DOI: 10.5061/dryad.4qrfj6qgx
title: Distance from available surface water of mammals in Ruaha National Park

description: In Africa, burgeoning human populations promote agricultural expansion and
 the associated demand for water. Water abstraction for agriculture from
 perennial rivers can be detrimental for wildlife, particularly when it
 reduces water availability in protected areas. Ruaha National Park (Ruaha
 NP) in southern Tanzania, one of the largest parks in Africa, contains
 important wildlife populations, including rare and endangered species. The
 Great Ruaha River (GRR) is the main dry-season water source for wildlife
 in the Park. Water offtake from this river for large-scale irrigation and
 livestock production up-stream of the Park has caused large expanses of
 this formerly perennial river within the Park to dry out during the dry
 season. The dry season distribution of a species in relation to surface
 water is considered an indicator of its dependence on water and ability to
 cope with the loss of surface water. We investigated how diminishing
 surface water availability during three dry seasons (2011–2013) affected
 herbivores’ distance to water in Ruaha NP. The distance held by herbivores
 to water is shaped by a range of factors including dietary category. We
 determined changes in the locations of available surface water throughout
 the dry season using standardized ground transects, close to and leading
 away from the GRR, to map the locations of nine herbivore species.
 Functional responses of herbivores, i.e. their change in distance to water
 between early and late dry season, indicated that distance to water was
 (i) shortest in buffalo and waterbuck (grazers), (ii) similar for plains
 zebra (grazer), elephant and impala (mixed feeders), (iii) larger in
 giraffe and greater kudu (browsers) and (iv) largest in generalist feeders
 (warthog, common duiker). The substantial species’ differences in surface
 water dependence broadly fit predicted species differences in their
 ability to cope with anthropogenic reduction in surface water in Ruaha NP.
description: The study was conducted during three dry seasons (2011–2013)
 within Ruaha NP in central Tanzania, which is located at the transition
 between the East African
 <em>Acacia</em>-<em>Commiphora</em> zone and the
 southern African <em>Brachystegia</em> miombo zone (Barnes
 1983). Counts of individuals of each species sighted
 along 10 ground transects that used an existing game viewing tract were
 conducted at intervals of approximately 14 days from June to October and
 once in November during the dry seasons of 2011, 2012 and 2013; thus each
 transect was surveyed 11 times per year. To visualize how the progress of
 the dry season was associated with changes in the distribution of species
 in the study area, transect data were divided into early (June-Aug, each
 transect surveyed 6 times) and late dry season (Sept-Nov, each transect
 surveyed 5 times). Transects were approximately 20 km (mean transect
 length 19,880 m ± 115.3 m [standard error of mean, S.E.M.]) in length.
 Five transects were alongside the GRR (A transects 6, 7, 8, 9 and 10), and
 five transects lead away from the GRR perpendicular to the course of the
 river (P transects 1, 2, 3, 4 and 5). This resulted in two transect
 categories (‘alongside’, ‘perpendicular’) that differed in their distance
 to the GRR, vegetation type and water availability at the start of the dry
 season. Hence, the main differences between transects influencing
 detectability of species are encapsulated in these two categories, whereas
 available surface water is a variable that changed during the dry season
 and was updated at each survey. To investigate the
 effect of the availability of surface water on herbivore distribution, we
 recorded as dynamic variables the locations of species sightings and their
 counts and the locations of available water surface in the following way:
 Data along transects were collected between 07:00 and 11:00 hours in the
 morning. Sightings of the nine most numerous herbivore species were
 recorded, including buffalo, waterbuck, zebra, giraffe, kudu, impala,
 elephant, warthog and common duiker. For each sighting, we also recorded
 species counts, i.e., the number of individuals of a species per sighting
 (group sizes), to account for large aggregations in areas containing
 larger sources of surface water. We did not include the hippopotamus
 because this species’ dependence on surface water in Ruaha NP is reported
 elsewhere (Stommel et al. 2016b). We restricted our sightings to animals
 within 100 m of the survey vehicle to maximize detection and assumed equal
 bias in detection across the study area. We could not formally account for
 imperfect detection and hence animals that potentially were missed, but we
 assume any bias to be similar in the two transect categories. For each
 sighting of an animal or a group of animals, the perpendicular distance
 between the animal sighting to the transect line was measured with a laser
 range finder (Bushnell, PinSeeker 1500 7x26). However, only the GPS
 coordinates (latitude, longitude) of the locations of each animal or group
 were taken when animals were first sighted to minimize possible error due
 to animals shifting position in response to an approaching vehicle. Thus
 locations were not routinely taken when animals were perpendicular to the
 vehicle, neither was perpendicular distance recorded. As the maximum
 distance for recording any animal to the right and left of the vehicle
 along all transects was set at 100 m, this method resulted in a maximum
 bias of ± 100 m for locations. The location of all
 available sources of surface water in the study area was also monitored
 from the beginning to the end of the dry season as detailed by Stommel et
 al. (2016a). That is, while driving along each transect, not only species
 identity and group size were recorded, but also the GPS location
 (latitude, longitude) of available water sources. We cannot exclude the
 possibility that we may have missed some small, ephemeral water sources
 such as small wildlife-dug waterholes, but we do not think we missed any
 major sources of surface water as these are well known to rangers in the
 dry season. To analyse whether herbivores change their
 location in relation to the location of surface water, which shifts
 spatially during the course of the dry season due to evaporation and the
 digging of water holes by wildlife, we calculated the Euclidean distances
 (m) from the species’ GPS location to the GPS location of the nearest
 surface water in the respective sampling week. Please note that both,
 species location and the position of surface water, can shift from one
 sampling period to the next, as the pools and water courses dry out. To
 calculate the nearest distance between species coordinates and water
 coordinates, we used the statistical software package R, version 4.2.1 (R
 Core Team 2022) and the function ‘dist’ for matrix calculations and
 selected the minimum distance to any water location for a respective
 species location.
description: # Distance from Available Surface Water of Mammals in Ruaha National Park
 The study was conducted
 during three dry seasons (2011-2013) within Ruaha NP in central Tanzania,
 which is located at the transition between the East African
 *Acacia*-*Commiphora* zone and the southern African *Brachystegia* miombo
 zone (Barnes 1983). Counts of individuals of each species sighted along 10
 ground transects that used an existing game viewing tract were conducted
 at intervals of approximately 14 days from June to October and once in
 November during the dry seasons of 2011, 2012, and 2013; thus each
 transect was surveyed 11 times per year. To analyze whether herbivores
 change their location in relation to the location of surface water, which
 shifts spatially during the dry season due to evaporation and the digging
 of water holes by wildlife, we calculated the Euclidean distances (m) from
 the species’ GPS location to the GPS location of the nearest surface water
 in the respective sampling week. To calculate the nearest distance between
 species coordinates and water coordinates, we used the statistical
 software package R, version 4.2.1 (R Core Team 2022), and the function
 `dist` for matrix calculations and selected the minimum distance to any
 water location for a respective species location. ## Description of the
 data and file structure * **`survey_id`** (numeric) — each day a survey
 was conducted, a unique identifier was provided as a consecutive number
 for each single observation. * **`water_id`** (character) — at each
 survey, each location of water availability (i.e. a water hole) was mapped
 and received a unique numbering * **`water_id_num`** (numeric) — see
 above, but transformed into a numeric to run the code * **`lon`**
 (numeric) — longitude. Angular coordinate in decimal degrees * **`lat`**
 (numeric) — latitude. Angular coordinate in decimal degrees * **`utm_x`**
 (numeric) — geographic X coordinate in UTM coordinate reference system *
 **`utm_y`** (numeric) — geographic Y coordinate in UTM coordinate
 reference system * **`transect_location`** (numeric) — location of the
 transect in relation to the Great Ruaha River (GRR). Either `1` (alongside
 the river) or `2` (perpendicular, leading away from the river) *
 **`transect_GRR`** (character) — location of the transect in relation to
 the Great Ruaha River (GRR). Either ‘alongside’ or ‘perpendicular’ *
 **`transect_id`** (numeric) — identifier of the transect as depicted in
 Figure 1 * **`river_class`** (character) — classification of the main
 river and corresponding perpendicular transects into `upstream`,
 `midstream`, and `downstream`. The R code shows the classification of
 `transect_id` into `river_class`. * **`year`** (numeric) — the year the
 survey was conducted, either 2011, 2012 or 2013 * **`week`** (numeric) —
 the calendar week the survey started beginning 1st of June. Per year, in
 11 consecutive weeks the survey was conducted * **`weeks_all`** (numeric)
 — the week the first survey started beginning 1st of June 2011 to have a
 consecutive time series, that is, the survey week in the second year
 starts with 12 (see above, 11 weeks per year). Altogether, 33 weeks were
 surveyed. * **`Juldate`** (numeric) — day of the year beginning 1st of
 June * **`dry_season`** (character) — division of the 11 weeks beginning
 1st of June into an `early` or `late` dry season. *
 **`distance_to_water`** (numeric) — the calculated minimum distance to
 available surface water in meters from the location of the animal
 observation. Per survey, the availability of surface water was mapped as
 well as the observation on the transect (see main text). *
 **`distance_to_water_rounded`** (numeric) — see above, but rounded to not
 contain decimals, as integer values are computationally more easy to
 handle than floating points. * **`Species`** (character) — shortcut of
 species name, with `B` = buffalo, `CD` = Common duiker, `E` = elefant, `G`
 = giraffe, `I` = impala, `K` = Greater Kudu, `Wb` = water buck, `Wh` =
 warthog, `Z` = zebra. * **`species_id`** (numeric) — see above in numbers
 * **`FEEDTYPE_A`** (numeric) — classification of feeding types into four
 classes * **`feed_type_A_char`** (character) — see above. Classification
 into `grazer`, `browser`, `mixed` feeder and `omni`vore. *
 **`species_count`** (numeric) — number of individuals counted per species
 and observation (`survey_id`) ## Sharing/Access information The data are
 hosted on GitHub:
 Please contact
 the authors in case you want to use the data. ## Code/Software R version
 4.2.1 The scripts for the analysis and figures are hosted on GitHub: