rainfall variability driving human-elephant conflict in east africa
TRANSCRIPT
Rainfall variability driving human-elephant
conflict in East Africa
Dabwiso Sakala
201248842
BLGY5191M – Final Project Report
MSc Biodiversity and Conservation
2018-2019
Supervisor
Prof. Jon Lovett (University of Leeds)
Dr. Lee Hannah (Conservation International)
Faculty of Biological Sciences
University of Leeds
LS2 9JT
Abstract
Rainfall variability as a driver of future human elephant conflicts in east Africa was analysed.
The principal focus was to determine future rainfall onset and cessation dates, duration of wet
and dry seasons, rainfall variation and occurrence of droughts. Research methods used
projected rainfall data for east Africa, with a focus on Kenya and Uganda, for the time period
2021 to 2030. With east Africa having a bimodal rainfall regime, onset and cessation dates for
the April-June (long rains) and October-December (short rains) rainy seasons were calculated.
Trend analysis was carried out for monotonic trends in rainy and dry season durations.
Intensity of dry periods and potential droughts was monitored by rainfall anomaly index (RAI),
with spatial distribution of dry periods assessed by consecutive dry days index (CDD).
Variation of rainfall in all years was assessed by coefficient of variation index (CV). Rainfall
variation, elephant habitat range, human population density and land use were used to predict
future human elephant conflict hotspots. Results indicated differences in duration of long rains
(51.8 ±18.8 days) and short rains (63 ±22.6 days). Variation in onset and cessation dates for
long rains was higher than that of short rains. Trend analysis found non-significant trends in
duration of rainy and dry seasons. RAI indicated 2028 to be the driest year with a complete
failure of long rains. Intensity and distribution of CDD and CV were generally clustered in the
Kenyan side. Conflict predictive mapping showed future HEC to be more intense in Kenya
than in Uganda. Understanding how climate change will affect human and elephant land uses
as a result of rainfall variation is essential in planning for HEC. Knowledge of the spatial
distribution and intensities of future conflicts can help in planning and maximising limited
resources by focusing on areas which matter.
Contents
Acknowledgments .............................................................................................................................. i
Introduction ......................................................................................................................................... 1
Methods ................................................................................................................................................ 3
Study area ......................................................................................................................................... 3
Precipitation data .............................................................................................................................. 4
Defining water seasons ................................................................................................................... 5
Onset and cessation ........................................................................................................................ 6
Rainfall and dry season trends ....................................................................................................... 6
Monitoring droughts ......................................................................................................................... 6
Rainfall variation ............................................................................................................................... 7
HEC predictive mapping ................................................................................................................. 7
Results .................................................................................................................................................. 8
Discussion ......................................................................................................................................... 14
Onset and Cessation ..................................................................................................................... 16
Droughts and HEC ......................................................................................................................... 17
Predictive HEC hotspots ............................................................................................................... 19
Elephant range shifts ..................................................................................................................... 20
Conclusion ......................................................................................................................................... 20
References ......................................................................................................................................... 22
i
Acknowledgments
I would like to express my gratitude to the Beit Trust and the University of Leeds for their
generosity in funding my MSc studies. I would also like to thank my project supervisors, Prof.
Jon Lovett (University of Leeds) and Dr. Lee Hannah (Conservation International) for their
valuable input in the formulating of my research topic and guidance throughout the entire
research.
1
Introduction
The complexity of Eastern Africa’s terrain and its geographic position results in a wide
spectrum of climatic conditions with major impacts on natural resources and socio-economic
activities (Ogallo, 1993). Rainfall pattern is highly variable in east Africa as seen by the 2010-
2011 droughts which triggered a food and water crisis for people and wildlife (Chen and
Georgakakos, 2015). These seasonal variations play a vital role in how elephants utilise their
habitats for foraging and meeting their water needs (Ashiagbor and Danquah, 2017). As
climate change is expected to alter global rainfall pattern (IPCC, 2014), it is projected that both
human and elephant land-uses will shift with the changing climate (Abdulkadir et al., 2013).
These changes might have direct effects on the frequency and intensities of human elephant
conflicts (HEC) especially in areas where humans and elephants share a common resource.
Studies exploring the impacts of climate change on biodiversity predict a decrease in genetic
diversity of populations and rapid species range shifts. Biodiversity response to climatic
changes is thought to revolve around three things: changing home range through dispersal,
timing of life cycle (phenology) or by adapting to new climatic conditions (Bellard et al., 2012).
Observational studies suggest that the occurrence of extreme events like prolonged droughts
and amplified precipitation will increase in the future (Easterling et al., 2000). Such kind of
climatic happenings and their impacts on elephant ecology has been experienced before in
previous years. A severe drought in 1993 experienced by elephants in Tarangire National
Park, Tanzania, disrupted their normal patterns of movements with some elephants moving to
areas outside the Park (Foley et al., 2008). Garstang et al., (2014) observed that elephant
movement patterns in north-western Namibia are influenced by rainfall onsets or wet episodes
within the dry seasons. The research found that elephant movements occurred just before rain
onset suggesting a response to an environmental factor. Similar observations have been
made in Kenya where elephants are quick to respond to changes in forage and water
availability driven by rainfall events (Bohrer et al., 2014).
Elephants are known to have movements with a stronger directional orientation towards water
resources in dry season than in wet seasons (Wato et al., 2018). This transition in land-use
due to limited water supply is also seen in human societies especially in agricultural
communities (Brücher et al., 2015). The effect of climate change on human societies is more
severe in regions whose livelihoods are heavily dependent on the ecosystem and agriculture
(Bamutaze et al., 2002). The Maasai people of Kenya for example have been moving from
pastoralism to agro-pastoralism with an increased cultivation along river banks and in swamps
(Okello, 2005). The need to provide food, water and shelter for people has led to massive
expansions of croplands, pastures, plantations and urban areas in many areas (Foley et al.,
2005). In places where humans and elephants live in close proximity, such land-use changes
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create conflicts between the two as they compete for space, water and food resources (Okello,
2005). In Kwando region of Namibia, an assessment of human wildlife conflict
incidences between 1991 and 1995 indicated that elephants were in conflict with rural
agriculturalist more than any other animal in the area (O’Connell-Rodwell et al., 2000). The
effect of seasonality on human wildlife conflicts in the case of elephants is mainly driven by
water and food availability. In most elephant habitat regions of Africa such as Marsabit
National Park in Kenya, water and forage availability are mainly determined by rainfall. Rural
communities surrounding this National Park are known to experience season influenced
incidences of human elephant conflicts (HEC), with higher incidences being reported during
dry seasons compared to wet seasons (Abdulkadir et al., 2013).
The effect of rainfall variability on human elephant conflict (HEC) has been observed in many
areas where humans and elephants coexist. Local communities in Mozambique living near
the Limpopo river increasingly choose river banks as their preferred cropping area due to
prolonged droughts (Givá and Raitio, 2017). These seasonal shifting between droughts and
rainfall affects the intensities of HEC during dry spells as more elephants move from the
interior of Limpopo National Park towards the Limpopo river. HEC incidences caused by
rainfall failure can be linked to changes in vegetation pattern, irrigation and water management
and agricultural cultivation during droughts (Zacarias and Loyola, 2018). Elephants have been
known to have distinct seasonal home ranges based on vegetation nutritional quality
(Shannon et al., 2015). With both human and elephant decisions heavily influenced by suitable
habitats, failure of rainfall has the potential for increased HEC as the two cross paths in their
search for limited resources.
Changes in seasonality has significant effects on elephant’s foraging behaviour. They display
an opportunistic migration strategy where they only stay in an area as long as forage and
water persist (Bohrer et al., 2014). This behaviour is seen by an increase in home ranges or
concentrating foraging activities in areas near water resources during dry season or droughts
(de Beer and van Aarde, 2008). In areas surrounding Tsavo National Park, Kenya, the
increase in elephant home range during dry season has been shown to be a major driver in
HEC incidences (Smith and Kasiki, 2000). HEC in this region is more dispersed during dry
season as compared wet season. Increased HEC during dry seasons has also been observed
in Asian elephants (Elephas maximus). Between the years 1990 and 2003, the number of
elephants killed in Southern Sri Lanka as a result of HEC were significantly correlated with
drought peaks (Zubair et al., 2005). A similar case is that of the pastoral communities in west
Kilimanjaro basin in Kenya and Tanzania. A drought in 2009 significantly increased HEC
incidences as more elephants migrated towards human dominated landscapes in search of
forage and water (Mariki et al., 2015).
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As HEC incidences are more likely to increase during droughts (Hazzah et al., 2013),
understanding the timing of future climate-related hazards like delayed rainfall and droughts
would be vital in implementing HEC mitigation strategies. East Africa is known to have frequent
droughts and rainfall variation with its future climate projected to alter both timing and
intensities of rainfall (Yang et al., 2014). Knowledge of the timing and spatial distribution of
rainfall variations is essential in understanding how human and elephants might compete for
resources in East Africa. Experts project that future human land-use changes will reduce
natural vegetation cover by 26-58% in biodiversity hotspots (Jantz et al., 2015). Understanding
the long-term rainfall variability and its impact on HEC creates a well-founded platform for
conflict mitigation strategies and sound policy formulation for natural resource management.
The purpose of this study was to understand how future changes in rainfall seasonality and its
variation in East Africa would impact human elephant conflicts (HEC) in a shared environment.
The study focussed on rainfall timings by looking at future projections of rainfall onsets and
cessations, duration of wet and dry seasons, and occurrence of droughts or failure of rainfall.
In addition, the study assessed how HEC distribution and intensities might be driven by the
interactions of future rainfall variations, human land-use and elephant habitat range.
Methods
Study area
The eastern African region is made up of eleven Countries (Sudan, South Sudan, Eritrea,
Djibouti, Ethiopia, Somalia, Kenya, Uganda, Rwanda, Burundi, Tanzania) standing on an
equatorial location characterised by a dry annual mean precipitation (Camberlin, 2018). Like
most areas near the equator, rainfall pattern follows a bimodal regime with rainy seasons
occurring around April to June (long rains) and October to December (short rains). The two
seasons indicate varying levels of influence from the Atlantic, Indian and Pacific Oceans
(Conway et al., 2005). These variations are thought to be a result of the annual cycle of
monsoonal winds combining with the annual cycle of the Indian ocean’s sea surface
temperatures (Yang et al., 2014). Complex topographical features in east Africa such as the
Great Rift Valley, mount Kenya and Kilimanjaro, and large water bodies like lake Victoria have
significant effects on rainfall patterns (Ogallo, 1993). The features together with regional
systems make seasonality to change rapidly over short distances (Nicholson, 1996).
The study area was defined by the two Countries Kenya and Uganda, covering an
approximate area of 829,000 km2. The area is located between 28°82´- 42°02´ N and -4°77´-
5°06´ E, bordering Ethiopia and South Sudan on the north and Tanzania on the southern part
(Fig. 1).
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Fig. 1. Map of the study area with its bordering countries in east Africa
Despite its equatorial location, east Africa has a relatively dry area with the highest rainfall
variation in the African continent (Ogallo, 1993). Short rains generally exhibits higher
interannual variability than long rains (Conway et al., 2005). However, onset for long rains
displays substantial year to year variations than the short rains (Camberlin et al., 2009). The
region has some of the poorest nations in the world with agriculture forming the principal
source of livelihood for many households in semi-arid areas (Gachimbi et al., 2003). Kenya
and Uganda have an estimated combined population of 96 million people with future
population projected to reach 120 million by the year 2030 (United Nations, 2019). The
countries have an elephant habitat range of 123,000 km2, of which 42% is outside protected
areas (Blanc, 2008).
Precipitation data
Global Circulation Models (GCMs), commonly referred to as Global Climate Models, are the
primary tools for predicting and understanding future global climates (Karl and Trenberth,
2003). However, specific regions are not well represented by GCMs because climate at any
specific location is heavily influenced by regional and local factors like topography and water
bodies (Ogallo, 1993). To provide climate information at a smaller scale, Regional Climate
Models (RCM) with a higher horizontal resolution are mainly used to study regional climate
changes (Flato et al., 2013). This study used RCM future precipitation data produced by the
Coordinated Regional Climate Downscaling Experiment (CORDEX) at a horizontal resolution
of 0.44° (~ 50 km2). The data was at a daily frequency, under Representative Concentration
Pathway (RCP) 8.5, and covered a timeframe ranging from 1st January 2021 to 31st December
2030.
5
Defining water seasons
Methods of defining climatological water seasons, rainfall onset and cessation for regions with
two wet seasons per year described by Dunning et al (2016) were adopted for this study. With
a bimodal annual rainfall cycle, each year has two periods when rain occurs. These are termed
as climatological water seasons. Using daily frequency data, climatological water seasons for
each year were found by computing the climatological cumulative daily rainfall anomaly on
day “d”, C(day), denoted by the following equation:
𝐶(𝑑𝑎𝑦) = ∑ 𝑄𝑖 − �̅�
𝑑𝑎𝑦
𝑖=1𝐽𝑎𝑛
Where 𝑄𝑖 is the projected daily mean precipitation, 𝑖 ranging from 1st January to 31st
December, and �̅� as annually averaged daily precipitation (annual total divided by 365).
A positive slope in the climatological cumulative daily rainfall anomaly C(day) indicates the
water season because this is when precipitation exceeds its annual average (Liebmann et al.,
2001). Figure 2 shows a smoothed 30-day running mean C(day) and daily mean precipitation
(𝑄𝑖) for the year 2021.
Fig. 2. Annual precipitation (blue line) with its smoothed 30-day running mean cumulative daily mean rainfall
anomaly (brown line). Minima (green dots) and maxima (red dots) were used to determine the start and end of the
2 climatological water seasons
Start and end of the two water seasons were identified by using minima and maxima in the
30-day smoothed C(day) curve. The first minima (𝑠𝑡𝑎𝑟𝑡1) for the year 2021 was on the 115
day (24th April) with maxima (𝑒𝑛𝑑1) on day 167 (15th June). Second minima on day 258 (14th
Sep) marked the start of the second water season and day 318 (13th Nov) as the end. This
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procedure was applied to each year of study (2021-2030) to establish their climatological water
seasons
Onset and cessation
Upon identification of the two water seasons, rainfall onset and cessation dates were worked
out by using the following equation:
𝐴(𝐷) = ∑ 𝑅𝑗 − �̅�
𝐷
𝑗=𝑠𝑡𝑎𝑟𝑡1−20
Where 𝑅𝑗 is rainfall on day 𝑗 with 𝑗 starting from day 𝑠𝑡𝑎𝑟𝑡1 − 20. 𝐴(𝐷) is calculated for each
day from 𝑠𝑡𝑎𝑟𝑡1 − 20 to 𝑒𝑛𝑑1 + 20 and for 𝑠𝑡𝑎𝑟𝑡2 − 20 to 𝑒𝑛𝑑2 + 20. A ±20 day buffer was
added so as to capture the correct onset and cessation of rains (Dunning et al., 2016). Using
𝐴(𝐷) for start1 to end1 (start2 to end2), minima and maxima in a 5-day running mean of 𝐴(𝐷)
curve were then used to determine rainfall onset and cessation dates. Coefficient of variation
for onset and cessation dates were calculated for both long and short rains across the 10-year
period using the formula:
𝐶𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 𝑜𝑓 𝑣𝑎𝑟𝑖𝑎𝑡𝑖𝑜𝑛 (𝐶𝑉) =𝑜𝑛𝑠𝑒𝑡/𝑐𝑒𝑠𝑠𝑎𝑡𝑖𝑜𝑛 𝑠𝑡𝑎𝑛𝑑𝑒𝑟𝑒𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛
𝑜𝑛𝑠𝑒𝑡/𝑐𝑒𝑠𝑠𝑎𝑡𝑖𝑜𝑛 𝑚𝑒𝑎𝑛
Rainfall and dry season trends
The study considered duration of long and short rains to be the number of days from rainfall
onset to cessation. Days from cessation of long rains to onset of short rains were considered
as the main dry season of the year. A Mann-Kendall Test for trend analysis on the duration
of long and short rains and dry seasons was performed in R-Studio software. Mann-Kendall
test is a non-parametric test which checks the no trend hypothesis versus the alternative of
the existence of increasing or decreasing trend (Eshetu et al., 2016). The test was done to
assess any trends in the three seasons across the 10-year period (2021-2030). Before
applying the test to each season, both autocorrelation and partial autocorrelation in the time
series were examined. With all correlation tests not being significant, a Mann Kendall test was
performed with alpha level taken at 0.05.
Monitoring droughts
Droughts are one of the most costly natural hazards, with their effects having significant
impacts on both human livelihoods and natural resources (WMO and GWP, 2016). The study
used Rainfall Anomaly Index (RAI) and Consecutive Dry Days index (CDD) to monitor possible
future droughts. Rainfall anomaly index (RAI) was calculated at a monthly frequency in R-
Studio software using the precintcon package. The RAI values were then judged based on
7
scale ranging from extremely humid (wet) to extremely dry (Alcântara Costa and Pontes
Rodrigues, 2017).
Consecutive dry days (CDD) is an index for the annual maximum number of consecutive dry
days (Kruger, 2006). It is a valuable drought indicator for the dry part of the year (Frich et al.,
2002). A ‘dry day’ was taken to have a mean daily precipitation of less than 1mm. The CDD
index for each year was calculated in Climate Data Operator (CDO) software version 1.9.6,
with results of the analysis mapped in ArcGIS and R-studio programmes. A Mann-Kendall
trend analysis of the highest CDD number from each year for the 10-year period was
performed to assess possible trends in the dry spells.
Rainfall variation
Rainfall variability, generally defined as the degree to which rainfall amounts vary across an
area or time, was monitored by using coefficient of variation index. Using Climate data
operator (CDO) programme, annual mean and standard deviation for each year (2021-2030)
were calculated. Coefficient of variation (CV) was calculated by dividing precipitation standard
deviation by the mean for each year. Results were processed in ArcGIS (10.6) while mapping
was done in R-studio (1.1.463).
HEC predictive mapping
Predictive maps of HEC hotspots based on the possible interactive effect of rainfall variability,
known elephant habitats, human population density, and agricultural lands were modelled for
the year 2024 and 2030. The two years were chosen based on how different rainfall variation
intensities were distributed. Predicting HEC hotspots based on rainfall variation spatial
distribution was done to understand how rainfall variability might drive changes in HEC. Table
1 and figure 3 below describes the data and its geographic distribution across the study area.
Table 1. description of datasets used in HEC predictive mapping
Data input Source
Rainfall variation
2024/2030 CV raster Calculated from precipitation data (rcp 8.5)
Africa human
Population 2015
1km resolution density
raster
World Pop
(https://www.worldpop.org/geodata/summary?id=139)
Africa Land Use
2016
Kenya & Uganda
Agricultural areas
RCMRD
(http://geoportal.rcmrd.org/)
Elephant Range
2008
Elephant range
IUCN
(https://www.iucnredlist.org/species/12392/3339343#habitat-
ecology)
8
Conflict modelling was done in ArcGIS using weighted overlay tools for modelling suitable
areas. Modelling was carried out by first calculating Euclidian distances for human population
density, agricultural areas, and elephant range. Each Euclidian distance and CV data were
reclassified using a scale of 1 to 9 with 9 being the highest possible conflict area. HEC areas
were modelled based on the weighting factors of 35% elephant range, 15% agricultural land,
25% human population density and 25% rainfall variation. HEC hotspots were taken to be
locations with the highest overlaps in the weighted data. Modelled conflict hotspots areas in
km2 were calculated for both years and amplified maps created.
Results
The study analysed variability in East Africa’s projected precipitation for the time period 2021
to 2030. For each climatological water season, rainfall onset and cessation dates where
calculated to determine the timing of rainfall in each year. Duration of rainfall was taken to be
the number of days from onset date to cessation. Long rains had a mean duration of 51.8
±18.8 days while short rains had 63 ±22.6 days. Figure 4 summarises the projected rainfall
timing for both long and short rains.
Fig. 4. Bar plot showing long and short rainfall timing for each year
Agricultural land Elephant habitat range
Fig. 3. Datasets used to create HEC predictive mapping
High
Low
Human population density
9
Timing of the two rains had substantial effects on duration of the dry seasons. Timing
fluctuations for the year 2030 for example caused an early cessation for long rains and delayed
onset for short rains causing a prolonged dry season duration of 192 days. Long rains for year
2027 showed the shortest duration while 2025 had the shortest short rains duration. Prolonged
dry seasons driven by delay in long rains onset can also be observed for 2027 and 2028.
Interannual variability for both long and short rains duration was approximately 36%. Mean
precipitation between long and short rains showed some variation, with short rains displaying
higher means 70% of the time (Fig.5).
Fig 5. Mean precipitation for long and short rainy season, with short rains having higher precipitation in 7 out of 10
years
Rainfall onset and cessation dates across the years showed some variations in the two rain
types. The mean onset day for long rains was 90 ± 24.8 days with mean cessation at 141.8 ±
25.1 days. Results showed coefficient of variation (CV) for long rains onset to be at 28% while
cessation had 18%. Mean onset day for short rains was on 267.1 ± 22.9 days and cessation
at 331.2 ± 17.2 days. This gave coefficient of variation in onset of short rains at 9% with
cessation 5%. Overall results indicated higher variability in both onset and cessation of long
rains than in short rains (Fig.6).
10
Fig. 6. Boxplots indicating variability in onset and cessations days of rains for long and short rains
The analysis employed a Mann-Kendall test to detect possible downwards or upward trends
in the yearly duration of rainy and dry seasons. The test results indicated none significant
monotonic trends in all the three seasons considered. Duration of long rains showed a non-
significant decrease while dry and short rainy seasons duration had a non-significant increase
(Fig. 7).
Fig. 7. Mann-Kendall trend test results showing no monotonic trends in the duration of rainy and dry seasons
Rainfall anomaly index results based on an intensity scale (table 2) ranging from extremely
dry to extremely wet months are shown in Figure 8. A monthly scale was used to show the
intensity and frequency of dry and wet periods through the years. The dry months are
displayed in red (negative anomalies) while blue bars show wet months (positive anomalies).
11
Table 2. Rainfall Anomaly index intensity scale (sourced: Alcântara Costa and Pontes Rodrigues, 2017)
Fig 8. Monthly frequency rainfall anomaly index(RAI) indicating the driest and wettest months
Results indicated 2028 to be the driest year with a failure of long rains, while years 2021 and
2025 having poor short rains. The normal months of rainfall in the study area should be from
April-June and October-December for long and short rains respectively. The data shows
substantial deviations from normal timings in most years with 2024 displaying rainfall covering
months of dry season.
Results of consecutive dry days index (CDD) analysis were mapped to show both the spatial
distribution and intensity of dry spells. With a mean CDD of 205.4 ±30.4 days, the number of
dry days ranged from 11 to 286 days, with the year 2028 having the highest dry days per time
period. The highest number of dry days showed some variation but were generally clustered
in the North and spreading across the Eastern side (Fig.9).
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Fig. 9. A 10-year timeline map of the spatial distribution and intensity of Consecutive Dry Days index (CDD)
A Mann-Kendall trend analysis for CDD indicated a positive none significant change in dry
days across the years (Fig.10).
Fig 10. Mann-Kendall trend test with no overall monotonic trends in CDD per time period
The analysis of rainfall variability using coefficient of variation (CV) index indicated some
spatial similarities with consecutive dry days. Just like CDD, annual rainfall patterns displayed
higher variability in the Kenyan side of the region than in Uganda (Fig.11). The years 2024
and 2025 had the least overall rainfall variations while years like 2021 and 2030 showed
almost of the region with high rainfall variability.
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Fig 11. Spatial distribution and intensity of rainfall variability
Human elephant conflict (HEC) predictive mapping results for the years 2024 and 2030 varied
in both conflict intensities and distribution. HEC hotspots for the year 2030 had a larger area
(185,400 km2) with higher intensities compared to the year 2024 (82,600 km2) (Fig 12 and 13)
Fig. 12. Human elephant conflict (HEC) predictive mapping for the year 2024 with conflict area hotspots (red and
orange) of 82,600 km2
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Fig.13. Human elephant conflict predictive mapping for the year 2030 with conflict area hotspots (red and orange)
of 185,400 km2
For both years, conflict intensities where more clustered to the Kenyan side of the region.
Regions with the highest overlaps had the highest intensities of conflicts. Elephant range had
an area overlap with agricultural land covering an area of 14,100 km2 which accounts for
11.5% of elephant habitat. Human population densities over 50 people per km2 overlapped
elephant range by 8.6%.
Discussion
Understanding regional factors driving human elephant conflicts (HEC) is fundamental to
formulating mitigation strategies that are location specific. The study considered how east
Africa’s rainfall variability in the face of climate change could influence the distribution and
intensities of future HEC. Timing of rainfall onset and cessation, together with rainfall failure
and occurrence of droughts formed the core areas investigated in relation to HEC in East
Africa. Results for timing of rainfall showed an overall longer duration and higher mean rainfall
for short rains than longer rains (Fig. 4 and 5). Variability in rainy season duration for both long
and short rains indicated a 36% variability across the years of study. These findings differ from
what would be anticipated as the long rains are expected to be more reliable in terms of the
15
amount of precipitation (Camberlin et al., 2009). Short rains are known to display higher
interannual variability than long rains because short rains are influenced by complex
interactions between the Indian and Pacific Oceans (Conway et al., 2005).
Long rains are considered to constitute the main agricultural season for many agriculturalist
in east Africa. Reduction in the amount of rainfall can have negative impacts on food
production systems which can lead to significant changes in human land uses (Conway et al.,
2005). These land use changes can include an increase in irrigation farming or cultivation in
wetlands and along river banks. It is known that agriculture and farming practices are largely
determined by the long term mean climatic conditions of an area (Bamutaze et al., 2002). With
East Africa’s main agricultural season (long rains) showing less rainfall, new land uses are
likely to cross paths with elephant habitat ranges increasing the risks of HEC as a result.
Conflicts between agriculturalist and elephants are already widespread across Africa. This
possess a significant threat to the long-term survival of elephants because 42% of their range
in the two Countries is outside protected areas.
Low rainfall trends in long rains observed in this study are not foreign to East Africa’s rainfall
patterns. Over the past decades, there has been growing evidence suggesting that the mean
rainfall for long rains is in decline (Yang et al., 2014). These below rainfall averages in long
rains have been thought to be driven by large scale sea surface temperatures changes in the
pacific ocean and other regions (Lyon and DeWitt, 2012). The amount of rainfall is clearly a
significant driver of vegetation quality and surface water availability. The observed low rainfall
in long rains could lead to low vegetation quality during the April to June climatological seasons
forcing both elephants and humans to compete for available surface water in the many areas.
It can therefore be expected that HEC would be high near surface water sources because
elephant densities, cultivation and human settlements tends to increase near water bodies
(Kusena, 2009).
A lot of people in East Africa are highly vulnerable in times of low rainfall as 70% of livelihoods
depends on rain-fed agriculture (Muthoni et al., 2019). This has been seen in the Maasai
communities of Kajiado District, Kenya. Local people have been favouring agricultural
expansion over pastoralism (Okello, 2005). However, the recent decline in rainfall totals has
contributed to the expansion of cultivation into elephant habitats causing an increase in HEC.
Similarly, research done in Narok County, Kenya, found a significant increase in human wildlife
conflicts (HWC) with increased agriculture (Mukeka et al., 2019). It was further observed that
HWC significantly decreased with increasing rainfall. With these trends, it can be said that the
possible overall decline in East Africa’s long rains (Fig.5) would increase probabilities of HEC
occurrences in the region.
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Onset and Cessation
Despite long rains being more reliable than short rains, their onsets have been known to
display significantly higher year to year variability (Camberlin et al., 2009). This is in line with
onset and cessation variation results obtained from this analysis (Fig.6). Rainfall onset and
cessation for long rains displayed 28% and 18% respectively. This variation was higher than
that of short rains. Higher onset and cessation variability in long rains indicates that there are
higher risks of failure or delay in long rains than in short rains. This variation is considered as
result of pressure gradients between the Indian and Atlantic Oceans. Variability in rainfall
timings can be thought to have larger impacts on human and elephant land uses more than
the intensities of rainfall. For some land uses such as agriculture, the amount of rainfall is not
as important as the timing because one can easily adapt to low rainfall if there is an assurance
that the rains will fall (Schulze, 2007). Fluctuations in onset and cessation increases the
chances of a prolonged dry seasons. In cases like the year 2030 (Fig.4) where long rain
cessation comes early with a delayed onset for short rains, results indicated a prolonged dry
season lasting over 6 months.
There is growing evidence supporting that elephant movements at the end of the dry season
is triggered by distant thunderstorms. Garstang et al (2014) observed 14 elephants for seven
years in north-western Namibia to determine if rainfall timing influenced elephant movements.
Statistically significant changes in elephant movements near onset of wet seasons were found.
This included movements made by elephants just before wet episodes during the dry season.
Similar findings were observed by Bohrer et al (2014) in Marsabit protected area, Kenya. Five
elephant breeding herds and five bachelors were observed for three years to investigate how
precipitation driven vegetation affects elephant movements. It was found that elephants were
quick to respond to vegetation changes and making migrations in response to rainfall onsets.
With timing of rainfall influencing elephant movements, delays in rainfall onset or early
cessation could result in delayed migration. Delayed elephant migrations in human dominated
landscapes could cause competition for a shared resources such as water. As availability of
surface water drives where humans develop cultivation and settlement, resource use overlap
with elephants could lead to significant human elephant conflicts (Kusena, 2009).
Resource overlap driven HWC can be expected during years with onset delays and early
cessation resulting in prolonged dry seasons. Duration of rains and dry seasons were
analysed for monotonic trends in the study period. Overall, non-significant increase in short
rains and dry season were observed, with non-significant decrease of long rains (Fig.7). All
three seasons showed signs of increase or decrease but no monotonic trends detected
possibly because of how small the sample was. The possible decline in long rains duration
observed could explain the low rainfall amount in long rains (Fig 5). The possible expanding
17
Rainy season Dry season
dry season duration (Fig.7) across the years could be an indication of increased risk of
droughts in East Africa.
Droughts and HEC
A monthly scale for rainfall anomaly index (RAI) was used to monitor possible rain failures and
occurrence of droughts (Fig 8). Analysis results showed a complete failure of long rains for
year 2028 with low short rains in October. The failure of long rains made 2028 to be the driest
year. Even though 2028 experienced the shortest dry season duration (Fig 7), its dryness
could be as a result of low mean rainfall during long rains, which is also observed for the year
2024 (Fig. 5). The relationship between RAI and timing of rainfall indicate years with poor
duration having relatively dry rainy season. This can be seen in long rains for 2027 and short
rains for 2025. Normal rainfall timing onset should occur every 3 months which should display
3 positive anomaly bars (blue bars) roughly on Month 4-6 and 10-12. RAI results however
show more dryness (red bars) in a lot of months which should be wet (Fig 8). These short
rainfall durations and failures like those observed in 2025 and 2028 can have significant effects
on elephant ranging behaviour.
Elephants consistently search for greener vegetation all year round. With vegetation quality
heavily driven by precipitation, failure of rainfall creates the need for elephants to use more
habitat for foraging due to low quality vegetation in the dry season (Shannon et al., 2015). An
increase in home range during dry seasons or droughts increases the probability of HEC as
human and elephant land uses are more likely to overlap. During the years 1994 and 1997,
Smith and Kasiki (2000) observed that HEC in areas surrounding Tsavo National Park in
Kenya were more dispersed during dry season than in wet season (Fig 14).
Fig. 14. Rainy and Dry season dispersion of Human Elephant Conflict in Taita Taveta District of Kenya (source: Smith and Kasiki, 2000)
18
Similar observations on effect of droughts on intensities of HEC have been observed in Asian
elephants as well. Zubair et al (2005) analysed 14-year (1990-2003) rainfall data with elephant
deaths caused by HEC in Southern Sri Lanka. The study found that long term trends of rainfall
deficit coincided with high elephant deaths (Fig. 15).
Fig. 15. Long term trends of elephant deaths and rainfall in Southern Sri Lanka (Source: Zubair et al., 2005)
In the two cases above, Taita Taveta District of Kenya and southern Sri Lanka, both the spread
and intensities of conflicts significantly peaked during dry season. With this information, it can
be said that the projected rainfall failure in 2028 would result in higher HEC compared to other
years (Fig 8). However, this is can only be possible if the spatial distribution of droughts or
rainfall failure covers areas where humans and elephants share resources.
The spatial distribution of droughts, prolonged dry seasons and rainfall failure was assessed
by using consecutive dry days index (CDD). With the analysis taking a dry day to have less
than 1 mm of rainfall, results showed the highest number of CDD to be in year 2028 (Fig 9).
However, distribution of these dry spells is clearly clustered in the Kenyan side of the region.
The known rainfall duration for both long and short rains is roughly 3 months (92 days) each,
which is equivalent to 184 days of total rainfall a year. The two rainy seasons are separated
by two periods of dry seasons (January-March and July-September) which is approximately
equal to 92 days each. Areas having CDD above 92 can be considered to have prolonged dry
season or complete failure of one rainy season. HEC can therefore be expected to be high in
these dry spell hotspot areas. The year to year variation in spatial distribution and dryness
intensity would cause shifts in levels of HEC depending on how dry it gets. Years with high
conflicts would be expected when CDD hotspots overlaps with human land use and elephant
habitats. HEC incidences caused by dry spells can be expected to be more frequent in the
central to southern part of the region having large elephant ranges with high human densities
19
(Fig 3). This is also true for the Ugandan side near the Congo border, though less affected by
dry spells in most years.
Trend results for indication of CDD increase were non-significant (p=0.06) but this could have
been as a result of the sample size (Fig 10). A positive slope might show possibilities of dry
spells getting more intense in East Africa. Consecutive dry days per time period is a result of
timing and the amount of rainfall an area receives. Knowing areas that are likely to receive
less rainfall provides more information on the risks of HEC as a result of dry spells in an area.
Variability of rainfall monitored by coefficient of variation (CV) indicating the risk of rainfall
failure showed that the Kenyan part of the region had more unstable rainfall patterns (Fig 11).
The Ugandan region seems to be less affected by dry spells and rainfall variability. This could
be as a result of large topographical features and water bodies such as Lake Victoria. Lake
Victoria is known to have a strong circulation of its own with significant influence on rainfall in
the region (Ogallo, 1993).
Predictive HEC hotspots
The success of elephant conservation demands knowledge of the drivers of HEC and how its
intensities are spatially distributed across a landscape (Kagwa, 2011). With rainfall variability
affecting HEC in east Africa, identifying potential future conflicts would be essential in elephant
conservation planning and HEC mitigation strategies. Potential HEC predictive mapping were
based on land use (agriculture), human population density, elephant habitat range and rainfall
variability. Agriculture was considered the main land use in mapping conflicts because almost
70% of livelihoods in east Africa depend on rain-fed agriculture (Muthoni et al., 2019). Variation
in rainfall would be expected to affect most land under cultivation forcing people do expand
agricultural lands near surface water sources or even into elephant territory. The years 2024
and 2030 were considered for HEC predictive mapping because of how rainfall variability in
the two years differed.
Areas with high rainfall variability for 2024 were clustered in the eastern part while the rest of
the region showed low variation (Fig 11). The year 2030 on the other hand displayed
considerable spread in rainfall variation with higher overlaps with elephant habitats and human
land uses. The effect of this variation is seen by the difference in conflict spread and intensity
between the two years (Fig 12 and 13). Conflict hotspots in 2030 covered an area twice as
that in 2024, with 2030 having more HEC intensities. Differences between the two years can
also be observed in the amount of rainfall and duration of seasons (Fig 5 and 7). Not only did
2024 get lower rainfall for both rains than 2030, duration of its dry season and CDD were less
than that of 2030. Overall, the two predictive maps points out how rainfall variability can
determine the spatial distribution of HEC.
20
Elephant range shifts
In response to climatic changes, species are sometimes forced to shift their habitat ranges by
dispersing to areas with suitable environmental conditions (Bellard et al., 2012). In the arid
environments of Mali and Namibia for example, elephants have been recorded to expand their
ranges as far as 12,800 km2 in search for scarce resources (Graham et al., 2009). It has been
suggested that species are likely to shift their ranges upwards along gradients of water
availability in response to changing climates (Kanagaraj et al., 2019). Kenya and Uganda’s
elephant habitat range are largely concentrated in areas with relatively high rainfall variability
and high probability of prolonged dry season and drought episodes. These habitats are
fragmented with a significant number of patches surrounded by human settlement and
agricultural lands (Fig 3). With results of this analysis showing possible increase in dry spells
and decline of rainfall for long rains, it can be said that elephant populations would shift their
range towards the western side with relatively stable rainfall pattern. However, due to
significant habitat fragmentation which is known multiply the effects of climate change (Opdam
and Wascher, 2004), range shifts would intensify HEC incidences as both human and
elephants compete for limited resources.
Conclusion
Variability of rainfall has been linked to have significant impacts on food production systems
in many parts of Africa (Conway et al., 2005). With human population on the rise in east African
Countries, demand for suitable lands for agriculture is likely to expand into elephant territories.
Kenya and Uganda’s human population is projected to increase by 24 million by the year 2030
(United Nations, 2019). This will create more demand for housing and agricultural land to meet
the need for settlement and food production. Results of this research points out to the
increased possibilities of rainfall onset and cessation failures, prolonged dry seasons and
occurrence of droughts in east Africa. These findings highlight the differences in the spatial
distribution and intensities of rainfall variations and its associated effects on human and
elephant land use. Predictive HEC mapping indicates how rainfall variability is a significant
driver of HEC in areas where humans and elephants share same resources.
Understanding the spatial distribution and intensities of future HEC can help institutions plan
and maximise limited resources by focusing on areas that matter. Furthermore, conservation
planning under climate change should consider migration corridors to enable effective
elephant dispersal in fragmented habitats (Kanagaraj et al., 2019). The analysis was solely
based on regional downscaled CORDEX data under representative concentration pathway
(rcp) 8.5. Since regional climate models (RCM) are simulated based on data from global
circulation models (GCM), they inherently carry weaknesses from the GCM they are based on
(Ogallo, 1993). Analysing effects of rainfall variability using different GCM’s and RCM’s over
21
a longer timescale would provide better understanding of how HEC will be affected in east
Africa.
22
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