. It was
designed to be a multipurpose dataset with a customizable classification system for
agricultural and environmental applications. There are 119 available land cover classes.
These are listed in Appendix A. The land cover classes were generalized and reclassified
to reflect the eleven classes that were used in the Galanti et al. (2006).
33
Galanti et al. (2006) determined percentage of habitat usage by elephants within
each land cover class during the dry season. The percent use was converted to the
likelihood from one to ten by creating a ratio to determine preferential scale. The land
cover class with the highest usage was open shrubland with 32.6 percent use. This was
classified as a 10. Each land cover class thereafter was divided by 32.6 and multiplied by
10 to derive habitat preference by the placement on the scale of 1 to 10 (Table 2). The
scaled values were accurate to the millionths place.
Table 2. Integrated Galanti Land Cover Classes into Preferential Scale
Percent Use
(Galanti et al. 2008) Scale (0 – 10) Agriculture
2.2 0.674847
Closed forest
1.7 0.521472 Open forest
17.8 5.460123
Savannah
7.4 2.269939 Savannah with trees and shrubs 17.9 5.490798 Closed shrubland
6 1.840491
Open shrubland
32.6 10.000000 Non-permanent water 13.3 4.079755 Permanent water
0.9 0.276074
Urban 0 0.000000
34
3.5 Slope
Elevation across the study area is not a significant factor to elephant movement.
There are no regions with significant hills or mountains except in the northwest region
and some patches of hills scattered throughout. For this reason, slope was used as a
derivative of elevation.
The elevation dataset was derived from SRTM provided on the ESRI Data and
Maps 9.3 Media Kit. The SRTM data is minimally processed by ESRI to remove sinks and
other anomalies. The data was extracted and clipped to the study area and then
processed through the Spatial Analyst slope tool to provide a degree slope on a per
raster basis.
Wall et al. (2006) found that elephants can navigate a slope of up to 30 degrees,
and that any additional inclination prevents their movement because of their physical
frame and walking mechanics, even when foraging potential exists beyond that
threshold. They also found a fairly consistent trend of elephants preferring flat ground,
with an overwhelming majority of elephants found between 1 and 5 degrees slope. To
incorporate this into the model, the slope raster was reclassified into five classes; 0 -5
degrees was ranked 10, 5 – 10 was ranked 7, 10 – 20 was ranked 5, 20 – 30 was ranked
2, and above 30 was ranked as 0 to reflect the impossible nature of elephant movement
beyond that point (Table 3). Wall et al. (2006) provide a graph with elephant fix density
(fixes per km2) in relation to slope gradient in degrees. A fix is a record noting the
geographic coordinate of the elephant occurrence. The graph in Wall et al. (2006)
indicated there were nearly 100 elephant fixes near 0 degree slope and very near 0 fixes
35
Table 3. Values used for slope criterion in model
Degrees slope Scale (0-10) 0 - 5
10
5 - 10
7
10 - 20
5
20 - 30
2 >30 0
at 30 degrees slope. This range of values was used to heuristically evaluate the ranking
of the slope classes.
3.6 Roads
The roads dataset was provided as ancillary information in the Africover dataset
produced by FAO. There was no distinction placed on the characteristics of the roads,
such as major or minor. Furthermore, the dataset may be slightly out of date, but
nonetheless provides a perspective on human presence within an elephant’s
interpretation of the landscape. The literature provides divergent viewpoints on the
influence of roads in an elephant’s habitat preferences. Ngene et al. (2009) found that
elephants in their study area are actually at ease around roads because they are
regularly patrolled by security forces who prevent poaching. They acknowledged,
however, that their case may be an exception, since many other studies found roads to
be avoided by elephants (Blom et al. 2005; Barnes et al. 1991). Elephants generally
avoid things that are associated with people (Harris et al. 2008). With this research in
mind, roads were buffered and all areas were classified as being a short, medium or high
36
Table 4. Results of distance to roads analysis
Distance (Km) Scale (0-10) 0 - 1 5 1 - 3 8 >3 10
distance from a road. On the ease of movement scales of one to ten, a high distance to
road was ranked as a 10, medium distance was an 8 and short distance was ranked as 5
(Table 4). These values were assigned somewhat arbitrarily and not based on any
numbers offered within the literature. They were chosen to give roads at least a partial
influence in the design of elephant movement corridors.
3.7 Human density
Similar to roads on elephant movement, human population is another factor
related to the presence of humans and the likelihood that elephants will make a
decision to travel one way or the other. Many of the same rationales were used to
quantify resistance, such as interpreting observations about human presence and
elephant preferences from literature and assigning a somewhat arbitrary number to
reflect the spirit of the statement. Harris et al. (2008) observed that female elephants
tend to stay at least 5 km from human settlements, but that male elephants were less
cautious around humans.
The human density data was derived from LandScan global population dataset
created by the Oak Ridge National Laboratory (ORNL) for the year 2008. Landscan is a
37
gridded, 1 kilometer resolution product that predicts population density based on a
series of inputs, including census counts, land cover, slope, urban areas, village locations
and high resolution imagery (ORNL 2009). The data is combined using a dasymetric
modeling approach. LandScan 2008 was obtained through special agreement with
ORNL to only use the data for educational purposes.
Since elephant movement through a matrix of resistance values was the goal of
this study, the LandScan product appeared to compliment the objectives very well. The
alternative would have been to use incomplete point data on village and settlement
locations and derive distances from population point, and then assign resistance values.
This may have made it easier to reflect statements made in the literature referring to
the exact distances elephants generally prefer from human populations. Unfortunately,
there is no authoritative point population dataset for the study area.
There was no literature found that directly referenced human density in
observing elephant behavior; however, it seemed logical to apply the scale of 0 to 10 to
the population density grid. Areas with highest population are most likely urban areas
while the sparsely populated regions have very few people per square kilometer.
To prepare the data for inclusion into the analysis, the LandScan product was
extracted by mask for the study area and reclassified to five categories of population
density. These were referred to as low, low-moderate, moderate, high-moderate and
high population density and assigned a preference factor of 10, 8, 3, 2, and 0
respectively (Table 5).
38
People per Km² Scale (0-10) 0 - 5
10
6 - 50
8
50 - 100
3
100 - 300
2
>300 0
The values were chosen based on reports that elephants tend to avoid human
population (Harris et al. 2008; Wall et al. 2006; Ngene et al. 2009). The ranking of the
values were not in relation to any graph or parameter set by the literature, rather a best
guess based on the notion in the literature that elephants do their best to avoid contact
with human populations.
3.8 Cost surface
The final composition of model data was then amalgamated into a weighted
conductance surface raster. Ngene et al. (2009) provided a percent factor contribution
to elephant distribution. Through a correlation matrix of explanatory variables, they
calculated that distance to water points, distance to seasonal rivers, elevation,
shrubland, forest, distance from settlements and distance from minor roads help to
explain 73 percent of elephant distribution (Ngene et al. 2009). The remaining factors
explaining 27 percent of elephant distribution were listed as agriculture, closed
grassland, mollic andisol and chromic combisol soil types. These types had on average a
less than significant influence on elephant distribution and therefore were excluded
Table 5. Values applied to weight human population density criterion in model
39
from use in the current analysis. To create an easily understood weighting system, the
significant explanatory variables were curved to represent a scale of 0 to 100 percent.
The land cover variables of forest and shrubland were combined to represent
the influence of land cover. It should be acknowledge that these two land cover types
do not compose the entirety of the land cover types used in the model. Furthermore,
Ngene et al. (2009) determined that other land cover types such as agricultural do not
factor as a significant predictive variable for elephant habitat preference. This is a
significant shortcoming of the model; however, there did not appear to be another
choice from the desktop analysis perspective. A combination of concepts derived from
literature will certainly create at least some theoretical dissonance within the
methodology. It did not seem prudent to dismiss execution the model for this reason,
and the land cover types suggested by Ngene et al. (2009) were used as surrogates for
all land cover types in Galanti et al. (2006).
Distance to water points and seasonal rivers from Ngene et al. (2009) were
combined to represent distance to water. Distance to minor roads from Ngene et al.
(2009) was considered distance to all roads, since the roads dataset did not have any
descriptive attributes. Therefore, explanatory variables to describe the ease of
movement, or conductivity of a cost surface cell was influenced 33 percent by distance
to water, 26 percent by land cover, 20 percent by elevation, 11 percent by human
density and 10 percent by distance to roads (Table 6) (Ngene et al. 2009).
The final amalgamation borrowed from concepts provided by Beier et al. (2007).
They suggest either using a geometric mean or an arithmetic mean as described in
40
Chapter 2. An arithmetic mean simply multiplies each factor’s interim value, in the case
of this analysis, the conductivity score of 0 to 10, by the weight being applied to the
distribution factor. The final values are then added together to form a final
conductance value. This means that if a value of 0 is present within the factors, it will
not necessarily result in a pixel value with maximum resistance. This is particularly
unhelpful when demonstrating the effect of potential slope values that exceed an
elephant’s capability. The geometric mean takes each factor and exponentiates the
factor’s interim value by the percentage of the distribution factor. The remaining values
are then all multiplied together. If a 0 exists in the conductance factors, the ultimate
score will result in a pixel with maximum resistance, or 0 (Beier et al. 2007). The
geometric mean was used in this analysis by using the Spatial Analyst Power tool with
each raster of conductance factors and then with a simple map algebra statement
multiplying the factors together. This resulted in a raster surface of landscape
conductance values ranging between 0 and 10.
It should be noted at this point that there was an issue with the Spatial Analyst
Math tools. The land cover dataset contained values with several decimal places.
Values with decimals occur in an ESRI GRID raster as a floating point grid. The Power
tool used to exponentiate the factors was unable to process the floating point values.
To overcome this obstacle, each factor was multiplied by 1,000,000. This allowed for at
least six additional significant digits in each factor, which minimized the loss of accurate
data and maintained the integrity of the conductance value. Each value’s magnitude
41
Table 6. Final amalgamation of movement cost surface values
was simply adjusted to occur in front of the decimal point. This resulted in a raster
surface of landscape conductance values ranging between 0 and 1,000,000.
Before advancing to the calculation of CMTC could commence, the preferential
value structure of the conductance surface needed to be inverted to represent
resistance rather than conductance. Using the Spatial Analyst Map Algebra tool, the
raster was multiplied by -1 and then 1,000,000 was added to it. This reversed the
conductance raster to place the most facilitative movement values at the low end of the
spectrum. This concluded the first half of the analysis, and the cost distance calculation
could commence.
3.9 Corridor modeling with CMTC
The second half of the analysis (Figure 5) consisted of finding the least cost
corridor between each PA centroid in the PA network. There were 107 total PAs. A
method was devised to run an analysis between PAs that were in direct path of each
other. Developing the list of PAs to run the least cost analysis was completed by
manually generating straight line paths between each PA centroid. So that PA names
were easily entered into path file names in the variety of GIS tools used, unique ID
Criterion
Weight given (%) Distance to water
33
Land cover
26 Slope
20
Human density
11 Distance to roads 10
42
numbers were assigned as a surrogate to the text names of the PAs. This significantly
facilitated ease of model execution. The original PA dataset, obtained from World
Database of Protected Areas (International Union for the Conservation of Nature and
United Nations Environment Programme 2010) was a collection of PAs in one shapefile.
The centroid of each PA polygon was used as the origin and/or destination of the least
cost corridor. Therefore, using the Data Management toolbox Feature to Point tool,
each polygon was converted to a point within the centroid of the polygon.
A simple model in Model Builder was created to extract each PA individually.
The Iterate Feature Selection tool was used to run through each row of the centroid
shapefile of PAs. Then the Copy Features tool was used to extract each PA individually.
In this way, each protected area was made into its own point feature class.
Figure 5. Flowchart for modeling corridors
43
To manage the list of least cost paths, the study area was compartmentalized
into five zones (Figure 6). Each zone was identified based on an evaluation of major PA
point clustering (Appendix B). There appeared to be three major zones of clusters.
These were identified as Zone A, Zone B and Zone C. In order to model between each of
the zones, two interzones were created; these were named Interzone A and Interzone
B. Zone A consisted of the twenty-nine PAs found in the northwest section of the study
area. Zone B consisted of fifty PAs found in the central portion of the study area. Zone
C consisted of thirty PAs found in the southern section of the study area. Interzone A
consisted of the eleven PAs that link Zone A and Zone B, and Interzone B contains
eleven PAs that link Zone B and Zone C.
A zone of analysis was drawn as a bounding box around the protected area
centroids using the Feature Envelope to Polygon tool in Data Management Tools in
ArcGIS Toolbox. Then, using the Buffer tool, ten kilometers were added to the bounding
boxes to provide enough distance from the exterior PAs to allow for ecological
processes to be modeled . To find the least cost path between PAs that connected
across the zone boundaries two sets of interzone areas were delineated in the same
fashion as each zone area. Interzone A was inadvertently cut short due to the
previously set analysis mask, however, this was only along the north sections and did
not impact corridor modeling.
44
Figure 6. The extent of each zone of analysis within study area.
45
3.9.1 Modeling corridor width and alternate dispersal corridors
At this point in the analysis modeling the correct corridor width becomes an
issue. Beier et al. (2007) suggested corridor width should be set according to the
species being modeled. There are two types of species, corridor dwellers that might
carry out an entire life cycle within the corridor, or passage species which simply may
use the corridor for movement between habitat patches. For this research, the African
elephant is considered a passage species since movement between protected areas are
being modeled. For corridor dwellers it might be best to set the width as the minimum
area of viability for the species. Additionally, if more than one species is being modeled,
it is possible that the width of the corridors will be set by overlapping requirements of
each species and that this will be an effective way to model species. Minimizing
bottlenecks is perhaps the most crucial component so that species always have free
access through the most inhospitable matrix of the corridor. It might also be
advantageous to decide corridor width through a stakeholder engagement process; so
that conservation goals can be reasonably assessed with the reality of local
circumstances (Bier et al. 2007). Ultimately, they do not provide a clear rationale why to
choose widening a corridor based on species habitat requirements or through the
stakeholder engagement process.
Pinto and Kiett (2008) criticized focusing on corridor width by expanding a least
cost path because it ignores potential alternate dispersal routes that exist amongst
comparable routes with similar costs of movement. They proposed focusing on
methods that find redundant least cost paths to evaluate corridors. Drawing upon
46
graph theory, they describe Conditional Minimum Transit Cost (CMTC) as a method to
find redundant least cost paths between origin and destination points. CMTC is an
analysis that can be carried out through ArcGIS processing of cost surface grids. The
research by Pinto and Keitt (2008) proposed to integrate species habitat preferences
into regional-scale depictions of habitat connectivity through the identification of
redundant least-cost corridors. This appeared to be most in line with the goals of this
analysis considering large protected area network study area. The advantage of this
approach is that redundancies are modeled through a user defined threshold of
conductive pixels across the landscape, and the top percentage of conductive pixels is
identified. The alternative is that a single least-cost path (one pixel in width) is
identified, which provides a less descriptive view of the landscape as the focal species
would experience it.
CMTC is relatively simple to conceptualize in a GIS. As described in Chapter 3, it
simply adds the two cost distance grids in both directions of movement. The cells with
the lowest values will emerge as the corridor. A cost distance raster is calculated using
the Spatial Analyst Distance tool Cost Distance. Once the cost distance in both
directions is calculated, a threshold must be set to determine cells that represent the
most conductive possible movement between PAs. Pinto and Keitt (2008) suggested
that this is somewhat of an arbitrary decision that can be made based on goals of the
analysis. In their study they chose to select the top 10 percent of cells with the lowest
resistance values. In this analysis 10 percent appeared to include too many cells, so the
percent value was reduced from 10 to 5.
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3.10 Calculating CMTC
The first step to calculate CMTC was to use the Cost Distance tool to calculate
the total value of cost of movement from each PA centroid. It was not necessary at this
point to focus on the destination of the corridor. As with the previous analyses the
processing of the cost distance results were completed through the
compartmentalization of the zones of analysis. The batch command of Cost Distance
was utilized to process through each zone. Each value was named according to its
respective PA unique ID it represented.
Obtaining the CMTC was completed by using the batch command and the Plus
tool in the Spatial Analyst toolbox to add the cost distance rasters from the source and
destination of the corridor. The manually generated connections can be viewed in
Appendix B; and the list of protected areas modeled for CMTC in Appendix C. In total,
194 PA connections were produced from the manually generated list (Table 7). Each of
the CMTC analyses was completed using the Plus tool in batch mode.
Table 7. Number of CMTC analyses completed
Zone of Analysis Number of PAs Number of CMTC analyses completed
Zone A 29 51 Zone B 49 83 Zone C 29 42 Interzone A 11* 11 Interzone B 11* 7 Total 107 194
*Interzone PAs are composed of PAs within Zones A though C and therefore are not counted in total
48
3.10.1 Finding five percent
Obtaining the top five percent of cells was a relatively complicated process.
Model Builder in ArcGIS was used to create a series of tools that processed the
information obtained for each PA corridor. The process began using the Iterate Rasters
tool to automatically run through the folder of CMTC rasters from each zone that had
been processed. Each cell value in the cost surface was in the magnitude of tens of
millions from the upscaling to rid each value of a decimal point so that Spatial Analyst
would perform more smoothly. When the cost distance analysis was performed, it
created some raster cell values in the tens of billions range since the operation
accumulates cost surface cell values as distance increases from the source cell. To
create cell values in a more manageable magnitude, each raster was divided by 100,000
to return it back to the millions range. Even without decimal values the divide tool
creates rasters that are floating point values; therefore each raster had to be converted
back to an integer with the Int tool. Next an attribute table was built for each raster
since one did not yet exist and would be needed to create a process that finds the top
five percent values in each CMTC raster. The next steps, described next, involved
creating a series of new fields for each newly created attribute table and then
computations were executed to get to the top five percent value of each raster grid.
The Summary Statistics tool was used to find the minimum and maximum value
of each CMTC raster which is now an integer raster with an attribute table and
generates an ESRI Info table. The Summary Statistics tool automatically created two
new fields for minimum and maximum of the corridor raster. Next, using the Add Field
49
tool, a field was added named “range” as a long integer, and the Calculate Field tool was
used to subtract the minimum value from the maximum value. This new value
represented the total range found in each CMTC raster so that a clean five percent could
be found. Next, using the Add Field, a new field was added named “fiveperc” as a long
integer and the Calculate Field tool was used to multiply “range” by 0.05, or 5 percent.
Finally the top value of the five percent needed to be found to establish the value that
represented up to five percent of all cell values. To do this a new field was added using
the Add Field tool, naming the new field “topvalue” as a long integer, and again the
Calculate Field tool was used to add the minimum value of the original raster found in
the “min” field first created by Summary Statistics, to the value found in the “fiveperc”
field, which was the value of five percent of the total range of raster values.
This model was executed five times through each of the zones of analysis. Once
this step was complete, a series of ESRI info tables existed with the value that
represented the top of the five percent range. This number was used as the main input
into the next step of the analysis, which was to extract the top five percent of values
from each least cost corridor raster.
3.10.2 Extracting five percent
This step extracted only the most conductive cells to movement so that the
network of PAs could be combined to display a landscape of high potential movement.
It was also possible that this step would provide individual least cost corridor
50
visualizations. With these two purposes in mind, it seemed appropriate that extracting
the most conductive cells while ignoring all other values was the best way to provide a
representation of the outputs of the analysis.
To accomplish a visualization of the most conductive cells, map algebra in the
Spatial Analyst Raster Calculator was used in batch mode to calculate multiple CMTC
corridors simultaneously. The value at the top of the five percent range of most
conductive cells in each corridor was manually inserted into a conditional statement
using map algebra. The value at the top of the five percent range was found in the
“fiveperc” field of the attribute table created in the previous step. All cells not meeting
the conditional requirement were set to NoData. This was repeated throughout the
CMTC corridor rasters in each zone of analysis.
To visualize the landscape of conductive cells, the Mosaic to New Raster tool in
the Data Management Tools toolbox was used to patch together each least cost
corridor. The minimum value of raster cells that fell on top of one another was used as
the mosaic output value so that the cell most conducive to movement would be
represented.
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Chapter 4: Results and discussion
Demonstrating ecological connectivity between protected areas amongst a
backdrop of coastal forest areas was the goal of this analysis. The product
demonstrated the potential for geospatial information to model movement of a
paradigm species through a habitat matrix framework that predicted movement paths.
The following results and discussion review the initial criteria used to model elephant
habitat preference used as a predictor of movement under the assumption an elephant
will likely move along a path in structural landscape of preferred habitat with low
functional resistance (i.e. low elevation, avoidance of human population, etc.).
To interpret the findings of the corridor modeling, there are several
considerations to take into account. First is the scale at which one is trying to interpret
the findings. Each of the zones in the study area has slightly different habitat
characteristics that should be considered. Furthermore, conservation activities that
take place in one locality of the network may not be comparable to the results of
another area. Therefore increasing and decreasing the scale in order to provide a more
useful interpretation may be important. Finally, analyzing the corridor found between
each PA may be necessary when considering what types of conservation action to take.
Placing the modeled corridor in the context of a local situation may provide a
cartographic scenario - such as its placement within the matrix of agriculture, natural
land and human population - that would provide a more meaningful graphic for
visualizing elephant movement across geographic space.
52
As the results are presented, the modeled corridors are presented in the local,
regional and network wide scales of analysis. This provides the best framework for
interpreting the results as a structural and functional part of the landscape, and acts as a
suggestion for how landscape-scale corridor models should be interpreted.
4.1 Cost surface results and discussion
The distance to water results (Figure 7) demonstrate the availability of drinking water
sources. The WWF Hydrosheds network of drainage channels forms a network pattern without
significant interruption across the landscape of the study area. The values incorporated into the
model were chosen to represent water availability during the dry season, since this is the time
of year that most limits the movement potential of elephants.
The results may represent an overestimation of available water sources since each
drainage network is not verified for the presence of water. The WWF Hydrosheds dataset was
processed by applying an algorithm to find the pathway of the lowest areas of elevation across
the landscape. This analysis usually results in the presence of water, since water typically
follows drainage pathways. The ubiquity of water availability across the landscape is further
perhaps overrepresented by the lack of any location farthest than the longest distance (more
than 26 km) the literature mentioned elephants were willing to travel without water (Harris et
al. 2008). It seems unlikely that there are no places in the study area more than 26 km from
water in the dry season, but it is difficult to verify without validated water presence data. Other
factors that are not included, such as subsurface hydrology, precipitation, temperature and
human use, may also govern the availability of a drinking water source for elephants, but are not
a component to this research.
53
Figure 7. Results of the distance to water analysis
The land cover dataset (Figure 8) displays a landscape that is composed of roughly 30
percent agricultural, 25 percent open forest, 20 percent open shrubland, with the other land
cover classes making up the final 25 percent. An elephant would likely perceive this landscape
to be somewhat hostile due to the agricultural areas, but largely accommodating to movement
in the remaining areas. The central region of the study area is largely devoid of agricultural
54
Figure 8. Land cover reclassification
activity and may present an elephant with the least amount of resistance to movement in any
direction.
The results of the slope analysis (Figure 9) show a landscape that has a low degree of
slope throughout much of the study area. Only in the northwest portions of the landscape are
there any significant degrees of slope that would impede movement. The areas coincide with a
55
matrix of agricultural land use and higher human density. The northwest portion of the
landscape may therefore see a higher cost of movement. The area in the southwest portion of
the study area does not pose any real impedance to movement since this region is deep within
Selous Game Reserve and will not have any potential corridors modeled in between.
Figure 9. Result of the slope analysis
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The distance to roads analysis (Figure 10) demonstrates a landscape that does not
contain a very robust network of roads. This will likely aid elephant movement across much of
the landscape. Roads are likely associated with agriculture and human density since they
represent not only human movement, but also economic corridors. At all edges of the study
area there are higher densities of roads. This will likely make the central portion of the study
area most accommodating to movement.
Figure 10. Result of the distance to roads analysis
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The final criterion for the development of the cost surface raster is human density
(Figure 11). Following many of the patterns found in the previous datasets, the edges of the
study area have higher levels of human density while the central portion remains largely devoid
of any significant human presence. The northwest, northeast and southeast areas have the
highest density and will likely prevent elephants from moving freely across the landscape. Also
revealed are several patches of human absence. The central patch is the largest, with two
medium patches to the northwest and a series of much smaller patches along the eastern
portion of the study area. These areas will likely improve the chances that an elephant will
choose to move in these regions rather than in areas of higher human density.
58
Figure 11. Human population density reclassification
The final cost surface (Figure 12) is the amalgamation of the input parameters of
distance to water, land cover, slope, distance to roads and human density. As many of the
criteria that discourage elephant movement were found roughly along the edges of the study
area, the final cost surface shows the highest costs of movement in the northwest, northeast
and southeast corners. There are several smaller pockets in the interior of the study are that
59
will likely produce resistance to movement and perhaps encourage an elephant to move around
these pockets of hostile matrix.
Figure 12. The final cost of movement surface
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4.2 Zonal characteristics discussion
As previously described, the PA network was subdivided into a set of five zones
where the cost surface raster was extracted and run amongst the set of PAs located
within the zone. This helped to compartmentalize the task of running 194 CMTC
analyses across the PA network. An unintended consequence of this is that the total
area of analysis changed within each zone (Figure 13). This is significant since the land
cover prediction values were developed using likelihood values based on potential
habitat availability which are represented by the land cover classes (Figure 14). The final
corridor was delineated using the top five percent of all values within the zone. Since
each zone has slightly different ranges of total values, the top five percent probably
changes a little bit between each zone. This is most likely the most significant when
considering the effects of land cover on the model output, since habitat availability is
distinctly different across the PA network.
In each zone of analysis there was a range of approximately 17,000 to 45,000
square kilometers. The study area in Galanti et al. (2006) was approximately 17,000
square kilometers. In addition to the differences in total size of each zone of analysis,
there are different habitat compositions within each zone. Each zone has slightly
different proportions of land cover types (Figure 14).
61
Figure 13. Total area in each zone
There was a certain amount of congruency, however, within each land cover
type. The percent composition was within a reasonable range with a few exceptions.
Interzone A and Zone C had much higher amounts of the open forest land cover type
than the rest of the zones. And the amount of open shrubland was somewhat more
variable than the rest of the land cover types throughout each of the zones. Open
shrubland was the land cover type most utilized by elephants, as pointed out by Galanti
et al. (2006).
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
50,000
Km²
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Figure 14. Percent of land cover type for each zone of analysis
4.3 CMTC results and discussion
As can be seen in each of the following representations of model results (Figures
15-23), the most important predictor of modeling connectivity using a least cost path
algorithm may be proximity. PAs that are near are most likely to have lower travel cost
and thus movement within the clusters of PAs is predictably conductive. When high
cost of movement does appear in areas with high clustering, or near proximate PAs,
there must be substantial restrictions to movement that originated with one of the
original parameters of the model, such as high human density or steep slopes. These
areas should be evaluated from a management perspective since they present risk to
0
5
10
15
20
25
30
35
40
45
Perc
ent o
f Tot
al A
rea
wit
hin
Zone
Galanti et al. 2006 Zone A Zone B Zone C
Interzone A Interzone B Entire Study Area
63
the degradation of habitat for elephants as well as potential sites of human elephant
conflict.
Another possible lesson taken from the fact that PAs that are in closer proximity
are more effective at providing high levels of conductivity is that the PAs should be
expanded to provide more habitat for elephants and the rest of the biodiversity that
depends on similar habitat. The PAs with the highest conductivity values could be
identified as targets for expansion. It might be expected that external threats degrade
the quality of the habitat in these areas, and therefore an expansion of the total area
currently under protection could be increased.
4.3.1 Regional zone results and discussion
In the cluster of PAs in Zone A (Figure 15), there are several sets of PAs that are
close to one another, but they are surrounded by a fairly high level of inhospitable
matrix. This is especially apparent in the southwest portion of the zone where the cost
of movement between Mikumi and Shikurifumi appears to be rather high. Similar
distances are present throughout the network without such high cost of movement
values. There must be considerable barrier to movement in this area, which is most
likely due to agricultural or human population density.
Several of the PAs are in a cluster pattern and could be used as a starting point
to strengthen the reserve network in contrast to the difficult terrain of the southwest
portion of the zone. The northern portion of the zone, however, appears to be of higher
64
habitat value since even in the case of longer distances between PA centroids, there is a
relatively low cost of movement.
There also appears to be a degree of clustering of PAs in Zone B where several
PAs surround Namakutwa Nyamulete. It is also likely in this area that human population
density is high. Therefore conservation effort could be established in these areas as the
matrix of hospitable habitat is less than would be expected.
There is considerable resistance to movement in the northern and the southern
sections of Zone B (Figure 16). This is a coastal region with higher human population
density than more inland. Given the current situation modeled, elephant movement
between these PAs appears to be very hostile.
65
Perhaps most importantly, the areas where movement is found to be the most
facilitated by ideal conditions for movement should be used as a core elephant zone.
Several of the PAs in this region are especially important because they contain coastal
forest ecosystem remnants. It would be of high conservation value to ensure that
functional corridors are established between these PAs.
Figure 15. Model result for Zone A
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Zone C contains up to four sets of clustered PAs (Figure 17). These clusters seem
to facilitate the movement of elephants relatively well with low cost of movement
values. There are however some clear corridors that have emerged from the analysis.
Figure 16. Model Result for Zone B
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High movement costs can be found along several places in the southeast portion
of the zone. Additionally, a large gap exists in the central southern region where it does
not appear that elephant movement is likely. This leaves the most southwestern PAs
isolated from movement in and out of PAs.
Figure 17. Model result for Zone C
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The results from the interzones had notably high resistance to movement. The
distances between PAs in these areas were greater than most other connections;
however, this fact might be of value since habitat patches in these areas could be
overlooked. The area between Zone A and Zone B has a paucity of PAs and could be
evaluated as a potential site for the establishment of more protection. Additionally,
along the western edge of Zone B and Zone C is among the highest resistance in the
study area. The southern edge of the study area has the highest values of resistance.
Additionally the corridors show up as very narrow pathways that would make
movement through this region extremely difficult for an elephant.
The exception to difficult movement across Interzone A (Figure 18) is the area
between Ruvu North Fuel and Kitulanghalo, and to a lesser degree directly south at
Mkungwe. This corridor is certainly a pathway most likely taken across this space. The
high amounts of open shrubland, the most preferred land cover type by elephants, is
perhaps in highest density in this section of Interzone A.
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Figure 18. Model result for Interzone A
The space in Interzone B is not as far a distance as that in Interzone A, but there
are a variety of outputs in this area (Figure 19). The eastern section contains a cluster of
smaller PAs where the distance between centroids is minimal. It also appears that the
habitat matrix is relatively hospitable for the movement of elephants. The western
section however is perhaps the exact opposite with among the highest cost of
70
movement values in the PA network. This may be in part due to the distance traveled
between Lungonya and Nyera Kipere; however, it seems undeniable that the matrix
restrains movement in this region. It may be important to analyze the threats to
conservation in this region and focus on containing additional pressures from
agriculture or human population growth.
Figure 19. Results from Interzone B
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4.3.2 Results from a single corridor
Many of the local results resembled a least cost pathway that did not
demonstrate very much differentiation between a straight line path, as was the case
with the Uluguru South to Kimboza path in Zone A (Figure 20). The habitat matrix
between the two PAs must be relatively hospitable to movement since there are no
major deviations or abrupt changes in course of the least cost pathway. It is in these
types of situations that the advantage of using CMTC and deriving a top percentage of
movement values becomes clear. The pathway reveals a corridor of sufficient width to
allow for movement across an area and thus could be used as a blueprint for
conservation planning. This is in contrast with methods that find a least cost path,
which does not give the context of the surrounding habitat matrix. A variety of
potential paths are visualized. Using CMTC, the total width of the corridor is
determined using a select group of movement values. In the case of our study, the top
five percent of values were used to represent the best corridor.
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Figure 20. Model result between Uluguru South and Kimboza in Zone A
Perhaps the most interesting corridor modeled throughout the network was the
Ntama to Lionja pathway found in Zone C (Figure 21). Rather than taking a shorter path
between the two PA centroids, the path took a circuitous and meandering form. The
most conductive pathway is to go the most circuitous way around what must be a very
inhospitable matrix. However, an alternate dispersal corridor also exists in a straight
across trajectory. Agriculture is a major land cover type in this area, in addition to
73
substantial human population density. These factors must present an elephant with
little choice but to move via less resistant pathways.
Figure 21. Model result between Ntama and Lionja in Zone C
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4.3.3 Entire study area results and discussion
Each of the zones were mosaicked together to produce a full visualization of the
PA network (Figure 22). The results here should be regarded with caution due to the
difference in habitat characteristics and total size in each zone, and its effect on the top
five percent slice of movement values. The results, however, are undeniably similar to
the outputs found in the compartmentalized view of each zone. There are areas of
clustered PAs where movement is relatively easy mostly due to the short distances
traveled between PA centroids, and several bottlenecks and hostile matrix areas
become apparent. When developing a conservation strategy for this area, a
visualization such as the one produced here would provide an excellent overview of
barriers to movement in the region.
Three major gaps in movement across the region emerge from the entire PA
Network. First is the Interzone A area in the north with high resistance to movement
values in the northern and southern portions of the interzone. The next two areas of
hostile matrix are in the south, leaving the southwestern section of the PA network
isolated from the rest of the region. Movement across this space is likely rather difficult
for elephants, and the region may be experiencing declines in elephant populations due
to the pressures exerted from habitat degradation due to human activities.
On the positive side a clear set of pathways exists between many of the PAs in
the network, especially along the coastal region of the network. The most abrupt
changes in movement values exist between Zone A and Interzone A.
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Visualizing the entire study area by classifying the regions into ten movement
classes further illuminates several zones where barriers to movement are higher and
perhaps helps to place corridors in a hierarchy of resistance and conductivity to
movement (Figure 23). As mentioned previously there are several specific areas where
resistance to movement is highest. These are both located in the interzones where the
distance traveled between PAs is high.
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Figure 22. Model results for entire PA network
77
Figure 23. Model result classified for PA network
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Chapter 5: Conclusion
This model of connectivity between protected areas was derived from estimates
found in literature to predict the habitat preferences of elephants in the southeastern
section of Tanzania. It is meant to provide a prototype of corridor modeling in the
region. A comprehensive model would include several other species with habitat
requirements different than the elephant as well as a habitat preference study with
point location data of elephant movement throughout the study area. This would avoid
several of the issues related to subjective translation, which Beier et al. (2008) described
as the incorporation of subjective measurements into the model. The criteria for
habitat selection were derived from Ngene et al. (2009) for elephants in eastern Kenya,
which has a very different ecology than southeastern Tanzania. This undoubtedly will
introduce uncertainty in the model. The values chosen for the habitat criteria were
derived from Galanti et al. (2006) in a region close to this study area, which may provide
some reliable estimation of elephant behavior. To test for certainty, there are several
different methods that could be used. One possible method could be to conduct a
sensitivity analysis to see how different weights given to each criterion might produce
different outcomes. Another could be to use field-based measurements from radio or
GPS collars to monitor the movement of elephants in the region. If the elephant’s
movement mimics the predictions given by the model, the results could be validated as
such. The predictions could be statistically evaluated using these field-derived points of
occurrence over time and using logistic regression or the Maximum Entropy modeling
79
program to complete accuracy metrics to reflecting the appropriate prediction values
(Pittiglio et al. 2012). These analyses are outside of the scope of this study, which was
composed entirely through desktop analysis without access to field-based occurrence
points or recent land cover data. This study does provide a starting point for completing
such an exercise, however, and can be used to develop a new study that takes these
considerations into account.
A better analysis of PA management strategies also needs to occur so that the PA
network is robustly managed enough to ensure that corridors identified between each
PA can be expected to have long term persistence. The PA data was obtained from the
WDPA and contains the initial attribute data describing the management authority and
the classification by IUCN, which describes a basic evaluation of PA management.
5.1 Least cost modeling
Least cost modeling might also be more effective using habitat patches as the
node of connectivity. PA centroids were used in lieu of habitat patches due to the
difficulty in defining a habitat patch. There would likely be some threshold for
determining patch suitability for elephants as well as some requirement for a large
enough area to qualify as a patch. This again was outside the scope of this report, but
should be evaluated if connectivity is going to be modeled more accurately in the area.
The likely benefits of such an analysis would include a derivation of elephant migration
pathways with the highest degree of certainty. This is especially clear when considering
80
the high resistance seen in Interzone A even though the region has the highest
percentage of open shrubland, which is by far an elephant’s preferred habitat.
The modeling process was based off of literature, however, elements of
improvisation did occur throughout due to the lack of a clear example to follow as well
as difficulty processing in the ArcGIS modeling environment. Employing a least-cost
model using CMTC across such a large landscape has never been published. Given these
circumstances, there were numerous instances when the best possible data and
methods were compromised. A new iteration of this model with better data and the
lessons learned during this modeling process would surely provide a more accurate
prediction of elephant movement across the southeast Tanzanian landscape.
5.2 The use of data
Much of the data acquired for use within the model had unique circumstances
that should be discussed as potential agents that may limit the accuracy of the model,
these include land cover, water, slope, human density and roads. Additionally the
process of constructing the model was one of improvisation based on examples cobbled
together from several research examples and trial and error within the GIS processing
environment. It is hoped that this discussion will provide some insight to the thought
process involved during the construction of this research process.
The land cover data was derived from the Africover Tanzania project by FAO.
There were no documents provided that verify the accuracy of the data, nor was it
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within the scope of this project to validate this land cover dataset. An evaluation of
accuracy for this data would have provided an element of credibility to the model.
Additionally the data was quite old. Imagery used to derive the land cover classification
was derived from the 1997 to 1998 timeframe. Imagery obtained from a more up-to-
date period would benefit the model by providing a more recent view of the landscape.
It was certainly considered an advantage that Galanti et al. (2006) used Africover data in
their research project. This may have provided an element of congruency, but
unfortunately it also may have given another opportunity for error to creep into the
analysis. Galanti et al. (2008) did not provide a document of how the land cover data
was reclassified to the eleven land cover classes identified as being surrogates for the
entirety of the land cover classes available in Africover. The process of reclassifying the
data used within this thesis project was completed using assumptions based on an
expectation of reasonable reclassifications. For example, the wide variety of agricultural
crops detailed in the Africover land cover dataset were generalized simply as
agriculture. A complete list of the land cover reclassification is provided in Appendix A.
The water data is of particular concern since it has the most influence out of all
of the model criteria. Additionally, Galanti et al. (2006) suggest that elephants have
different movement behaviors in the wet season, ranging from December to May, and
the dry season, ranging from June to November (Voeten and Prins 1999). Galanti et al.
(2006) provide separate values for elephant habitat used in the wet and dry season as
elephants may choose to forage in areas of higher primary productivity that provide
nourishment even in the dry season. The other study used to provide values in the
82
model, Ngene et al. (2009), incorporated water into their model using permanent
drinking water points and seasonal rivers as separate measurements of elephant
preference. This thesis combined the two measurements made in Ngene et al. (2009)
and classified them as one measurement of water source. Additionally complicating the
distance to drinking water issue was the use of WWF Hydrosheds data, combined with
lakes data from Tanzania GIS User’s Group. These two data sources do not distinguish
between wet and dry season availability of water. The WWF Hydrosheds data only
provides a representation of the drainage network, rather an actual mapping of
perennial river networks.
These combined issues decrease the credibility of the distance to water criterion
used in the model, especially since it was declared that the model was being sensitized
to respond to the habitat requirements of elephants in the dry season. This uncertainty
should be considered while evaluating the use of the model in conservation planning,
however, the construction of a flawless model was not the intent of this thesis. More
importantly, the issues with water data, as well as its commanding influence on
elephant behavior should provide future GIS researchers with the evidence needed to
focus on the water issue in as great of detail as possible.
The slope data could have been potentially misused during the modeling
process. Values for elephant’s ability to navigate slope came from Wall et al. (2006)
which were combined with the value Ngene et al. (2009) developed for explaining
elephant behavior in regards to elevation. The difference between slope and elevation
83
was not distinguished in the thesis model and therefore would need to be evaluated
more robustly in a new modeling scenario.
The roads data could be improved in several different ways. The improvement
status of roads was never distinguished in the model. All roads, whether used
frequently or not, paved or dirt, were counted as having equal influence in the model.
Additionally the roads data was derived as ancillary data delivered in the same database
that the Africover land cover data was delivered in. Although it is not verified, it would
be reasonable to assume that roads may have been constructed since the 2003 release
of this dataset.
5.3 Model processing issues
In the process of constructing the model methodology several issues arose that
deserve mention in this discussion so that new research on this topic may benefit from
the lessons learned. First, was the use of the Summary Statistics tool in ArcGIS Toolbox.
The Summary Statistics tool gives a variety of statistics in raster data, such as minimum
and maximum value, average and range. It was not known at the time of construction
that range was an output of the tool and its use was inadvertently disregarded during
the modeling process which made the modeling process slightly more complicated than
necessary. In the model the total range of values were arranged to subtract the
maximum value from the minimum value to produce a total range value.
84
Also of note during the modeling process was the use of magnitudes to derive
cost of movement values that could be processed in the ArcGIS modeling environment.
Section 3.9 describes the process of developing the cost surface used to derive the
CMTC corridors. It was found that the raster file types output from process contained
decimal values and therefore would not process successfully in ArcGIS ModelBuilder. To
remedy the situation the values were multiplied by 1,000,000 to ensure at least six
significant digits. Maintaining this level of detail of each value was not derived from
literature, rather was assumed to be a conservative enough to capture enough available
data values to provide a proper valuation of the cost surface as an elephant may
experience the landscape. This decision created a cost surface with resistance values
ranging from 0 to 1,000,000. When the CMTC analysis was completed corridor values
ranged from zero to the tens of billions.
At this point, it was discovered that values in the tens of billions would also not
process successfully in ModelBuilder. Therefore the values were reduced by 100,000 to
return all values to at least the tens of millions magnitude. A brief review of the data
values indicated that all values could be reduced by five digits without any loss of data.
This review, however, turned out to be flawed. It was discovered later that the lowest
values in the CMTC corridors was actually 613,260,000. Dividing this value by 100,000
results in a value of 6132.6; further, when the Int tool is used to remove the decimal
value and create only integer values this data value is lost. This may have occurred
more than once in the model. A full investigation of data values that were in the
hundreds of millions range and therefore would have lost this unit of value was not
85
completed. The results were compromised to a slight degree, but it was deemed
reasonable to accept this small degree of loss. These values were likely especially
prevalent when the distance between origin and destination was minimal. In the vast
majority of cases, there was minimum value lost.
5.4 The exclusion of scale sensitivity
The issue of scale is also of concern for the analysis of the model. Improvements
could be made to a new iteration of this model by taking into account the sensitivities of
the differences in scale used while modeling. It was assumed that scale is a non-factor
throughout the model, as can be evidenced by the use of zones of analysis with
different total areas and the evaluation of connectivity using the same modeling process
and data at the single corridor level all the way up to the region-wide model
5.5 Final thoughts
Given the quantity of problems associated with the data and the flaws in the
modeling process, the question arises whether this thesis research has any value at all.
It is clear that improvements made to the data would certainly increase the credibility of
the modeling exercise. It is also clear that the amount of inputs needed to model
connectivity are abundant and not easily obtained. Therefore, it is hoped that this
discussion may serve as evidence to the need to increase the quality of data in the
86
southeast Tanzanian region. Developing data for regional water availability during the
dry season, for instance, might serve as a critical input to a variety of research projects
trying to model wildlife occurrence and movement in the region. As discussed in
Chapter 1, the area is home to a multitude of endemic species and some of the last
remaining vestiges of east African coastal forest. Improving the data availability so that
more research may contribute to conservation in the region would be of immense
value. The added value of a thesis project such as this, is that may serve as a prototype
for conservation planning in one of the biologically outstanding places on planet earth.
87
Appendix A: Africover land cover classes reclassified
Africover Land Cover Class Galanti et al. (2008)
Reclassification
1 Forest Plantation - Acacia mearsi 1 Agriculture
2 Forest Plantation - Eucalyptus spp. 1 Agriculture
3 Forest Plantation - Pinus spp. 1 Agriculture
4 Forest Plantation - Teak 1 Agriculture
5 Forest Plantation - Needle Leaved Evergreen 1 Agriculture
6 Forest Plantation - Large Fields 1 Agriculture
7 Rainfed Tree Crop, Large Fields 1 Agriculture
8 Rainfed Tree Crop, Medium Fields 1 Agriculture
9 Rainfed Tree Crop, Plus 1 Herbaceous Crop, Medium Fields 1 Agriculture
10 Rainfed Orchard (1 add. Herbaceous Crop), Medium Fields - Cashew 1 Agriculture
11 Rainfed Tree Crop, Clustered Medium Fields 1 Agriculture
12 Rainfed Tree Crop (1 add. Herbaceous Crop) - Clustered Medium Fields 1 Agriculture
13 Rainfed Tree Crop, Small Fields 1 Agriculture
14 Rainfed Tree Crop (1 add. Herbaceous Crop), Small Fields 1 Agriculture
15 Rainfed Tree Crop (1 add. Shrub Crop), Small Fields 1 Agriculture
16 Rainfed Tree Crop, Clustered Small Fields 1 Agriculture
17 Rainfed Tree Crop (1 add. Herbaceous Crop), Clustered Small Fields 1 Agriculture
18 Rainfed Tree Crop (1 add. Shrubs Crop), Clustered Small Fields 1 Agriculture
19 Rainfed Tree Crop, Isolated Small Fields 1 Agriculture
20 Rainfed Tree Crop (1 add. Herbaceous Crop), Isolated Small Fields 1 Agriculture
21 Rainfed Shrub Crop, Large Fields 1 Agriculture
22 Rainfed Shrub Crop, Large Fields - Tea 1 Agriculture
23 Rainfed Shrub Crop, Medium Fields 1 Agriculture
24 Rainfed Shrub Crop, Medium Fields - Tea 1 Agriculture
25 Rainfed Shrub Crop, Small Fields 1 Agriculture
26 Rainfed Shrub Crop, Clustered Small Fields 1 Agriculture
27 Rainfed Shrub Crop, Isolated Small Fields 1 Agriculture
28 Rainfed Shrub Crop (1 add. Herbaceous Crop), Small Fields 1 Agriculture
29 Rainfed Shrub Crop (1 add. Herbaceous Crop), Clustered Small Fields 1 Agriculture
30 Rainfed Shrub Crop (1 add. Shrub Crop), Small Fields - Coffee, Banana 1 Agriculture
31 Rainfed Shrub Crop (1 add. Herbaceous Crop), Isolated Small Fields 1 Agriculture
32 Irregularly Flooded Cereals, Small Fields - Rice 1 Agriculture
33 Irrigated Herbaceous Crop, Large Fields 1 Agriculture
34 Irrigated Herbaceous Crop, Large Fields - Sugarcane 1 Agriculture
35 Irrigated Herbaceous Crop, Small Fields 1 Agriculture
36 Post Flooding Herbaceous Crop, Medium Fields 1 Agriculture
37 Post Flooding Herbaceous Crop, Medium Fields, Clustered 1 Agriculture
38 Post Flooding Herbaceous Crop, Small Fields 1 Agriculture
88
39 Post Flooding Herbaceous Crop, Clustered Small Fields 1 Agriculture
40 Post Flooding Herbaceous Crop, Isoloated Small Fields 1 Agriculture
41 Rainfed Herbaceous Crop, Large to Medium Fields 1 Agriculture
42 Rainfed Herbaceous Crop, Large to Medium Fields - Sisal 1 Agriculture
43 Rainfed Herbaceous Crop, Large Fields 1 Agriculture
44 Rainfed Herbaceous Crop, Large Fields - Sugarcane 1 Agriculture
45 Rainfed Herbaceous Crop, Large Fields - Sisal 1 Agriculture
46 Rainfed Herbaceous Crop, Large Fields - Wheat 1 Agriculture
47 Rainfed Herbaceous Crop, Medium Fields 1 Agriculture
48 Rainfed Herbaceous Crop, Medium Fields - Wheat 1 Agriculture
49 Rainfed Herbaceous Crop, Clustered Medium Fields 1 Agriculture
50 Rainfed Herbaceous Crop, Isolated Medium Fields 1 Agriculture
51 Rainfed Herbaceous crop, Small Fields 1 Agriculture
52 Rainfed Herbaceous Crop, Clustered Small Fields 1 Agriculture
53 Rainfed Herbaceous Crop, Isolated Small Fields 1 Agriculture
54 Rainfed Herbaceous Crop (2 add. Herbaceous Crops), Small Fields 1 Agriculture
55 Rainfed Herbaceous Crop (2 add. Herbaceous Crops), Clustered Small Fields 1 Agriculture
56 Rainfed Herbaceous Crop (2 add. Herbaceous Crops), Isolated Small Fields 1 Agriculture
57 Rainfed Herbaceous Crop (2 add. Herbaceous Crops), Isolated Small Fields 11 Urban
58 Vegetated Urban Areas 2 Closed forest
59 Closed woody (broadleaved deciduous) with sparse 2 Closed forest
60 Open woody with closed to open herbaceous 3 Open forest
61 Closed trees (needlelaved evergreen) 2 Closed forest
62 Closed trees with closed to open shrubs 2 Closed forest
63 Closed low trees with closed to open shrubs 2 Closed forest
64 Open general trees with closed to open shrubs 3 Open forest
65 Open general trees with open shrubs 3 Open forest
66 Open general trees (broadleaved evergreen) with open shrubs 3 Open forest
67 Open trees (broadleaved deciduous) with closed to 3 Open forest
68 Open trees (broadleaved deciduous) with open herbaceous and sparse shrubs 7 Open shrubland
69 Open low trees (broadleaved deciduous) with open herbaceous and sparse shrubs 7 Open shrubland
70 Very open trees (broadleaved deciduous) with 3 Open forest
71 Very open low trees (broadleaved deciduous) with open herbaceous and sparse shrubs 7 Open shrubland
72 Very open low trees (broadleaved deciduous) with open tall herbaceous and sparse shrubs 7 Open shrubland
73 Closed multilayered trees (broadleaved evergreen) 2 Closed forest
74 Closed Shrubs 6 Closed shrubland
75 Closed medium shrubs (broadleaved deciduous) - Fern 6 Closed shrubland
76 Open general shrubs with closed to open herbaceous 7 Open shrubland
77 Open general shrubs with closed to open herbaceous and sparse trees 7 Open shrubland
78 Open shrubs with closed to open herbaceous and 7 Open shrubland
79 Very open shrubs with closed to open herbaceous 7 Open shrubland
80 Very open shrubs with closed to open herbaceous and sparse trees 7 Open shrubland
89
81 Sparse shrubs with sparse herbaceous 7 Open shrubland
82 Closed to very open herbaceous 4 Savannah
83 Closed to very open herbaceous with sparse shrubs 5 Savannah with trees and shrubs
84 Closed to very open herbaceous with sparse trees 5 Savannah with trees and shrubs
85 Sparse herbaceous 7 Open shrubland
86 Cereals, Rice - Large Fields 1 Agriculture
87 Cereals, Rice - Medium Fields 1 Agriculture
88 Cereals, Rice - Small Fields 1 Agriculture
89 Closed herbaceous on temporarily flooded and - fresh water 8 Non-permanent water
90 Closed herbaceous with sparse trees on temporarily flooded land - fresh water 8 Non-permanent water
91 Closed Herbaceous (on permanently flooded land - Fresh Water) 8 Non-permanent water
92 Closed to very open herbaceous with sparse shrubs on temporarily flooded land - fresh water 8
Non-permanent water
93 Closed shrubs on temporarily flooded land - fresh water 8 Non-permanent water
94 Closed shrubs (broadleaved evergreen) on permanently flooded land - brackish water 9 Permanent water
95 Open shrubs with closed to open herbaceous on 8 Non-permanent water
96 Very open shrubs with closed to open herbaceous on temporarily flooded land - fresh water 8
Non-permanent water
97 Closed trees on temporarily flooded land - fresh water 8 Non-permanent water
98 Open general woody with closed to open herbaceous on temporarily flooded land 8
Non-permanent water
99 Closed trees (broadleaved evergreen) on permanently flooded land - brackish water 9 Permanent water
100 Open general trees with closed to open herbaceous on temporarily flooded land - fresh water 8
Non-permanent water
101 Urban areas (general) 11 Urban
102 Rural settlements 11 Urban
103 Refugee camp 11 Urban
104 Port 11 Urban
105 Airport 11 Urban
106 Industrial area - general 11 Urban
107 Quarry 11 Urban
108 Bare rock 7 Open shrubland
109 Bare soil 7 Open shrubland
110 Bare soil very stony 7 Open shrubland
111 Salt crusts 7 Open shrubland
112 Sand 10 Waterbodies
113 Artificial Lakes or Reservoirs 10 Waterbodies
114 River 10 Waterbodies
115 Natural Lakes 10 Waterbodies
116 River banks 10 Waterbodies
117 Lake shore 10 Waterbodies
118 Sand beaches 10 Waterbodies
90
119 Snow 10 Waterbodies
91
Appendix B: Modeled PA connections
92
93
94
Interzones
95
Appendix C: List of corridors between PAs modeled Zone A PA number Origin Protected Area Name
PA number Destination Protected Area Name
1 Wami-Mbiki to 2 Morogoro Fuel
1 Wami-Mbiki to 5 Kitulanghalo
2 Morogoro Fuel to 3 Nguru ya Ndege
2 Morogoro Fuel to 4 Dindili
3 Nguru ya Ndege to 4 Dindili
3 Nguru ya Ndege to 7 Nyandira
3 Nguru ya Ndege to 6 Mindu
4 Dindili to 5 Kitulanghalo
4 Dindili to 12 Mkungwe
4 Dindili to 9 Pangawe East
5 Kitulanghalo to 12 Mkungwe
6 Mindu to 10 Konga
6 Mindu to 28 Bunduki 3
7 Nyandira to 10 Konga
7 Nyandira to 8 Pangawe West
7 Nyandira to 11 Uluguru North
8 Pangawe West to 9 Pangawe East
9 Pangawe East to 11 Uluguru North
9 Pangawe East to 13 Mangala
9 Pangawe East to 15 Ruvu
9 Pangawe East to 12 Mkungwe
11 Uluguru North to 16 Bunduki
11 Uluguru North to 27 Milawilila
12 Mkungwe to 15 Ruvu
12 Mkungwe to 23 Mkulazi
13 Mangala to 14 Kimboza
13 Mangala to 27 Milawilila
14 Kimboza to 15 Ruvu
14 Kimboza to 19 Uluguru South
14 Kimboza to 20 Kasanga
15 Ruvu to 22 Chamanyani
15 Ruvu to 23 Mkulazi
16 Bunduki to 27 Milawilila
16 Bunduki to 28 Bunduki 3
17 Vigoza to 28 Bunduki 3
96
17 Vigoza to 19 Uluguru South
17 Vigoza to 18 Nyandiduma and Nyandira
18 Nyandiduma and Nyandira to 19 Uluguru South
18 Nyandiduma and Nyandira to 29 Shikurifumi
19 Uluguru South to 29 Shikurifumi
19 Uluguru South to 20 Kasanga
20 Kasanga to 21 Mvuha
20 Kasanga to 29 Shikurifumi
20 Kasanga to 25 Kilengwe
21 Mvuha to 22 Chamanyani
21 Mvuha to 26 Vigoregoro
23 Mkulazi to 26 Vigoregoro
24 Mikumi to 29 Shikurifumi
24 Mikumi to 25 Kilengwe
25 Kilengwe to 29 Shikurifumi
25 Kilengwe to 26 Vigoregoro
Zone B
PA number Origin Protected Area Name
PA number Destination Protected Area Name
30 Ruvu North Fuel to 31 Pande
30 Ruvu North Fuel to 34 Ruvu South
31 Pande to 35 Pugu
33 Mbinga to 35 Pugu
33 Mbinga to 37 Forest Reserve Name Unknown (TZA) (Mangrove) No.20
34 Ruvu South to 35 Pugu
33 Mbinga to 37 Forest Reserve Name Unknown (TZA) (Mangrove) No.20
34 Ruvu South to 35 Pugu
34 Ruvu South to 38 Masanganya
35 Pugu to 36 Kazimzumbwi
36 Kazimzumbwi to 33 Mbinga
37 Forest Reserve Name Unknown (TZA) (Mangrove) No.20 to 41
Forest Reserve Name Unknown (TZA) (Mangrove) No.22
40 Forest Reserve Name Unknown (TZA) (Mangrove) No.23 to 41
Forest Reserve Name Unknown (TZA) (Mangrove) No.22
40 Forest Reserve Name Unknown (TZA) (Mangrove) No.23 to 43 Marenda
40 Forest Reserve Name Unknown (TZA) (Mangrove) No.23 to 51 Mchungu
39 Masanganya to 43 Marenda
36 Kazimzumbwi to 38 Masanganya
38 Masanganya to 45 Mtanza Msona
39 Masanganya to 42 Mtita
42 Mtita to 43 Marenda
40 Forest Reserve Name Unknown (TZA) to 51 Mchungu
97
(Mangrove) No.23
39 Masanganya to 42 Mtita
42 Mtita to 43 Marenda
42 Mtita to 46 Ruhai River
42 Mtita to 47 Ngulakula
47 Ngulakula to 44 Kingoma
52 Forest Reserve Name Unknown (TZA) (Mangrove) No.25 to 33 Mbinga
48 Nyumburuni to 33 Mbinga
44 Kingoma to 48 Nyumburuni
43 Marenda to 44 Kingoma
44 Kingoma to 51 Mchungu
51 Mchungu to 50 Kikale
51 Mchungu to 52 Forest Reserve Name Unknown (TZA) (Mangrove) No.25
58 Nyamwage Village to 48 Nyumburuni
50 Kikale to 58 Nyamwage Village
52 Forest Reserve Name Unknown (TZA) (Mangrove) No.25 to 59 Mohoro
48 Nyumburuni to 58 Nyamwage Village
48 Nyumburuni to 78 Yelya Village
48 Nyumburuni to 57 Katundu
47 Ngulakula to 48 Nyumburuni
46 Ruhai River to 47 Ngulakula
45 Mtanza Msona to 46 Ruhai River
55 Kipo to 47 Ngulakula
45 Mtanza Msona to 55 Kipo
55 Kipo to 57 Katundu
55 Kipo to 56 Rupiange
56 Rupiange to 57 Katundu
56 Rupiange to 61 Ngarambe-Tapika
56 Rupiange to 76 Kichi Hill
57 Katundu to 76 Kichi Hill
57 Katundu to 78 Yelya Village
47 Ngulakula to 57 Katundu
76 Kichi Hill to 72 Mbinga
76 Kichi Hill to 75 Lungonya
61 Ngarambe-Tapika to 75 Lungonya
75 Lungonya to 72 Mbinga
72 Mbinga to 74 Ulabo
73 Mchonga to 74 Ulabo
71 Tong-omba to 73 Mchonga
70 Kibambo to 71 Tong-omba
67 Kilungulungu to 70 Kibambo
98
66 Kiwengoma to 67 Kilungulungu
63 Nambunju Village to 66 Kiwengoma
62 Tawi Village to 66 Kiwengoma
62 Tawi Village to 63 Nambunju Village
76 Kichi Hill to 62 Tawi Village
62 Tawi Village to 78 Yelya Village
78 Yelya Village to 77 Mbwara Village
77 Mbwara Village to 64 Namakutwa Nyamulete
64 Namakutwa Nyamulete to 68 Mchekela
68 Mchekela to 69 Kianika
64 Namakutwa Nyamulete to 68 Mchekela
69 Kianika to 79 Rufiji-Mafia-Kilwa Marine
64 Namakutwa Nyamulete to 79 Rufiji-Mafia-Kilwa Marine
64 Namakutwa Nyamulete to 65 Tamburu
58 Nyamwage Village to 59 Mohoro
58 Nyamwage Village to 65 Tamburu
65 Tamburu to 79 Rufiji-Mafia-Kilwa Marine
59 Mohoro to 60 Mohoro River
59 Mohoro to 65 Tamburu
79 Rufiji-Mafia-Kilwa Marine to 81 Forest Reserve Name Unknown (TZA) (Mangrove) No.27
60 Mohoro River to 81 Forest Reserve Name Unknown (TZA) (Mangrove) No.27
81 Forest Reserve Name Unknown (TZA) (Mangrove) No.27 to 33 Mbinga
Zone C
PA number Origin Protected Area Name
PA number Destination Protected Area Name
80 Kisangi Village to 82 Forest Reserve Name Unknown (TZA) (Mangrove) No.28
80 Kisangi Village to 85 Migeregere Village
80 Kisangi Village to 83 Kikole Village
82 Forest Reserve Name Unknown (TZA) (Mangrove) No.28 to 85 Migeregere Village
82 Forest Reserve Name Unknown (TZA) (Mangrove) No.28 to 88
Forest Reserve Name Unknown (TZA) (Mangrove) No.30
85 Migeregere Village to 88 Forest Reserve Name Unknown (TZA) (Mangrove) No.30
85 Migeregere Village to 86 Ruhatwe Village
84 Kikole Village to 86 Ruhatwe Village
86 Ruhatwe Village to 89 Mitundumbea
86 Ruhatwe Village to 87 Mitaurure
88 Forest Reserve Name Unknown (TZA) (Mangrove) No.30 to 89 Mitundumbea
88 Forest Reserve Name Unknown (TZA) (Mangrove) No.30 to 90 Kiwawa Village
87 Mitaurure to 89 Mitundumbea
87 Mitaurure to 91 Rungo
99
91 Rungo to 92 Ngarama
90 Kiwawa Village to 92 Ngarama
89 Mitundumbea to 90 Kiwawa Village
92 Ngarama to 94 Pindiro
92 Ngarama to 95 Ndimba
94 Pindiro to 95 Ndimba
93 Kihundu Village to 94 Pindiro
91 Rungo to 93 Kihundu Village
93 Kihundu Village to 109 Malehi
109 Malehi to 108 Matapwa
108 Matapwa to 96 Ruawa Munimburo
95 Ndimba to 96 Ruawa Munimburo
100 Chitoa to 96 Ruawa Munimburo
100 Chitoa to 101 Litipo
107 Ntama to 101 Litipo
106 Nndawa Village to 107 Ntama
105 Rondo to 106 Nndawa Village
105 Rondo to 99 Mkangala
98 Nandimba to 108 Matapwa
98 Nandimba to 99 Mkangala
98 Nandimba to 105 Rondo
98 Nandimba to 105 Rondo
98 Nandimba to 104 Lionja
109 Malehi to 97 Nyera Kiperere
109 Malehi to 98 Nandimba
97 Nyera Kiperere to 103 Angai
103 Angai to 104 Lionja
104 Lionja to 107 Ntama
Interzone A
PA number Origin Protected Area Name
PA number Destination Protected Area Name
1 Wami-Mbiki to 30 Ruvu North Fuel
5 Kitulanghalo to 30 Ruvu North Fuel
12 Mkungwe to 30 Ruvu North Fuel
12 Mkungwe to 34 Ruvu South
23 Mkulazi to 30 Ruvu North Fuel
23 Mkulazi to 34 Ruvu South
23 Mkulazi to 38 Masanganya
23 Mkulazi to 42 Mtita
23 Mkulazi to 45 Mtanza Msona
26 Vigoregoro to 45 Mtanza Msona
100
25 Kilengwe to 45 Mtanza Msona
Interzone B
PA number Origin Protected Area Name
PA number Destination Protected Area Name
75 Lungonya to 97 Nyera Kiperere
75 Lungonya to 87 Mitaurure
72 Mbinga to 83 Kikole Village
72 Mbinga to 80 Kisangi Village
74 Ulabo to 80 Kisangi Village
69 Kianika to 85 Migeregere Village
79 Rufiji-Mafia-Kilwa Marine to 82 Forest Reserve Name Unknown (TZA) (Mangrove) No.28
101
References
Adriaensen, F., Chardon, J.P., De Blust, G., Swinnen, S., Villalba, E., Gunlinck, H., and Matthysen, E., 2003. The application of ‘least-cost’ modeling as a functional landscape model. Landscape and Urban Planning, 64:233-247.
Angel, S., Sheppard, S.C., and Civco, D.L., 2005. The Dynamics of Global Urban
Expansion. Department of Transport and Urban Development, The World Bank. Barnes, R.F.W., Barnes, K.L., Alers, A., and Blom, A., 1991. Man determines the
distribution of elephants in the rain forests of northeastern Gabon. African Journal of Ecology, 29 (1): 54 – 63.
Beier, P., Majka, D., and Jenness, J., 2007. Conceptual Steps for designing wildlife
corridors [online]. Available from http://www.corridordesign.org/ [Accessed date 1 September 2010]
Beier, P., Majka, D.R., and Spencer, W.D., 2008. Forks in the road: choices in procedures
for designing wildland linkages. Conservation Biology. 22 (4): 836-851. Blanc, J.J., Barnes, R.F.W., Craig, G.C., Dublin, H.T., Thouless, C.R., Douglas-Hamilton, I.,
and Hart, J.A., 2007. African Elephant Status Report 2007. Occasional Paper of the IUCN Species Survival Commission No. 33. International Union for the Conservation of Nature and Natural Resources.
Blom, A., Zalinge, R., Heitkonig, I.M.A., and Prins, H.H.T., 2005. Factors influencing the
distribution of large mammals within a protected central African forest. Oryx, 39 (4): 381 – 388.
Burgess, N.D., Dickinson, A., and Payne, N.H., 1993. Tanzanian coastal forest – new
information on status and biological importance. Oryx, 27 (3): 169-173. Burgess, N.D. and Clarke, P., 2008. Towards a Protected Area Network in the Coastal
Forests Ecoregion of Tanzania: Analysis and Recommendations. Unpublished. Document prepared as an input to the Global Environment Facility proposal for the Tanzanian Coastal Forests.
Buys, P., Deichmann, U., and Wheeler, D., 2006. Road Network Upgrading and Overland
Trade Expansion in Sub-Saharan Africa. Development Research Group, The World Bank.
102
Caro, T.M. and O’Doherty, G., 1999. On the use of surrogate species in conservation Biology. Conservation Biology, 13 (4): 805 – 814.
Chetkiewicz, C.B., Colleen C., and Boyce, M.S., 2006. Corridors for conservation:
integrating pattern and process. Annual Review of Ecology, Evolution and Systematics, 37: 317-342.
Cushman, S.A., McKelvey, K.S., and Schwartz, M.K., 2009. Use of empirically derived
source-destination models to map regional conservation corridors. Conservation Biology, 23 (2): 368-376.
Di Gregorio, A., 2002. Tanazania – Multipurpose Landcover Database Metadata. Food
and Agriculture Organization of the United Nations. Dobson, A.P., Borner, M., Sinclair, A.R.E., Hudson, P. J., Anderson, M.T., Bigurube, G.,
Davenport, T.B.B., Deutsch, J., Durant, S.M.E., Estes, R.D., Estes, A. B., Fryxell, J., Foley, C., Gadd, M.E., Haydon, D., Holdo, R., Holt, R.D., Homewood, K., Hopcraft, J.G.C., Hilborn, R., Jambiya, G.L.K., Laurenson, M.K., Melamari, L. Morindat, A.O., and Ogutu, J.O., 2010. Road will ruin Seregeti. Nature. 467: 272-273.
Donovan, T.M. and Flather, C.H., 2002. Relationships among North American songbird
trends, habitat frafmentation, and landscape occupancy. Ecological Applications, 12 (2): 364 – 374.
Douglas-Hamilton, I., Krink, T., and Vollrath, F., 2005. Movements and corridors of
African elephants in relation to protected areas. Naturwissenschaften, 92: 158 – 163.
Driezen, K., Adriaensen, F., Rondinini, C., Doncaster, C.P., and Matthysen, E., 2007.
Evaluating least-cost model predictions with empirical dispersal data: A case-study using radiotracking data of hedgehogs (Erinaceus europaeus). Ecological Modeling. 209: 314-322.
Epps, C.W., Mutayoba, B.M., Gwin, L.,and Brashares, J.S., 2011. An empirical evaluation
of the African elephant as a focal species for connectivity planning in East Africa. Diversity and Distributions, 17 (4): 603 – 612.
Fahrig, L., 2003. Effects of habitat fragmentation on biodiversity. Annual Review of
Evolution Systematics, 34 (1): 487 – 515. Frankam, R., 2005. Genetics and extinction. Biological Conservation, 126 (2): 131-140.
103
Galanti, V., Preatoni, D., Martinoli, Wauters, L.A., and Tosi, G., 2006. Space and habitat use of the African elephant in the Tarangire-Manyara ecosystem, Tanzania: implications for conservation. Mammalian Biology, 71 (2): 99 – 114.
Gibbs, H.K., Ruesch, A.S., Achard, F.C., Holmgren, P.M.K., Ramankutty, N., and Foley,
J.A., 2010. Tropical forests were the primary sources of new agricultural land in the 1980s and 1990s. Proceedings of the National Academy of Sciences of the United States of America. 107 (38): 16732 – 16737.
Harris, L.M. and Hazen, H.D., 2006. Power of maps: (counter) mapping for conservation.
ACME, 4 (1): 99-130. Harris, G.M., Russell, G.J., Van Aarde, R.I., and Pimm, S.L., 2008. Rules of habitat use by
elephants Loxodonta Africana in southern Africa: insights for regional management. Oryx, 42 (1): 66 – 75.
International Union for the Conservation of Nature and United Nations Environment
Programme. 2010. The World Database on Protected Areas (WDPA). UNEP-WCMC. Cambridge, UK. www.protectedplanet.net
LaRue, M.A. and Nielsen, C.K., 2008. Modelling potential dispersal corridors for cougars
in midwestern North America using least-cost path methods. Ecological Modeling, 212: 372-381.
Lehner, B., Verdin, K., and Jarvis, A., 2008. New global hydrography derived from
spaceborne elevation data. Eos, Transactions, AGU, 89(10): 93-94. McRae, B.H., Dickson, B.G., Keitt, T.G., and Shah, V.B., 2008. Using circuit theory to
model connectivity in ecology, evolution, and conservation. Ecology, 89 (10): 2712 – 2724.
Myers, N., Mittermeier, R.A., Mittermeier, C.G.F., Gustavo A.B., and Kent, J., 2000.
Biodiversity hotspots for conservation priorities. Nature, 403: 853-858. Naugton-Treves, L., Holland, M.B., and Brandon, K., 2005. The role of protected areas in
conserving biodiversity and sustaining local livelihoods. Annual Review of Environmental Resources, 30 (1): 219-252.
Newmark, W.D., 1993. The role and design of wildlife corridors with examples from
Tanzania. Ambio, 22 (8): 500-504. Newmark, W.D., 1996. Insularization of Tanzanian parks and the local extinction of large
mammals. Conservation Biology, 10 (6): 1549-1556.
104
Ngene, S.M., Skidmore, A.K., Gils, H.V., Douglas-Hamilton, I., and Omondi, P., 2009. Elephant distribution around a volcanic shield dominated by a mosaic of forest and savanna (Marsabit, Kenya). African Journal of Ecology, 47: 234 – 245.
ORNL, 2009. Landscan 2008 Global Population Database. Pinto, N. and Keitt, T.H., 2008. Beyond the least-cost path: evaluating corridor
redundancy using a graph theoretic approach. Landscape Ecology, 24 (2): 253-266.
Pittiglio, C., Skidmore, A.K., Van Gils, H.A.M.J., and Prins, H.H.T., 2012. Identifying transit
corridors for elephant using a long time-series. International Journal of Applied Earth Observation and Geoinformation, 14(1): 61-72.
Primm, S.L., Russell, G.J., Gittleman, J.L., and Brooks, T.M., 1995. The future of
biodiversity. Science, 269: 347-350. Rabinowitz, A. and Zeller, K.A., 2010. A range-wide model of landscape connectivity and
conservation for the jaguar, Pantera onca. Biological Conservation, 143(3): 939-945
Ricketts, T.H., 2001. The matrix matters: effective isolation in fragmented landscapes.
The American Naturalist. 158 (1): 87-99. Rouget, M., Cowling, R.M., Lombard, A.T., Knight, A.T., and Kerley, G .I.H., 2006.
Designing large-scale conservation corridors for pattern and process. Conservation Biology, 20 (2): 549-561.
Shah, V.B. and McRae, B.H., 2008. Circuitscape: a tool for landscape ecology.
Proceedings of the 7th Python in Science Conference (SciPy 2008): 62-66. Shaffer, M.L., 1981. Minimum population sizes for species conservation. Bioscience. 31
(2): 131-134. Simberloff, D., 1998. Flagships, umbrellas, and keystones: is single -species management
passe in the landscape era? Biological Conservation, 83 (3): 247 – 257. Taylor, P.D., Fahrig, L., Henein, K., and Merriam, G., 1993. Connectivity is a vital element
of landscape structure. Oikos, 68 (3): 571-573. United Nations Environment Programme, 2008. Selous Game Reserve World Heritage
Site Description. [online] Available from: http://www.unep-wcmc.org/sites/wh/index.html [Accessed date 1 September 2010]
105
United Nations, 2005. World Population Prospects: The 2004 Revision. Publication of Department of Economic & Social Affairs.
Urban, D. and Keitt, T., 2001. Landscape connectivity: a graph-theoretic perspective.
Ecology, 82 (5): 1205- 1218. Urban, D.L., Minor, E.S., Tremi, E.A., and Schick, R.S., 2009. Graph models of habitat
mosaics. Ecology Letters, 12 (3): 260-273. Voeten, M.M., and Prins, H.H.T., 1999. Resource partitioning between sympatric wild
and domestic herbivores in the Tarangire region of Tanzania. Oecologia, 120 (2): 287-294.
Wall, J., Douglas-Hamilton, I., and Vollrath, F., 2006. Elephants avoid costly
mountaineering. Current Biology, 16: 527-529. White, F., 1983. The Vegetation of Africa. United Nations Educational, Scientific and
Cultural Organization, Paris.