Sonar Surveys for Bat Species Richness and Activity in the Southern Kalahari Desert, Kgalagadi Transfrontier Park, South Africa

diversity

Article Sonar Surveys for Bat Species Richness and Activity in the Southern Kalahari Desert, Kgalagadi Transfrontier Park, South Africa

Rick A. Adams 1,* and Gary Kwiecinski 2

1 School of Biological Sciences, University of Northern Colorado, Greeley, CO 80639, USA 2 Department of Biology, University of Scranton, Scranton, PA 18510, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-970-351-2057

Received: 27 March 2018; Accepted: 10 September 2018; Published: 18 September 2018

Abstract: Kgalagadi Transfrontier Park is located in northwestern South Africa and extends northeastward into Botswana. The park lies largely within the southern Kalahari Desert ecosystem where the Auob and Nassob rivers reach their confluence. Although these rivers run only about once every 100 years, or shortly after large thunderstorms, underground flows and seeps provide consistent surface water for the parks sparse vegetation and diverse wildlife. No formal studies on bats have previously occurred at Kgalagadi. We used SM2 + BAT ultrasonic detectors to survey 10 sites along the Auob and Nassob rivers from 5–16 April 2016. The units recorded 3960 call sequences that were analyzed using Kaleidoscope software for South African bats as well as visual determinations based on call structure attributes (low frequency, characteristic frequency, call duration, and bandwidth). We identified 12 species from four families: Rhinolophidae: Rhinolophus fumigatus. Molossidae: Chaerephon pumilus, and Sauromys petrophilus, Tadarida aegyptiaca; Miniopteridae: Miniopteris schreibersi (natalensis), Vespertilionidae: Laephotis botswanae, Myotis tricolor, Neoromicia capensis, N. nana, Pipistrellus hesperidus, Scotophilus dinganii, and S. viridus. The most abundant species during the survey period was N. capensis. We also used paired-site design to test for greater bat activity at water sources compared to dry sites, with dry sites being significantly more active. We conclude that species richness is much higher than previously known from this region and that more species may be present during the warmer months of the year. In addition, activity of bats during the dry season in Kgalagadi would likely be more concentrated around drinking opportunities, thus allowing for better detection of species richness in the area.

Keywords: bats; Kalahari; South Africa; Kgalagadi Transfrontier Park; species richness

1. Introduction Sub-Saharan Africa is highly vulnerable to climate change, which will have negative effects on biodiversity and regional economies (GNP), many of which rely heavily on tourism such as in South African national parks [1]. Kgalagadi Transfrontier Park is in northwestern South Africa and encompasses the southern end of the Kalahari Desert that stretches nearly 600,000 km2 across Botswana, Namibia, and South Africa, and supports a highly rich and unique fauna and ﬂora [2]. The establishment of transfrontier parks, such as Kgalagadi, that span two or more countries, allowing for unimpeded wildlife movements, are instrumentally important to conservation [3]. Although surveys have identiﬁed the composition of large- [4] and some small-bodied [5] terrestrial mammals in this area, little is known of bat species richness [6]. In addition, bats are uniquely sensitive bioindicators of ecosystem health [7]. Because of their unique physiology and morphology making them highly susceptible to evaporative water losses thereby affecting reproductive outcomes, bats may

Diversity 2018, 10, 103; doi:10.3390/d10030103 www.mdpi.com/journal/diversity Diversity 2018, 10, 103 2 of 10 actDiversity as ‘canaries 2018, 10, x inFOR a PEER global REVIEW coal mine’ for predicting large-scale cascading effects of climate-change2 of 11 induced droughts, particularly in arid, water-stressed environments [8]. Although climate change is a‘canaries long-term in a threat, global currentcoal mine’ land for use predicting practices large-scale such as wood-cutting cascading effects and commercialof climate-change cattle induced grazing aredroughts, more immediate,particularly and in arid, perhaps water-stressed more devastating environments [9]. Thus, [8]. itAlthough is essential climate that change baseline is surveys a long- forterm bat threat, species current richness land and use abundance practices such be conducted as wood-cutting to track and future commercial population cattle changes grazing of sensitiveare more speciesimmediate, [7]. and perhaps more devastating [9]. Thus, it is essential that baseline surveys for bat species richnessOur and intention abundance was to be survey conducted for undocumented to track future batpopulation speciesin changes the park of usingsensitive sonar species call capture [7]. and identificationOur intention ofwas call to survey sequences for undocumented to species. Our bat hypothesis species in the was park that using there sonar were call more capture than theand threeidentification species informallyof call sequences listed onto species. the Park’s Our website hypothesis (the was Cape that serotine, there wereNeoromicia more than capensis the, Egyptianthree species slit-faced informally bat, Nycteris listed on thebaica the Park’s, and website Egyptian (the free-tailed Cape serotine, bat, Tadarida Neoromicia aegyptiaca capensis), based, Egyptian upon estimatedslit-faced distributionalbat, Nycteris thebaica ranges, of and South Egyptian African batsfree-tailed [6]. A secondbat, Tadarida hypothesis aegyptiaca was that), based bat activity upon levelsestimated would distributional be higher at ranges water sourcesof South than African at dry bats sites [6]. if A conditions second hypothesis were hot and was dry. that bat activity levels would be higher at water sources than at dry sites if conditions were hot and dry. 2. Materials and Methods 2. Materials and Methods 2.1. Study Area 2.1. Study Area We worked in the South Africa region of Kgalagadi Transfrontier Park (Figure1). Rainfall is <2.22We cm/year worked [ 10in ],the thus South supporting Africa region a landscape of Kgalagadi of sand Transfrontier dunes, sparsePark (Figure ground 1). vegetation,Rainfall is and<2.22 occasional cm/year [10], acacia thus trees supporting [9,11] (Figure a landscape1). Water availabilityof sand dunes, is scarce sparse within ground the park. vegetation, Two major and rivers,occasional the Nossobacacia trees and [9,11] Auob, (Figure flow only1). Water after availability significant rainsis scarce during within the the wet park. season Two that major normally rivers, stretchesthe Nossob from and January Auob, flow through only April.after significant However, rain theres during are several the wet areas season where that underground normally stretches water seepsfrom January to the surface, through thus April. providing However, consistent there are drinking several areas opportunities where underground for wildlife, water even seeps during to the drysurface, season. thus providing consistent drinking opportunities for wildlife, even during the dry season.

Figure 1.1. Map of southern Africa showing location of Kgalagadi Transfrontier Park, South Africa. Inset showsshows approximateapproximate sonar survey locations where SM2SM2 ++ BAT sonarsonar detectorsdetectors werewere deployeddeployed along the Auob ((left)left) and Nassob (right) (right) river basins, as well as near the confluence conﬂuence of these two rivers (see(see TableTable1 for1 for GPS GPS placement placement data). data). Blue Blue and blackand bl asterisksack asterisks indicate indicate paired sitespaired wherein sites wherein one detector one wasdetector positioned was positioned at a water at source, a water and source, the other and th positionede other positioned about 1 km about away 1 km at a away dry site. at a dry site.

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Table 1. Site speciﬁc data for sonar surveys, including survey date, latitude, longitude, and elevation. Colors indicate sites paired as dry and wet.

Site Survey Date Latitude Longitude Elevation Kamqua 10 April 2016 S 26◦01020.700 E 20◦24017.300 904 m Mata Camp 10 April 2016 S 25◦49026.9800 E 20◦00025.1300 952 m Samevloeiing Water Hole 11 April 2016 S 26◦28010.300 E 20◦36055.900 869 m Twee Rivieren Gate 11 April 2016 S 26◦28022.300 E 20◦36048.000 870 m Houmoed Water Hole 12 April 2016 S 26◦20022.500 E 20◦35027.600 878 m Houmoed Dry Site 12 April 2016 S 26◦20053.600 E 20◦35040.400 873 m Leeuwdril Water Hole 13 April 2016 S 26◦23007.500 E 20◦41051.600 875 m Leeuwdril Dry Site 13 April 2016 S 26◦22035.700 E 20◦41050.600 878 m Melkvlei 14 April 2016 S 26◦08019.400 E 20◦51025.800 892 m Dikbaardskolk 14 April 2016 S 26◦46013.700 E 20◦43046.900 926 m

2.2. Data Collection Each night we deployed two SM2 + BAT sonar recorders with SMX-US microphones (Wildlife Acoustics Inc., Concord, MA, USA) set to run from sunset to sunrise. A total of 10 locations was surveyed (two survey locations per night over ﬁve nights) along either the Nassob and Auob rivers or near their conﬂuence (Figure1, Table1). Each SM2 unit was set to record full spectrum sonar calls at a sampling rate of 192 KS/s and also recorded temperatures at 1-min intervals. In addition, we used a pair-wise design to sample six sites (3 pairs) wherein one SM2 was placed directly at a water source, with the other placed approximately one kilometer away at a dry location. Methods used in this study were approved by the University of Northern Colorado IACUC #1510C-RA-B-18.

2.3. Data Analysis Sonar recordings were analyzed for structural data and species identification using Kaleidoscope software (Wildlife Acoustics Inc.). In addition, all call sequences were reviewed using call structure data, which included the following properties: Fc (average characteristic frequency defined as the “body” of the call, i.e., the portion of the call consisting of the flattest slope), Fmax (average maximum frequency detected in the call), Fmin (average minimum frequency detected in the call), and average call duration (duration of the call). We also considered the number of pulses matching the auto classification results, using only call sequences that were ≥0.70 matching. Visual sonograms were generated by Sonobat 4.1 bat analysis software (Arcata, CA, USA) and were compared with published literature [12,13]. Activity of bats at a particular site was defined as the raw number of call sequences recorded from individual passes at that site. For temperature data comparisons among paired sites, we used recorded data separated by 5-min intervals to reduce redundancy but maintain a level of resolution that would allow observations of any sudden temperature changes common in a desert environment. We used X2 analysis to compare bat activity levels between wet and dry sites and Tukey-Kramer to compare temperatures among paired sites.

3. Results

3.1. Sonar Capture Results A total of 3960 call sequences was recorded across the 10 sites. Of these, 1425 sequences were identiﬁable to species using call matching rates ≥70% and represented 12 species from the families Rhinolophidae, Molossidae, Miniopteridae, and Vespertilionidae (Table2). We did not ﬁnd evidence of the Egyptian slit-faced bat, Nycteris thebaica (family Nycteridae), as indicated informally in the park brochure, during our survey, and thus, this species’ presence remains questionable. Parameters for select species’ sonar call sequences are presented in Table3 with representative sonograms. Visual depiction of call sequences of selected individuals of each species are represented in color-coded sonograms using Sonobat 4.1 bat analysis software (Arcata, CA, USA) Diversity 2018, 10, x FOR PEER REVIEW 4 of 11

grouped by family and sequence durations have been standardized from 0–100 milliseconds (x-axis) Diversityto allow2018 for, 10 comparisons, 103 among species. 4 of 10

Table 2. Families and affiliated species of bats identified by their sonar calls from Kgalagadi Transfrontier (FiguresPark2–5 in). 2016. Sonograms are grouped by family and sequence durations have been standardized from 0–100 milliseconds (x-axis) to allow for comparisons among species. Rhinolophidae Molossidae Miniopteridae Vespertilionidae Table 2. Families and affiliated species of bats identified by their sonar calls from KgalagadiLaephotis Transfrontier botswanae Park in 2016. Myotis tricolor Chaerephon pumilus Neoromicia capensis Miniopteris schreibersi RhinolophusRhinolophidae fumigatus Sauromys Molossidae petrophilus Miniopteridae VespertilionidaeNeoromicia nana (natalensis) Tadarida aegyptiaca LaephotisPipistrellus botswanae hesperidus MyotisScotophilus tricolor dinganii Chaerephon pumilus Neoromicia capensis Miniopteris schreibersi Rhinolophus fumigatus Sauromys petrophilus NeoromiciaScotophilus nana viridus (natalensis) Tadarida aegyptiaca Pipistrellus hesperidus Table 3. Call parameters by species provided by Kaleidoscope bat analysisScotophilus software. dinganii Fc = mean characteristic frequency, Fmax = mean maximum frequency, Fmin = meanScotophilus minimum viridus frequency, Dur = mean duration, N = sample size, MR = Matching Rate (i.e., number of calls in sequence that Tablematched 3. Call species parameters identification by species with 1 provided = 100% match). by Kaleidoscope bat analysis software. Fc = mean characteristic frequency, Fmax = mean maximum frequency, Fmin = mean minimum frequency, Species Family Fc Dur Fmax Fmin N MR Dur = mean duration, N = sample size, MR = Matching Rate (i.e., number of calls in sequence Rhinolophusthat matched fumigatus species identiﬁcationRhinolophidae with 1 = 100%53.9 match). 40.1 54.4 53.6 1 0.9 Chaerophon pumilus Molossidae 22.9 ± 0.3 5.6 ± 1.5 25 ± 0.1 22.4 ± 0.4 2 0.9 SauromysSpecies petrophilus FamilyMolossidae Fc 24.0 ± 0.2 Dur 4.3 ± 0.7 Fmax 30.3 ± 3.2 Fmin 23.7 ± 0.1 N MR 2 1 TadaridaRhinolophus aegyptiacus fumigatus RhinolophidaeMolossidae 53.9 21.6 ± 1.2 40.1 4.9 ± 0.6 54.4 25.2 ± 1.1 53.6 21.2 ± 1.1 1 50.9 1 Chaerophon pumilus Molossidae 22.9 ± 0.3 5.6 ± 1.5 25 ± 0.1 22.4 ± 0.4 2 0.9 MiniopterisSauromys petrophilusschreibersi MolossidaeMiniopteridae 24.0 ± 47.60.2 ± 6.5 4.3 ± 0.7 2.6 ± 0.4 30.3 ± 76.73.2 ± 7.7 23.7 ± 53.20.1 ± 4.9 2 3 1 1 LaephotisTadarida aegyptiacusbotswanae VespertilionidaeMolossidae 21.6 ± 38.81.2 ± 0.7 4.9 ± 0.6 2.2 ± 0.3 25.2 ± 68.51.1 ± 0.5 21.2 ± 37.91.1 ± 0.9 5 3 1 1 NeoromiciaMiniopteris schreibersicapensis MiniopteridaeVespertilionidae 47.6 ± 36.96.5 ± 0.6 2.6 ± 0.4 3.5 ± 0.5 76.7 ± 39.67.7 ± 4.3 53.2 ± 36.74.9 ± 0.6 3 10 1 1 Laephotis botswanae Vespertilionidae 38.8 ± 0.7 2.2 ± 0.3 68.5 ± 0.5 37.9 ± 0.9 3 1 NeoromiciaNeoromicia capensis nana VespertilionidaeVespertilionidae 36.9 ± 61.50.6 ± 8.9 3.5 ± 0.5 3.7 ± 0.3 39.6 ± 74.34.3 ± 6.2 36.7 ± 61.10.6 ± 3.5 10 2 1 0.8 ScotophilusNeoromicia dinganii nana Vespertilionidae Vespertilionidae 61.5 ± 33.98.9 ± 2.4 3.7 ± 0.3 3.3 ± 0.9 74.3 ± 40.96.2 ± 2.1 61.1 ± 33.43.5 ± 2.0 2 40.8 1 ScotophilisScotophilus dinganiiviridus VespertilionidaeVespertilionidae 33.9 ± 37.52.4 ± 1.6 3.3 ± 0.9 4.3 ± 0.3 40.9 ± 70.62.1 ± 6.6 33.4 ± 36.72.0 ± 1.8 4 3 1 0.9 Scotophilis viridus Vespertilionidae 37.5 ± 1.6 4.3 ± 0.3 70.6 ± 6.6 36.7 ± 1.8 3 0.9 PipistrellusPipistrellus hesperidus VespertilionidaeVespertilionidae 43.3 ± 43.33.1 ± 3.1 2.3 ± 2.30.03 ± 0.03 53.6 ± 53.61.6 ± 1.6 42.9 ± 42.93.4 ± 3.4 2 0.85 2 0.85 MyotisMyotis tricolor tricolor VespertilionidaeVespertilionidae 41.8 ± 41.84.3 ± 4.3 2.9 ± 0.9 2.9 ± 0.9 81.5 ± 81.56.2 ± 6.2 38.8 ± 38.82.2 ± 2.2 6 6 1 1

FigureFigure 2. 2.Representative Representative sonogram sonogram forRhinolophidae for Rhinolophidae presented presented using Sonobat using 4.1.Sonobaty-axis 4.1. is frequency Y-axis is infrequency kilohertz in (kHz) kilohertz and x (kHz)-axis isand time x-axis standardized is time standardized over 100 milliseconds over 100 milliseconds (msec). Blue (msec). coloration Blue indicatescoloration lowest indicates sound lowest intensity sound of the intensity call, yellow of the indicates call, yellow medium indicates intensity, medium and red intensity, indicates highestand red intensity or loudest portion of the call. Green display represents an oscillogram of the call sequence.

Diversity 2018, 10, x FOR PEER REVIEW 5 of 11 Diversity 2018, 10, x FOR PEER REVIEW 5 of 11 indicates highest intensity or loudest portion of the call. Green display represents an oscillogram of indicates highest intensity or loudest portion of the call. Green display represents an oscillogram of Diversitythe2018 call, sequence.10, 103 5 of 10 the call sequence.

Figure 3. Representative sonograms for three bat species from the family Molossidae presented using Figure 3. RepresentativeRepresentative sonograms sonograms for for three three bat batspecie speciess from from the family the family Molossidae Molossidae presented presented using Sonobat 4.1. Y-axis is frequency in kilohertz (kHz) and x-axis is time standardized over 100 usingSonobat Sonobat 4.1. Y-axis 4.1. yis-axis frequency is frequency in kilohertz in kilohertz (kHz) (kHz)and x and-axisx -axisis time is timestandardized standardized over over100 milliseconds (msec). Blue coloration indicates lowest sound intensity of the call, yellow indicates 100milliseconds milliseconds (msec). (msec). Blue Blue coloration coloration indicates indicates lowe lowestst sound sound intensity intensity of of the the call, call, yellow indicates medium intensity, and red indicates highest intensity or loudest portion of the call. Green display medium intensity, andand redred indicatesindicates highesthighest intensityintensity oror loudestloudest portionportion ofof thethe call.call. Green display represents an oscillogram of the call sequence. represents anan oscillogramoscillogram ofof thethe callcall sequence.sequence.

FigureFigure 4. RepresentativeRepresentative sonogram sonogram for for MiniopterisMiniopteris schreibersii schreibersii (family(family Miniopteridae) Miniopteridae) presented presented using using Figure 4. Representative sonogram for Miniopteris schreibersii (family Miniopteridae) presented using SonobatSonobat 4.1. 4.1. Yy-axis is frequency frequency in kilohertz kilohertz (kHz) (kHz) and and x-axis-axis is is time time standardized standardized over over 100 100 milliseconds milliseconds Sonobat 4.1. Y-axis is frequency in kilohertz (kHz) and x-axis is time standardized over 100 milliseconds (msec).(msec). Blue Blue coloration coloration indicates indicates lowest lowest sound sound intens intensityity of of the the call, call, yellow yellow indicates indicates medium medium intensity, intensity, (msec). Blue coloration indicates lowest sound intensity of the call, yellow indicates medium intensity, andand red red indicates indicates highest highest intensity intensity or loudest or loudest portion portion of the call. ofthe Green call. display Green represents display an represents oscillogram an and red indicates highest intensity or loudest portion of the call. Green display represents an oscillogram ofoscillogram the call sequence. of the call sequence. of the call sequence. 3.2. Species Richness and Activity The most prominent species recorded and distributed among all sites was the Cape serotine (Neoromicia capensis). Two other species were also recorded at all 10 sites: the yellow-bellied house bat (Scotophilus dinganii) and the green house bat (S. viridus). Although distributed among only eight of the ten sites, the Egyptian free-tailed bat (Tadarida aegyptiaca) was the second most common bat we recorded in the park (Figure6).

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Figure 5. Representative sonogram for bats in the family Vespertilionidae presented using Sonobat 4.1. Figure 5. Representative sonogram for bats in the family Vespertilionidae presented using Sonobat 4.1. y-axis is frequency in kilohertz (kHz) and x-axis is time standardized over 100 milliseconds (msec). Y-axis is frequency in kilohertz (kHz) and x-axis is time standardized over 100 milliseconds (msec). Blue coloration indicates lowest sound intensity of the call, yellow indicates medium intensity, and red Blueindicates coloration highest indicates intensity lowest or loudest sound portion intensity of the of call. the Green call, displayyellow representsindicates anmedium oscillogram intensity, of the and Diversity 2018, 10, x FOR PEER REVIEW 7 of 11 red indicatescall sequence. highest intensity or loudest portion of the call. Green display represents an oscillogram of the call sequence.

3.2. Species Richness and Activity The most prominent species recorded and distributed among all sites was the Cape serotine (Neoromicia capensis). Two other species were also recorded at all 10 sites: the yellow-bellied house bat (Scotophilus dinganii) and the green house bat (S. viridus). Although distributed among only eight of the ten sites, the Egyptian free-tailed bat (Tadarida aegyptiaca) was the second most common bat we recorded in the park (Figure 6).

Figure 6. Distribution of activity levels for the 12 bat species identified in Kgalagadi Transfrontier Park Figure 6. Distribution of activity levels for the 12 bat species identified in Kgalagadi Transfrontier in 2016. Species abbreviations in figure represent the first three letters of the generic and specific names Park in 2016. Species abbreviations in figure represent the first three letters of the generic and specific and correlate with the list provided + indicates highest detection rates. names and correlate with the list provided + indicates highest detection rates.

The species richness varied by site, with Dikbaardskolk having the highest richness with seven species and Leeuwdril Water Hole having the lowest with three species. Most sites were dominated by one or two species, usually Neoromicia capensis. However, sites varied considerably in species richness (Figure 7A–C).

Figure 7. Distribution of activity at each site. (A) Neoromicia capensis (NEOCAP) was the most abundant species in the park and was found to occur at all sites. (B) Second most common species was Scotophilus dinganii (SCODIN), recorded at eight sites, followed by Tadarida aegyptiaca (TADAEG), Scotophilus viridus (SCOVIR), and Scotophilus viridus (SCOVIR), which were found at seven of 10 sites. (C) Least number of passes were recorded for Myotis tricolor (MYOTRI), Sauromys petrophilus (SAUPET), Chaerephon pumilus (CHAPUM), Miniopteris schriebersii (MINSCH), Neoromicia nana (NEONAN), Pipistrellus hesperidus (PIPHES), Rhinolophus fumigatus (RHIFUM), and Laephotis

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Figure 6. Distribution of activity levels for the 12 bat species identified in Kgalagadi Transfrontier

DiversityPark2018 ,in10 2016., 103 Species abbreviations in figure represent the first three letters of the generic and specific 7 of 10 names and correlate with the list provided + indicates highest detection rates.

TheThe species species richness richness variedvaried byby site, with Dikbaa Dikbaardskolkrdskolk having having the the highest highest richness richness with with seven seven speciesspecies and and Leeuwdril Leeuwdril Water Water Hole Hole having having the the lowest lowest with with three three species. species. MostMost sitessites werewere dominated by oneby orone two or species, two species, usually usuallyNeoromicia Neoromicia capensis capensis. However,. However, sites variedsites varied considerably considerably in species in species richness (Figurerichness7A–C). (Figure 7A–C).

Figure 7. Distribution of activity at each site. (A) Neoromicia capensis (NEOCAP) was the most Figure 7. Distribution of activity at each site. (A) Neoromicia capensis (NEOCAP) was the most abundant species in the park and was found to occur at all sites; (B) Second most common species abundant species in the park and was found to occur at all sites. (B) Second most common species was Scotophilus dinganii (SCODIN), recorded at eight sites, followed by Tadarida aegyptiaca (TADAEG), was Scotophilus dinganii (SCODIN), recorded at eight sites, followed by Tadarida aegyptiaca (TADAEG), Scotophilus viridus (SCOVIR), and Scotophilus viridus (SCOVIR), which were found at seven of 10 sites; Scotophilus viridus (SCOVIR), and Scotophilus viridus (SCOVIR), which were found at seven of 10 sites. (C) Least number of passes were recorded for Myotis tricolor (MYOTRI), Sauromys petrophilus (SAUPET), (C) Least number of passes were recorded for Myotis tricolor (MYOTRI), Sauromys petrophilus Chaerephon pumilus (CHAPUM), Miniopteris schriebersii (MINSCH), Neoromicia nana (NEONAN), (SAUPET), Chaerephon pumilus (CHAPUM), Miniopteris schriebersii (MINSCH), Neoromicia nana Pipistrellus(NEONAN), hesperidus Pipistrellus(PIPHES), hesperidusRhinolophus (PIPHES), fumigatus Rhinolophus(RHIFUM), fumigatus and Laephotis(RHIFUM), botswanae and Laephotis(LAEBOT). WH = Water Hole, DS = Dry Site, TW = Twee Rivieren gate, a dry site paired with Samevloeiing WH.

3.3. Wet versus Dry Site Comparisons For our wet versus dry sites, pooled nightly bat activity was significantly higher at dry sites (X2 = 182.5, P < 0.001). Only at Houmoed was activity higher at the water source than its paired dry site. Because data were recorded among paired-sites on different nights and temperature can affect activity levels at water sources in arid environments, we used the Tukey-Kramer analysis to test for significant differences and found that Houmoed was significantly higher in mean nightly temperature than those recorded at the other sites sampled on different nights (Figure8). Activity at water sources was highest at sunset, after which activity declined with slight increases at dawn for all three sites (Figure9A–D). Temperatures were highest at sunset and tapered off throughout the night. Houmoed had the highest sunset temperature of 28.2 ◦C, recorded at 1624 h with Samevloeiing and Leeuwdril having slightly lower sunset temperatures at 27 ◦C and 27.4 ◦C, respectively. Drops in temperatures throughout the nocturnal period were steepest between sunset and midnight for Leeuwdril and Houmoed, whereas at Samevloeiing, temperature declined less dramatically and held more steadily throughout the night (Figure6D). Diversity 2018, 10, x FOR PEER REVIEW 8 of 11

botswanae (LAEBOT). WH = Water Hole, DS = Dry Site, TW = Twee Rivieren gate, a dry site paired with Samevloeiing WH.

3.3. Wet versus Dry Site Comparisons For our wet versus dry sites, pooled nightly bat activity was significantly higher at dry sites Diversity 2018, 10, x FOR PEER REVIEW 8 of 11 (Χ2 = 182.5, P < 0.001). Only at Houmoed was activity higher at the water source than its paired dry site. Because data were recorded among paired-sites on different nights and temperature can affect botswanae (LAEBOT). WH = Water Hole, DS = Dry Site, TW = Twee Rivieren gate, a dry site paired activity levels at water sources in arid environments, we used the Tukey-Kramer analysis to test for with Samevloeiing WH. significant differences and found that Houmoed was significantly higher in mean nightly temperature than those recorded at the other sites sampled on different nights (Figure 8). 3.3. Wet versus Dry Site Comparisons For our wet versus dry sites, pooled nightly bat activity was significantly higher at dry sites (Χ2 = 182.5, P < 0.001). Only at Houmoed was activity higher at the water source than its paired dry site. Because data were recorded among paired-sites on different nights and temperature can affect activity levels at water sources in arid environments, we used the Tukey-Kramer analysis to test for significant differences and found that Houmoed was significantly higher in mean nightly temperatureDiversity 2018 than, 10, 103those recorded at the other sites sampled on different nights (Figure 8). 8 of 10

Figure 8. Distribution of activity between paired dry and wet sites sampled simultaneously. Wet sites indicate drinkable water source for bats. Dry = reference sites approximately 1 km from wet sites. Overall, Χ2 analysis indicated significantly higher activity at dry sites. Open dots indicate mean temperature with standard deviations over the recording periods. Only Houmoed showed more activity at the water source than the reference dry site. Tukey-Kramer analysis on mean temperatures (gray dots with bars indicating standard deviations) indicated that the night Houmoed was sampled (mean = 23.1 °C, SD = 3.6) was significantly higher (DF = 430, CV = 3.15, P = 0.0001) than on the other sampling nights (Leeuwdril 19.8 °C, SD 3.9 and Samevloeiing 23.3 °C, SD = 3.1).

FigureActivity 8. Distribution at water sources of activity was between highest paired at drysunset and wet, after sites which sampled activity simultaneously. declined Wet with sites slight Figureincreasesindicate 8. Distributionat drinkabledawn for waterallof activithree sourcety sites between for (Figure bats. paired Dry 9A–D). = dry reference Temperatures and wet sites sites approximately weresampled highest simultaneously. 1 km at sunset from wet and sites.Wet tapered sites indicateoff throughoutOverall, drinkable X2 analysisthe water night. indicated source Houmoed signiﬁcantlyfor bats. had Dry the higher =highest reference activity sunset sites at dry temperatureapproximately sites. Open of dots 128.2 km indicate °C,from recorded meanwet sites. at Overall,1624temperature h with Χ2 analysisSamevloeiing with standardindicated and deviations significantlyLeeuwdril over having higher the recording slightly activity periods. lower at dry sunset Onlysites. Houmoed temperaturesOpen dots showed indicate at more27 °C mean and temperature27.4 activity°C, respectively. at with the water standard Drops source indeviations than temperatures the reference over throthe dry ughoutrecordin site. Tukey-Kramer theg periods. nocturnal analysis Only period onHoumoed meanwere temperaturessteepest showed between more activitysunset(gray and at dots the midnight withwater bars source for indicating Leeuwdril than the standard referenceand Houmoed, deviations) dry site. indicatedwhereas Tukey-Kramer thatat Samevloeiing, the night analysis Houmoed on temperature mean was temperatures sampled declined (mean = 23.1 ◦C, SD = 3.6) was signiﬁcantly higher (DF = 430, CV = 3.15, P = 0.0001) than on the other (grayless dramatically dots with bars and indicating held more standard steadily deviat throughoutions) indicated the night that (Figure the night6D). Houmoed was sampled sampling nights (Leeuwdril 19.8 ◦C, SD 3.9 and Samevloeiing 23.3 ◦C, SD = 3.1). (mean = 23.1 °C, SD = 3.6) was significantly higher (DF = 430, CV = 3.15, P = 0.0001) than on the other sampling nights (Leeuwdril 19.8 °C, SD 3.9 and Samevloeiing 23.3 °C, SD = 3.1).

Activity at water sources was highest at sunset, after which activity declined with slight increases at dawn for all three sites (Figure 9A–D). Temperatures were highest at sunset and tapered off throughout the night. Houmoed had the highest sunset temperature of 28.2 °C, recorded at

1624 Diversityh with 2018 Samevloeiing, 10, x FOR PEER andREVIEW Leeuwdril having slightly lower sunset temperatures at 279 of °C 11 and 27.4 °C, respectively. Drops in temperatures throughout the nocturnal period were steepest between sunset and midnight for Leeuwdril and Houmoed, whereas at Samevloeiing, temperature declined less dramatically and held more steadily throughout the night (Figure 6D).

Figure 9. Activity (defined as number of passes) at the three paired sites in relation to three time-blocks Figure 9. Activity (defined as number of passes) at the three paired sites in relation to three time-blocks representing activity periods (1850–2320 h, 2321–0121 h, and 0122–0522 h) (A) Leeuwdril (13 April 2016); representing activity periods (1850–2320 h, 2321–0121 h, and 0122–0522 h) (A) Leeuwdril (13 April (B) Houmoed (4 April 2016); and (C) Samevloeiing (11 April 2016). Blue = water sources and 2016), (B) Houmoed (4 April 2016), and (C) Samevloeiing (11 April 2016). Blue = water sources and Orange = dry sites. Most activity at water sources was consistently higher in the earliest time blocks, Orange = dry sites. Most activity at water sources was consistently higher in the earliest time blocks, whereas activity was more variable at dry sites but generally increased between sunset and 2200 h; whereas activity was more variable at dry sites but generally increased between sunset and 2200 h. (D) Mean changes in temperature from sunset to sunrise during the sampling periods showed declines (D) Mean changes in temperature from sunset to sunrise during the sampling periods showed over the sampling periods, with Leeuwdril and Houmoed showing steepest and nearly identical declines over the sampling periods, with Leeuwdril and Houmoed showing steepest and nearly declines. Samevloeiing showed a more gradual temperature decline throughout the night. identical declines. Samevloeiing showed a more gradual temperature decline throughout the night. 4. Discussion 4. Discussion Our hypothesis that we would discover higher species richness in the park than was previously documentedOur hypothesis was supported. that we would Of the discover 12 species higher we species identiﬁed richness for in Kgalagadi the park than Transfrontier was previously Park, documented was supported. Of the 12 species we identified for Kgalagadi Transfrontier Park, only three were expected based upon current estimated ranges for South African Bats and these were Nycteris thebaica, N. capensis and T. aegyptiaca [6]. Our survey found evidence of two of these species, but we did not record sonar calls from N. thebaica. Because our survey was done during the end of the wet season, it is possible that individuals of some species had migrated to other localities for the dry season. Our hypothesis that wet sites would support greater bat activity than dry sites was not supported statistically. Rather, we found overall greater activity at dry sites than at water sources. However, Houmoed did have higher activity at the wet site on the warmest survey night and thus temperature may influence activity patterns in these desert bats [14,15]. In addition, much of South Africa is being affected by climate change with anticipated increases in mean annual temperatures accompanied by reductions in rainfall and increasing desertification [1,16,17]. Therefore, understanding regional bat ecology is important, as bats have been shown to be reliable bioindicators of environmental health and effects of climate disruption, especially in arid regions [7]. For example, insectivorous bats showed significant declines in reproductive output in arid regions of the Rocky Mountains in North America, and this correlated with a lack of water availability in drought years [8,18,19]. In addition, these same populations showed long-term shifts towards male-biased sex ratios [20] and possible disruptions of altitudinal migratory patterns [21] in relation to increasing regional temperatures and reduced precipitation. Kgalagadi Transfrontier Park provides protected areas for a high biodiversity of bats. The vast biome known as Flynbos/Chapparel composed of the Kalahari xeric savanna ecoregion extends through western South Africa as well as throughout much of Botswana and Namibia [10,11]. We feel that more research will yield further records of undocumented bat species as well as the reliance of bats on available water sources, especially during the dry season, in the park. Indeed, the distribution and ecology of insectivorous bats throughout Africa continues to be largely unknown. Because bats provide important contributions to food webs and ecological services [22,23], better understanding of their natural histories will lend new insights into the region’s overall ecology and improvements in conservation efforts.

Funding: Funding was provided by the University of Northern Colorado Faculty Research and Development Program, RDF_RAA S2015.

Diversity 2018, 10, 103 9 of 10 only three were expected based upon current estimated ranges for South African Bats and these were Nycteris thebaica, N. capensis and T. aegyptiaca [6]. Our survey found evidence of two of these species, but we did not record sonar calls from N. thebaica. Because our survey was done during the end of the wet season, it is possible that individuals of some species had migrated to other localities for the dry season. Our hypothesis that wet sites would support greater bat activity than dry sites was not supported statistically. Rather, we found overall greater activity at dry sites than at water sources. However, Houmoed did have higher activity at the wet site on the warmest survey night and thus temperature may inﬂuence activity patterns in these desert bats [14,15]. In addition, much of South Africa is being affected by climate change with anticipated increases in mean annual temperatures accompanied by reductions in rainfall and increasing desertification [1,16,17]. Therefore, understanding regional bat ecology is important, as bats have been shown to be reliable bioindicators of environmental health and effects of climate disruption, especially in arid regions [7]. For example, insectivorous bats showed signiﬁcant declines in reproductive output in arid regions of the Rocky Mountains in North America, and this correlated with a lack of water availability in drought years [8,18,19]. In addition, these same populations showed long-term shifts towards male-biased sex ratios [20] and possible disruptions of altitudinal migratory patterns [21] in relation to increasing regional temperatures and reduced precipitation. Kgalagadi Transfrontier Park provides protected areas for a high biodiversity of bats. The vast biome known as Flynbos/Chapparel composed of the Kalahari xeric savanna ecoregion extends through western South Africa as well as throughout much of Botswana and Namibia [10,11]. We feel that more research will yield further records of undocumented bat species as well as the reliance of bats on available water sources, especially during the dry season, in the park. Indeed, the distribution and ecology of insectivorous bats throughout Africa continues to be largely unknown. Because bats provide important contributions to food webs and ecological services [22,23], better understanding of their natural histories will lend new insights into the region’s overall ecology and improvements in conservation efforts.

Author Contributions: R.A.A. developed the study design, collected and analyzed data, wrote original draft and edited subsequent drafts of the manuscript. G.W. assisted in the collection of field data and provided comments on drafts of the manuscript. Funding: Funding was provided by the University of Northern Colorado Faculty Research and Development Program, RDF_RAA S2015. Acknowledgments: We thank SANParks, South Africa for allowing us to conduct research and their support in providing game guards and accommodations. We especially thank the staff at Kgalagadi Transfrontier Park for their assistance and hospitality. We also acknowledge the University of Northern Colorado and the University of Scranton for funding this project. Conflicts of Interest: The authors declare no conflict of interest.

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