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Journal of Mammalogy, 97(2):483–489, 2016 DOI:10.1093/jmammal/gyv192 Published online December 26, 2015 Similarities in perceived predation risk prevent temporal partitioning of food by rodents in an African grassland Natalia Banasiak and Adrian M. Shrader* School of Life Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville 3209, South Africa (NB, AMS) * Correspondent: [email protected] One way in which animals coexist is through temporal separation of feeding activities. This separation directly reduces interference competition, but potentially not exploitive competition. To reduce exploitive competition, coexisting species tend to utilize different microhabitats and/or achieve different feeding efforts across microhabitats. However, 1 factor that has generally not been considered with regards to its impacts on competition, and thus coexistence, is predation risk. As different predators are active during the day and at night, the location of safe areas across the landscape can vary temporally. If so, then temporally separated prey species would likely forage in different areas thus reducing exploitive competition. However, if predation risk across the landscape is similar for nocturnal and diurnal species, then both could restrict their foraging to the same microhabitats, thus increasing exploitive competition. To explore these alternative possibilities, we manipulated grass height in an African grassland to create microhabitats that varied in predation risk. We then estimated perceived predation risk of both nocturnal and diurnal rodents in these microhabitats by recording giving-up densities (GUDs) in artificial resource patches. We found that despite differences in predators, both nocturnal and diurnal rodents preferred feeding in the same microhabitats, and they achieved similar feeding efforts within these microhabitats. This suggests that despite the prevention of interference competition through temporal partitioning, the spatial similarities in perceived predation risk in relation to cover likely increase exploitive competition between these rodents. However, as both nocturnal and diurnal rodents were present in the study area, it is likely that some other mechanism (e.g., varied diets) allows them to coexist. Key words: coexistence, competition, feeding effort, giving-up densities © 2015 American Society of Mammalogists, www.mammalogy.org Due to similarities in diet and/or habitat requirements, many species have the potential to compete for key resources. However, through differences in resource partitioning of their diets (Kinahan and Pillay 2008; Symes et al. 2013), spatial habitat selection (Abramsky et al. 1990; Morris 1996; Bramley 2014) and temporal separation of feeding activities (KronfeldSchor and Dayan 2003; Gutman and Dayan 2005), many species reduce interspecific competition and are thus able to coexist (Kotler and Brown 1988). For example, Symes et al. (2013) found that 6 rodent and 2 shrew species coexisted in a South African grassland–woodland mosaic by separating their niches (i.e., reducing competition) through different combinations of diet, space, and time. Competition, however, comes in 2 forms—interference and exploitative competition (Nicholson 1954), which affect coexistence differently. Interference competition entails one species preventing another from accessing resources (e.g., food) through direct confrontation (e.g., fights, chases—Case and Gilpin 1974), or indirectly via one species avoiding certain areas at certain times due to fear of injury from potential aggressive encounters with another species (Ziv et al. 1993). In contrast, exploitative competition (also called scramble competition) is when one species lowers food availability for other species through feeding (Parker 2000) or by scaring prey eaten by both species away (i.e., behavioral resource depression— Charnov et al. 1976). One way in which species can reduce interference competition is through temporal partitioning of feeding (Richards 2002; Kronfeld-Schor and Dayan 2003; Gutman and Dayan 2005). By foraging at different times, heterospecifics prevent direct interactions. However, exploitative competition between these species would still take place if they fed in the same areas unless food was replenished in the period between visits (Schoener 1974). A 2nd way in which exploitative competition could be reduced by temporal partitioning is if different resources (e.g., prey species) are available at different times (i.e., reduced resource overlap). For example, the availability and presence of different insect species varies both spatially 483 484 JOURNAL OF MAMMALOGY and temporally and thus insectivorous birds and bats potentially reduce exploitative competition by foraging at different times (Speakman et al. 2000). A 3rd way in which temporal partitioning could reduce exploitive competition, and thus promote coexistence, would be if resource patches are renewed once a day, and each species has a resource density at which it forages more efficiently than its competitor/s (Brown 1989). For example, in the Negev Desert, Israel, 2 gerbil species coexist due, in part, to differences in their feeding efficiencies. Specifically, the gerbil (Gerbillus pyramidum) feeds more efficiently when food availability is high, in contrast to the smaller G. allenbyi that feeds more efficiently when food availability is low (Kotler et al. 1993; Ziv et al. 1993). Temporal separation as a mechanism leading to coexistence (Schoener 1974) has been found in a wide range of animal species (e.g., Gutman and Dayan 2005; Leisnham et al. 2014; and examples in Kronfeld-Schor and Dayan 2003). However, 1 thing that has not been extensively considered is how predation risk affects microhabitat use and feeding effort of temporally separated species and thus ultimately their coexistence. Jones et al. (2001) studied 2 temporally separated spiny mice, with Acomys cahirinus being nocturnal and A. russatus diurnal. In winter, the 2 species easily coexisted, as they did not compete for food. This was because their use of the different microhabitats (e.g., under and away from boulders) varied as did their feeding effort within the microhabitats. However, in summer, when snakes were active, both species of spiny mice fed more in open areas away from boulders to avoid snakes. As a result, this spatial shift in landscape use increased the overlap in the use of more open microhabitats by the 2 spiny mice species. However, as the feeding effort of the 2 species differed within the habitats and they continued to use the available microhabitats to different degrees, the spiny mice were able to coexist (Jones et al. 2001). Nevertheless, microhabitat selection may not promote coexistence if the food resource (i.e., prey) moves between the microhabitats, even if the competitors do not. As different predators are active at different times, safe areas across the landscape may vary temporally (Jacob and Brown 2000; Jones et al. 2001). For example, aerial predators (e.g., falcons and hawks) tend to be more active during the day, while terrestrial predators (e.g., cats) tend to hunt more at night. In response, prey species may consider areas under cover to be safer during the day, as it can reduce detection by predators, compared to at night when terrestrial ambush predators may use cover to hide in (Mohr et al. 2003; Kotler et al. 2004). As spatial differences in perceived predation risk can influence both largeand small-scale space use (i.e., a landscape of fear—Shrader et al. 2008; Van Der Merwe and Brown 2008; Laundré et al. 2013), then under these circumstances temporally separated prey species would likely forage in different areas. This spatial separation would reduce exploitative competition (Morris 1996), while temporal partitioning would prevent interference competition (Kronfeld-Schor and Dayan 2003). This remains true even if temporal partitioning is the result of previous interference between the prey species. As a result, under these circumstances, there are 2 mechanisms (i.e., spatial and temporal separation) reducing competition and thus facilitating the coexistence of these species (Kotler and Brown 1988). However, if nocturnal and diurnal species perceive landscape features similarly with regards to predation risk (e.g., safety under cover for rodents), then this perception could increase exploitative competition as both species would restrict their foraging to the same habitats and/or patches, albeit at different times of the day. If this was the case, it may be possible that the benefits gained from reducing interference competition through temporal partitioning would be counteracted by increased exploitive competition generated by similarities in perceived predation risk. To explore these alternative possibilities, we manipulated grass height to alter perceived predation risk in an African grassland utilized by both nocturnal and diurnal rodents. Different predators were active at night compared to during the day at our study area. Specifically, the majority of raptors (e.g., long-crested eagles, Lophaetus occipitalis; jackal buzzards, Buteo rufofuscus; and black-shouldered kites, Elanus caeruleus) were active during the day, compared to only 2 species of owls (barn owl, Tyto capensis; and spotted eagle owl, Bubo africanus) that were active at night. In contrast, threats from terrestrial predators were likely greater at night compared to day, as many African mammalian predators of rodents are nocturnal (e.g., white-tailed mongoose, Ichneumia albicauda; genets, Genetta spp.—Skinner and Chimimba 2005). If nocturnal and diurnal rodents used the landscape differently with regards to perceived predation risk, then we would expect that they would feed in different microhabitats (e.g., open versus closed cover) or their feeding effort would differ within the same microhabitats, which would reduce exploitative competition. Alternatively, if perceived predation risk resulted in these 2 sets of rodents using microhabitats similarly, then, despite temporal separation preventing interference competition, there would be an increase in interspecific exploitative competition. Materials and Methods Study site.—We conducted our study from 11 April to 21 June 2012 (autumn into winter) in an 8 ha natural grassland located at the University of KwaZulu-Natal, Ukulinga Research Farm in Pietermaritzburg, KwaZulu-Natal, South Africa (30°24′S, 29°24′E). Grasslands within Africa provide a good place to test the effects of temporal separation on coexistence. This is because they are inhabited by both nocturnal and diurnal omnivorous rodent species (Taylor 1998; Skinner and Chimimba 2005), food in the form of seed rain from grasses is not pulsed and thus seeds fall throughout the 24 h cycle over a number of months (Lyaruu 1999), and a range of nocturnal and diurnal invertebrates are present that are also eaten by these rodents (Samways et al. 1996). Both aerial and terrestrial predators of rodents were present at the study site. Daytime aerial predators observed included long-crested eagles, jackal buzzards, and black-shouldered kites, while nocturnal aerial predators comprised both barn and spotted eagle owls. Terrestrial predators active during the day BANASIAK AND SHRADER—PREDATION AND COEXISTENCE485 included slender mongoose (Galerella sanguinea) and secretary birds (Sagittarius serpentarius), while nocturnal terrestrial predators included white-tailed mongoose, genets, feral cats (Felis catus), serval (F. serval), and caracal (F. caracal). We did not observe snakes (e.g., puff adders, Bitis arietans; brown house snake, Lamprophis capensis; and rinkhals, Hemachatus haemachatus) in the study area, but they may have been present. However, since we conducted our study during autumn and winter, snakes were unlikely to have been very active. Experimental design.—To determine the nocturnal and diurnal rodents’ use and feeding effort within the different grass height microhabitats, we established four 3 × 3 m plots in 3 separate sites (N = 12 plots) in a grassland around a 20 × 10 m woodland patch (Fig. 1). We selected the location of the sites so that the ground at each site had similar slopes. Moreover, we set up these sites so that all of the plots were 5 m from the woodland patch in a straight line, with the sites separated from each other by a minimum of 10 m to reduce the probability of the same rodents utilizing more than 1 site. However, the use of more than 1 site by some of the same individuals would likely not alter the results of the study, as the habitat and foraging decisions of these few individuals would unlikely skew the overall microhabitat use patterns of the whole rodent community. Within a site, individual 3 × 3 m plots were separated by 1 m of uncut grass (> 150 cm high). For each 3 × 3 m plot, we manipulated grass height by cutting the grass to either 5 cm, 20 cm, 40 cm, or leaving the grass uncut to act as a control (> 150 cm). The order of grass height plots was randomly assigned at each site. We chose these heights as 5 cm provided no cover for the rodents, 20 cm provided patchy cover as it included only the fibrous parts of the grass tufts, at 40 cm the grass became leafier providing cover, while the uncut (> 150 cm) grass offered maximum cover at the study site. To generate an index of available cover for rodents in the different grass height treatments, we measured photosynthetically active radiation in µmol m−2 sec−1 using an AccuPAR Fig. 1.—Layout of the experimental design indicating the location of the 3 sites around the 20 × 10 m woodland patch (indicated by shaded trees). Each site comprised four 3 × 3 m plots (indicated by open squares), with each plot containing a randomly selected different grass height treatment (5 cm, 20 cm, 40 cm, and >150 cm). model LP-80 ceptometer (Decagon Devices, Inc., Pullman, Washington). We took 4 light measurements from each 3 × 3 m microhabitat plot by pushing the tip of the meter under the grass along the ground from each corner toward the opposite corner. We then used the mean of these photosynthetically active radiation measurements as the index of cover for the plot. We repeated this for each of the plots in 2 sites. We omitted the 3rd site, as shade from the trees partially covered some of the treatments. To ensure consistency, we took all the measurements on a single cloudless day between 1100 h and 1300 h (i.e., solar noon). Using the mean index values from all the plots, we were able to estimate differences in cover between the grass height treatments (i.e., microhabitats). Cover did not differ between the 40 and > 150 cm treatments (Fig. 2). We quantified utilization and feeding effort within the different grass height microhabitats by measuring giving-up densities (GUDs; i.e., the amount of food left in a patch once a forager has finished eating—Brown 1988) in artificial patches. Foraging in a patch is a trade-off between costs and benefits. As a result, an animal should forage in a patch until its quitting harvest rate (H) in the patch equals the sum of the metabolic (C), predation risk (P), and missed opportunity costs (MOC) of foraging in that patch (H = C + P + MOC—Brown 1988). As harvest rate is dependent on the density of food within a patch, GUDs can act as an index of quitting harvest (Schmidt et al. 1998). Hence, animals show greater preference for a patch through greater feeding effort, resulting in lower GUDs (Brown 1988). We provided the rodents with food in artificial resource patches comprised plastic circular trays (20 cm diameter × 2 cm deep). To create diminishing returns to harvest rate and thus making these artificial patches similar to natural food patches (Kotler and Brown 1990), we filled the trays with a mixture of 80 g of whole sunflower seeds and approximately 700 g of filtered sand. By filling the artificial patches with sand, the rodents would need to dig in the sand to obtain seeds in a similar manner to how they would forage for natural seeds (Kotler et al. 1994). In each grass height microhabitat (i.e., plot), we set up a 3 × 3 grid of these artificial resource patches where each artificial patch was separated by 1 m. To maximize the perceived predation risk within the microhabitats, we set up the grid in the Fig. 2.—Photosynthetically active radiation (PAR; µmol m−2 sec−1) as an index of the amount of cover provided by the different grass height microhabitats. Error bars represent 95% confidence intervals. 486 JOURNAL OF MAMMALOGY middle of each 3 × 3 m plot. This resulted in the outer artificial resource patches of the grid (N = 8 patches) being 50 cm from the uncut grass at the edges of the plot. Prior to data collection, we habituated the rodents to the artificial patches by initially filling the trays with only sunflower seeds. We left these artificial resource patches out in the microhabitats over the 24 h cycle for 2 weeks. Each day, we checked these artificial patches in the morning and late afternoon to determine patch use and refilled any seeds that the rodents had removed. After 2 weeks, we filled each artificial patch with the sunflower seed and sand mixture. We then let the rodents forage in these artificial resource patches for an additional week before we started collecting data. To quantify differences in the nocturnal and diurnal rodents’ use of and feeding effort in the different microhabitats, we collected GUDs twice a day, at 0700 h and 1630 h, respectively. We estimated GUDs by sifting and weighing the seeds left in the artificial patches. We then reset these artificial resource patches by mixing 80 g of seed into the 700 g of sifted sand. As all the trays were set up in the same manner, we were able to compare the GUDs from the different microhabitats (Kotler et al. 1994). Once we had completed the feeding experiment, we used Sherman livetraps to determine which rodent species fed from the artificial resource patches. To do this, we placed a 2 × 2 grid of Sherman livetraps (each trap separated by 1 m) into the middle of the grid of artificial patches in the different microhabitats (N = 48 traps). We baited the traps with sunflower seeds mixed in peanut butter. In addition, we added a piece of cotton wool into the traps for the rodents to use as bedding material to help protect them against the cold. We trapped over 7 days and nights (i.e., 336 trap days and nights), checking the traps twice a day, at 0700 h and 1630 h, to determine the nocturnal and diurnal rodent species, respectively. Rodents were identified to species level using Taylor (1998) and then released where they were caught. We did not mark the rodents, as we were only interested in species’ presence and not abundance. The research on the live animals followed guidelines of the American Society of Mammalogists (Sikes et al. 2011) and the University of KwaZulu-Natal animal ethic protocols. The University of KwaZulu-Natal animal ethics committee approved all experimental procedures under reference number 012/12/Animal. No animals were harmed during the study. Data analysis.—As the data were not normally distributed, we used a Friedman Test, which is the non-parametric alternative to a repeated measures analysis of variance, to compare the feeding effort (i.e., GUDs) within the different grass height microhabitats (5 cm, 20 cm, 40 cm, >150 cm) and time of day (day, night). We used the Friedman Test, as the GUDs recorded in the each of the different sites were likely from the same individuals over the 2 weeks. We then used Wilcoxon Signed Ranks tests with a Bonferroni correction for the a posteriori analyses. This resulted in the significance level being adjusted to P = 0.0017 to account for all combinations required. All analyses were conducted in SPSS 19.0. Results Rodent species livetrapped within our study site included the nocturnal Natal multimammate mouse (Mastomys natalensis; N = 5 individuals), African pygmy mouse (Mus minutoides; N = 1 individual), and the diurnal four-striped grass mouse (Rhabdomys pumilio; N = 15 individuals—Taylor 1998). Despite being classified as diurnal, the four-striped grass mouse sometimes also forages at night (Perrin and Kotler 2005; Abu Baker and Brown 2010). However, we did not capture fourstriped mice during the night. Moreover, nocturnal foraging of this mouse is limited to times when night time temperatures are above 5°C (Perrin and Kotler 2005). As temperatures during our study were below 5°C each night, it is unlikely that the four-striped grass mice foraged nocturnally. Rodent feeding activity differed significantly between the grass height microhabitats (Friedman test: χ2 = 713.09, P < 0.0005; Fig. 3). In contrast to expectations, we found that the nocturnal and diurnal rodent communities had the same microhabitat utilization pattern and achieved similar GUDs (i.e., feeding effort) in the different microhabitats (all P > 0.0017; Fig. 3). Specifically, both sets of rodents achieved greater feeding effort (i.e., achieved lower GUDs) when grass height was ≥ 40 cm compared to when it was ≤ 20 cm (all P < 0.0005). In addition, feeding effort (i.e., GUDs) did not differ significantly between the 40 and > 150 cm microhabitats (day: P = 0.623, night: P = 0.173) nor between the 5 and 20 cm microhabitats (day: P = 0.487, night: P = 0.777; Fig. 2). Finally, in the more open microhabitats where grass height was < 20 cm, the rodents only fed in the artificial patches around the edges of the grids (i.e., those closest to the > 150 cm high uncut grass) and never in the central patch. Discussion Temporal partitioning of feeding times is one way in which animals can reduce interference competition and thus coexist (Kronfeld-Schor and Dayan 2003). However, if these temporally separated species utilize microhabitats to the same extent, they could still experience exploitative competition through the reduction of food availability. Despite differences in cover and Fig. 3.—Mean giving-up densities (GUDs) of the rodent community at 4 different grass heights for both day (□) and night (▲) foraging. Error bars represent 95% confidence intervals. BANASIAK AND SHRADER—PREDATION AND COEXISTENCE487 the predators that were active during night and day, we found that both nocturnal and diurnal rodents had the same pattern of microhabitat utilization. Moreover, both sets of rodents showed the same feeding effort in each of the different microhabitats, and achieved greater feeding effort (i.e., lower GUDs) in patches where the grass height was ≥ 40 cm. This suggests that, despite the prevention of interference competition through temporal partitioning, the spatial similarities in perceived predation risk in relation to cover likely increase exploitive competition between these rodents. As a result, temporal partitioning of feeding activity alone is not a sufficient mechanism to allow for coexistence between these rodent species. Vegetation structure that creates cover is a key component that reduces perceived predation risk of rodents (Hughes and Ward 1993; Kotler and Blaustein 1995; Mohr et al. 2003; Abu Baker and Brown 2010). This is due to a lower probability of detection and capture by both aerial and ambush predators. In contrast, open areas provide opportunities to spot potential predators at greater distances but do not provide concealment from predators. Species such as fox squirrels (Sciurus niger), thirteen-lined ground squirrels (Ictidomys tridecemlineatus), degus (Octodon degus), southern African ground squirrels (Xerus inauris), and the nocturnal Allenby’s gerbil (Gerbillus andersoni allenbyi) rely heavily on vision as a means to detect predators (Thorson et al. 1998; Ebensperger and Hurtado 2005; Edwards and Waterman 2011; Embar et al. 2011). However, the lower feeding effort showed by gerbils in open habitats suggests that open sightlines aid predators more to locate prey than it helps prey to detect approaching predators (Embar et al. 2011). The fact that the rodents in our study preferred to feed in microhabitats with high cover suggests that they do not rely on long-range visual detection of approaching predators to reduce predation risk and their preferred antipredator strategy is concealment. The preference for microhabitats with greater cover suggests that both the nocturnal and diurnal rodents in our study perceived the open microhabitats to be more dangerous. This could be an attempt to reduce predation risk from pursuing predators (e.g., birds of prey, mongoose), as grass height can influence predator hunting success (Janes 1985; Kotler et al. 2004; Embar et al. 2011). This may also explain why when both sets of rodents fed in the open microhabitats (i.e., 5 and 20 cm), they restricted their feeding to the artificial patches on the periphery of the 3 × 3 grid. By feeding in these patches, they were closer to the uncut >150 cm tall grass surrounding the plot compared to the resource patch in the middle of the grid. Thus, if chased they would be able to get within the sounding tall grass quicker, thus reducing predation risk, than if they were feeding in the middle of the grid. For example, Simmons (2000) found that the success of African marsh harrier (Circus ranivorus) hunting on rodents was lowest in tall vegetation. In addition, owls tend to be more cautious and less maneuverable when hunting in dense vegetation (Longland and Price 1991; Rohner and Krebs 1996). However, antipredator behaviors may be altered or combined in multipredator systems, such that by sticking to cover rodents also escape detection from both aerial and terrestrial predators (Lima 1998; Stankowich and Blumstein 2005). In contrast to Jones et al. (2001), both the nocturnal and diurnal rodents in our study used the same microhabitats. As GUDs provide an index into the feeding effort within the different microhabitats (Jones et al. 2001), our results suggest that, despite a reduction in interference competition, there is potential for a high degree of exploitive competition between these nocturnal and diurnal rodents. However, the presence of both sets of species indicates that, even if there was extensive competition between the species, there must be some other mechanism that allows them to coexist (Kotler and Brown 1988). One way in which these species could coexist is through the partitioning of their diets (Kotler and Brown 1988; Jones et al. 2001). All three of the species in our study were omnivorous, dividing their diets between seeds, vegetation, and insects (Skinner and Chimimba 2005). As we only provided seeds as a food source in our artificial patches, it is possible that under unmanipulated conditions these rodents could reduce exploitive competition within the microhabitats by feeding on different food types. However, the similar feeding effort on the seeds within the different microhabitats suggests that, despite the potential to reduce exploitive competition, these species still compete to some degree. For example, Kronfeld-Schor and Dayan (1999) found that 2 species of spiny mice reduced interference competition as they were temporally separated. Nevertheless, an extensive overlap in food preferences by the 2 species suggested that if food were limiting, then the temporal separation would not reduce exploitive competition. However, potential differences in arthropods ingested by the 2 spiny mice, either through different arthropods being active at different times (i.e., food availability) or different preferences for specific arthropods by the 2 species (i.e., resource partitioning), may have reduced exploitive competition thus enabling their coexistence (Kronfeld-Schor and Dayan 1999). A potential concern that could be directed toward our study is that the limited number of species that we recorded in the grassland may not be a true reflection of the rodent community. However, as the grassland was far from water and human dwellings, the number of rodent species that could potentially live within this grassland is limited (Taylor 1998). In fact, the species that we caught make up the 3 most common rodent species living in the grasslands of KwaZulu-Natal, South Africa (Taylor 1998). The only other rodent species that may have been present is the Angoni vlei rat (Otomys angoniensis). However, as we did not come across any of its distinctive runways through or around our plots, it is unlikely that these solitary, purely herbivorous rodents (Bronner and Meester 1988) were in the section of the grassland where we conducted the study. However, if they were present, the fact that they have a completely different diet means that they would not directly compete with the other species for food and thus would be able to coexist. As a result, our results reflect the general rodent community within KwaZulu-Natal grasslands and thus likely provide insight into the coexistence of these rodents across the province. Up to this point, we have focused on competition as a way to discuss our observations. However, it is possible that food was not limiting in the grassland and hence the rodents were not competing for resources. If this were the case, then it is 488 JOURNAL OF MAMMALOGY not surprising that both the nocturnal and diurnal rodents displayed similar feeding efforts within the same microhabitats. Therefore, our results would simply reflect the rodents’ microhabitat use in relation to perceived predation risk. However, density dependence generally governs rodent population size (e.g., Saitoh et al. 2008; Previtali et al. 2009; Letnic et al. 2011). Thus, if food availability were high, we would expect that rodent populations would increase rapidly, due to their high reproductive outputs, to a point where they were at equilibrium with available resources. As a result, it would be unlikely that these larger rodent populations would have access to excess food. Additionally, rainfall on the farm in 2011 was below average (708 mm) compared to a 32-year mean of 790 mm (Kirkman et al. 2014). As grass growth is determined by rainfall, it is unlikely that the below-average rainfall would have resulted in an excessive seed set. Thus, exploitive competition for food between the rodent species seems the most likely outcome. Competition, however, may not be limited to differences between the rodent species. It is also possible that other seedeating taxa such as birds and insects may compete for food within the grassland. A number of seed-eating birds have been observed on the farm including bronze mannikin (Spermestes cucullatus), red-collared widow (Euplectes ardens), and longtailed widow (E. progne; A.M. Shrader, University of KwaZuluNatal, pers. comm.). However, as these species move over large areas (i.e., locally migratory in winter—Hockey et al. 2005) and are thus not restricted to the grassland where we conducted our study, the total impact they could have on food availability is unlikely to be great. In contrast, harvester ants (Messor capensis), which feed extensively on seeds, could reduce food availability for other species in areas surrounding their colonies. However, Milton and Dean (1993) found that harvester ants only removed between 10% and 18% of the annual seed biomass produced by plants. As a result, these ants may reduce the availability of seeds, but the extent is unlikely to result in heavy exploitive competition with rodents. Ultimately, our results highlight the potential role that predation risk can have on competition between species and thus their coexistence. Similarities in perceived predation risk can result in temporally separated species utilizing microhabitats to the same extent, thus decreasing the effectiveness of temporal partitioning in reducing exploitive competition. Moreover, as predators can remain or move in and out of areas, changes in predation risk can vary over temporal scales. As a result, through influences on the competitive interactions between species, predation risk could potentially alter community composition and dynamics. Acknowledgments Natalia Banasiak thanks the University of KwaZulu-Natal G. Langmuir bursary for financial support. AMS thanks the National Research Foundation (NRF) for financial support. B. Kotler, J. Wilson, C. Pavey, J. Merritt, and N. Hagenah provided valuable comments on the manuscript. Literature Cited Abramsky, Z., M. L. Rosenzweig, B. Pinshow, J. S. Brown, B. P. Kotler, and W. A. Mitchell. 1990. Habitat selection: an experimental field test with two gerbil species. Ecology 71:2358–2369. Abu Baker, M. A., and J. S. Brown. 2010. Islands of fear: effects of wooded patches on habitat suitability of the striped mouse in a South African grassland. Functional Ecology 24:1313–1322. Bramley, G. N. 2014. Habitat use by kiore (Rattus exulans) and Norway rats (R. norvegicus) on Kapiti Island, New Zealand. New Zealand Journal of Ecology 38:64–75. Bronner, G. N., and J. A. J. Meester. 1988. Otomys angoniensis. Mammalian Species 306:1–6. Brown, J. S. 1988. Patch use as an indicator of habitat preference, predation risk, and competition. Behavioural Ecology and Sociobiology 22:37–47. Brown, J. S. 1989. Coexistence on a seasonal resource. The American Naturalist 133:168–182. Case, T. J., and M. E. Gilpin. 1974. Interference competition and niche theory. Proceedings of the National Academy of Sciences 71:3073–3077. Charnov, E. L., H. O. Gordon, and K. Hyatt. 1976. Ecological implications of resource depression. The American Naturalist 110:247–259. Ebensperger, L. A., and M. J. Hurtado. 2005. On the relationship between herbaceous cover and vigilance activity of degus (Octodon degus). Ethology 111:593–608. Edwards, S., and J. M. Waterman. 2011. Vigilance and grouping in the southern African ground squirrel (Xerus inauris). African Journal of Ecology 49:286–291. Embar, K., B. P. Kotler, and S. Mukherjee. 2011. Risk management in optimal foragers: the effect of sightlines and predator type on patch use, time allocation, and vigilance in gerbils. Oikos 120:1657–1666. Gutman, R., and T. Dayan. 2005. Temporal partitioning: an experiment with two species of spiny mice. Ecology 86:164–173. Hockey, P. A. R., W. R. S. Dean, and P. G. Ryan (eds.). 2005. Roberts birds of southern Africa. 7th edition. The Trustees of the John Voelcker Bird Book Fund, Cape Town, South Africa. Hughes, J. J., and D. Ward. 1993. Predation risk and distance to cover affect foraging behaviour in Namib Desert gerbils. Animal Behaviour 46:1243–1245. Jacob, J., and J. S. Brown. 2000. Microhabitat use, giving-up densities and temporal activity as short- and long-term anti-predator behaviors in common voles. Oikos 91:131–138. Janes, S. W. 1985. Habitat selection in raptorial birds. Pp. 159–188 in Habitat selection in birds (M. L. Cody, ed.). Academic Press Ltd., London, United Kingdom. Jones, M., Y. Mandelik, and T. Dayan. 2001. Coexistence of temporally partitioned spiny mice: roles of habitat structure and foraging behavior. Ecology 82:2164–2176. Kinahan, A. A., and N. Pillay. 2008. Does differential exploitation of folivory promote coexistence in an African savanna granivorous rodent community? Journal of Mammalogy 89:132–137. Kirkman, K. P., et al. 2014. Responses to fire differ between South African and North American grassland communities. Journal of Vegetation Science 25:793–804. Kotler, B. P., Y. Ayal, and A. Subach. 1994. Effects of predatory risk and resource renewel on the timing of foraging activity in a gerbil community. Oecologia 100:391–396. BANASIAK AND SHRADER—PREDATION AND COEXISTENCE489 Kotler, B. P., and L. Blaustein. 1995. Titrating food and safety in a heterogeneous environment: when are the risky and safe patches of equal value? Oikos 74:251–258. Kotler, B. P., and J. S. Brown. 1988. Environmental heterogeneity and the coexistence of desert rodents. Annual Review of Ecology and Systematics 19:281–307. Kotler, B. P., and J. S. Brown. 1990. Rates of seed harvest by two species of Gerbiline rodents. Journal of Mammalogy 71:591–596. Kotler, B. P., J. S. Brown, A. Bouskilla, S. Mukherjee, and T. Goldberg. 2004. Foraging games between gerbils and their predators: seasonal changes in schedules of activity and apprehension. Israel Journal of Zoology 50:255–271. Kotler, B. P., J. S. Brown, and A. Subach. 1993. Mechanisms of species coexistence of optimal foragers: temporal partitioning by two species of sand dune gerbils. Oikos 67:548–556. Kronfeld-Schor, N., and T. Dayan. 1999. The dietary basis for temporal partitioning: food habits of coexisting Acomys species. Oecologia 121:123–128. Kronfeld-Schor, N., and T. Dayan. 2003. Partitioning time as an ecological resource. Annual Review of Ecology, Evolution, and Systematics 34:153–181. Laundré, J. W., et al. 2013. The landscape of fear: the missing link to understand top-down and bottom-up controls of prey abundance? Ecology 95:1141–1152. Leisnham, P. T., S. L. La Deau, and S. A. Juliano. 2014. Spatial and temporal habitat segregation of mosquitoes in urban Florida. PLoS One 9:e91655. Letnic, M., et al. 2011. Resource pulses, switching trophic control, and the dynamics of small mammal assemblages in arid Australia. Journal of Mammalogy 92:1210–1222. Lima, S. L. 1998. Nonlethal effects in the ecology of predator-prey interactions. BioScience 48:25–34. Longland, W. S., and M. V. Price 1991. Direct observations of owls and Heteromyid rodents: can predation risk explain microhabitat use? Ecology 72:2261–2273. Lyaruu, H. V. M. 1999. Seed rain and its role in the recolonization of degraded hill slopes in semi-arid central Tanzania. African Journal of Ecology 37:137–148. Milton, S. J., and W. R. J. Dean. 1993. Selection of seeds by harvester ants (Messor capensis) in relation to condition of arid rangeland. Journal of Arid Environments 24:63–74. Mohr, K., S. Vibe-Petersen, L. Lau Jeppesen, M. Bildsøe, and H. Leirs. 2003. Foraging of multimammate mice, Mastomys natalensis, under different predation pressure: cover, patchdependent decisions and density-dependent GUDs. Oikos 100:459–468. Morris, D. W. 1996. Coexistence of specialist and generalist rodents via habitat selection. Ecology 77:2352–2364. Nicholson, A. J. 1954. An outline of the dynamics of animal populations. Australian Journal of Zoology 2:9–65. Parker, G. A. 2000. Scramble in behaviour and ecology. Philosophical Transactions of the Royal Society of London, B. Biological Sciences 355:1637–1645. Perrin, M. R., and B. P. Kotler. 2005. A test of five mechanisms of species coexistence between rodents in a southern African savanna. African Zoology 40:55–61. Previtali, M. A., M. Lima, P. L. Meserve, D. A. Kelt, and J. R. Gutiérrez. 2009. Population dynamics of two sympatric rodents in a variable environment: rainfall, resource availability, and predation. Ecology 90:1996–2006. Richards, S. A. 2002. Temporal partitioning and aggression among foragers: modeling the effects of stochasticity and individual state. Behavioral Ecology 13:427–438. Rohner, C., and C. J. Krebs. 1996. Owl predation on snowshoe hares: consequences of antipredator behaviour. Oecologia 108:303–310. Saitoh, T., et al. 2008. Effects of acorn abundance on density dependence in a Japanese wood mouse (Apodemus speciosus) population. Population Ecology 50:159–167. Samways, M. J., P. M. Caldwell, and R. Osborn. 1996. Groundliving invertebrate assemblages in native, planted and invasive vegetation in South Africa. Agriculture, Ecosystems and Environment 59:19–32. Schmidt, K. A., J. S. Brown, and R. A. Morgan. 1998. Plant defenses as complementary resources: a test with squirrels. Oikos 81:130–142. Schoener, T. W. 1974. Resource partitioning in ecological communities. Science 185:27–39. Shrader, A. M., J. S. Brown, G. I. H. Kerley, and B. P. Kotler. 2008. Do free-ranging domestic goats show `landscapes of fear’? Patch use in response to habitat features and predator cues. Journal of Arid Environments 72:1811–1819. Sikes, R. S., W. L. Gannon, and the Animal Care and Use Committee of the American Society of Mammalogists. 2011. Guidelines of the American Society of Mammalogists for the use of wild mammals in research. Journal of Mammalogy 92:235–253. Simmons, R. 2000. Harriers of the world: their behaviour and ecology. Oxford University Press, New York. Skinner, J. D., and C. T. Chimimba. 2005. The mammals of the southern African sub-region. Cambridge University Press, Cambridge, United Kingdom. Speakman, J. R., et al. 2000. Activity patterns of insectivorous bats and birds in northern Scandinavia (69° N), during continuous midsummer daylight. Oikos 88:75–86. Stankowich, T., and D. T. Blumstein. 2005. Fear in animals: a metaanalysis and review of risk assessment. Proceedings of the Royal Society of London, B. Biological Sciences 272:2627–2634. Symes, C. T., J. W. Wilson, S. M. Woodborne, Z. S. Shaikh, and M. Scantlebury. 2013. Resource partitioning of sympatric small mammals in an African forest-grassland vegetation mosaic. Austral Ecology 38:721–729. Taylor, P. 1998. The smaller mammals of KwaZulu-Natal. University of Natal Press, Pietermaritzburg, South Africa. Thorson, J. M., R. A. Morgan, J. S. Brown, and J. E. Norman. 1998. Direct and indirect cues of predatory risk and patch use by fox squirrels and thirteen-lined ground squirrels. Behavioral Ecology 9:151–157. Van Der Merwe, M., and J. S. Brown. 2008. Mapping the landscape of fear of the Cape ground squirrel (Xerus inauris). Journal of Mammalogy 89:1162–1169. Ziv, Y. Z., A. Abramsky, B. P. Kotler, and A. Subach. 1993. Interference competition and temporal partitioning in two gerbil species. Oikos 66:237–246. Submitted 23 April 2015. Accepted 20 November 2015. Associate Editor was Chris R. Pavey.