Matthew Wood

Simon Chamaillé-Jammes

Almuth Hammerbacher

Adrian M Shrader
email: adrian.shrader@up.ac.za

African elephants can detect water from natural and artificial sources via olfactory cues

Keywords: 2-methylisoborneol, dimethyl sulphide, geosmin, smell of water, surface water, volatile organic compounds

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Introduction

For mammals, food and water are essential resources. Water is important for many vital physiological processes such as digestion [START_REF] Silanikove | Effects of water scarcity and hot environment on appetite and digestion in ruminants: a review[END_REF], thermoregulation [START_REF] Cain | Mechanisms of Thermoregulation and Water Balance in Desert Ungulates[END_REF], lactation [START_REF] Maltz | Milk Production in the Desert: Lactation and Water Economy in the Black Bedouin Goat[END_REF], maintenance of blood plasma volume [START_REF] Mo | Water deprivation and its effect on some blood constituents in Idmi Gazelle, Gazella gazella[END_REF], and body-fluid homeostasis [START_REF] Noda | Central regulation of body-fluid homeostasis[END_REF]. Moreover, it even influences food intake (e.g. [START_REF] Shrader | Providing water for goats in arid landscapes: effects on feeding effort with regard to time period, herd size and secondary compounds[END_REF]. Thus, water is vital for life.

Finding water is more difficult in some environments compared to others. For instance, large mammalian herbivores (hereafter, large herbivores) living in arid environments are challenged by the limited distribution of water sources across the landscape. Permanent water bodies are rare, and the many ephemeral water sources such as pans, streams, shallow pools, and puddles dry up as the dry season progresses [START_REF] Chamaillé-Jammes | Climate-driven fluctuations in surfacewater availability and the buffering role of artificial pumping in an African savanna: Potential implication for herbivore dynamics[END_REF][START_REF] Naidoo | Mapping and assessing the impact of small-scale ephemeral water sources on wildlife in an African seasonal savannah[END_REF][START_REF] Redfern | Surface water availability and the management of herbivore distributions in an African savanna ecosystem[END_REF]. Many species in these regions have evolved behavioural, physiological, and morphological mechanisms to reduce water loss or obtain more water from food (see [START_REF] Cain | Mechanisms of Thermoregulation and Water Balance in Desert Ungulates[END_REF][START_REF] Kihwele | Quantifying water requirements of African ungulates through a combination of functional traits[END_REF] for details). Yet, many large herbivores remain dependent on surface-water and need to drink every day or two [START_REF] Kihwele | Quantifying water requirements of African ungulates through a combination of functional traits[END_REF][START_REF] Western | Water availability and its influence on the structure and dynamics of a savannah large mammal community[END_REF]. Accordingly, surface water is a key factor that determines these mammals daily and seasonal activities and movement [START_REF] Gaylard | Surface water availability: Implications for heterogeneity and ecosystem processes[END_REF][START_REF] Mckee | Spatial distributions and resource selection by mule deer in an arid environment: Responses to provision of water[END_REF][START_REF] Naidoo | Mapping and assessing the impact of small-scale ephemeral water sources on wildlife in an African seasonal savannah[END_REF][START_REF] Redfern | Surface-water constraints on herbivore foraging in the Kruger National Park, South Africa[END_REF][START_REF] Thrash | Dry season herbivore densities around drinking troughs in the Kruger National Park[END_REF]Valls-Fox et al. 2018a). As the availability and distribution of the ephemeral water sources are dependent on rainfall patterns, the predictability of their locations will vary both within and between years [START_REF] Redfern | Surface water availability and the management of herbivore distributions in an African savanna ecosystem[END_REF]. The question that arises then is: how do water-dependant mammals locate these ephemeral water sources? One animal that is heavily dependent on water is the African savanna elephant (Loxodonta africana) [START_REF] Chamaillé-Jammes | Climate-driven fluctuations in surfacewater availability and the buffering role of artificial pumping in an African savanna: Potential implication for herbivore dynamics[END_REF][START_REF] Gaylard | Surface water availability: Implications for heterogeneity and ecosystem processes[END_REF][START_REF] Tsalyuk | Temporal variation in resource selection of African elephants follows long-term variability in resource availability[END_REF][START_REF] Tshipa | Partial migration links local surface-water management to large-scale elephant conservation in the world's largest transfrontier conservation area[END_REF]Valls-Fox et al. 2018b). Elephants generally drink on a daily basis but can go 2-3 days between drinks [START_REF] Chamaillé-Jammes | African Elephants Adjust Speed in Response to Surface-Water Constraint on Foraging during the Dry-Season[END_REF][START_REF]Ecological guidelines for waterpoints in extensive protected areas[END_REF]. [START_REF] Bax | Some preliminary observations on the food of elephant in the Tsavo Royal National Park (East) of Kenya[END_REF] suggested that elephants were able to track rainfall events and the subsequent re-establishment of temporary water sources. While this might be true, [START_REF] Bax | Some preliminary observations on the food of elephant in the Tsavo Royal National Park (East) of Kenya[END_REF] did not explore the cognitive processes driving the elephants' movements. Thus, it is unclear what cues the elephants may have used to determine when to leave and in which direction to travel.

One possibility is that elephants rely on spatial memory to make both small and largescale movements between permanent water sources [START_REF] Polansky | Elucidating the significance of spatial memory on movement decisions by African savannah elephants using state-space models[END_REF][START_REF] Presotto | Spatial mapping shows that some African elephants use cognitive maps to navigate the core but not the periphery of their home ranges[END_REF]). Yet, this does not explain how elephants detect when ephemeral water sources contain surface water, or how they might track rainfall events. One possibility is that elephants may use olfactory cues to locate surface water [START_REF] Presotto | Spatial mapping shows that some African elephants use cognitive maps to navigate the core but not the periphery of their home ranges[END_REF]. However, to date there are no data to support these claims for elephants or any other mammal. Nevertheless, elephants possess an exceptional olfactory sense [START_REF] Shoshani | Frequently asked question about elephants[END_REF][START_REF] Shoshani | Elephant brain. Part I: gross morphology, functions, comparative anatomy, and evolution[END_REF], which can likely discriminate between subtle differences in structurally related odours [START_REF] Niimura | Extreme expansion of the olfactory receptor gene repertoire in African elephants and evolutionary dynamics of orthologous gene groups in 13 placental mammals[END_REF]. For example, elephants can differentiate between organic compounds that differ by a single carbon chain [START_REF] Rizvanovic | Olfactory Discrimination Ability of Asian Elephants (Elephas maximus) for Structurally Related Odorants[END_REF]. In addition, they can olfactorily distinguish between ethnic groups of humans (i.e. Maasai and Kamba) that they frequently encounter [START_REF] Bates | Elephants Classify Human Ethnic Groups by Odor and Garment Color[END_REF], discriminate between the scents of different humans including related individuals [START_REF] Von Dürckheim | African elephants (Loxodonta africana) display remarkable olfactory acuity in human scent matching to sample performance[END_REF], recognise related and unrelated elephants by urine scents [START_REF] Bates | African elephants have expectations about the locations of out-of-sight family members[END_REF], and the smell of lion dung [START_REF] Valenta | The sensory ecology of fear: African elephants show aversion to olfactory predator signals[END_REF]. Moreover, they make foraging decisions across a range of spatial scales using only olfactory cues [START_REF] Schmitt | African elephants use plant odours to make foraging decisions across multiple spatial scales[END_REF], can locate marula fruit with the highest sugar content using scent alone [START_REF] Nevo | Sweet tooth: Elephants detect fruit sugar levels based on scent alone[END_REF], and can find preferred plant species through a 'noisy' olfactory background [START_REF] Mcarthur | Plant volatiles are a salient cue for foraging mammals: elephants target preferred plants despite background plant odour[END_REF]. With regards to water, [START_REF] Ramey | Desert-dwelling African elephants (Loxodonta africana) in Namibia dig wells to purify drinking water[END_REF] suggested that elephants in the Kunene region of Namibia may have avoided drinking from small water holes containing high faecal coliform bacteria levels by keying off of either olfactory cues or the taste of the water.

Three volatile organic compounds (VOCs) generally associated with water are geosmin, 2-methylisoborneol (2-MIB), and dimethyl sulphide [START_REF] Izaguirre | Geosmin and 2-methylisoborneol from cyanobacteria in three water supply systems[END_REF][START_REF] Jenkins | Odorous compounds in natural waters. Some sulfur compounds associated with blue-green algae[END_REF][START_REF] Jüttner | Biochemical and Ecological Control of Geosmin and 2-Methylisoborneol in Source Waters[END_REF]. Geosmin and 2-MIB are known to be produced by aquatic microorganisms like cyanobacteria as well as other actinomycetes [START_REF] Izaguirre | Geosmin and 2-methylisoborneol from cyanobacteria in three water supply systems[END_REF][START_REF] Jüttner | Biochemical and Ecological Control of Geosmin and 2-Methylisoborneol in Source Waters[END_REF]. These and other bacteria are common and abundant within natural water reservoirs [START_REF] Izaguirre | Geosmin and 2-methylisoborneol from cyanobacteria in three water supply systems[END_REF]. In some cases, however, geosmin and 2-MIB are not inherently part of water sources but rather are present in the surrounding soil [START_REF] Jüttner | Biochemical and Ecological Control of Geosmin and 2-Methylisoborneol in Source Waters[END_REF]. When dry soil is exposed to rainfall, both geosmin and 2-MIB are released and give off distinctive musty odours akin to damp soil or the smell associated with the first rains of the season [START_REF] Bear | Nature of Argillaceous Odour[END_REF][START_REF] Izaguirre | Geosmin and 2-methylisoborneol from cyanobacteria in three water supply systems[END_REF][START_REF] Jüttner | Biochemical and Ecological Control of Geosmin and 2-Methylisoborneol in Source Waters[END_REF][START_REF] Suffet | Olfactory and chemical analysis of taste and odor episodes in drinking water supplies[END_REF]). Dimethyl sulphide (DMS) is another compound frequently associated with water sources, giving off a natural gas, sulphur-like odour [START_REF] Jenkins | Odorous compounds in natural waters. Some sulfur compounds associated with blue-green algae[END_REF]. Its biosynthesis is associated with blue-green algae that occur in most natural water sources [START_REF] Jenkins | Odorous compounds in natural waters. Some sulfur compounds associated with blue-green algae[END_REF]. Although most commonly found in marine environments, where it is used by seabirds to locate good foraging patches [START_REF] Nevitt | Sensitivity to dimethyl sulphide suggests a mechanism for olfactory navigation by seabirds[END_REF], it has also been identified and isolated in fresh water sources [START_REF] Bechard | Volatile organic sulfides from freshwater algae[END_REF].

Despite the importance of surface water to elephants and their excellent sense of smell, little is known about the degree to which elephants can detect the olfactory cues given off by water. Additionally, despite humans being able to detect geosmin, 2-MIB, and dimethyl sulphide at very low concentrations [START_REF] Izaguirre | Geosmin and 2-methylisoborneol from cyanobacteria in three water supply systems[END_REF][START_REF] Jenkins | Odorous compounds in natural waters. Some sulfur compounds associated with blue-green algae[END_REF][START_REF] Young | Taste and odour threshold concentrations of potential potable water contaminants[END_REF], it is unclear whether elephants can detect these VOCs, and use them to locate re-established temporary water sources. To address this knowledge gap, we conducted two olfactory choice experiments on semi-tame elephants to explore the extent to which they could detect water from both natural (dams) and artificial (trough) water sources using only olfactory cues. We did this, by presenting the elephants with two buckets with holes drilled into the lids, one with and one without water, so that the odours given off by the water could escape. The elephants were then allowed to smell each bucket before making a choice. In addition, we determined the olfactory profiles of these different water sources. We then determined whether they were able to detect geosmin, 2-MIB, and dimethyl sulphide by smell, and whether these key VOCs were present in the water sources.

We predicted that if the elephants were able to detect water using olfactory cues, that they would choose the buckets containing the water from the study site. However, as the distillation process likely removes VOCs from the water, we predicted that the elephants would not be able to locate the distilled water (i.e. show a pattern of random selection). Yet, if the distillation process did not remove key compounds that the elephants recognised and associated with water, then they should also be able to locate the distilled water. Due to their excellent sense of smell, we predicted that the elephants would be able to detect the three key compounds associated with water (i.e. geosmin, 2-MIB, and dimethyl sulphide). Moreover, we predicted that these compounds would be found in the water sources at the study site, and thus likely act as cues for the elephants. Yet, if these compounds were not present, it would suggest that the elephants were using some other compound or compounds to locate the water.

Materials and methods

We conducted the study during the dry season from 20-Aug to 8-Sept-2019 at the Adventures with Elephants sanctuary near Bela Bela, South Africa (24°46'53.8"S+27°57'03.3"E).

Adventures with Elephants operates as an education centre where people can interact with and learn about elephants. To ensure the safety and comfort of the elephants, the professional elephant handlers carried out the experiments and issued the verbal commands under our direction. To determine the extent to which elephants use olfactory cues to detect and locate water at small spatial scales, we conducted choice experiments on five semi-tame, adult African elephants (see details below). All experiments were conducted in a covered open area where the elephants are fed and where educational interactions take place. Each elephant has its own section within this area, which made it possible for us to interact with each elephant separately.

Elephants

The elephants we used in the study consisted of two males (Chova 23 years of age, Chishuru 21 years), and three females (Shan 18 years, Mussina 17 years, Nuanedi 17 years). These animals are housed in a barn at night but free-range over the 500 ha property during the day.

The handlers follow the elephants as they forage, but do not limit what and where they eat or how far they travel. Yet, during the day the elephants are brought to the covered interaction area at 08:30, 12:00 and 15:00. We limited our experiments to the 8:30 and 12:00 time slots.

Experimental design

To obtain the data, we followed a similar experimental design to [START_REF] Schmitt | African elephants use plant odours to make foraging decisions across multiple spatial scales[END_REF].

Specifically, we conducted scent-based choice experiments using four different sources of water. We collected the water from three locations on the property from which the elephants drink on a daily basis. We did this prior to the elephants arriving at the interaction area, thus they did not see where the water came from. The water sources comprised two dams (upper and lower) that refill via rain and runoff, and a plastic trough (artificial water source) that is refilled with underground water provided via a pump. The purpose of using these water sources was that each would likely have a different odour profile. Visually, there were noticeable differences between the water sources with the upper dam having a white cloudy appearance (due to the clay soil of the dam) compared to brownish water of the lower dam, and the clear underground water found in the trough, which also contained a small amount of green algae. Despite the visual differences, we could not detect any olfactory differences between the different water types. Finally, as a control, we used type II distilled water that was distilled at the University of Pretoria, as the distillation process would have likely greatly reduced the odour profile.

Experiment 1: Detecting water via olfactory cues

For the choice experiments, we used two identical 25-litre plastic buckets (32 cm x 32 cm x 40 cm) placed side-by-side (Fig. 1). The buckets each contained a 5-litre glass beaker (Superduty, low form, CC Imelmann, Robertsham). We ensured that the beaker was held in the centre of the bucket by wrapping a blanket around the outside of the beaker (Fig. 1a). We wrapped the beaker to prevent it from moving, which could produce noise that the elephants may use to determine which bucket contained the water, and to prevent water from spilling out of the beaker when the bucket was moved between trials. Before inserting the beaker into the blanket, we covered the blanket with a 50-litre clear plastic trash bag to prevent the blankets from getting wet during the trials, and thus potentially providing an additional olfactory clue for the elephants.

In each experiment, we poured 3.5 litres of water into the beaker of one of the two buckets and left the beaker in the second bucket empty. To ensure that the elephants could only use olfactory and not visual cues to find the water, we drilled 28, 10 mm holes (separated by ~3 cm) into the lids of the buckets to allow any odours given off by the water to escape (Fig. 1a). Once the lids were attached, the two buckets (one with and one without water) were placed side by side ~5 m in front of the elephant being tested (Fig. 1b). To ensure that the elephant did not know which bucket contained the water prior to smelling, the handlers instructed the elephant to turn 180˚ and face away from where the buckets would be placed. Once the buckets were in place the elephant was instructed to turn and walk up to the buckets. As with [START_REF] Schmitt | African elephants use plant odours to make foraging decisions across multiple spatial scales[END_REF], upon reaching the buckets the elephant was instructed to smell each bucket, remove its trunk, and then choose which bucket it wanted by placing its trunk on the lid of the bucket. To prevent the elephants from knocking the buckets over when they smelt, two trainers held the buckets in place on the ground (Fig. 1b). Once the elephant had made its choice, it was rewarded with the contents of the bucket it chose (i.e. water or no water), while the other bucket was taken away without them seeing its contents. This contingency trained the elephants to learn that whatever they chose, they received.

During the experiments, the trainers knew which bucket contained the water but were not aware of where the water came from (i.e. dams, trough, or distilled). As they knew the location of the water, it is possible that the trainers may have inadvertently signalled to the elephants where it was. However, if this was the case, then we would expect that the elephants would consistently locate the water and that there would be no differences in their ability to locate the different water types. As neither of these things happened (see results and supplementary material), we are confident that the trainers did not signal the location of the water to the elephants and that our results thus reflect the elephants' ability to find water using olfactory cues.

Over a three-day period, we tested the ability of all five elephants to locate the four water types using olfactory cues alone. On these days, we ran trials at 08:30 and at 12:00. To ensure that the elephants were thirsty, the handlers prevented them from drinking for a minimum of two hours prior to being tested. This was easy, as during the dry season the only surface water available on the property is found in the two dams and the trough. The water that we used in the different trials was collected ~30 minutes prior to each trial. For logistical reasons, we exposed all five elephants to the same water type during one of the time slots. Thus, we tested two water types each day. In each time slot, the ability of each elephant to locate the water was tested five times. This resulted in 25 separate choice tests in each time slot (i.e. 5 elephants tested 5 times). To control for any side bias or handler effect, the location of the bucket with water, as well as the handlers holding the buckets were randomized for each trial. In addition, we wiped down both lids of the buckets with the same cloth between each trial, and periodically swapped the lids between buckets. For each elephant, new lids were put onto the buckets, and the used ones were cleaned with water, rubbed down with sand, and left to dry in the sun. These were then used again in a later trial.

Training session

Prior to conducting the experiments, we pre-trained the elephants in the procedure. This pretraining involved first presenting them with the two buckets, one with a beaker with water from one of the dams on the property, one with an empty beaker. However, in these initial trials, we removed the lids so that the elephants could smell, touch, and see what was in each bucket. We followed the same experimental procedure as above (i.e. approach, smell, smell, choose). Once all five of the elephants were consistently choosing the bucket containing the water (~2 days), we then put the lids on and continued the training. It only took one day for the elephants to figure out the experimental design with the lids on and demonstrate that they could select the bucket containing the water (i.e. locate the water >75% of the time). As a result, we started data collection the following day.

Odour profiles of the different water sources

To determine the odour profiles of the four water sources (i.e. two dams, a plastic trough, distilled water), we collected 4 x 3 L water samples from each source on the day we exposed the elephants to the water from that source. We then stored these water samples in sealed glass bottles in a refrigerator for two weeks to prevent the VOCs from dissipating prior to analysis.

To determine the odour profiles, we decanted 300 µL from each sample into separate 1.5 mL glass vials with a septum and then incubated at 23 °C for approximately 24 h. The headspace of each vial was sampled for 40 min using a solid-phase-micro extraction fibre coated with 50/30 µm divinylbenzene/carboxen on polydimethylsiloxane solid support (Supelco, Merck). The fibre was injected into the inlet of an Agilent 7890 gas chromatograph coupled to an Agilent 7000D mass spectrometer (GC-MS) equipped with an Agilent J&W DB-Wax column. The inlet of the GC was set at 220 °C and the mobile phase flow rate at 1 mL/min. The initial temperature of 40 °C was held for 2 minutes after which the temperature was ramped up by 5 °C min -1 to a final temperature of 180 °C with an additional 5 min hold at 220 °C before re-equilibration to 40 °C. The volatiles eluting from the column were diverted to the ion source of the MS which was kept at 320 °C and ionized at 70 eV. We set the MS to scan mode with a low mass of 40 m z -1 and a high mass of 450 m z -1 .

Unfortunately, when we ran the first three samples (one each of the lower dam, trough, and distilled water), there was an air leak in the GC. As such we discarded the output of these samples, which left us with 13 samples in total. To view the data, we used MassHunter software (Agilent) and then used the NIST v. 98 library (National Institute of Standards and Technology, USA) for tentative identifications of the analytes. We identified potentially important metabolites by comparing the chromatograms and locating unique metabolites not found in the distilled water.

Experiment 2: VOCs associated with water

In addition to determining whether elephants could locate water via olfactory cues alone, we ran a second experiment to determine whether they could detect geosmin, 2-methylisoborneol (2-MIB), and dimethyl sulphide, olfactorily. As we were only interested in whether the elephants were able to detect these compounds, and not their degree of sensitivity to these different compounds, we standardised the compounds by creating a 1% solution of each by diluting them into dipropylene glycol, a relatively odourless solvent. For the choice experiments, 2 ml of the solutions were poured into separate Eppendorf tubes.

As with the first experiment, we ran an odour-based choice experiment. However, in this experiment we used two plastic boxes (29 cm x 21.5 cm x 16.5 cm) with 15, 1 cm diameter holes drilled into the lids. In each box, we taped an open 2 ml Eppendorf tube to the inside wall of the container, halfway up the side (~8 cm from the top and bottom). In one box, the Eppendorf contained 2 ml of the VOC solution, while the Eppendorf in the second box contained only 2 ml of dipropylene glycol. As in the first experiment, we had the elephants smell each box and then choose the one that they wanted. However, in contrast to the first experiment, the elephants were not given what was inside the box. Rather, if they chose the box containing the VOC, we gave them four animal food pellets (Alzu Game Feeds: Grazer, South Africa) as a reward. This acted as a positive reinforcement and thus, if the elephants made a significant number of correct choices, this would indicate that the elephants could detect the VOCs associated with water. Similar to the first experiment, the location of VOCs and handlers holding the boxes were randomised between each trial.

Additionally, to ensure that the elephants were not associating the positive reward with another odour cue (e.g. the smell of the handler deposited on the box, or another random odour), we wiped down the boxes and lids with alcohol between each trial, as well as switching the lids randomly during each trial.

Prior to the experiments, we trained the elephants to learn that if they indicated to the box containing the VOC, we would give them the food pellets as a reward. This was done by first having the elephants smell the VOC and then immediately giving them the pellets. This helped them associate the VOC with a reward. We did this 10 times for each elephant for each VOC. We, however, only exposed the elephants to one VOC a day, thus it took 3 days to do the initial training. We then introduced the elephants to the two boxes and ran the experimental procedure 10 times for each VOC for each elephant during one day.

Statistics

As both experiments comprised a series of binary choices (i.e. selection between two buckets or boxes), and we tested the same elephants multiple times, we treated the results from each individual elephant as repeated measures in generalized estimating equations (GEE's; see [START_REF] Schmitt | African elephants use plant odours to make foraging decisions across multiple spatial scales[END_REF] for similar procedure). The GEEs incorporated an exchangeable correlation matrix and binomial error distribution with a logit link function. As such, the GEEs allow modelling the proportion of time elephants make a given choice and comparing it to a 50% distribution expected under random selection for that given choice. Data were back transformed from logit-scale for graphical representation. We used the means and 95% confidence intervals (CI) to determine whether the elephants' choice differed from the expected 50% random selection for each of the water types.

To statistically analyse the odour profiles, we converted the GC-MS raw data to .mzXML file format using the msConvert GUI in the Protewizard tool [START_REF] Kessner | ProteoWizard: open source software for rapid proteomics tools development[END_REF].

We then uploaded the converted data to XCMS online [START_REF] Tautenhahn | XCMS Online: a web-based platform to process untargeted metabolomic data[END_REF]) for peak picking and retention time correction. Once the retention time was corrected, we logtransformed the deconvoluted dataset and analysed it in R using the Metaboanalyst v. 3 [START_REF] Chong | MetaboAnalystR: an R package for flexible and reproducible analysis of metabolomics data[END_REF] platform.

We conducted a standard principal component analysis to determine the similarity of the four water sources. We then ran a random forest analysis to determine the specific analyte differences between the water sources [START_REF] Biau | Analysis of a random forests model[END_REF]. Finally, we then ran separate 1-way ANOVAs to determine if the quantities of five key volatiles (see below) varied between the different water samples taken from the four water sources (N = 13 samples). For the principal component analysis, the random forest analysis, and the ANOVAs, we log transformed the data for normality.

Results

Experiment 1: Detecting water via olfactory cues

Using only olfactory cues, the probability that elephants located water when it came from either one of the two dams or from the trough, was much higher than expected by chance (i.e. the 95% CI of the marginal means were above the 0.5 expectation; Fig. 2a). Yet, this was not the case for the distilled water (Fig. 2a), for which elephants' successes were highly variable (i.e. the 95% CI of the marginal means overlapped the 0.5 expectation), leading to their selection of the water source to be random and not significantly different from chance (GEE: χ2= 2.119, df= 3, p= 0.548, Fig. 2a). Individually, the elephants' ability to locate the different water types varied (Table SM1 supplementary material).

Odour profiles of the different water sources

When we explored the scent profiles of the water sources, the PCA showed a clear distinction between the distilled water and the three water sources from the property (Fig. 2b). These three water sources had very similar odour profiles (Fig. 2b, Fig SM1 supplementary material), but each contained unique VOCs that we identified using the peaks on the chromatograms (Figs. SM2 & SM3 supplementary material). These were a nitrogenous volatile (F 3,9 = 348.53, p<0.0001), aliphatic hydrocarbon (F 3,9 = 134.64, p<0.0001), putatively oxygenated terpenoid (F 3,9 = 202.63, p<0.0001), and two separate unique phenolic volatiles (Phenolic volatile 1: F 3,9 = 255.49, p<0.0001; Phenolic volatile 2: F 3,9 = 430.76, p<0.0001).

We found two of the unique compounds in the water from the two dams, which included the nitrogenous volatile and the putatively oxygenated monoterpene (Fig. SM2). There were two unique phenolic volatiles emanating from the upper dam samples that we also detected to a lesser degree in the trough water samples (phenolic volatile 1 and 2; Fig. SM2). We found the aliphatic hydrocarbon in the water from the two dams and the trough (Fig. SM2).

These volatiles could not be specifically identified, as closely matching spectra were not available in the NIST v. 98 spectral library. Therefore, no candidate compounds were identified for retention index calculations (See mass spectra in Fig. SM3). Thus, we provide a tentative identification based on the mass spectra (aliphatic vs phenolic vs nitrogenous).

For the distilled water, we detected a greater number of unique compounds compared to the other three water sources (Fig. 2b). However, these were mainly plasticisers and other impurities derived from processing of the water at the University of Pretoria.

Experiment 2: VOCs associated with water

When presented with the VOCs commonly emitted from water (i.e. geosmin, 2methylisoborneol and dimethyl sulphide), the elephants were able to detect and locate all three of the VOCs using olfactory cues alone (GEE: χ2= 35.138, df= 2, p< 0.001; Fig. 3).

However, the ability to locate the specific VOCs varied between individuals (Table SM2 supplementary material). Interestingly, none of these three VOCs were found in any of the scent profiles of the three water sources (i.e. the two dams and the trough) found on the property.

Discussion

Small ephemeral water sources (e.g. pans, pools, puddles) are key for water-dependant herbivores living in arid environments, yet the temporary nature of these water sources makes predicting their location and availability difficult [START_REF] Redfern | Surface water availability and the management of herbivore distributions in an African savanna ecosystem[END_REF]. Using choice experiments, we found that the elephants were able to detect water at a small spatial scale using only olfactory cues. Furthermore, we found that they were able to detect three VOCs commonly associated with natural water sources and rainfall, namely geosmin, 2-MIB, and dimethyl sulphide.

Despite geosmin, 2-methylisoborneol, and dimethyl sulphide being considered common to the odour profiles of water sources [START_REF] Izaguirre | Geosmin and 2-methylisoborneol from cyanobacteria in three water supply systems[END_REF][START_REF] Jenkins | Odorous compounds in natural waters. Some sulfur compounds associated with blue-green algae[END_REF][START_REF] Jüttner | Biochemical and Ecological Control of Geosmin and 2-Methylisoborneol in Source Waters[END_REF], and us finding that the elephants could detect these VOCs, we did not find these VOCs in the odour profiles of the water we used in our experiments. As such, we are unable to comment on the extent to which elephants may use these key VOCs as cues when searching for water. Nevertheless, the elephants still located the water from the three water sources found on the property olfactorily.

The results of the PCA indicated that the odour profiles of the three water sources on the property were very similar, and that they all differed to that of the distilled water. As such, it is possible that the elephants used specific VOCs, other than geosmin, 2methylisoborneol, and dimethyl sulphide, or combination of VOCs (e.g. aliphatic hydrocarbons) found within the odour profiles of the water sources that were eliminated from the distilled water during the distillation process. However, as each water source contained unique VOCs, which would result in different odour profiles, it is possible that the elephants may have learnt and now recognise the specific odour profiles of these water sources. As such, they could have used these odours to locate the water from the sources on the property.

Either way, our results suggest that elephants do not solely rely on the presence of geosmin, 2-methylisoborneol, or dimethyl sulphide to locate water, which opens up an interesting and important field of future study.

Ultimately, our study is the first to demonstrate that elephants can detect water using olfactory cues alone. Moreover, despite us only focussing on a few individuals, the fact these elephants were unrelated, semi-tame, and free ranging suggests that our findings go beyond these individuals and likely apply to all African savanna elephants. Yet, as we focused on a small spatial scale, the extent to which elephants can detect water at larger spatial scales (e.g. km) remains unclear. Nevertheless, the fact that the elephants in our study were able to detect water olfactorily opens the possibility that they, especially with their exceptional sense of smell [START_REF] Niimura | Extreme expansion of the olfactory receptor gene repertoire in African elephants and evolutionary dynamics of orthologous gene groups in 13 placental mammals[END_REF][START_REF] Shoshani | Frequently asked question about elephants[END_REF][START_REF] Shoshani | Elephant brain. Part I: gross morphology, functions, comparative anatomy, and evolution[END_REF], may be able to detect water sources at distances beyond their visual range. Moreover, the VOC's being given off by the larger surface area of a body of water would likely be greater than those emitted from the small surface area of the beakers we used in our experiment [START_REF] Aydin | Biogenic volatile organic compound (BVOC) emissions from forested areas in Turkey: Determination of specific emission rates for thirty-one tree species[END_REF][START_REF] Marneweck | Ritualised Dung Kicking by White Rhino Males Amplifies Olfactory Signals but Reduces Odour Duration[END_REF].

This same logic may also apply to the suggestion that elephants can track rainfall events [START_REF] Bax | Some preliminary observations on the food of elephant in the Tsavo Royal National Park (East) of Kenya[END_REF]. For example, geosmin and 2-MIB are released by bacteria in the soil when rain hits dry ground [START_REF] Asquith | Distribution, abundance and activity of geosmin and 2-methylisoborneol-producing Streptomyces in drinking water reservoirs[END_REF][START_REF] Gerber | Geosmin, an earthy-smelling substance isolated from actinomycetes[END_REF][START_REF] Izaguirre | Geosmin and 2-methylisoborneol from cyanobacteria in three water supply systems[END_REF].

As rain tends to fall over large areas, it is possible that the volume of VOCs released by the newly wet soil may allow elephants to detect key VOCs or combination of VOCs over large spatial scales. This then may allow elephants to track rainfall events and the subsequent reestablishment of temporary water sources [START_REF] Bax | Some preliminary observations on the food of elephant in the Tsavo Royal National Park (East) of Kenya[END_REF][START_REF] Garstang | Response of African Elephants (Loxodonta africana) to Seasonal Changes in Rainfall[END_REF]).

Yet, the distance that these VOCs can travel and the concentration at which elephants can detect them is unknown. Moreover, it is also possible that elephants may rely on auditory cues generated by thunderstorms to track distant rainfall events [START_REF] Garstang | Response of African Elephants (Loxodonta africana) to Seasonal Changes in Rainfall[END_REF]. As such, further study is required to determine the distances and concentrations at which elephants can detect olfactory cues associated with water, and whether they can use these to track rainfall.

Despite our focus on elephants, other mammalian herbivore species may also rely on olfactory cues to locate surface water. For example, [START_REF] Western | Water availability and its influence on the structure and dynamics of a savannah large mammal community[END_REF] suggested that the largescale movements of wildebeest (Connochaetes taurinus), impala (Aepyceros melampus), and zebra (Equus burchelli), in Amboseli National Park, Kenya were correlated with rainfall events. In addition, the distribution of water-dependent herbivores like wildebeest, zebra, and sable antelope (Hippotragus niger) can change depending on the availability of ephemeral and artificial water points [START_REF] Redfern | Surface water availability and the management of herbivore distributions in an African savanna ecosystem[END_REF][START_REF] Smit | Do artificial waterholes influence the way herbivores use the landscape? Herbivore distribution patterns around rivers and artificial surface water sources in a large African savanna park[END_REF]). Yet, the extent to which the movements of these herbivores are driven by olfactory cues is unclear. Nevertheless, our results indicate that elephants can use olfactory cues to locate water. Thus, it is possible that both the small-and large-scale movements of a wide range of herbivore species may be driven by the detection of olfactory cues given off by both large and small water sources.

Fig. 1

 1 Fig.1Experimental setup of experiment one, with a) the blankets used to surround the beaker

Fig. 2 a

 2 Fig. 2 a) Probability of elephants selecting for water when given the choice between water

Fig. 3

 3 Fig. 3 Probability of elephants selecting the different volatile organic compounds compared

  Fig. 1

Acknowledgements

We thank S. Hensman and the rest of the team at Adventures with Elephants for allowing us to run the experiments. T. Bester and M. Schmitt assisted with data collection, and J. Joubert helped with the analysis of VOCs. J. Katz and three anonymous reviewers provided valuable comments on the manuscript.

Funding

The work was supported the University of Pretoria to AMS and the French Agence Nationale de la Recherche [ANR-16-CE02-0001-01] to SCJ.

Data Availability

Upon publication, all data will be available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.h9w0vt4hc (Shrader et al., 2021) Compliance with ethical standards

Authors' contributions M.W., S. C-J and A.M.S conceived the ideas, designed the methodology, and led the writing of the manuscript. M.W. and A.M.S. collected and analysed the data. A.H. conducted and interpreted the olfactory analysis. All authors contributed critically to the drafts.

Conflict of interests


Matthew Wijers
email: matthew.wijers@gmail.com

Paul Trethowan

Byron Du Preez

Simon Chamaillé-Jammes

Andrew J Loveridge

David W Macdonald

Andrew Markham

The influence of spatial features and atmospheric conditions on African lion vocal behaviour

Keywords: Biologging, Panthera leo, Roar, Vocal behaviour, Vocalisation

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

structure and production [START_REF] Garcia | Animal vocal communication: function, structure and production mechanisms[END_REF]. Vocal signalling is known to fulfil a variety of biological functions including territorial defence [START_REF] Darden | Acoustic territorial signalling in a small, socially monogamous canid[END_REF] contacting members in a social group [START_REF] Rendall | Vocal recognition of individuals and kin in free-ranging rhesus monkeys[END_REF], foraging [START_REF] Rydell | Capture success of little brown bats (Myotis lucifugus) feeding on mosquitoes[END_REF], navigation [START_REF] Moss | Probing the natural scene by echolocation in bats[END_REF] and deterring predators [START_REF] Zuberbühler | The predator deterrence function of primate alarm calls[END_REF]. Long distance vocal communication functions, in general, as a spacing mechanism between territorial individuals and allows distantly separated group members to locate each other [START_REF] Marler | Animal communication signals[END_REF][START_REF] Mitani | Contexts and social correlates of long-distance calling by male chimpanzees[END_REF]. Like other behaviours, an animal's decision to vocalise or remain silent is likely to be based on a trade-off between the relative costs and benefits of calling under certain conditions [START_REF] Mcfarland | Decision making in animals[END_REF].

Vocal signalling can be costly for the emitter as it may reveal information about identity, fitness, behaviour and location to potentially hostile listeners. Studies on territorial species have shown that nomadic individuals avoid vocalising in order to reduce the risk of attracting attention from territory owners [START_REF] Harrington | Wolf Howling and Its Role in Territory Maintenance[END_REF][START_REF] Grinnell | Roaring and social communication in African lions: the limitations imposed by listeners[END_REF][START_REF] Campioni | Social status influences microhabitat selection: breeder and floater Eagle Owls Bubo bubo use different post sites[END_REF]. Dominant individuals may also show varying degrees of preference and avoidance behaviour depending on their location within their home range. For example, territorial swift foxes (Vulpes velox; [START_REF] Darden | Acoustic territorial signalling in a small, socially monogamous canid[END_REF] and eagle owls (Bubo bubo; [START_REF] Delgado | Vocal behaviour and neighbour spatial arrangement during vocal displays in eagle owls (Bubo bubo)[END_REF] have been reported to increase vocal effort in the core of their home ranges while tawny owls (Strix aluco; [START_REF] Sunde | A telemetry study of the social organization of a tawny owl (Strix aluco) population[END_REF] appear to prefer vocalising in peripheral areas. Variation in vocal intensity may also be influenced by the distribution of important resources within a territory such as nests, access to mates, or areas of highest food availability [START_REF] Jacobsen | Territorial calls in the Little Owl (Athene noctua): spatial dispersion and social interplay of mates and neighbours[END_REF][START_REF] Penteriani | Owl dusk chorus is related to the quality of individuals and nest-sites[END_REF].

In addition to spatial variation in the costs and benefits of vocalising, the efficacy of longdistance acoustic communication is subject to changes in atmospheric conditions which result in varying degrees of signal degradation. Factors such as wind speed, temperature and humidity can significantly increase or reduce an animals' calling area [START_REF] Wiley | Physical Constraints on Acoustic Communication in the Atmosphere: Implications for the Evolution of Animal Vocalizations[END_REF]Larom et al., 1997b). [START_REF] Garstang | Atmospheric controls on elephant communication[END_REF] found that the range of elephant vocal signals doubled in magnitude under optimum atmospheric conditions. Such optimum conditions are generally characterised by low wind speeds and low-level vertical changes in temperature known as temperature inversions (Larom et al., 1997b). Studies on wolf and coyote vocalisations have shown an overlap between peak periods of vocalisation and the hours of best sound transmission [START_REF] Harrington | Wolf Howling and Its Role in Territory Maintenance[END_REF][START_REF] Laundre | Temporal variation in coyote vocalization rates[END_REF].

African lions are well known for their impressive, long-distance vocalisations commonly referred to as 'roars'. These signals typically consist of a series of moans leading to several fullthroated roars and end with a sequence of short grunts [START_REF] Mccomb | Roaring and numerical assessment in contests between groups of female lions, Panthera leo[END_REF][START_REF] Grinnell | Roaring and social communication in African lions: the limitations imposed by listeners[END_REF]. Lion vocal communication has been well studied particularly in relation to roar characteristics [START_REF] Stander | Characteristics of lion roars in Etosha National Park[END_REF] individual recognition [START_REF] Mccomb | Female lions can identify potentially infanticidal males from their roars[END_REF][START_REF] Gilfillan | Cross-modal individual recognition in wild African lions[END_REF], information content of the signal [START_REF] Mccomb | Roaring and numerical assessment in contests between groups of female lions, Panthera leo[END_REF][START_REF] Pfefferle | Do acoustic features of lion, Panthera leo, roars reflect sex and male condition?[END_REF] and the limitations imposed by unwanted receivers [START_REF] Grinnell | Roaring and social communication in African lions: the limitations imposed by listeners[END_REF]. The majority of these studies have used playback experiments to test hypotheses and thus there are few cases where researchers have relied on records of natural, spontaneous roaring events to investigate aspects of lion vocal behaviour [START_REF] Stander | Characteristics of lion roars in Etosha National Park[END_REF][START_REF] Pfefferle | Do acoustic features of lion, Panthera leo, roars reflect sex and male condition?[END_REF][START_REF] Lehmann | Home range utilisation and territorial behaviour of lions (Panthera leo) on Karongwe Game Reserve, South Africa[END_REF]. This preference may be partly related to the difficulty in obtaining sufficient quantities of roar records especially as wild lions vocalise almost exclusively at night [START_REF] Stander | Characteristics of lion roars in Etosha National Park[END_REF][START_REF] Pfefferle | Do acoustic features of lion, Panthera leo, roars reflect sex and male condition?[END_REF]. Apart from the observations of Schaller (1972) and [START_REF] Stander | Characteristics of lion roars in Etosha National Park[END_REF] little is known about the temporal or spatial patterns of lion vocal behaviour that may arise as a result of spatial and temporal variations in the costs and benefits of communication. [START_REF] Lehmann | Home range utilisation and territorial behaviour of lions (Panthera leo) on Karongwe Game Reserve, South Africa[END_REF] documented an apparent preference for roaring along drainage lines and within 500m of water but were limited by sample size and therefore encouraged further research on this topic. At a home range scale, lions are known to positively select areas closer to water which are characterised by high prey abundances [START_REF] Davidson | Environmental determinants of habitat and kill site selection in a large carnivore: scale matters[END_REF]. Such areas within a territory are high value resources and are therefore likely to be more intensely protected with increased territorial behaviour such as scent marking and roaring [START_REF] Darden | Acoustic territorial signalling in a small, socially monogamous canid[END_REF].

In this study, we aimed to investigate whether lions exhibit spatial preferences for vocalising, specifically with regards to proximity to rivers and water points and an individual's location within its home-range. Previously, it has been impossible to manually obtain sufficient data on spontaneous lion vocalisations with concurrent location information. To overcome this issue, we developed novel acoustic-accelerometer biologgers combined with innovative machine learning techniques to detect lion vocalisations from accelerometer data alone. Generally, animal activity recognition from accelerometers has been limited to locomotion and feeding and therefore, this work presents the first example of vocalisation detection using the same approach [START_REF] Grünewälder | Movement activity based classification of animal behaviour with an application to data from cheetah (Acinonyx jubatus)[END_REF][START_REF] Lush | Use of tri-axial accelerometers to assess terrestrial mammal behaviour in the wild[END_REF][START_REF] Wang | Movement, resting, and attack behaviors of wild pumas are revealed by tri-axial accelerometer measurements[END_REF]. We hypothesised that lions would show a preference for vocalising nearer rivers and water points as an attempt to retain and protect these valuable territory features and avoid roaring outside their home ranges due to the risks of provoking conflict with neighbouring individuals. We also aimed to test how lion vocal behaviour is influenced by atmospheric conditions. In this case we hypothesised that conditions with lower temperatures and wind speed and higher absolute humidity would be preferred given that such conditions maximise the calling range [START_REF] Harris | Absorption of Sound in Air versus Humidity and Temperature[END_REF][START_REF] Griffin | The importance of atmospheric attenuation for the echolocation of bats (Chiroptera)[END_REF]Larom et al., 1997b).

Methods

Study Site

The study was conducted in the Bubye Valley Conservancy (BVC), a privately-owned wildlife area in southern Zimbabwe located between latitudes 21.209 and 21.851 S and between longitudes 29.798 and 30.521 E. The BVC measures approximately 3400 km 2 in area and hosts a variety of indigenous megafauna including a large lion population (~ 500 individuals at the time of the study; du Preez et al., 2014a) organised into resident female prides with associated territorial male coalitions as well as vagrant individuals. Mopane woodland savannah dominates the majority of habitat within the conservancy with some riparian woodland occurring along seasonal river lines. Annual rainfall is low, averaging 351 mm, and falling mostly during the summer months from November to March (du Preez et al., 2014b). Daytime temperatures are generally high in summer, regularly exceeding 40 C, with mild conditions in winter. Permanent surface water is artificially provided at a relatively high density (approximately 5.9 / 100km 2 ). The study was conducted in the south-western section of the Conservancy where an ongoing lion research project was established in 2009.

Biologgers

Between January and July 2014, we fitted thirteen adult lions (9 males and 4 females, > 4 years old) with custom designed accelerometer biologgers (Biotrack / University of Oxford) that recorded accelerometer data at 16 Hz in 3 dimensions. In November 2014, eight adult lions (5 males and 3 females) were fitted with custom designed acoustic-accelerometer biologgers (Biotrack / University of Oxford) that recorded audio (8 bit, 16 kHz mono) and accelerometer data (32 Hz, 3 dimensions) simultaneously [START_REF] Wijers | Listening to Lions: Animal-Borne Acoustic Sensors Improve Bio-logger Calibration and Behaviour Classification Performance[END_REF]. Six (4 males and 2 females) of these study animals were recaptures from the initial accelerometer biologger deployment. All biologgers were manufactured to attach onto existing GPS satellite (GPS-PTT) collars (Africa Wildlife Tracking, Pretoria, South Africa) and measured ~ 50 x 20 x 30 mm with a mass of < 150 g. Collars were secured to the lions using metal brackets provided by manufacturer that were designed to prevent abrasion. Biologgers were retrieved by recapturing each study animal which could be easily relocated as the BVC perimeter is fenced and thus prevents movement of lions beyond the Conservancy boundary. The GPS collars recorded 16 geographical locations per day at an hourly interval between 17:00 and 07:00 local time and one point in the middle of the day at 14:00. The mean  SE 50% circular error probable of the collars was 9.94  0.89 m (du Preez et al., 2014a). From the accelerometer biologgers we collected a total of 1069 lion days of useable data from 12 lions (no data was recorded for one male lion). Due to the higher power requirements for recording audio, the acoustic-accelerometer biologgers provided a comparatively smaller dataset with a total of 60 lion days of concurrent audio and accelerometer data from all 8 individuals.

Animal Capture Procedure

Each study animal was chemically immobilised using 75-100 mg Zoletil (Virbac RSA (Pty) Ltd, Halfway House, South Africa) combined with 5 mg medetomidine (Kyron Laboratories, Johannesburg, South Africa). Immobilisation drugs were delivered intramuscularly by 1 cc darts (Pneudart, Williamsport, Pennsylvania, USA) projected from a Dan-Inject CO 2 -pressurised dartgun (Dan-Inject, Børkop, Denmark) at a distance of 15-20 m from the animal. Following clear signs of immobilisation and a time period of approximately 15 minutes, the animal was carefully approached and blindfolded. The front legs were then secured together with a rope and earplugs inserted to reduce auditory stimuli. At approximately 60 minutes after initial drug injection, ~ 25 mg atipamazol (Antisedan, Pfizer Animal Health, Johannesburg, South Africa) was administered to reverse the effects of medetomidine allowing the animal to recover within 15 -90 mins.

Weather data

Weather data which included air temperature, relative humidity and wind speed were obtained using a portable weather station (HOBO® Weather Station Data Logger H21-001, Onset Computer Corporation, Massachusetts, USA). The unit was erected in an open area in the study site and set to record measurements at 5-minute intervals [START_REF] Trethowan | Getting to the core : Internal body temperatures help reveal the ecological function and thermal implications of the lions' mane[END_REF].

Because we were interested in the sound attenuation effects of humidity, we converted relative humidity (RH) to absolute humidity (AH). AH is a more appropriate measure than RH as the interaction of water molecules with oxygen contributes towards the largest proportion of molecular attenuation [START_REF] Griffin | The importance of atmospheric attenuation for the echolocation of bats (Chiroptera)[END_REF]. AH in g/m 3 was calculated from relative humidity and temperature using the following formula [START_REF] Mander | How to convert relative humidity to absolute humidity[END_REF]

:
where RH is relative humidity in % and T is temperature in C. Data were then summarised into hourly means for each variable.

Spatial variables

Two spatial variables were used in this study: i) distance to closest river or water point and ii) position relative to home range. Using GIS layers of rivers and water points within the study site, we generated a raster proximity surface (cell size = 50 meters) in Quantum GIS 2.14 (QGIS Development Team, 2016) indicating the distance of each cell to the closest river or water point. Home range zones were estimated from GPS collar data using the local convex hull (LoCoH) method [START_REF] Getz | LoCoH: Nonparameteric Kernel Methods for Constructing Home Ranges and Utilization Distributions[END_REF] with heuristic value k = n (n = number of locations). Three home range zones were defined: i) core (within 50% isopleth), ii) peripheral (between the 50% isopleth and 90% isopleth) and iii) outside (beyond the 90% isopleth).

Roar Classifier

From the raw audio recordings recovered from the acoustic-accelerometer biologgers we manually labelled the start and end times of roar events in Audacity 2. 1.1 (Audacity Team, 2015). A total of 296 roars were found for the five male lions (Table A1). The three females did not roar and could only be heard uttering soft moans. We further labelled random segments of audio according to other identified, non-vocalising, behaviours (run, walk, feed, drink, rest) which could be recognised from characteristic acoustic cues (e.g. footfalls) as described in [START_REF] Wijers | Listening to Lions: Animal-Borne Acoustic Sensors Improve Bio-logger Calibration and Behaviour Classification Performance[END_REF]. All behaviour labels were then matched with concurrent accelerometer data to obtain a labelled dataset for training classification algorithms. Figure 1 illustrates the synchronised audio and accelerometer data for a single roar bout. Considering that the training dataset contained no female roars, we chose to limit further analyses to males as it was impossible to validate a roar classifier for females.

Since roar events can overlap with active behaviours such as walking we noticed distinct differences in the roar acceleration signal when an animal was stationary compared to when it was active. Active behaviours appeared to override the roar acceleration signal making it more difficult to determine the presence or absence of a roar. Given that our objective was to investigate patterns of lion vocal behaviour, it was essential that the classifier functioned with high precision (no false positives). Initial attempts to build a classifier that could identify roars in both active and stationary behavioural states produced a high number of false positives. We therefore decided to exclude periods of active behaviour from our analysis and focus on vocalisations occurring while the animal was stationary (a comparison between active and stationary behaviour is shown in Figure A1). We reasoned that this approach was valid as the majority (68%) of roars heard in the audio data occurred when animals were stationary. This finding was also consistent with the observations of [START_REF] Stander | Characteristics of lion roars in Etosha National Park[END_REF] who found that 70% of roars emitted by lions from Etosha National Park occurred while animals were sitting, lying or standing.

A second important consideration in our analysis was being able to link location with a roar event. Given that the GPS satellite collars only recorded location on the hour at an hourly interval we systematically sampled the accelerometer data by extracting 20-minute windows centred on the hour associated with each GPS point (Fig. 2). We chose a 20-minute period as a trade-off between maximising the time available to detect roars and minimising the probability of detecting active behaviour which would result in the window being excluded from further analyses. Although lions are stationary for most of the day, rest periods are frequently interrupted by brief periods of activity. We used the same sampling method to investigate the diel distribution of roars but used all hours of the day (instead of the GPS point hours) in order to determine vocalisation rates during daylight hours as well as at night.

To extract roar events from accelerometer data we built a hierarchical classifier consisting of hidden Markov models (HMM) to distinguish between active and stationary behaviour, and a random forest (RF) to differentiate between roars and non-roars (Fig. 3). We first trained 3state HMMs for active and stationary behaviour (a time window containing both active and stationary periods was classified as active) based on overall dynamic body acceleration (ODBA) which is a single, integrated measure of body motion [START_REF] Gleiss | Making overall dynamic body acceleration work: On the theory of acceleration as a proxy for energy expenditure[END_REF]. ODBA sequences could then be tested against each HMM to determine which model (active or stationary) was most likely to produce the given sequence (based on log-likelihood). A 4-fold cross-validation procedure resulted in 100% recall and precision indicating perfect classification of active and stationary sequences. Accelerometer windows that were classified as stationary in the first step were then passed to the RF (1000 trees and (number of variables) considered at each split) which was trained to classify each second of the 20 min window as 'roar', 'rest' or 'other' (features used for RF classification are described in Table A2). The RF classification performance was tested using 5-fold cross validation where each fold represented data from only one of the five individual lions. Roars were classified with 93.3% recall and 86.1% precision (Table A3). We used this approach to simulate the scenario where the classifier would be required to predict behaviour on individual animals not included in the training set.

The predicted series of behaviours generated by the RF were then converted into 1s and 0s where a '1' indicated a roar and a '0' indicated a non-roar. This allowed for the application of a gaussian filter which effectively filtered out isolated 1s that were likely to be false positives (considering that a single lion roar lasts for approximately 40 s). We selected a classification threshold of 0.82 which was found to yield high roar precision (100%) and satisfactory recall (68.9%). All stages of roar classification were carried out in Python using the hmmlearn 0.2.2 (hmmlearn Development Team, 2019) and Scikit-learn [START_REF] Pedregosa | Scikit-learn: Machine Learning in Python[END_REF] libraries.

Diel distribution of roars

To determine the diel distribution of roars from the accelerometer data, we first calculated the total number of roars (frequency) in each hour for each individual. The hourly frequencies were then divided by the total number of stationary windows in each hour to account for potential variations in sampling effort across hours. For each individual, hourly frequencies were normalised by expressing each frequency as a fraction of the maximum frequency in order to compare distribution patterns across individuals. The resulting relative hourly frequencies were averaged across each individual and plotted with a loess smoothing function and 95% confidence intervals to remove high-frequency variability [START_REF] Cleveland | Robust locally weighted regression and smoothing scatterplots[END_REF]. We used the same method to plot the diel distribution of roars obtained from the raw audio recordings on the acoustic-accelerometer biologgers. However, because all data were used, it was not necessary to account for any variation in sampling effort.

Effect of location and atmospheric conditions on vocalisation likelihood

Lion vocalisations were analysed at two levels. Firstly, we simply considered the presence or absence of vocalisations in each 20-min window (roar occurrence). Secondly, we accounted for the number of vocalisations in each window in order to investigate possible differences in vocalisation preferences when lions choose to roar repetitively. In the latter case, we only considered windows in which two or more roars were detected (repetitive roars). Because lions primarily roar during the night, we restricted our analyses to the period between 18:00 and 06:00 local time.

To test whether the likelihood of roar occurrence and repetitive roaring was a function of home range location and proximity to rivers and water points, we compared roar location characteristics to all non-roar stationary locations. Generalised linear mixed models (GLMM) with a binomial distribution and logit link function were used from the 'lme4' package in R [START_REF] Bates | Fitting linear mixed-effects models using lme4[END_REF]. The response variable was coded as 1 (roar) and 0 (no roar). In each model lion identity was included as a random intercept, which accounted for individual-level variation in vocalisation behaviour and allowed for inference to the population level. This useavailability design allowed for robust comparison of locations chosen by lions for roaring to those available to them. We considered five candidate models consisting of each of the explanatory variables alone, combined and in interaction. Using the same GLMM approach, we also tested for effects of atmospheric conditions on vocalisation likelihood. Specifically, we investigated whether roar likelihood was a function of temperature, wind speed and absolute humidity. All three weather variables were standardised (mean-centred and divided by the standard deviation (SD)) to allow comparison of results within the model. Eight potential models were considered that consisted of the explanatory variables alone and in all possible combinations. Due to gradual changes in atmospheric conditions through the night and through the year we included random intercepts for hour and for month. A random intercept was also included for individual identity. All variables were checked for collinearity using variance inflation factors which were found to be < 3 and could therefore be included in the models [START_REF] Zuur | A protocol for data exploration to avoid common statistical problems[END_REF]. Model selection was carried out using the R package MuMin [START_REF] Bartoń | MuMIn: Multi-Model Inference[END_REF]) with all potential models ranked according to Akaike Information Criterion corrected for small sample sizes (AICc). The model with the lowest AICc was considered the most plausible following the recommendations of [START_REF] Arnold | Uninformative parameters and model selection using Akaike's information criterion[END_REF] for small sets (< 10) of a priori models. Model fit was assessed using Nagelkerke's R 2 , which is a pseudo R-squared test that provides an estimate (between 0 and 1) of the amount of variation that can be explained by the model and is defined as one minus the ratio of the likelihood under the null model to the likelihood of the fitted model [START_REF] Nagelkerke | A Note on a General Definition of the Coefficient of Determination[END_REF][START_REF] Xu | R-squared inference under non-normal error[END_REF]. Predictor effects were interpreted in terms of odds ratios (OR) and their 95% confidence intervals (CI).

Results

Home range analyses indicated that 7 out of the 8 male lions with functioning accelerometers were territorial males with clearly defined, static home ranges (Fig. 4 and Fig. A2). One of the study males (A4) was not resident in any one area and appeared to only move along the boundary fence (Fig. A3). Of the seven territorial males, all belonged to separate coalitions except for lion A6 which was a singleton. All males were associated with at least one pride of females with which they would have likely sired cubs, however, specific details pertaining to this are not available. Small overlaps were evident between the 90% LoCoH isopleths (< 16%), while areas within the 50% LoCoH isopleths appeared to be used exclusively by each tagged individual and their associated pride members (although, realistically, nomadic males would also pass through these areas). A total of 990 roars were detected from the 7 territorial males (mean  SE = 1.98  0.33 roars/day) with only 19 roars (0.63 roars/day) detected from the nomadic male. Due to the low number of detected roars and in order to reduce possible bias as a result of social status, the nomadic male was excluded from further vocalisation analyses.

In total, 8701 time windows were analysed of which 641 contained roars (176 of these contained > 1 roar), 4444 contained non-roar stationary behaviour and 3616 contained active behaviours and were therefore excluded (Table A5). The roar and non-roar stationary windows (Figure A2) were used to model vocal behaviour in relation to the atmospheric and spatial variables.

Diel distribution of roars

The diel distribution of classified roars derived from the accelerometer data indicated that the territorial male lions roared predominantly during the night (Fig. 5). Relative frequency of roars increased steeply between 5pm and 8pm and then stabilised until shortly after midnight, before peaking between 3am and 5am. Relative frequency then steeply decreased at sunrise between 5am and 7am with relatively few roars detected during daylight hours. The hourly distribution of roars obtained from the acoustic biologgers shows an almost identical pattern with an obvious peak just before dawn between 3am and 5am and further validates the accelerometer classifier.

Effect of atmospheric conditions on vocalisation likelihood

The likelihood of roar occurrence in relation to atmospheric conditions was best explained by a model consisting of temperature, wind speed and absolute humidity which had a Nagelkerke R 2 of 0.022 (Table 1). Wind speed had the greatest effect with the odds of vocalisation decreasing by ~ 32 % for every 1 SD (0.733 m/s) increase in speed (OR: 0.677, CI: 0.590 -0.778). Comparatively, a 1 SD (4.97 C) rise in temperature decreased the odds of vocalisation by ~ 25 % (OR: 0.753, CI: 0.617 -0.919) while a 1 SD (4.46 g/m 3 ) decrease in absolute humidity decreased the odds of vocalisation by ~ 23 % (OR: 0.773, CI: 0.630 -0.949). When assessing the relationship between repetitive vocalisations and atmospheric conditions, the best model consisted of all three atmospheric predictor variables, as was the case for roar occurrence, and had a Nagelkerke R 2 of 0.030 (Table 1). The odds of repetitive roars occurring decreased by ~ 44 % with every 1 SD (0.733 m/s) increase in wind speed (OR: 0.559, CI: 0.416 -0.751), decreased by ~ 45 % for every 1 SD (4.97 C) rise in temperature (OR: 0.551, CI: 0.389 -0.781) and decreased by ~ 35 % for every 1 SD (4.46 g/m 3 ) decrease in absolute humidity (OR: 0.645, CI: 0.457 -0.909). Further details on the atmospheric conditions during the study can be found in Table A4.

Effect of location on vocalisation likelihood

The best model explaining the likelihood of roar occurrence in relation to spatial variables consisted of home range location as the only explanatory variable and had a Nagelkerke R 2 of 0.009 (Table 2). A Tukey post-hoc analysis revealed that the odds of a lion roaring outside its home range was ~ 54% lower compared to its home range core (OR: 0.459, CI: 0.289 -0.730) and ~ 43% lower compared to the periphery (OR: 0.571, CI: 0.353 -0.919). No significant difference was found between the likelihood of roar occurrence in the home range periphery and the core (OR: 0.805, CI: 0.644 -1.001). The best model explaining the likelihood of repetitive vocalisations consisted of an interaction between home range location and the distance to nearest river or water point and had a Nagelkerke R 2 of 0.023 (Table 2). The effect of distance to rivers and water points therefore depended on home range location (Fig. 6) with the odds of repetitive vocalisations increasing by 86% for every 1 km decrease in distance to rivers and water points in the home range core (OR: 1.863, CI: 2.835 -1.224) and decreasing by 82% for every 1 km decrease to rivers and water points outside the home range boundary (OR: 0.179, CI: 0.045 -0.703). No significant effect was detected for the influence of distance to rivers and water points on the likelihood of repetitive roars within the periphery (OR: 1.017, CI: 0.727 -1.423)

Discussion

Recording animal vocal behaviour with simultaneous location information is challenging, particularly for species such as lions which are mostly active at night and therefore difficult to identify. The use of biologgers provides a unique opportunity to monitor animal movement continuously and thereby overcomes many of the challenges associated with direct observation. Lion vocalisations involve consistent movements of the animal's head and neck which can be captured by three-dimensional accelerometer loggers. To our knowledge, this study is the first to retrieve animal vocalising behaviour from accelerometery data. Although our detection method was limited to stationary behaviour and retrieved ~69% of roar events, it still provided more than three times the number of roars as the raw audio recorded by the acoustic-accelerometer biologgers. On-animal audio recordings may be preferable for detecting all roars emitted by tagged animals but the lifetime of an acoustic biologger is considerably lower (~ 8 days) than an accelerometer biologger (~ 100 days) due to the higher power requirements for recording audio.

The diel distribution of roars obtained from both biologgers indicated that lions vocalise mainly at night with a distinct peak just before dawn. This pattern corresponds closely with the observations reported by Schaller (1972) and [START_REF] Stander | Characteristics of lion roars in Etosha National Park[END_REF] who also noted a consistent peak in the hours before sunrise. Larom et al. (1997b) suggested that lion calling behaviour is likely to be driven by predictable atmospheric fluctuations that enhance the range of vocal signal propagation. Optimal conditions occur when there is a strong surface temperature inversion and no wind as acoustic energy is refracted downwards and thereby increases sound levels near the ground (Larom et al., 1997a). Although it was not possible to determine the strength of daily temperature inversions, our results indicated that lions avoided vocalising with increasing wind speed. Strong winds cause vertical and horizontal mixing of air and thus prevent the development of a surface temperature inversion.

Furthermore, wind has the added effect of causing fluctuations in the received signal and introduces considerable low frequency noise that overlaps with low frequency vocal signals, such as lion roars, resulting in acoustic masking [START_REF] Wiley | Physical Constraints on Acoustic Communication in the Atmosphere: Implications for the Evolution of Animal Vocalizations[END_REF]Larom et al., 1997b).

Considering the multiple negative effects of wind on signal propagation and reception it is probable that lions actively avoid vocalising during these periods as doing so would result in minimal benefit. Similar avoidance behaviour has been recorded for maned wolves [START_REF] Rocha | Temporal and environmental influences on long-distance calling by free-ranging maned wolves (Chrysocyon brachyurus)[END_REF] and timber wolves [START_REF] Joslin | Movements and home sites of timber wolves in alǵonquin park[END_REF]. In addition to the effects of wind speed, our results also indicated significant effects for temperature and absolute humidity with lions showing an apparent preference for vocalising in lower temperatures and higher humidity. For frequencies below 1 kHz, sound attenuation decreases with increasing humidity and increases with increasing temperature [START_REF] Harris | Absorption of Sound in Air versus Humidity and Temperature[END_REF][START_REF] Griffin | The importance of atmospheric attenuation for the echolocation of bats (Chiroptera)[END_REF]. Low frequency vocalisations emitted in cold, humid conditions are therefore likely to travel further compared to signals emitted in warm, dry conditions. Considering that the fundamental frequency of a roar typically ranges between 40 Hz and 250 Hz, it is likely that lions also choose to exploit these conditions by roaring more frequently when temperatures are low and humidity is high in order to maximise calling area.

Although atmospheric conditions are likely to be a primary factor influencing lion vocal behaviour, spatial attributes may also impact an individuals' decision to vocalise. When assessing patterns of roar occurrence, the primary spatial feature affecting vocalisation likelihood was the animal's position relative to its home range. Despite little difference in the likelihood of roar occurrence between core and peripheral areas, lions appeared to strongly avoid vocalising outside of their home range. This result is consistent with the findings of [START_REF] Grinnell | Roaring and social communication in African lions: the limitations imposed by listeners[END_REF] who reported, from anecdotal observations, that resident male lions in the Serengeti and Ngorongoro crater refrain from roaring beyond their territory boundaries. This avoidance behaviour can be attributed to the increased costs of engaging in conflict with other territorial males and the reduced benefits of vocalising away from owned resources. Similar 'low profile' behaviour is commonly displayed by nomadic individuals that do not possess a territory as was evident in this study where the nomadic male vocalised less than the territorial males [START_REF] Harrington | Wolf Howling and Its Role in Territory Maintenance[END_REF][START_REF] Grinnell | Roaring and social communication in African lions: the limitations imposed by listeners[END_REF][START_REF] Campioni | Social status influences microhabitat selection: breeder and floater Eagle Owls Bubo bubo use different post sites[END_REF]. The locations of repetitive vocalisations appeared to be influenced by proximity to the nearest river or water point with the effect being dependent on the animal's position relative to its home range. Within the home range core, lions showed a preference for vocalising repetitively while closer to rivers and water points but avoided doing so when outside of their home range. Several studies have shown that rivers and water points are important features for lion prey acquisition and reproduction [START_REF] Mosser | Serengeti real estate: Density vs. fitness-based indicators of lion habitat quality[END_REF][START_REF] Valeix | How key habitat features influence large terrestrial carnivore movements: waterholes and African lions in a semi-arid savanna of north-western Zimbabwe[END_REF][START_REF] Davidson | Seasonal Diet and Prey Preference of the African Lion in a Waterhole-Driven Semi-Arid Savanna[END_REF]. Lions may therefore choose to increase vocalisation effort when closer to valuable territory features within their home range core in order to deter potential invaders. Other species such as fallow bucks (Dama dama) are known to increase vocalisation rates as a threat display directed at rival males particularly in contexts requiring protection of a resource such as access to females [START_REF] Mcelligott | Context-related vocalization rates of fallow bucks , Dama dama[END_REF]. The apparent avoidance of repetitive vocalisations closer to rivers and water points beyond an individual's home range boundary reflects the increased risks of inviting attacks from aggressive resident males that may be more willing to engage in conflict when access to a valuable resource is challenged.

While the objective of this work was to investigate the influence of spatial features and atmospheric conditions on lion vocalisations, it is important to note that there are other factors that can affect an animal's decision to vocalise or remain silent. For example, some predatory animals reduce vocalisation rates during foraging as prey are able detect vocal signals and respond with antipredator behaviour [START_REF] Deecke | The vocal behaviour of mammaleating killer whales: communicating with costly calls[END_REF]. Social factors may also play a role; in this study the three lionesses did not produce full throated roars and could only be heard uttering soft moans, likely as a means of short distance communication between other members of the pride. Although this was unexpected, the presence of small cubs in the prides may have contributed to a temporary cessation of long-distance vocalisation. Similar behaviour has been reported for wolves where adults appeared to stop howling until pups had reached an age of 6-9 weeks; a strategy that is thought to be employed to protect pups from predators [START_REF] Joslin | Movements and home sites of timber wolves in alǵonquin park[END_REF]. In the case of lions, avoiding long-distance vocalisation may reduce the risks associated with attracting potentially infanticidal males [START_REF] Grinnell | Maternal grouping as a defense against infanticide by males : evidence from field playback experiments on African lions[END_REF].

We acknowledge, however, that 3 lionesses does not constitute an adequate sample size and therefore further work would be required to support this assumption. Another social factor that can influence the frequency and timing of roars is an individual's proximity to its coalition or pride members. Lions are known to roar frequently in chorus with other members of the pride which conveys information relating to pride size and thus deters potential competitors [START_REF] Mccomb | Roaring and numerical assessment in contests between groups of female lions, Panthera leo[END_REF]. Roaring close to and in chorus with companions is likely to be advantageous and potentially preferred to roaring alone. However, given that lions are able to recognise the roars of familiar conspecifics and rely on vocalizations and scent to locate each other, individuals may also vocalize frequently while searching for distant companions [START_REF] Mccomb | Female lions can identify potentially infanticidal males from their roars[END_REF][START_REF] Gilfillan | Cross-modal individual recognition in wild African lions[END_REF]. Although the vocal behaviour of pride females in response to spatial features and atmospheric conditions is likely to be similar to that of pride males, we acknowledge that the findings presented in this research are based entirely on territorial males. We would therefore encourage additional studies on this topic to explore female vocal behaviour in similar detail.

In this study we have shown that lion vocal behaviour is influenced by both spatial and atmospheric variables. Although vocalisation behaviour is also dependent on other behavioural and social contexts, lions appear to select conditions that maximise calling area, as suggested by Larom et al. (1997b), and also adjust vocal effort depending on their location in order to defend resources and minimise inter-pride conflict. Understanding how environmental factors influence animal vocal behaviour is not only important for interpreting patterns relating to species ecology but can also directly benefit species conservation through passive acoustic monitoring (PAM; [START_REF] Marques | Estimating animal population density using passive acoustics[END_REF]. To our knowledge, no study has attempted to monitor lions from their vocalisations, but with rapidly advancing technology, PAM could provide an alternative, cost effective option for future lion research and conservation initiatives. This work has highlighted some important considerations for PAM system design such as the time of peak vocalisation and the influence of habitat features and environmental conditions. Furthermore, the methods employed in this study to obtain data on lion vocalisations may also be applicable to other species that are capable of carrying biologgers and that move in a unique and consistent manner while vocalising. In future, acoustic biologgers could be improved by incorporating on-board classification of vocalisations to eliminate the requirement for continuous audio and thereby improve device lifetime. for lion A5 appears to overlap considerably with that of lion A3, data collection for these two lions did not overlap temporally (Table A5). Lion A3 was no longer present in the area occupied by A5 at the time it's biologger was fitted. Figure A3. GPS points for lion A4 which was a vagrant during the study period and therefore not included in the analysis.

FiguresFigure 1 .

 1 Figures

  Figure 1. Roar spectrogram (a) with corresponding 3-axis accelerometer data (b). X, Y and Z

Figure 2 .

 2 Figure 2. Schematic representation of the sampling approach used to link behaviour with

Figure 3 .

 3 Figure 3. Flow diagram illustrating the sequence of steps in the roar classifier.

Figure 4 .

 4 Figure 4. Map showing location of the study site in Zimbabwe and the positions of each lion

Figure 5 .

 5 Figure 5. Diel distribution of roars obtained from accelerometer biologgers (a) and from

Figure 6 .

 6 Figure 6. Relationship between relative likelihood of repetitive vocalisation and the interaction

Figure

  Figure A1. Three-axis accelerometer data for active and stationary windows.

Figure A2 .

 A2 Figure A2. Maps showing 90% and 50% LoCoH isopleths for each lion with stationary points

Table A3 . Confusion matrix of actual behaviours (rows) vs predicted behaviours (columns) for random forest roar classifier.

 A3

	Activity	Roar	Rest	Active	Recall %	Precision %
	Roar	3522	82	171	93.3	86.1
	Rest	24	4807	54	98.4	96.7
	Active	545	81	4445	87.7	95.2

Table A4 . Atmospheric conditions during the study.

 A4

	Condition	Mean  SE	Max	Min
	Temperature (C)	21.07  0.06	41.56	3.83
	Absolute Humidity (g/m 3 ) 13.23  0.05	22.05	1.00
	Wind Speed (m/s)	0.50  0.01	4.72	0.00

Table A5 . Accelerometer only dataset -extracted roars for the 8 male lions.

 A5

			Attributes			20-min Time Windows		Data Periods
	Lion	Sex	Status Coalition Roars Stationary Active Total	Start (dd/mm)	End (dd/mm)
	A1	Male	Pride	2	188	713	480	1381 28 Jan	27 Apr
	A2	Male	Pride	2	41	178	166	385	28 Jan	20 Feb
	A3	Male	Pride	2	123	993	898	2014 29 Jan	06 Jun
	A4	Male	Nomad Singleton 7	249	211	467	29 Jan	28 Feb
	A5	Male	Pride	2	94	500	440	1034 16 Jul	16 Sep
	A6	Male	Pride	Singleton 84	989	813	1886 19 Jan	21 May
	A8	Male	Pride	2	35	148	128	311	28 Jan	16 Feb
	A11 Male	Pride	2	76	923	691	1690 29 Jan	17 May

Acknowledgments

We are grateful to the John Fell Fund and The Beit Trust for funding this research. We thank T. Kuiper for his helpful comments on initial versions of the manuscript. For facilitating the collection of the weather data we thank Prof. A. Fuller and Dr. A. Haw.

We are also grateful to the shareholders and management staff of the Bubye Valley

Tables

 Table 1.Model selection statistics for mixed effects logistic regression analysis of the relationship between lion vocal behaviour and atmospheric conditions.

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