Driving factors of temporary and permanent shallow lakes in and around Hwange National Park, Zimbabwe

Small aquatic ecosystems in semi-arid environments are characterised by strong seasonal water level fluctuations. In addition, land use as well as artificial pumping of groundwater to maintain water resources throughout the dry season may affect the functioning of aquatic ecosystems. In this study, we investigated pans situated in and around Hwange National Park, Zimbabwe, where certain waterholes are artificially maintained during the dry season for conservation purposes. We monitored 30 temporary and permanent waterholes for 7 months across the wet and dry seasons in 2013, and analysed them for standard parameters to investigate seasonal variations, assess the effects of land use and pumping on lake functioning, and determine the driving factors of these aquatic systems. Results show an increase in conductivity, hardness, and turbidity when temporary pans dry up and permanent ones are filled with groundwater. Prominent parameters explaining the diversity of aquatic ecosystems are water hardness, conductivity, turbidity, and the presence of vegetation. Seasonality differences in certain parameters suggest the influence of water level fluctuations associated with rainfall, evaporation, and pumping activities. Further, the distinction between turbid pans and those with clear water and vegetation suggests the alternative functioning of pans. Land use had no significant effects, while the effects of pumping are discussed. In times of water scarcity, animals gather around artificially maintained waterholes and foul water with faeces and urine, thus inducing water eutrophication

Factors affecting the characteristics of trips conducted between visits to waterholes.

<p>Distance traveled (A), maximum distance to start/end waterhole (B) and mean speed (C) were regressed against explanatory variables in linear mixed models with elephant identity as a random effect on intercept and slopes. The waterhole at the beginning of the trip is either different (commuting trip) or the same (looping trip) than the waterhole at the end of the trip. Estimates of the reference intercept (looping trips) and of deviations associated to other levels of explanatory variables are presented, with 95% confidence intervals obtained by parametric bootstrap with 10000 samples. Estimates for which the 95% confidence interval do not include zero are in bold.</p

Factors affecting elephant speed between visits to waterholes.

<p>Speed was regressed against explanatory variables in linear mixed models with elephant identity as a random effect on intercept. The 'Progression' variable was included as a quadratic predictor (i.e. the value and the square of the value ('Prediction²') was included in the model). The waterhole at the beginning of the trip is either different (commuting trip) or the same (looping trip) than the waterhole at the end of the trip. Progression in the trip was expressed as percent of the total trip duration. Estimates for the reference intercept (looping trips) and for the deviations associated to other levels of explanatory variables are presented, with 95% confidence intervals obtained by parametric bootstrap with 10000 samples. Estimates for which the 95% confidence interval do not include zero are in bold.</p

Characteristics of trips conducted between visits to waterholes.

<p>(A) Frequency of trip durations; (B) Relationship between distance traveled and trip duration; (C) Relationship between the maximum distance to waterholes visited at the start/end of the trip and trip duration; (D) Relationship between mean travel speed and trip duration. Note that in the wet season when elephants are not constrained by water availability mean speed is 0.33±0.52 s.d. km/h. All panels show differences between commuting trips (when different waterholes are visited at the beginning and at the end of the trip; white-filled symbols, dotted lines) and looping trips (when the same waterhole is visited at the beginning and at the end of the trip; black-filled symbols, solid lines). Note that the difference between commuting and looping trips in panel (C) is not significant (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0059164#pone-0059164-t001" target="_blank">Table 1</a>).</p

Relationship between speed and progression in the trip.

<p>(A) Speed of elephants within trips, in relation to the progression in the trip (expressed as percent of the total duration of the trip). Each line represents data for a single trip. Trips are defined as the movement between visits to waterholes, and elephants are therefore closer to water at the beginning and at the end of the trip. The waterhole at the beginning of the trip is either different (commuting trip) or the same (looping trip) than the waterhole at the end of the trip. A statistical model revealed that speed was best related to progression in the trip, trip type (commuting vs. looping) and some interactions with trip duration (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0059164#pone-0059164-t002" target="_blank">Table 2</a>). Panel (B) shows the model predictions. Note that in the wet season when elephants are not constrained by water availability mean speed is 0.33±0.52 s.d. km/h.</p

Data from: Reactive responses of zebras to lion encounters shape their predator-prey space game at large scale

The predator–prey space game and the costs associated with risk effects are affected by prey 1) proactive adjustments (when prey modify their behaviour in response to an a priori assessment of the risk level) and 2) reactive adjustments (when prey have detected an immediate threat). Proactive adjustments are generally well-studied, whereas the frequency, strength and duration of reactive adjustments remain largely unknown. We studied the space use and habitat selection of GPS-collared zebras Equus quagga from 2 to 48 h after an encounter with lions Panthera leo. Lion–zebra encounters generally occurred close to artificial waterholes (< 1 km). Two hours after an encounter, zebras were more likely to have fled than stay when the encounter occurred in more risky bushy areas. During their flight, zebras selected grasslands more than usual, getting great visibility. Regardless of their initial response, zebras finally fled at the end of the night and reached areas located far from waterholes where encounters with lions are less frequent. The large-scale flights (∼4–5 km) of zebras led to a local zebra depression for lions. Zebras that had fled immediately after the encounter resumed their behaviour of coming close to waterholes on the following day. However, zebras that had initially stayed remained far from waterholes for an extra 24 h, remaining an elusive prey for longer. The delay in the flight decision had different short-term consequences on the lion–zebra game. We reveal that the spatial context of the encounter shapes the immediate response of prey, and that encountering predators induces strong behavioural responses: prey flee towards distant, safer, areas and have a constrained use of key resource areas which are at the heart of the predator–prey game at larger spatio-temporal scales. Nighttime encounters were infrequent (once every 35 days on average), zebra responses were short-lived (< 36 h) but occurred over a large spatial scale (several km)

RSF_Lions

Data used to asses habitat selection function of lions (RSF). Column headings: Id = id of lion, DateTime = local date and time of the GPS fixes of lions, Year = year, isNight = binary variable indicating if GPS fixes occurred at nighttime (1) or daytime (0), DistWater = distance to artificial waterhole in km, Case = binary response with 1 for GPS fixes and 0 for random locations, Grassland = binary variable with 1 if location occurred in grassland and 0 if not, Bushland-1 = binary variable, Bushland-2 = binary variable, Bushland-3 = binary variable, Woodland = binary variable, Wooded Bushland = binary variable (reference category)

SSF_ImmediateFlight_NextDay

Data used to asses the step selection function (SSF) of zebras using the immediate-flight tactic the next day after an encounter with lions (question 4). Column headings: id = id of zebra, FakeTime = 2-case, case = binary variable with 1 for an observed step and 0 for a random step, strata = id of the stratum where an observed step is paired with 10 random steps, cluster = id of the cluster (group of non independent strata) used for robust variance, TacticCont = categorical variable indicating if the step occurred after an encounter ("Tactic") or during the proactive phase ("Control"), Grassland = binary variable with 1 if the step ended in grassland and 0 if not, Bushland-1 = binary variable, Bushland-2 = binary variable, Bushland-3 = binary variable, Woodland = binary variable, Spline1-4 = spline variable for step length in m, Step Length = length of the step in m

Encounter_ClassificationEM

Data used to classify the immediate spatial responses of zebras after an encounter with lions between stay and flight. Column headings: Id = id of zebras, DateTime = local date and time of the GPS fixes of zebras, DistToLion = distance between zebra and lion at the time of the encounter in m, NetDisplacement = net displacement of zebra two hours after the encounter with lion in m