Ecology and Behavioral Studies of Elephant in the Selous-Mikumi Ecosystem

ECOLOGY AND BEHAVIORAL STUDIES OF ELEPHANT IN THE SELOUS-MIKUMI ECOSYSTEM

Annual report

Edward Kohi and Alex Lobora

©TAWIRI December 2018

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Table of Contents

1. Background ...... 3

2. Introduction ...... 5

3. Study objectives ...... 6

4. The study area, Materials and Methods ...... 7

4.1 The Study area, Selous-Mikumi ecosystem ...... 7

4.2 Animal location data ...... 8

4.3 Data preparation ...... 9

4.4 Hidden Markov Model ...... 9

5. Data analyses ...... 10

6. Results ...... 11

7. Inferences during the reporting period: ...... 22

8. Acknowledgements ...... 24

9. References ...... 25

Appendices ...... 33

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1. Background About 15% of the land worldwide is currently designated as protected areas (henceforth “PAs”) for biodiversity conservation (Juffe-Bignoli et al., 2014), and efforts are underway to further extend this to 17% of all terrestrial land and 10% of coastal and marine areas by 2020 (Di Minin & Toivonen, 2015). These efforts indicate the importance that countries and the international communities attach to the role PAs play in preserving natural landscapes and reducing biodiversity loss over long-term (Wyman & Stein, 2010; Schulz et al., 2010; Gao & Liu, 2010; Gibbs et al., 2010; Lung & Schaab, 2010; Leroux et al., 2010; Azadi & Hasfiati, 2011; Estes et al., 2012; Bailey et al., 2016). However, poverty, population pressures and escalating demand for natural resources compounded by conflicting national policies, poor governance and weak institutions (Rao et al., 2009), have influenced the ability of PAs to fulfil their role (Shalaby & Tateishi, 2007; Bakr et al., 2010). While the expansion of PAs coverage and their contribution to nature conservation is well recognized (Jenkins & Joppa, 2009; Leverington et al., 2010; Machumu & Yakupitiyage, 2013), there is also increasing argument surrounding PAs’ effectiveness in protecting biodiversity loss due to the wide spreading anthropogenic activities within and outside their boundaries (Gibson et al., 2011; Laurance et al., 2011, 2012; Ghimire & Pimbert, 2013; Clark et al., 2013; WWF, 2016).

The landscape outside PAs is rapidly changing and human population growth rates are significantly higher on the margins of PAs than average rates for rural areas due to a range of benefits to local communities as a result of conservation management (Wittemyer et al., 2008; Guerbois & Fritz, 2017). This high density and activity creates potential for conflict between humans and elephants, which can have extreme consequences (Monney et al., 2010). Provision of food and safe water for humans in the continent is a fundamental a challenge, particularly in the face of a growing population (Hall et al., 2017). Supporting this growing population and improving quality of life will require more land to be developed and converted to agriculture in addition to increasing yields through biotechnology (Tester & Langridge 2010; Tilman & Clark 2015); further

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fragmentation and encroachment on habitats is inevitable. Elephants typically occupy a large range (up to ~18700 km2; Lindeque & Lindeque, 1991). Competing resource use and development of both settlements and infrastructure may disrupt the ability of the animals to freely move between and utilize the resources, thus creating additional challenges for the elephants. A recent study found that across a range of animals, persistence in proximity to growing human populations is associated with a reduction in range size, likely explained by physical constraints on habitat space and the enhanced resources around human populations (Tucker et al., 2018). Human-elephant conflict is likely to continue to increase as human populations develop further and therefore a need for understanding the behaviour of elephants within an increasingly fragmented human landscape in order to devise strategies to develop appropriate conservation actions.

African elephants (Loxodonta spp.) are among the most iconic and charismatic terrestrial animals. The species is widely distributed across the continent, with about half a million elephants occupying ~ 3 million km2 (Thouless et al., 2016). The species occur in three habitat types, and are typically subdivided into: forest elephants; savanna elephants; desert elephants. Based on molecular, morphological and life history evidences, the savanna elephant (Loxodonta Africana), and forest elephant (Loxodonta cyclotis) are increasingly recognized as distinct species (Roca et al., 2001; Comstock et al., 2002; Ishida et al., 2011); desert elephants are considered isolated populations of savanna elephants which have adapted to occupy desert habitats. There is also evidence of recent restriction of gene-flow between populations of savanna elephant (Comstock et al., 2002; Okello et al., 2008), suggesting a historically more continuous, freely-mixing elephant population that has been disrupted by anthropogenic fragmentation. Generally, the effects of habitat fragmentation are strong for species that require large, continuous ranges such as African elephants. To satisfy their high nutritional and water requirements (Laws, 1970), elephants in a fragmented landscape are likely to need to leave the safety of PAs to exploit alternative resources, or reach other PAs.

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Tanzania maintains a variety of PA categories which allow different levels of legal restrictions on resource use including fully protected areas (henceforth “FPAs”) (constituting 17% of land surface) comprising of national parks (only allow photographic tourism), game reserves (permit tourist hunting and sometime photographic), forest reserves (some permit selective logging) and the Ngorongoro Conservation Area (NCA) which is similar to national parks but allows cattle grazing by indigenous Maasai pastoralists (MNRT, 2007; Stoner et al., 2007). The rest of the PAs (constituting 18% of the country’s land surface) are considered as lesser protected (henceforth “LPAs”) and include Game Controlled Areas (GCAs) and Open Areas (OAs) where extractive resource use is permitted under license (MNRT, 2007). Wildlife Management Areas (WMAs) comprise Tanzania’s newest protection category that aims to promote wildlife management at the village level by allowing rural communities and private land holders to manage wildlife on their land for their own benefit and devolving management responsibility of the settled areas and areas outside unsettled PAs to rural people and the private sector (USAID, 2013; WWF, 2014).These areas provide a refuge for many species, and management and monitoring programmes within them help to identify population declines and inform interventions. However, these most of these PAs were not established according to an overall plan for an optimal network of areas, and many are now threatened by agriculture and settlement (Rodrigues et al., 2004a&b).

2. Introduction This report covers updates on elephant activity patterns across the Selous-Mikumi landscape over the past twelve months (December 2017 to the end of November 2018). It includes an analysis of 19 of 20 elephants collared in the Selous Game Reserve and adjacent WMA’s (N=15) as well as five (N=5) in Mikumi National Park (Figure 1). One collar which was deployed in Selous/Matambwe sector in December 2017 has never transmitted data and therefore excluded from the analysis. The study is using GPS satellite collars (GPS Iridium satellite collar) from Savannah tracking Ltd (http://www.savannahtracking.com/). The units upload the elephant positions (latitude/longitude) to geostationary satellites from where the information is transmitted to

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a data retrieval centre for distribution to the clients and programmed to take GPS fixes every 60min. Due to the battery’s lifespan of three years, data will be collected until the end of the battery’s life presumably towards the end of 2021.

3. Study objectives The study short-term aim’s to understand elephant activity patterns in relations to the various land protection status (Protected vs unprotected land), time (day and night), the season of the year (dry vs wet) to help develop immediate conservations actions including conflict mitigation measures. The long-term study objective is to relate biophysical and anthropogenic variables to generate an understanding of the species behavioural patterns in relation to these covariates over long periods. Previous studies reported elephants to move faster in corridors compared to when in PAs, suggesting that the elephants were responding to the risks associated with leaving PAs and switching behaviour to minimize the risk (Douglas-Hamilton et al., 2005).

Elephants have been observed to have an acute awareness of potential risk and shown to change movement behaviour in response to crossing PA boundaries to minimize risk (Douglas-Hamilton et al., 2005). This has been shown to involve switching to faster movements to reduce time spent outside of PAs, or by switching to nocturnal patterns to reduce the risk of detection. Understanding changes in movement behaviour in relation to human activities will help to inform approaches to mitigate human-elephant conflict. Another area of interest is the patterns of diurnal and nocturnal activity in elephants, with a common suggestion that elephants change their temporal behaviour inside and outside of PAs (Wittemyer et al., 2017). Although elephants are typically diurnal, their sleep pattern can be irregular and flexible (Gravett et al., 2017) and elephant habitat selection differs at different times of day (Fullman et al., 2017). A recent study proposed that a night-day speed ratio can serve as an indicator of increased poaching levels, as higher speeds were observed at night than during the day for areas of high poaching risk (Ihwagi et al., 2018). With a grant from WWF Sweden and WWF Tanzania, this project aims to provide answers to some of the unknown elephant behaviour using 20 collared elephants

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in and around the Selous-Mikumi Ecosystem. Our analysis used a Hidden Markov Model (HMM) to characterize movement behaviour of the collared elephants and to then test behaviour against protection and time of day during the reporting period.

4. The study area, Materials and Methods 4.1 The Study area, Selous-Mikumi ecosystem The Selous Game Reserve (SGR) is one of the largest faunal reserves in the world, located in Southern Tanzania covering an area of about 50,000 km2, roughly the size of Rwanda and Burundi combined (Figure 1). It is a UNESCO World Heritage Site since 1982 due to the diversity of its wildlife and undisturbed nature it contains. Habitats include grassland, typical Acacia savanna, wetlands and extensive Miombo woodlands. Although wildlife populations are high, densities of animals are lower (due to the large size) than in the more regularly visited the northern tourist circuit of Tanzania (Baldus, 2009). Most of the reserve (seven of eight sectors) remains set aside for game hunting through a number of privately leased hunting concessions, apart from a section north along the Rufiji River (Matambwe sector) which has been designated a photographic zone. In 1976, the SGR had about 109,000 elephants, then the largest in the world (Vira & Ewing, 2014) but by 2013, the numbers dropped to about 13,000 - a 66% drop from 2009 to 2013 (TAWIRI, 2014). Mikumi national park (MINAPA) landscape on the other hand, ranges from 430m to 2270 m above sea. This range of elevation combined with climatic and other geographical factors contribute to a diverse and complex habitat mosaic in this park. The influences of the coastal zone, the massifs of Uluguru, Udzungwa and Rubeho, the dry bushland and extensive miombo woodland (Norton et al., 1987) have all integrated to form six different mosaics namely woodlands, open woodland, wooded grassland, Mkata flood plain, riverine forest and Afromontane forest.

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Figure 1: Collared individuals within the landscape. Mikumi National Park and Selous Game Reserve are IUCN management category II and IV respectively.

4.2 Animal location data The dataset used in this analysis involved 19 of 20 elephants collared in the Selous- Mikumi ecosystems from December 2017 to November 2018. All the dataset are in one hourly fixes.

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4.3 Data preparation We performed data preparation and analysis in R (R Core Team 2017). In order to meet the requirements of the Hidden Markov Model (HMM) that the time series should be even, we constructed a filtering pipeline to detect time steps beyond a set tolerance (> 1 + 0.4 hr). Where there were gaps in the time series, locations were inserted at the appropriate time, with NAs for coordinates. The filter required input of specific thresholds allowing us to specify the strictness of the filter. The filter also detected cases where locations were deemed to have not changed for > 150 hours (1 collar- Suzy); long periods of inactivity were labelled as a collar failure, collar drop or mortality and were removed from subsequent analyses. The filter split the series for each elephant into appropriate sequences (referred to here as bursts). This filtering allowed high-quality bursts from 19 of 20 Animals to pass through to the HMM. We then coded the time series as day (07:00 – 19:00) or night (19:01 – 06:59).

4.4 Hidden Markov Model We used the package MoveHMM (Michelot et al., 2016) to fit an HMM in this analysis. The package is able to fit the model to data from multiple individuals allowing inference of general behavioural states across these individuals rather than being modelled separately and producing incomparable states. We combined all bursts into one dataset, each coded with a distinct burst ID. With data filtered into high-quality bursts, we used the prepData function of the package to convert the location data into trajectory data. We selected Gamma and von Mises distributions for steps and angles respectively and selected reasonable initial values to fit the model (table 1). Gamma and von Mises are commonly used distributions for fitting HMMs to movement data (Michelot et al., 2016). The model output indicate the probabilities of an animal to be in a certain state at each time, to which we applied the Viterbi algorithm (Zucchini et al., 2016) to extract the most likely state sequence.

Table 1: Initial values for the Hidden Markov Model. A gamma distribution was chosen for step length, and the circular von Mises distribution was selected for turning angles.

State 1 State 2 State 3

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Step mean (km) 0.069 0.349 1.161 Step SD 0.064 0.241 0.615 Angle mean (radians) -0.120 0.029 -0.005 Angle concentration (von Mises Kappa) 0.139 0.838 1.727

5. Data analyses Due to the difficulties associated with the lack of aerial support (helicopter), collars were deployed in three different time intervals namely five collars in the Selous Game Reserve (Matambwe sector) in December 2017, two collars in Mikumi National Park in March 2018 and the last group of 13 collars were deployed across the ecosystem in September 2018. For this reason, datasets are grouped and analyzed in two major groups namely the first two groups as one dataset (Dec 2017 & March 2018) and September 2018 as the other (September 2018). The two datasets are analyzed and discussed separately.

In order to test whether animals were spending less time outside of PAs at night, we aggregated counts of locations by protection (PA) and time of day and then fit a mixed model using the lme4 package (Bates et al., 2015), with the individual animal as a random effect. To test for differences in the distribution of states according to protection and time of day, we aggregated counts of locations by state, protection and time of day, then fit a second mixed model.

Home range estimation There is a consensus in conservation cycles that a species home range is an area that an animal uses to satisfy its requirements for mating, food, water, escape routes from enemies, resting sites, and look up positions (Delany, 1982). Estimates of elephant home range have been achieved using three methods: squared grids (Douglas-Hamilton et al., 2005), fixed kernel density estimation (Leggett, 2006) and minimum convex polygon (Foguekem et al., 2007; Ipavec et al., 2007). We used the latter method to estimate the smallest possible convex polygon around the XY locations for each elephant using adehabitatHR package in R (R Core Team, 2017). The first step involved i) calculating the centroid of all the points, ii) calculating the distances from the centroid to

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all the points, iii) removing all points with a distance greater than a selected quantile and iv) using a convex hull algorithm to estimate the minimum convex polygon for each of the collared individuals.

6. Results Data for the six (Dec 2017 & March 2018 dataset) elephants and 13 (September 2018 dataset) elephants were retained for the HMM, divided up into 8 and 13 bursts respectively (Appendix A). In total, 44,302 and 25,242 locations were modelled of which 2.4% and 1.8% were missing locations for the first and second datasets respectively. Furthermore, about 95% and 75.5% of all locations modelled dataset were inside PAs for the first and second dataset respectively, though time spent in PAs varied between individuals (Figure 2). The proportion of time spent by the six individuals inside PAs combined (first dataset) was 97% compared to 77% for the 13 individuals combined (Figures 2a&b).

The three behavioural states identified have contrasting step lengths and all states have very small turning angles (table 2) suggesting there were more directional movements. For state 1, the angle concentration parameter is small (table 2) which is approaching a uniform angle distribution. Concentration parameters are higher for States 1 and 3 (table 2), suggesting that these states may be more directional. The Viterbi decoded states indicate that the majority of time is spent in State 2, smaller proportions of time are spent in States 1 and 3 (Figure 3). The three states can be described simply as State 1: Very short hourly displacement with little directionality [inactive]; State 2: Medium hourly displacement with some directionality [active] and State 3: Long hourly displacement with some directionality [travelling].

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Figure 2(a)

2(b)

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Figure 2: 2(a): Proportion of time spent in protected areas by each of the six elephants. The dotted line indicates the mean proportion of time in Protected Areas (0.97). 2(b): Proportion of time spent in protected areas by each of the 13 elephants. The dotted line indicates the mean proportion of time in Protected Areas (0.77). Elephants prefixed with SGR and MINAPA are from the Selous Game Reserve and Mikumi National Parks respectively.

(3a)

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(3b) Figure 3: 3(a&b) Proportion of time spent in different states by individual elephants. States 1 (blue), 2 (green), and 3 (pink) represent inactivity, activity and travelling respectively. Elephants prefixed with SGR and MINAPA are from the Selous Game Reserve and Mikumi National Parks and their surrounds respectively.

Table 2: 2(a) Summary of the 3 behavioural states characterized by step length and turning angle for the six elephants for a period of about 12 months. Mean step Step SD Mean angle Angle concentration (km) (km) (radians) (von Mises κ) State 1 0.02 0.01 3.14 0.65

State 2 0.20 0.18 0.03 0.50

State 3 0.74 0.48 0.05 1.58

2(b) Summary of the 3 behavioural states characterized by step length and turning angle for the 13 elephants over a period of three months. Mean step Step SD Mean angle Angle concentration (km) (km) (radians) (von Mises κ) State 1 0.09 0.08 -0.02 0.22

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State 2 0.40 0.26 -0.01 1.17

State 3 1.26 0.64 -0.01 2.19

The three behavioral states are relatively stable (transition probability matrix), and paths were characterized by phases of consecutive steps in the same state.

The first group of six elephants combined appear to have spent a significant amount of time inside PAs than outside during the past 12 months (Figure 4a) suggesting the importance of PAs in protecting wildlife over long-term. Similarly, the remaining group of 13 elephants combined appear to have also spent a reasonable amount of time inside PAs than outside during the past three months (Figure 4b).

4(a) 4(b) Figure 4: Relative proportions of locations at night inside and outside protected areas for a) the six individuals during the past 12 months and b) for the 13 individuals during the three months period.

For the first group of six elephants, we found a significant three-way interaction between state, time of day and protection, implying that elephants spent different proportions of time in different states during the day and night depending on whether they were inside or outside

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PAs during the past 12 months (Figure 5a). Time of day had a stronger effect than level of protection (PA) on the distribution of behavioral states. State 1 occurs more frequently at night than in the day and is more common outside of PAs (figure 5a). State 2 is also observed more frequently outside of PAs relative to inside, but this state is more common during the day (figure 5a). Time of day appears to have less impact on State 3, though less time is spent in State 3 when outside of PAs (Figure 5a). A more of less trend is observed for the second set of 13 elephants (5b).

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(5a)

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(5b) Figure 5: Relative proportions of time spent by elephants in each state, split by time of day and whether inside or outside Protected areas, (5a) for the six elephants during the past 12 months and (5b) for the group of 13 elephants during the past three months. a) Time spent in State 1 [inactive]. b) Time spent in State 2 [active] c) Time spent in State 3 [travelling].

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For both sets of datasets, we found a two way interaction between state and season of the year. State 1 was more frequent during dry season whereas state 2 was more frequent in the wet season (Figure 6), suggesting elephants may have small home ranges during the wet season but tend to make the most of it.

(6a)

(6b)

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Figure 6: the proportions of the three states during day and night for 6a) the first set of six elephants during the past 12 months and 6b) the second set of 13 elephants during the past 3 months

Home range estimation Home range sizes among the 19 individuals varied among them but not between sexes. Rebecca was recorded with the smallest home range while Veronica had the biggest even through the latter female was only collared about three months ago whereas Rebecca has been on the ground for a year (Table 3; Appendix B). Overall, many of individuals collared about three months ago had the biggest home ranges as opposed to the ones collared over a year ago (Table 3). Our close observation further suggests that individuals that roamed between and outside protected areas occupied larger home range (60%) than those restricted to PAs. On average, elephants used 423 km2 when outside and 259 km2 when in protected areas. Looking at the two males and females collared over a year ago, we could not detect any significance difference to suggest that sex had anything to do with the home range sizes. Other covariates will be integrated in the subsequent reports to deduce the meaning of the observed elephant home ranges in this ecosystem.

Table 3: Home range for the 19 individuals across the Selous-Mikumi Ecosystem

Elephant name Home Range (Km2) Time period Location Sex Alan_Matambwe 125 One year In PA M Kajuni_Matambwe 205 One year In PA M 3 months M Meingataki_MINAPA 377 In PA 3 months F Haika_MINAPA 214 In PA 3 months F Joan_Mikumi 97 In PA 3 months F M1_Miguruwe 375 In PA 3 months F M2_Miguruwe 479 In PA 3 months F Nyangabo_MINAPA 193 In PA 3 months F Salma_Liwale 485 In PA Rebecca_Matambwe 41 One year In PA F Alicia_Matambwe 277 One year Out&Overlap btn PA F

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Doma_MINAPA 245 3 months Out&Overlap btn PA F 3 months F Eleonora_Kalulu 507 Out&Overlap btn PA 3 months F Fyumagwa_Likuyu 339 Out&Overlap btn PA 3 months F Kingupira_lower 231 Out&Overlap btn PA 3 months F Kingupira_Upper 567 Out&Overlap btn PA 3 months F Masenga_Kalulu 451 Out&Overlap btn PA 3 months F Shayo_Likuyu 524 Out&Overlap btn PA 3 months F Veronica_Liwale 667 Out&Overlap btn PA

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7. Inferences during the reporting period:

 Both our three and 12 months data reveals a significant three-way interaction between state, time of day and protection, implying that elephants spent different proportions of time in different states (resting, feeding or travelling) during the day and night depending on whether they were inside or outside PAs.

 Our results further suggests a two way interaction between state and season of the year, implying that elephants spent different proportions of time in the two states (resting & feeding). State 1 [resting] appear to be more frequent during the wet season whereas state 2 [feeding] is more frequent in dry season.

 The results support the hypothesis that high activity states will be more common in the day than at night. Time of day appeared to have a stronger effect than the level of protection (i.e. PA) on the distribution of behavioral states. State 1 occurs more frequently at night than in the day and is more common outside of PAs (Fig 5). State 2 is also observed more frequently outside of PAs relative to inside, but this state is more common during the day (Fig 5). Time of day appears to have less of an impact on State 3, though less time is spent in State 3 when outside of PAs (Fig 5).

 The distribution of states by time of day matches the expected activity patterns of a diurnal animal. State 1 was significantly more common at night that in the day, which supports our interpretation as a sleeping/resting state, as elephants typically sleep at night for short periods (Gravett et al., 2017).

 State 1 was only observed a few time, consistent with elephant sleep/rest being a relatively small part of the day (Figure 3a). State 2 was the most common state across all elephants (Figure 3). This forage state is more common in the day than at night, which complements the pattern observed for State 1. State 3 occurs much

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less frequently. However, contrary to expectations, we didn’t observe a big difference between day and night for State 3 (Figure 5).

 There was an interactive effect of protection (PAs) and time on state, the increase in state 3 at night when outside of PAs was observed. Additionally, we did observe that more time was spent outside of PAs at night. The three derived behavioural states can be related to a realistic picture of elephant activity.

 The use of more time inside PAs suggests that these areas provide safe haven for elephants, but to satisfy elephant daily requirements (food and water), they often have to cross boundaries into unprotected lands where they risk contact with people. As human densities tend to be high on the boundaries of PAs, we would expect conflict between humans and elephants to occur. A study human-wildlife conflict in Mozambique found that 67% of people killed by elephants were in the north of the country, around Niassa (Dunham et al., 2010) and also found high frequency of crop raiding and retaliatory killings of elephants in the region. Conflicts arising from interactions between people and elephants can be devastating for both, and is a key issue for the integration of conservation and development.

 Conflict involving elephants around the Selous has been demonstrated, with communities severely restricted by the losses from this conflict (Gillingham & Lee, 2003), however elephants were not perceived to be as damaging as other smaller species.

 Mammals move to place themselves in optimum conditions for as long as possible to enable them to build body reserves, which are important for enhancing their breeding success including walking and foraging (Sinclair, 1983). Elephants have a highly complex structures (Douglas-Hamilton et al., 2005) and their ranging patterns are largely influenced by an array of factors. Selous-Mikumi ecosystem may be one of the best few remaining ecosystems in the world that provide big

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habitats for understanding the actual elephant home range. In this analysis, elephants that roam outside the PA recorded up to 667 km2, while those inside recorded as low as 41km2. The low home range is highly likely attributed by the presence of forage and permanent water in Beho Beho area. Home range size of 2 up to about 6,900 km was recorded in this ecosystem in the past between the Selous-Niassa corridor (Mpanduji & Ngomello, 2007). However, the home ranges presented here only paints a picture of elephant home ranges over a short period of time and the actual ranges will be known at the end of the project.

 Elephants have an acute awareness of potential risk and as revealed by previous and our study, they have been shown to change movement behaviour depending on whether they are within or outside PAs.

 This study further demonstrate that modern GPS technology and analytical methods allow powerful analysis of movements at a fine temporal resolution.

8. Acknowledgements We would like to thank the donors WWF Sweden and WWF Tanzania country offices for their financial support as well as the Tanzania Wildlife Management Authority (TAWA) and the Tanzania National Parks for permission to carry out this study in their areas of jurisdiction. TAWIRI and COSTECH are also acknowledged for reviewing our initial proposal and subsequently granting permission to carry out this study.

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Appendices

Appendix A: Summary of the filtered elephant data and inferred behavioural states for the six elephants (December 2017 and March 2018 dataset). Quality scores are the ratio of total:present locations.

ele.ID Bursts Locations NAs quality LocsInPA S1 S2 S3 PropInPA

1 sel_Alicia_Matambwe_SGR 3 8200 244 0.97 6609 0.23 0.41 0.37 0.83

2 sel_Allan_Matambwe_SGR 1 8301 159 0.98 8142 0.01 0.66 0.33 1.00

3 sel_Haika_MINAPA 1 6129 14 1.00 6115 0.05 0.62 0.33 1.00

4 sel_Kajuni_Matambwe_SGR 1 7241 241 0.97 7000 0.12 0.57 0.31 1.00

5 sel_Nyangabo_MINAPA 1 6129 76 0.99 6053 0.05 0.75 0.20 1.00

6 sel_Rebecca_Matambwe_SGR 1 8302 331 0.96 7971 0.03 0.80 0.17 1.00

Summary of the filtered elephant data and inferred behavioural states for the 13 elephants (September 2018 dataset). Quality scores are the ratio of total:present locations.

ele.ID Bursts Locations NAs quality LocsInPA S1 S2 S3 PropInPA 1 sel_Doma_MINAPA 1 1941 24 0.99 8 0.41 0.49 0.10 0.00 2 sel_Eleonora_Kalulu_SGR 1 1942 32 0.98 1853 0.26 0.58 0.16 0.97 3 sel_Fyumagwa_Likuyu_SGR 1 1942 18 0.99 1422 0.34 0.52 0.14 0.74 4 sel_Joan_MINAPA 1 1942 84 0.96 1858 0.51 0.42 0.07 1.00 5 sel_Kingupira_lower_SGR 1 1941 50 0.97 1228 0.30 0.53 0.17 0.65 6 sel_Kingupira_upper_SGR 1 1942 21 0.99 1099 0.26 0.55 0.19 0.57 7 sel_M1_Miguruwe_SGR 1 1942 12 0.99 1930 0.25 0.63 0.12 1.00 8 sel_M2_Miguruwe_SGR 1 1942 17 0.99 1925 0.35 0.48 0.18 1.00 9 sel_Masenga_Kalulu_SGR 1 1941 6 1.00 1915 0.26 0.62 0.13 0.99 10 sel_Meingataki_MINAPA 1 1942 25 0.99 1917 0.48 0.32 0.20 1.00 11 sel_Ngusaru_Liwale_SGR 1 1941 77 0.96 596 0.23 0.55 0.23 0.32 12 sel_Salma_Liwale_SGR 1 1942 52 0.97 1890 0.25 0.58 0.17 1.00 13 sel_Shayo-Likuyu_SGR 1 1942 34 0.98 1410 0.34 0.49 0.17 0.74

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Appendix B: Activity patterns of all collared individuals during the reporting period

Allan’s activity patterns from December 2017 to November 2018

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Alicia’s activity patterns from December 2017 to November 2018

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Rebecca’s activity patterns from December 2017 to November 2018

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Kajuni’s activity patterns from December 2017 to November 2018

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Haika’s activity patterns from March 2018 to November 2018

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Nyangabo’s activity patterns from March 2018 to November 2018

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Doma’s activity patterns from September 2018 to November 2018

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Miguruwe1’s activity patterns from September 2018 to November 2018

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Miguruwe2’s activity patterns from September 2018 to November 2018

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Salma’s activity patterns from September 2018 to November 2018

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Veronica’s activity patterns from September 2018 to November 2018

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Shayo’s activity patterns from September 2018 to November 2018

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Meingataki’s activity patterns from September 2018 to November 2018

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Masenga’s activity patterns from September 2018 to November 2018

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Kingupira upper’s activity patterns from September 2018 to November 2018

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Kingupira lower’s activity patterns from September 2018 to November 2018

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Joan’s activity patterns from September 2018 to November 2018

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Fyumagwa’s activity patterns from September 2018 to November 2018

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Eleonora’s activity patterns from September 2018 to November 2018

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