DO MEGAHERBIVORES AND SHRUB ENCHROACHMENT CHANGE SMALL MAMMAL BEHAVIOR AND COMMUNITY STRUCTURE?

Anne A. Loggins

University of Florida

22 April 2016 2 BACKGROUND

Grasslands and savannas cover 20-30% of the planet’s land surface, directly supporting 1/5 of human populations (Parr et al. 2014), yet these areas are under increasing pressure from human impacts. Increased , intensive agriculture, altered fire regimes, and resource extraction all threaten unique biodiversity found in grasslands and savannas (Shorrocks 2007, Anderson et al. 2012, Parr et al. 2014). Savannas are distinctive grasslands containing woody and grass components that fluctuate in dominance depending on precipitation and disturbance levels (Sankaran et al. 2005, Shorrocks 2007). Changes in the frequency and intensities of disturbances are causing many savannas across the globe to shift towards homogenous extremes of grassy or woody species (Cremene et al. 2005, Eldridge et al. 2011, Anderson et al. 2012, Parr et al. 2014). Without regular disturbance, savannas may experience shrub encroachment, where woody species increase at the expense of grasses. Increasing shrub encroachment in savannas can reduce plant and animal diversity (Eldridge et al. 2011). A scarcity of woody species in savannas creates another extreme of vegetation, and open grasslands caused by intense disturbance may similarly decrease biodiversity (Cumming et al. 1997, Sankaran et al. 2008, Pringle et al. 2010, Treydte et al. 2009, Asner and Levick 2012). Species richness is often highest in areas where woody species and grasses co-occur than in dense shrub cover alone (Blaum et al. 2007, Blaum et al. 2009, Ratajczak et al. 2012, Sirami and Monadjem 2012, Abu Baker and Brown 2014). Shifts towards extremes of shrub-encroachment or open landscapes are occurring in savannas across the globe, from northern Australia to Africa to the North American plains (Eldridge et al. 2011, Anderson et al. 2012, Ratajczak et al. 2012, Parr et al. 2014). The lack of heterogeneity in these homogeneous vegetation extremes may threaten savanna ecosystem functioning (Tilman et al. 2014).

Protected areas aim to preserve the biodiversity; however, focusing on certain functional groups, especially large animals, may not necessarily benefit other important species (Rodrigues et al. 2004, Gardner et al. 2007). Some protected areas in African savannas have been so effective in preserving megafauna that they now contain dense elephant populations (van Aarde and Jackson 2007, Pienaar et al. 2012). Paradoxically, by maintaining these megaherbivores at high densities protected areas are potentially negatively affecting biodiversity by creating open savanna habitats (Hiscocks 1999, Langvelde et al. 2003, van Aarde et al. 2006, Asner and Levick 2012). Elephants reduce woody cover by reshaping vegetation and damaging trees, a “Keystone Structure” in savannas (Tews et al. 2004) providing vertical and ground level cover, food resources, nesting sites, and nutrient cycling in an otherwise homogeneous system (Belsky 1994, Treydte et al. 2009). By reducing the distributions of trees and shrubs in the landscape, elephants may cause trophic cascades that impact critical components of the savanna structure (Hiscocks 1999, Sankaran 2008, Asner and Levick 2012).

One potential cascading effect of high elephant densities is the decline of small mammal diversity in some protected areas (Caro 2001). Conversely, the absence of large increases shrub encroachment, which could also cause small mammal diversity declines (Blaum et al. 2007, Abu Baker and Brown 2014). Small mammals are critical in the savanna system as seed predators, ecosystem engineers, prey species, and promoters of nutrient cycling (Avenant 2011). They can change the composition of grassland habitats by reducing plant biomass (Brown and Heske 1990, Miller 1994, Manson et al. 2001). Closely linked with certain habitats, small mammal species shift across the shrub cover gradient in response to the opposing vegetation extremes (Fig. 1), thus altering their ecological roles (Keesing 2000). As savanna functionality potentially depends on small mammal diversity (Avenant and Cavellini 2007), it is critical to understand how changes in savanna structure are causing shifts in small mammal communities in order to improve the management of protected areas to better preserve grassland biodiversity.

Vegetation Structure 3 The homogeneity of vegetation in open savannas could explain the lower diversity of small mammal species (Fig. 2). Savanna landscapes with a diversity of woody and grassy species could support more species while vegetation dominated by either functional group may limit species richness (Tews et al. 2004, Treydte et al. 2009). However, we still struggle to understand how these vegetation types shape activity patterns and habitat use across multiple species. Within a given savanna ecosystem, species may respond differently to environments with differing proportions of trees, shrubs, and grass. While some small mammal species may decline in very open environments lacking shrubs, others may disappear from landscapes under high shrub encroachment, as high shrub density may reduce resource availability for more grass-dependent species (Blaum et al. 2007, Abu Baker and Brown 2014). Measurements of habitat heterogeneity should be defined at the appropriate spatial scale for small mammals and on a species-specific basis (Cramer and Willig 2002). Species-level responses may lead to community-level shifts, as some species may respond quickly to lower thresholds of shrub cover or tree scarcity, while others may tolerate higher levels of change in either direction (Long et al. 2012).

Landscapes of Fear In order to understand why vegetation structure affects small mammals and leads to larger-scale community shifts, it is critical to examine small mammal behavioral decisions in different environments (Cramer and Willig 2002). In open, grass-dominated systems the fear of may be the mechanism determining small mammal community composition (Fig. 2). Predator intimidation can affect prey behavior and reduce prey populations (Schmitz et al. 1997), with non-lethal effects accounting for an estimated 50-85% of the total predator effect (Preisser et al. 2005). Predators influence prey habitat use, foraging, reproduction, and movement (Brown et al. 1999, Laundre et al. 2010). As a consequence of the Marginal Value Theorem, optimal foragers remain in a resource patch until the nutritional benefits of feeding no longer outweigh the possible risks of predation (Charnov 1976, Brown 1988, Brown et al. 1999). Small mammals primarily forage where they feel safest from predators (Stephens et al. 2007). In grassland ecosystems safety for small mammals is often correlated to the availability of shrub cover, leaf debris, or burrowing soil, depending on the species (Kotler et al. 1991, Longland et al. 1991, Mohr et al. 2003, Long et al. 2012). For some small mammals the height of the grass can change the safety of a habitat, with short or mowed grass perceived as riskier than tall grass (Jacob et al. 2000, Banasiak and Shrader 2015). Even when predator abundances are similar across habitats, the mammals’ perceived predation risk will be higher in open areas due to evolved avoidance responses (Preisser et al. 2005). These behavioral responses may limit use in areas lacking cover, shifting small mammals towards microhabitats offering better protection. Effects of small-scale habitat selection may extend into community-level shifts, whereby certain small mammal species avoid entire landscapes due to their lack of shrub or grass cover.

PROJECT OBJECTIVES

My overall goal was to understand how vegetation structure shapes small mammal communities in southern African savannas and then test a mechanism explaining how small-scale habitat use may lead to shifts in community composition. My first objective was to understand the relationships between savanna vegetation and small mammal species, functional groups, and communities across the shrub cover gradient. I predicted that the presence or absence of grass, vegetative cover, and the combination of these factors will influence small mammal community turnover, as individual species may respond to grass, trees, and shrubs in different ways. Responses would likely vary by functional groups, as a species’ diet may influence whether grass or vegetative cover become limiting resources at either habitat extreme. I expected small mammal species that depend heavily on grass or grass seeds (see Table 1) to decline in abundance as grass biomass decreases. Alternatively, some species may decline in open, grass-dominated areas lacking shrubs, since the high abundance of grass resources may have led to competitive exclusion of those species that are less efficient at consuming high densities of resources. As grass and forb species diversity may decline without 4 large trees in the landscape (Treydte et al. 2009), species that specialize on certain herbaceous species may similarly decline. I expected species that prefer woodland habitats to be scarce in open areas lacking a frequent and even distribution of shrubs across the landscape.

My next objective was to determine if open savannas alter the landscape of fear for some small mammals. I hypothesized that the fear of predation while in open terrain would cause many small mammal species to forage in more enclosed habitats, driving community shifts along the shrub cover gradient. I predicted that Giving-Up Densities (see Proposed Methods) would be higher as the distance from available shrub cover increased and the grass height decreased, as small mammals would abandon food resources faster in riskier areas (Fig. 5; purple line). I predicted higher overall GUDs, adjusted for differences in small mammal abundances, would come from habitats with less overall cover as compared with shrub-encroached sites. My third and final objective was to determine if small mammal habitat use changes when individuals are exposed to higher levels of perceived threat. I predicted that increasing perceived predation risks would trigger more risk-averse behavioral responses and shift small mammals even closer to covered, “safe” habitats than their previous responses in Objective 2, changing the slope of their response behavior due to the added threats (Fig 5; red line). The loss of certain vegetation structures in open landscapes could affect the presence of predators as well as their small mammal prey (eg. fewer trees may lead to fewer owl nesting sites), which may affect perceived predation risks if small mammals were responsive to reductions in predator cues. GUDs in sites with fewer predators would rise under the perceived predation risk treatments, as small mammals modify their behavior to respond to the additional risks.

METHODS

Study Sites and Species

This research was based at the Savannah Research Center in the Mbuluzi Game Reserve in Swaziland (see Map A, B.2). We sampled small mammals on 10 established 30.25 ha grids in Mbuluzi Game Reserve (30 km², 2 grids), Mlawula Nature Reserve (165 km², 2 grids), and Hlane Royal National Park (220 km², 2 grids) in Swaziland, and on the basaltic supersite in Kruger National Park, South Africa (19,000 km², 4 grids, Map B.1). These reserves and parks are managed for wildlife conservation and ecotourism with minimal resource extraction. Elephants were extirpated from both sites by 1920 (Blanc et al. 2003), and have since reestablished themselves in Kruger National Park but not in Swaziland.

My trapping grids in Swaziland were in the lowveld region adjacent to the Lubombo Mountains on basaltic soils. The vegetation on these sites is predominantly acacia-dominated savannah, (Mucina 2006). The lowveld subtropical climate has a unimodal rainfall pattern with the wet season occurring from October- March (75% of the annual rainfall) and the dry season occurring from April-September (25% of the annual rainfall; Matondo et al. 2004). Yearly precipitation in the region ranges from 550-725mm (Matondo et al. 2005). Acacia nigrescens and Sclerocarya birrea are the most common tree species in the savannas. The dominant grass species include Themeda spp. and Panicum maximum and Dichrostachys cinerea is the dominant shrub (Sirami & Monadjem 2012). Grids in Swaziland have varying levels of woody cover (10- 90% cover; McCleery et al. unpublished data).

In order to match the ecological characteristics of the Swaziland reserves, we placed grids on the southern basaltic supersite in Kruger National Park, 100km from Swaziland. Tree communities in basaltic soils are especially vulnerable to elephant damage (Asner and Levick 2012). As of 2012 estimates, Kruger supports over 16,000 elephants, an approximate density of 0.8 individuals/km² that is still growing (Pienaar et al. 2012). A density of 0.5 elephants/km² is sufficient to significantly alter vegetation structure in savannas (Cumming et al. 1997), and the high elephant population in Kruger is reducing woody cover across the 5 park, especially 5-9m tall trees (Anser and Levick 2012). Average annual rainfall in these sites is 525mm/year, ranging from 400-600mm/year (Asner and Levick 2012; Gertenbach 1980). Rainfall seasons are similar to Swaziland and the most common woody species include Sclerocarya birrea and Acacia nigrescens (Levick and Asner 2009).

A minimum of 11 small mammal species, ranging in size from 5-100g, are present across these reserves, in the orders Rodentia, Eulipotyphla, and Macroscelidea. These species occupy habitats ranging from open grasslands, mixed woodlands, cultivated farmlands, and rocky terrain (Skinner and Chimimba 2005, Hurst et al. 2013). Shrew species are insectivorous, while rodent species are herbivorous, granivorous, or omnivorous, with seasonal shifts in diet (Table 1; Skinner and Chimimba 2005, Monadjem et al 2015, Abu Baker and Brown 2014, Monadjem et al. 2015, Bergstrom 2013). All rodents are omnivorous to a certain degree, consuming arthropods in different proportions throughout the year (Skinner and Chimimba 2005, Hurst et al. 2013). All species are nocturnal except Lemniscomys rosalia and Elephantulus brachyrhynchus, which are crepuscular, and shrews (Crocidura spp.), which are active during the day and night (Skinner and Chimimba 2005).

Table 1. Habitat preferences, average adult mass (g), functional group, and relative prevalence of small mammal species trapped in Kruger National Park, South Africa, and Mlawula, Mbuluzi, and Hlane Reserves, Swaziland from Dec 2013 - June 2016. Species Mass Diet Habitat Prevalence a Aethomys ineptus 78 Granivore- Grassland/woodland Low herbivore Dendromys melanotis 9 Granivore- Grassland with shrubs, tall grass Low Gerbilliscus leucogaster 70 Savanna/woodland, burrows Low Lemniscomys rosalia 57 Herbivore- Grassland, tall grass Mid granivore Mastomys natalensis 46 Granivore- Wide tolerance, savanna, agriculture High omnivore Micaelamys namaquensis 48 Herbivore- Woodland/rocky areas Low granivore Mus minutoides 6 Omnivore Wide tolerance, savanna, agriculture Mid Saccostomus campestris 48 Granivore- Savanna/woodland, burrows Low omnivore Steatomys pratensis 23 Graminivore- Open grassland/woodland, burrows Mid granivore Crocidura sp. 3-17 Insectivore Wide tolerance, savanna, agriculture Low Elephantulus 44 Insectivore Shrub cover Low brachyrhynchus

Objective 1: Vegetation Structure

My first objective was to determine how savanna vegetation gradients influence small mammal species occupancy. I analyzed how variation in vegetation structure, including trees, shrubs, and grass, shape small mammal functional groupings and communities across the shrub cover gradient. I used a two-year dataset to profile small mammal community compositions under different vegetative conditions. Small mammals a Relative prevalence based upon the total number of unique individuals captured (Low: < 50 captures; Mid: 50-300 captures; High: >300 captures). 6 and vegetation were surveyed in each of 10 systematically placed 30.25 ha grids in reserve areas (6 in Swaziland, 4 in Kruger NP). Within each grid there were 9 50m² plots nested in a 3x3 design spaced 250 m apart (Fig. 3). Twenty H. B. Sherman folding traps (3 x 3.5 x 9", Tallahassee, FL, USA) were baited with peanut butter and oats in each plot over 4 consecutive nights in the austral winter and summer seasons from November 2013 - January 2016. We continued this trapping and vegetation monitoring protocol during my field season in May to June 2016 but these data are not yet incorporated into the model. We identified all small mammal species and measured their weight, sex, age (adult or juvenile), hindfoot, body, and tail lengths. We ear-tagged new individuals (1005-1, National band Co., Newport, KY, USA). Home ranges of the small mammals do not exceed 250m (Monadjem and Perrin 1998) and plots were considered independent samples, though nested within grids because of their close proximity to each other within the landscape.

We surveyed vegetation yearly in April-June at the end of the growing season when grass biomass is at its peak. We measured tree density, shrub cover, canopy cover, and grass biomass for each plot during each sampling period using standardized monitoring protocols. We counted every tree in the > 2m height class on the plot. We estimated the percentage of shrub cover along the full length of the two outer transect lines using the Line-Intercept Method (Canfield 1941). We measured canopy cover with a concave spherical densiometer every 10m along the same transect lines, determining whether the canopy was opened or closed for each of 96 points. We used a Disc Pasture Monitor that was calibrated to Kruger National Park to estimate grass biomass every 5m along each of the two outer transects (Zambatis et al. 2006).

Data Analysis: I reduced the data from the small mammal trapping within plots to a binary presence/absence variable for each species representing its occupancy per plot, with plots as the replicates within grids. I used multi-species occupancy modeling to compare the small mammal communities across vegetation covariates (Dorazio et al. 2006). Multispecies occupancy models are able to account both for the variation in species occupancy and the inability to accurately detect all individuals. This framework allowed me to account for the variability in detection across species, improving the occupancy estimates by using the variation from both rare and common species to estimate parameters. This model can also be used to measure how different environmental variables affect occupancy on a species-specific and community level. I compared the effects of season, vegetation, and grid on small mammal species presence, functional groupings based on diet, and the overall community structure. My predictor variables were gradients of tree density, shrub cover, tree canopy cover, and grass biomass across plots. My response variables were species occupancy and community composition. In the future I will also assess the turnover, β-diversity, and community similarity, and how both functional groups and the overall community respond to changing gradients of vegetative cover.

I used an occupancy modeling approach that accounts for detection to conduct my analysis (MacKenzie et al. 2006). I built my generalized linear model in R and linked it under a Bayesian hierarchical framework in JAGS (R Development Core Team 2013, Plummer 2003). I modified detection with season (fixed effect, winter/summer) and year (random effect, Year 1 or 2) as these parameters may influence the trap success for the different species. I modified the probability of occupancy with the four vegetation covariates and the random effect of grid. Accounting for grid as a random effect addressed potential autocorrelation of the plots. Along with my additive models, I will also build models that incorporate potential interactions between the vegetation covariates, as occupancy for one vegetation category may change as a function of another covariate. For example, grass biomass may be negatively correlated to tree canopy cover as trees can shade out grass. I will test for collinearity of my vegetation covariates to control for confounding effects of correlated variables.

Objectives 2 and 3: Small Mammal Habitat Selection and Perceived Predation Risk 7

My next objective was to assess whether changes in savanna cover may change small mammals’ perceived predation risk across the landscape of fear. I examined how small mammals use vegetative cover on both small and large scales and multiple patch-levels, as species may respond differently to aggregates of shrubs within large open landscapes as compared with small open clearings within largely wooded landscapes (Abu Baker and Brown 2010). I tested for differences in perceived predation risk across vegetation conditions. I used a giving-up density (GUD) experiment (Brown 1988) to assess the mammals’ perceived risks along a stratified gradient of shrub cover in the study sites in Kruger and Swaziland. I assessed small mammal fine-scale habitat selection between shrubs and grass of varying heights and density. To isolate the effects of risk perception, I used a Before/After treatment, manipulating the level of predation risk by adding auditory cues and measuring the mammals’ responses to cover in the landscape prior to and after these treatments.

I attached a Reconix Hyperfire Infrared Camera (programmed to 40cm short-range focus and set to high sensitivity) to a screw in a cross-bar at the top of a 60cm-long stake so that I could easily unscrew it to check the camera settings and battery condition each day. These stakes were hammered into the ground so that cameras pointed downwards 50cm above a 30cm plastic tray containing the food resource (hulled millet seeds) and inedible substrate (sand). Using the camera traps, I determined which species utilized the GUD stations, ensuring against confounding influences on GUD results (Mohr et al. 2003, Bedoya-Perez et al. 2013, Abu Baker and Brown 2014). I placed a ruler within each GUD tray to enable the identification of small mammals in the photos based on body proportions.

I first habituated the small mammals to the GUD stations by filling the trays with millet seeds, since the majority of small mammal species consume seeds as a large portion of their diet (Monadjem et al. 2015; Table 1). These seed trays were left in the sites for a minimum of three days. Once I saw evidence that small mammals were using the trays (droppings, seed fragments, or shells), I mixed 600g of sand with 40ml of hulled millet seeds per tray. The amount of seed and sand used was adjusted during a trial period to ensure that animals were not consuming all seeds in a given night (Abu Baker and Brown 2014, Banasiak and Shrader 2015). Mixing the food with sand mimics a natural foraging scenario where there are associated costs with time spent foraging, as per Brown (1988). Seed and sand trays were left in the sites for a minimum of 2 days before data collection began, to ensure that small mammals were fully habituated to the mixture. I measured the volume of seeds remaining in each GUD station each day, removed them, and refilled the trays to full capacity for the next night, ensuring that 600g of sand remained. I monitored the GUD trials over the course of 5 nights before and 10 nights during the increased predation risk treatments. I set the camera to take 3 photos in rapid fire in 1-minute increments so that I could estimate the amount of time each species spent foraging at each station. Using the frequency of individual visits to the food trays, I will calculate an activity index per species per station (Mohr et al. 2003) and generate activity histograms per species to estimate temporal variability in foraging between the species (Abu Baker and Brown 2014).

I conducted the GUD experiments in the austral winter from May-August 2016 in the Mbuluzi Game Reserve. I chose areas with a stratified cover gradient from shrub cover to grass and evidence of rodent activity on the pre-baited seed trays. I placed GUD stations along selected gradients that started 1m within a shrub, along the edge between shrub and grass, and 50cm, 1m, and 3m into an open grassy area (Fig. 4). I measured the height of the shrubs and grass along this gradient, defining long grass as > 40cm and short grass as < 35 cm, as small mammal savanna species respond to grass height at this threshold (Banasiak and Shrader 2015). I estimated the visual obstruction of the vegetation surrounding each tray using the Robel pole (Robel et al. 1970). To quantify ground cover I estimated the percentage of ground covered by shrub and grass within one square meter surrounding each tray. I measured the distance from each tray to the nearest shrub as an estimate of how dispersed the shrubs were spread out around the open area. I will test for interaction effects of large-scale cover availability, as determined by the distances between my shrub 8 gradient and other shrubs, with fine-scale cover measured as the distance from shrub cover, visual obstruction, and ground cover. Using the GUD data, I compared the community-level responses to vegetation gradients. Using the camera trap data on activity patterns per species, I will compare how different species respond to these large and small scale vegetative cover classes and how these responses may explain community-level responses. I will compare the responses of small mammals such as Mus minutoides that are present over a range of vegetation conditions as well as species that are more restricted to certain cover classes, like Lemniscomys rosalia and Micaelamys namaquensis.

After estimating the ambient perceived predation risk based on the giving up densities and activity levels at each station and cover class, I applied a randomized sequence of predator and control treatments over a 10- day period. Each day I played one of three treatments: all control, diurnal control with nocturnal predator, and all predator. Artificial playbacks were randomly spaced every 30-60 minutes. I used lizard buzzard calls as diurnal predation risk treatment, as lizard buzzards prey on ground-dwelling species, including rodents. I used owl calls as my nocturnal predation risk treatment. Owls are nocturnal small mammal specialist predators and small mammals recognize owl calls as a threat and activate appropriate behavioral responses (Eilam et al. 1999). As sudden noises may also affect small mammal behavior, I applied a procedural control and broadcast non-threatening bird calls (doves for diurnal controls and nightjars for nocturnal controls). I conducted three to four GUD transect experiments at a time, spaced a minimum of 50m apart, and played the same calls simultaneously across all transects. I replicated the entire experiment at four sites in the Mbuluzi Game Reserve. I conducted the GUD experiment without auditory calls for 5 days at one additional site at Mbuluzi and at four sites in the basaltic supersite at Kruger Park.

Data Analysis: I will analyze the GUD data using a generalized linear model that accommodates non-normal distributions in Program R (R Development Core Team 2009). I will monitor how the GUD responses and the activity levels of the different small mammal species change as the distance from cover increases (arranged in a gradient from high to low cover levels). Distance to cover may interact additively or multiplicatively with predation risk. I will include the vegetation height, visual obstruction, and ground cover estimates as random covariates that may influence how the small mammal species use the GUD stations and may interact with predation risk. I will also monitor the effect of site (four total) and transect (three or four per site) and compare the responses before and after the control, control-noise, and predation risk treatments. I will build models for the entire community at once, using the GUD responses, and for individual species, using the activity levels. I will evaluate my models using Akaike’s Information Criteria (AIC), adjusted for small sample sizes (AICc; Akaike 1973).

Table 2. Names and types of predictor (X) and response (Y) variables and their respective measurements for the proposed Giving-Up Density (GUD) study in Kruger National Park, South Africa, and Mbuluzi Game Reserve, Swaziland, in May-Sep 2016. Name X/Y Type (#) Measurement (units) GUD Y Continuous Proportion of seeds remaining as an estimate of the community-level giving-up density Activity Level Y Continuous Frequency of visits to the food trays on a species-specific scale Distance from X (Defined Range) Fixed -1m, 0m, 0.5m, 1m, 3m from shrub cover Cover Gradient (5) Vegetation X (Covariate) Continuous Vegetation height at the GUD tray scale Height Gradient Visual X (Covariate) Continuous Shrub and grass density at the GUD tray scale Obstruction Gradient Ground Cover X (Covariate) Continuous Shrub and grass % cover at the GUD tray Gradient scale 9 Predator X Categorical All predator calls, all control calls, diurnal (4 factors) control calls with nocturnal predator calls, control (no noise) Time X Fixed (2) Before/After treatment Park X Fixed (2) Mbuluzi, Kruger Park Site X Random 4 within each park Transect X Random 3-4 transects within each site

PRELIMINARY RESULTS

Obejective 1: Vegetation and Small Mammal Communities

Woody species are less common in Kruger Park as compared with Swaziland (Figure 6). Tree densities, shrub cover, and canopy were more variable in Swaziland. Grass biomass was slightly higher in Kruger Park.

Small mammal communities responded positively to shrub cover, increasing in occupancy probability up to 50% as shrub cover reached 100% (Figure 7). Small mammals also increased slightly in occupancy with high levels of grass biomass. Tree density and canopy cover had little effect on the probability of species occupancy.

Species varied in their individual responses to changing vegetation gradients, especially to tree density and canopy cover (Figure 8). The majority of species increased in occupancy probability as shrub cover increased. Similarly, most species increased in occupancy as grass biomass increased. Two granivorous species (Mastomys natalensis and Saccostomus campestris) declined in occupancy as shrub cover increased. Possible thresholds for species turnover may exist at high levels of tree canopy cover and shrub cover, as some species declined while others increased. I intend to look further at species turnover and how these multiple vegetation components may interact to shape small mammal communities.

Objective 2: Community Giving Up Densities and Species Present

Prior to adding predation risk treatments, small mammal species consumed the most seeds closer to shrub cover, in some areas nearly consuming all seeds present (Figure 9). They varied in their seed consumption at the edge of the shrub/grass transition zone, with some transects showing very little activity while others consumed nearly half the seeds present. Very few seeds were consumed away from the edge of the shrub, with giving up densities averaging 90% for all distances.

Though I have yet to complete a full analysis of small mammal activity patterns related to the giving up densities of the community, I have collected photographs of eight rodent species across 15 transects in 5 sites in the Mbuluzi Game Reserve (Figure 10). Lemniscomys rosalia is the most commonly recorded species, present on 13 out of the 15 transects, with Mus minutoides and Mastomys natalensis slightly less prevalent. I intend to measure the activity patterns across all species, transects, and tray location, as well as how these different species interact with the fine-scale vegetation that I have measured at the tray level.

Objective 3: Manipulating Perceived Predation Risk

The average giving up densities of the small mammal community did not vary by treatment, though giving up densities were slightly higher for the initial five control days (Figure 11). Rodents continued to eat the most seed closest to shrub cover, while consuming little seed further into the open grassland. The 10 full treatment days and the control call days had the lowest seed consumption for the tray in the bush. These results may change when other factors like the weather, day, moon phase, and vegetation variation around each transect are taken into account.

DISCUSSION

Objective 1: Vegetation Shapes Small Mammal Communities

Vegetation components are driving small mammal occupancy, with shrub cover as the strongest determinant of species presence or absence, though grass biomass may also be important. Small mammal species respond differently to trees, shrubs, and grass levels, with some species declining at high levels of trees and shrubs while others increase in occupancy. Two granivores decline with increased shrub cover, possibly because the rely more heavily on grass seeds which may be less abundant as woody cover increases at the expense of grass cover. No other trends were detected based on dietary functional group, perhaps because these small mammals consume a variety of resources and their vegetation requirements may be more related to other factors, like predation risk, than their dietary needs. Some species require loose soil for burrowing while others depend on debris for shelter, suggesting that some species may need shrubs and trees that provide fallen branches and other debris, while others may be less affected by changes in woody cover. There is the potential for species turnover at very high levels of tree canopy cover and shrub cover, and this will be explored further. It is also possible that species interact and influence the occupancy of other species, which may also be related to vegetation type and the diversity of vegetation components available in the system.

These patterns in small mammal responses to vegetation may explain why most small mammal species are less abundant in Kruger Park, with its open grasslands, than in Swaziland, with its largely woodier vegetation structure.

Objective 2: Savannah Rodents Preferentially Forage Under Shrub Cover

Small mammals consume more seeds under shrub cover, suggesting that they may be spending more time foraging in these areas. Few individuals were photographed in open areas furthest from shrub cover, and few seeds were consumed at those distances. The rodent activity patterns, once analysed, will likely confirm that most activity occurs under shrub cover. The level of activity may vary by species and fine- scale vegetation around the tray. Some species may journey to the outermost trays to feed, though feeding less there than at the bush, while other species may only be found feeding at the trays under cover. The level of rodent activity and seed consumption declined sharply as small mammals traveled further from the shrub, and even trays at the transition zone had much higher giving up densities than trays within the shrub level. This suggests that small mammals utilize shrub areas more heavily when foraging because shrubs are perceived to be safer from predators and even vegetation that borders open grassy areas are perceived to be too exposed.

Objective 3: Savannah Rodents May Not React to Auditory Predator Cues

Rodents strongly prefer to forage under woody cover without any manipulation of perceived threat, and they showed no change in this behavior when potential threats were artificially increased. Possibly the threat of aerial predators is already high in these areas, given the high densities of trees, and shrubs as raptor habitat, and small mammals have no other refuge than the shrubs to forage in safety. It is also possible that rodents in these savannahs use other cues of predators to alert them to danger, and that the auditory calls of nocturnal and diurnal raptors do not trigger additional anti-predator behaviors. The 11 rodents consumed more seeds under predation treatments than in control day/nights, which suggests either that more individuals found the tray and fed in the bush because of the increased threat, or that the same individuals fed more intensively without leaving the tray to forage elsewhere like they may do on control day/nights. It is also likely that the number of days had an effect on the rodents’ behavior, as the treatment calls occurred when rodents had already been exposed to the seeds mixed with the sand for a minimum of seven days at the first predator treatment day/night and a minimum of 17 days at the last day/night.

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FIGURES

Figure 1. A model of the vegetation spectrum in southern African savannas with open savannas, high elephant densities, and low predicted small mammal diversity (left), mixed savannas, low elephant densities, and high predicted small mammal diversity (middle), and closed savannas, no elephants, and low predicted small mammal diversity (right).

Figure 2. A simplified model of elephant and shrub cover effects on small mammals in southern African savannas with potential drivers reducing diversity at either extreme: (1) decreased vegetative structural diversity leading to simplified habitat and (2) fear of predation.

13 Figure 3. A 30.25 ha Grid sampling design with 9 50 m² plots spaced 250m apart. There are 4 grids located in Kruger National Park, South Africa, and 2 grids each in the Mbuluzi Game Reserve, Mlawula Nature Reserve, and Hlane National Park in Swaziland.

Figure 4. A model of the locations of GUD stations and camera traps 1m within a shrub, at the edge, and 50cm, 1m, and 3m away from the shrub into open grassy areas.

Figure 5. The predicted effects of vegetative cover on small mammal giving up densities in southern African savannas under current perceived predation risks (purple) and increased predation risk treatments (red).

14 Figure 6. The average tree density, shrub cover, tree canopy cover, and grass biomass for the study grids in Kruger National Park, South Africa, and Mbuluzi Game Reserve, Mlawula Nature Reserve, and Hlane National Park in Swaziland from Nov 2013 to June 2015. Error bars are standard deviations of the mean.

Figure 7. The responses of the small mammal community’s occupancy probability to the actual gradient of tree canopy cover, tree density, shrub cover, and grass biomass present throughout the study plots in Kruger Park, South Africa, the Mbuluzi Game Reserve, Mlawula Nature Reserve, and Hlane National Park, Swaziland from Nov 2013 to June 2015.

Figure 8. The responses of individual small mammal species’ occupancy probability to the actual gradient of tree canopy cover, tree density, shrub cover, and grass biomass present throughout the study plots in Kruger Park, South Africa, the 15 Mbuluzi Game Reserve, Mlawula Nature Reserve, and Hlane National Park, Swaziland from Nov 2013 to June 2015. Species are color-coded by dietary functional groups. Dotted lines indicate possible thresholds in vegetative cover where species turnover may occur.

Average Giving Up Densities for 11 Transects for Control Days

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1

Proportion of Seeds Remaining Seedsof Proportion 0 1m in Bush Edge 50cm Out 1m Out 3m Out Bush ---> Tray Location ---> Open Grass Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Site 8 Site 9 Site 10 Site 11

Figure 9. The average giving up densities from 11 transects across 5 sites in the Mbuluzi Game Reserve, Swaziland in June- August 2016. Averages were calculated from the first five days of the experiment from the proportion of seeds left behind in seed trays left 1m in the bush, on the edge, and 50cm, 1m, and 3m out into open grassland.

Small Mammal Species Photographed at Seed Trays

Saccostomus campestris Aethomys ineptus/Micaelamys namaquensis Steatomys pratensis Dendromus mystacalis

Species Mastomys natalensis Mus minutoides Lemniscomys rosallia 0 2 4 6 8 10 12 14 Total Number of Transects

Figure 10. The total number of transects that recorded each small mammal species present at at least one seed tray on the camera traps in 15 transects across 5 sites in the Mbuluzi Game Reserve, Swaziland from June-August 2016. Aethomys ineptus and Micaelamys namaquensis are grouped together due the difficulty in distinguishing them in the photographs.

16 Average Giving Up Densities for 10 Transects By Treatment 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2

Proportion of Seeds Remaining Seeds of Proportion 0.1 0 1m in Bush Edge 50cm Out 1m Out 3m Out Bush ---> Tray Location ---> Open Grass Control Control Calls Control Day/Treatment Night Full Treatment

Figure 11. The average giving up densities from 10 transects that received the full combination of treatment possibilities across 4 sites in the Mbuluzi Game Reserve, Swaziland in June-August 2016. Averages were calculated from the first five days of the experiment (control), the two days of control noise (Control Calls), and four days each of the day/night predator treatment (Full Treatment) and the day-control/night-predator treatment (Control Day/Treatment Night). proportion of seeds left behind in seed trays left 1m in the bush, on the edge, and 50cm, 1m, and 3m out into open grassland.

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20 MAPS

Map A. Location of study sites in South Africa and Swaziland. Stars mark the location of the furthest study plots, with the arrow marking the 100km distance between the two.

21 1)

22 2) Swaziland Grids

Map B. 1) Study grids in southern Kruger National Park, South Africa; 2) Study grids in Swaziland: Mbuluzi Game Reserve (purple), Mlawula Nature Reserve (green), Hlane Royal National Park (blue). Maps are shown at different scales, though all grids are the same size (30.25 ha).