ORI GIN AL PA PER

Functional relationships reveal keystone effectsof the gopher tortoise on vertebrate diversity in a longleafpine savanna

Christopher P. Catano1,2• I. Jack Stout1

Received: 13 September 2014 / Revised: 17 March 2015 / Accepted: 28 March 2015� Springer Science+Business Media Dordrecht 2015

Abstract Keystone species are important drivers of diversity patterns in many ecosys-

tems. Their effects on ecological processes are fundamental to understanding community

dynamics, making them attractive conservation targets for ecosystem management.

However, many studies assume keystone effects are constant. By developing functional

relationships of species’ effects and assessing how they vary with context, we can design

more efficient conservation strategies to maintain keystone impacts. The threatened gopher

tortoise (Gopherus polyphemus) is presumed to be a keystone species promoting biodi-

versity in endangered longleaf pine ecosystems of the Southeastern Coastal Plain, USA.

Although many commensals use tortoise burrows, their putative keystone influence on

emergent diversity patterns lacks critical evaluation. We quantified the functional rela-

tionship between tortoise burrow density and non-volant vertebrate diversity in a longleaf

pine savanna, located in central Florida. Tortoise burrow density had a positive effect on

vertebrate diversity and evenness but did not affect species richness. This relationship was

robust across fire disturbance regimes and was the primary factor explaining diversity at

the local scale. Our results demonstrate keystone effects of the gopher tortoise through an

ecosystem engineering mechanism. Continued gopher tortoise population declines will

have large, negative impacts on vertebrate diversity in this biodiversity hotspot. Therefore,

maintaining gopher tortoise populations is critical to effectively conserve dependent spe-

cies and the function of endangered longleaf pine ecosystems. We show that developing a

functional understanding of keystone relationships (not a binomial categorization) can lead

to important insights into community processes.

Communicated by Pedro Aragon.

& Christopher P. Catanochcatano@gmail.com

1 Department of Biology, University of Central Florida, 4000 Central Florida Blvd., Orlando,FL 32816, USA

2 Present Address: Department of Biology, Washington University in St. Louis, St. Louis,MO 63130, USA

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Biodivers ConservDOI 10.1007/s10531-015-0920-x

Keywords Akaike’s information criterion � Biodiversity conservation �Ecosystem engineer � Evenness � Richness � Southeastern coastal plain

Introduction

Biodiversity is vital to maintain ecosystem functions and services (Hooper et al. 2005;

Balvanera et al. 2006; Cardinale et al. 2006, 2012); however, the mechanisms that structure

biodiversity are poorly understood in many ecosystems (Messmer et al. 2011). The key-

stone species concept has been important in applied and community ecology to explain

community structure and dynamics. Keystone species have a disproportionately large ef-

fect, relative to their abundance or biomass, on biodiversity and other ecosystem functions

(Paine 1969, 1974; McLaren and Peterson 1994; Power et al. 1996). Because ecological

communities are often highly influenced by interactions among species, loss or decline of

keystones can result in negative effects on community structure including loss of diversity

(Ebenman and Jonsson 2005). Conservation and management of keystone species can

therefore be an effective strategy to maintain biodiversity and ecosystem functions

(Simberloff 1998).

Despite their importance, historical emphasis has predominantly rested on qualitatively

categorizing species as a keystone or not, rather than deciphering the causal mechanisms in

which alleged keystones influence their communities. As a result, many species have been

claimed as keystones with insufficient critical evaluation (Power et al. 1996). Anecdotal

claims of keystone status do little to further our understanding of community dynamics;

effectively undermining the concept’s applied use (Hurlbert 1997) and possibly inhibiting

practical or more effective conservation strategies. Despite promising criteria to quantify

keystone relationships (see Power et al. 1996; Kotliar 2000) or their cascading effects

(Ebenman and Jonsson 2005), many studies continue to neglect such methodologies.

Applying such quantitative frameworks to a priori hypotheses can allow us to determine

the degree to which biodiversity depends on keystone relationships. In addition, a focus on

an arbitrary binomial categorization of species as either keystones or not is overly sim-

plistic (Hurlbert 1997; Kotliar 2000). ‘Keystoneness’ of a species is not a species-specific

trait, but rather a function of historical and ecological context (e.g., abundance, scale,

disturbance, etc.) (Fauth 1999; Kotliar 2000). For example, changes in disturbance regimes

and setting have been shown to mitigate species’ keystone effects in both terrestrial and

aquatic ecosystems (McLaren and Peterson 1994; Menge et al. 1994; Fauth 1999; Smith

et al. 2003). Therefore, instead of assuming this is a constant trait, researchers should

determine how other factors influence the functional relationships and magnitude of

keystone species’ effects. Understanding variation in functional relationships allows re-

searchers to detect the domain in which keystone species are most influential, and can

therefore guide management seeking to maximize their effects on community or ecosystem

functions (Kotliar 2000).

The gopher tortoise (Gopherus polyphemus) is asserted to be a keystone species in

longleaf pine savannas of the Southeastern Coastal Plain, USA. (Eisenberg 1983; Guyer

and Bailey 1993). These grasslands host many endemic species and are considered a

biodiversity hotspot and one of the most biodiverse ecosystems in North America (Noss

2013; Noss et al. 2014). Extensive habitat loss, illegal harvesting, disease, and fire sup-

pression have resulted in gopher tortoise population declines in excess of 80 % in the last

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century (Auffenberg and Franz 1982). The gopher tortoise is federally listed as threatened

in the western portion of its range and has recently been added as a candidate species for

protection under the Endangered Species Act in the remainder of its range (USFWS 2011).

The gopher tortoise is currently listed as threatened in Florida where habitat loss remains a

serious issue and many of the remaining populations reside. Despite these protections,

gopher tortoise population declines are reported on public lands (McCoy et al. 2006). It is

urgent that we understand the role of the gopher tortoise in maintaining diversity in this

North American biodiversity hotspot to anticipate cascading effects from their declines and

to develop more effective management strategies to maintain native biodiversity.

Like many other fossorial vertebrates (see Bravo et al. 2009 and Davidson et al. 2012),

gopher tortoises are ecosystem engineers (Kinlaw and Grasmueck 2012) that increase

heterogeneity in both abiotic and biotic characteristics of the habitat primarily through the

construction of extensive burrows across the landscape (Diemer 1986; Kaczor and Hartnett

1990). Their ecosystem engineering recycles leached nutrients to the surface and provides

suitable sites for pioneer plant species, but their putative impact on animal diversity makes

them a focal point of habitat management. The burrow and excavated sand mound at its

entrance are important to various faunal elements by creating stable microclimates, and

providing shelter from adverse environmental extremes such as heat, cold, fire, and aridity.

Also, the burrows are used as sites for feeding, mating, or nesting for a number of com-

mensal species (Landers and Speake 1980; Eisenberg 1983; Lips 1991). At last count, 60

vertebrates and 302 invertebrate species have been observed in association with tortoise

burrows (Jackson and Milstrey 1989). The influence of burrows as refugia is thought to

have a disproportionately positive effect on faunal diversity and is the justification for the

gopher tortoise’s designation as a keystone species (Eisenberg 1983; Guyer and Bailey

1993).

These observations have been useful to advocate for the importance of this threatened

species. Unfortunately, there are currently substantial limits to our understanding of the

dynamics between gopher tortoises and the myriad of commensals. For example, burrow

use by commensal species can be obligatory or facultative and may vary in frequency (Cox

et al. 1987). The importance of burrows to commensal populations has been demonstrated

for only a small subset of vertebrates [e.g., eastern indigo snake for shelter and to find

mates (Drymarchon couperi) (Landers and Speake 1980), Florida mouse for nesting

(Podomys floridanus) (Eisenberg 1983; Layne and Jackson 1994), and the gopher frog to

avoid desiccation (Rana capito) (Roznik and Johnson 2009); the latter two obligately

depend on burrows for persistence in xeric sandhill habitat]. Furthermore, despite their

importance in management plans, there are no studies that have empirically quantified the

effects of the gopher tortoise on community-wide faunal diversity metrics (e.g., species

richness, evenness, or diversity), or how these relationships vary with context. Therefore,

substantial uncertainties remain hampering our ability to predict how longleaf pine com-

munities will respond to continued gopher tortoise population declines.

In this study, we empirically quantified the functional relationship between gopher

tortoises and vertebrate richness, evenness, and diversity in a longleaf pine sandhill habitat.

It is important to incorporate all three components of diversity to determine gopher tortoise

impacts because each metric reveals a different aspect of the community composition (i.e.,

interplay of species presence and relative abundance). Idiosyncratic responses could

therefore suggest different mechanisms in which tortoises impact resident communities.

Further, we addressed how the relationship with diversity varied with burrow density and

activity (i.e., percentage of burrows that were occupied by tortoises), fire disturbance

frequency, and their influence relative to other drivers of sandhill faunal diversity (e.g.,

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prescribed fire, habitat structure, ground cover diversity, and proximity to seasonal ponds).

Specifically, we evaluated the following three hypotheses: (1) The functional relationship

between gopher tortoise burrow density and diversity would be positive, supporting a

keystone effect. Also, only burrow density, not whether tortoises were present, would

impact diversity therefore suggesting a causal link to an ecosystem engineering mechan-

ism. (2) Under more extreme fire regimes (i.e., higher fire frequency), burrow density

would have a larger effect on diversity because of their potential as a source of refuge for

wildlife to escape fire-related mortality. (3) Despite the importance of tortoise burrows, fire

frequency would be a more important predictor of vertebrate diversity because of its large-

scale effects altering habitat structure, composition, and vegetation biomass. We focused

on herpetofaunal and small mammal assemblages because they are among the most con-

spicuous users of tortoise burrows and include species with known dependencies on these

structures. Because there is substantial information regarding their ecologies and distri-

butions, we can make stronger inferences regarding the effect of tortoises on vertebrates

using correlative inference than for other commensals such as invertebrates.

Materials and methods

Study area

We conducted our study in longleaf pine sandhill habitat at Wekiwa Springs State Park

(WSSP) in central Florida, USA (28�4405000N, 91�2904400W). Sandhill is a xeric habitat and

at WSSP exists as a contiguous, relatively large area (approximately 600 ha), with Candler

fine sand as the primary soil type. The vegetation composition is dominated by an un-

derstory of wiregrass (Aristida spp.) and an overstory of longleaf pine (Pinus palustris) and

infrequent oak species, primarily turkey oak (Quercus laevis). Active management and

spring/summer prescribed fire treatments consistent with historical variation (1–3 years;

Stambaugh et al. 2011) are prioritized and have been implemented for over three decades.

WSSP is one of relatively few remaining intact protected sandhill sites approximating

historical natural conditions in the region. The average burrow density, *3.5 burrow ha-1

(Catano et al. 2014), is comparable to other highly suitable sites such as the Wade Tract in

southern Georgia (Guyer et al. 2012).

Tortoise burrow survey and fauna sampling

We conducted tortoise burrow surveys in June 2011 along north–south transects spaced

10 m to locate burrows across 450 ha of the longleaf pine savanna landscape (we ex-

cluded 150 ha that would be burned with prescribed fire during the study interval).

Tortoise burrow position was determined using global positioning system (Garmin

GPSMAP� 60Cx) with 1 m accuracy. Assessment of external burrow features is nec-

essary to determine gopher tortoise habitat use because they spend approximately 90 %

of their time underground (McCoy et al. 2006; Castellon et al. 2012; Guyer et al. 2012).

We classified burrows as active or abandoned using standard methodology (see Hermann

et al. 2002). Active burrows are elliptical at the entrance, approximating the shape of a

tortoise’s carapace, and exhibit tracks or plastron scrapes. Abandoned burrows may be

partially or completely collapsed, occluded by plant material, and do not show evidence

of recent tortoise use (Smith et al. 2005). Ashton et al. (2008) demonstrate that across

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habitats, the number of active burrows correlates strongly (r = 0.9) with the number of

tortoises.

Burrow densities were calculated in 5000 m2 grid cells overlain on the survey areas

using ArcGIS software version 10.0. Because fire is a crucial process structuring sandhill

habitat (Russell et al. 1999; Reinhart and Menges 2004), we selected 16 sample units using

a stratified random design based on relative burrow density categories within two fire

frequency categories: 1–3 years burn intervals which represents the historical fire fre-

quency (n = 8) and 4–7 years burn intervals (n = 8) (Fig. 1). Burrow surveys were re-

conducted with complete area coverage within each of the 16 selected sample units to

Fig. 1 Study area and sample locations at Wekiwa Springs State Park, Florida, USA. Shading correlateswith elevation (3–32 m a.s.l) derived from a 1 m resolution digital elevation model. Lighter shaded area isprimarily upland longleaf pine savanna habitat. White points indicate center of sample units in frequent fireregime (1–3 years interval); black points indicate sample units in less frequent fire regime (4–7 yearsinterval)

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ensure all burrows were located and classified. This method ensures we missed few

adult/subadult burrows, which dominate the structure of populations at carrying capacity

(Nomani et al. 2008).

We sampled vertebrate relative abundances within each site using a trapping array

consisting of drift fences and Sherman live traps. Drift fences had three 8 m arms radiating

out from a center point at 120� angles (to reduce directional bias) with four 19 L pitfall

traps (1 at the terminus of each arm and 1 at the center of the array) and six double-opening

funnel traps attached midway and to both sides of each arm (see Campbell and Christman

1982). One drift fence array was placed at the center of each sample unit. Five randomly

placed Sherman traps were used to live trap small mammals within 20 m of each sample

unit center. We standardized sampling intervals and intensity among sample units by

opening and clearing traps at all sites concurrently over five day periods. We checked all

traps daily over each sampling period (30 replicate sample events per site). We sampled

June 2011–January 2012 to encompass annual thermal extremes and the most active season

for sandhill vertebrates.

For each sample unit, we calculated diversity, evenness, and richness. We calculated

diversity using the Shannon exponential diversity measure (D) (Jost 2006, 2007; Eq. 1).

D ¼ exp �XS

i¼1

pilnpi

!ð1Þ

where S is the observed number of species and pi is species frequency. Evenness (relative

proportions of species) was calculated using the Shannon evenness index (J) (Maurer and

McGill 2011; Eq. 2);

J ¼ D=S ð2Þ

where D is diversity calculated from the exponential of the Shannon entropy index and S is

observed number of species. Richness was calculated using the sample-based Chao2 es-

timator SChao2

� �, an incidence-based, non-parametric estimate of richness (Colwell and

Coddington 1994; Chao et al. 2009; Eqs. 3, 4).

SChao2 ¼ Sobs þm� 1

1

� �Q1 Q1 � 1ð Þ2 Q2 þ 1ð Þ

� �ð3Þ

var SChao2

� �¼ m� 1

m

� �Q1 Q1 � 1ð Þ2 Q2 þ 1ð Þ

� �þ m� 1

m

� �2Q1 2Q1 � 1ð Þ2

4 Q2 þ 1ð Þ2

!

þ m� 1

m

� �2Q2

1Q2 Q1 � 1ð Þ2

4 Q2 þ 1ð Þ4

!ð4Þ

where Sobs is the number of species observed, m is the sample size, Q1 is the number of

species found in exactly 1 sample, and Q2 is the number of species found in exactly 2

samples. Chao2 is an accurate and precise extrapolation of minimum richness of the

assemblage (Colwell and Coddington 1994; Brose et al. 2003; Walther and Moore 2005;

Chao et al. 2009; Gotelli and Colwell 2011). Because samples were taken in a systematic,

uniform way and Q1/m\50 %, adequate sample sizes were accumulated and comparisons

using this estimate are valid (Colwell and Coddington 1994; Magurran 2004). We used

EstimateS software version 8.2.0 (http://viceroy.eeb.uconn.edu/estimates; Colwell 2000) to

calculate Chao2 richness, Shannon evenness, and Shannon exponential diversity. We

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bootstrapped each diversity measure with 1000 iterations to increase precision of the

estimate.

Habitat and vegetation sampling

We used light detection and ranging (LiDAR) remote sensing to acquire three-dimensional

vegetation structural data (Lefsky et al. 2002; Vierling et al. 2008). The National Center for

Airborne Laser Mapping (NCALM; http://www.ncalm.org) acquired lidar data for our

study site in summer 2011. The data were collected using an Optec Gemini Airborne Laser

Terrain Mapper (with 5–35 cm elevation accuracy, laser pulse repetition frequency (PRF)

of 70 kHz, and an average density of 7 points m-2). The full validation report and data set

are available at http://opentopo.sdsc.edu/gridsphere/ (doi: 10.5069/G94M92GW). We

created a 1 m resolution digital elevation model (DEM) from the ground lidar returns

(points where objects were detected) using FUSION version 2.90 (McGaughey 2010). We

used FUSION to calculate the number of non-ground lidar returns occurring within 1 m

height intervals in each 5000 m2 sample unit. We then estimated canopy cover as [(number

of returns [3 m in height/total number of returns) 9 100 %]. Canopy cover was relati-

vized to account for slight spatial variability in point density inherent to the airborne

acquisition process.

Ground cover can also influence animal habitat use in southeastern pine forests (Litt

et al. 2001; Vitt et al. 2007; Baxley and Qualls 2009). Three 15 m transects were chosen

using a stratified random design to assess variation in ground cover types at each sample

site. Measured to the nearest centimeter, we recorded cover of grasses, shrubs, herbs, bare

ground, leaf litter, and woody debris along each transect. We calculated the mean percent

cover for each ground cover type and estimated ground cover functional diversity using the

exponential of the Shannon index (Eq. 1). Proximity to ephemeral ponds could also in-

fluence animal presence and was recorded using ArcGIS based on the Euclidean distance

from the sample center to the nearest pond edge.

Statistical analysis

We used general linear models (Kutner et al. 2005) and information theoretic approaches

(Burnham and Anderson 2002) to determine how gopher tortoises influence richness

ðSChao2Þ, evenness (J), and diversity (D). First, we evaluated the support for multiple

functional relationships that describe how the vertebrate assemblages respond to burrow

density. The potential relationships included an intercept only model (no effect of burrow

density), a linear relationship (consistent effect at all burrow densities), and a second-order

polynomial to capture non-linearity (e.g., if burrow density only becomes important after a

threshold is reached). We used Akaike’s Information Criterion with bias correction (AICc)

to evaluate the support for each functional relationship (Burnham and Anderson 2002).

AICc model ranking is useful because one can weigh the strengths of different competing

hypotheses while quantifying the support for each (Quinn and Keough 2002; Johnson and

Omland 2004; Anderson 2008). The best model was selected based on the model weight

xi ¼e�1=2Di

� �

PR

r¼1e

�1=2Dr� �

0BB@

1CCA; a posterior probability representing the probability that model i

is the best in the set (Anderson 2008). For richness only, we used weighted least squares

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(WLS) regression because the variance of each estimate was known and residuals were not

normally distributed (i.e., heteroskedastic). Each point was weighted by the inverse of the

calculated richness variance (Eq. 4), giving more weight to estimates with higher precision

(Kutner et al. 2005). Next, for the most supported relationship we fit a model with and

without an interaction between burrow density and the proportion of active burrows to

determine whether their impact depends on tortoise presence or simply the number of

burrows available. Based on the model weight, we calculated evidence ratios Eij ¼ xi�xj

to determine the likelihood of the best model compared to the other.

Second, to test the effect of fire frequency on the relationship between burrow density

and diversity (D) we calculated the evidence ratio for an additive model with burrow

density and the fire regime factor, and for a model with the interaction of these predictors.

The additive model would support a consistent effect of burrow density on diversity

independent of fire regime, and the interaction model would support the hypothesis that fire

disturbance alters the relationship between burrow density and diversity.

Third, we evaluated a priori hypotheses about which processes were most important for

explaining variation in sandhill vertebrate diversity. All of the variables we considered

represent different processes demonstrated to be important in previous studies. These

included abiotic factors (distance to water and fire frequency), vegetation/habitat factors

(percent canopy cover, ground cover diversity, and coarse woody debris (CWD) volume),

and a biotic factor (gopher tortoise effect represented by burrow density). We developed

specific models to determine whether vertebrate diversity is most determined by abiotic

factors, vegetation/habitat related factors, gopher tortoise ecosystem engineering, or a

combination of these. A null (intercept only) model was also included in the set for a total

of nine models. We ranked the models based on AICc weights and calculated evidence

ratios to determine empirical support for selecting the best model over other suitable

models. We then used the best model to assess the relative importance of the different

variables to predict local vertebrate diversity. We ensured the suitability of the best model

by calculating the coefficient of determination adjusted for the number of parameters in the

model (Adj. R2), residual analysis, and goodness-of-fit. All analyses were performed using

R version 3.0. We ensured sample independence and quantified residual spatial structure of

all dependent variables after model fitting using SAM Version 4.0 (Rangel et al. 2010).

Results

Twenty-two species (10 reptiles, 6 amphibians, and 6 small mammals) were sampled over

the study interval (Table 1). Moran’s I values for diversity and richness oscillated ran-

domly around zero indicating the absence of spatial autocorrelation in their variation at all

distance classes \4 km. Evenness was slightly positively spatially correlated; however,

absence of correlation in model residuals implies spatial structuring of tortoise burrow

densities accounted for observed autocorrelation. Both evenness and total diversity of these

assemblages were associated with an increase in burrow density, exhibiting a linear

functional relationship (Table 2). The linear relationship between diversity and burrow

density had an 80 % probability of being best in the set, the nonlinear functional form had

only 20 % probability, and the model of no effect had a 0 % probability of being best. The

linear relationship between evenness and burrow density had an 82 % probability of being

best in the set, the nonlinear functional form had only 13 % probability, and the model of

no effect had a 4 % probability of being best. There was an 88 % probability that there was

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no relationship between richness and burrow density. Furthermore, it is 42 times more

likely (E = 41.88) that diversity is responding only to burrow density and that there is no

interaction between burrow density and the proportion of burrows that tortoises are ac-

tively using; 46 times more likely for evenness (E = 46.29). Therefore, diversity increased

linearly, with burrow density explaining almost 70 % of the variation; b1 = 0.176,

SE = 0.033, R2 = 0.68 (Fig. 2). Evenness also increased linearly, with burrow density

Table 1 Scientific and common names of all species trapped during the study period (22 species total).Spearman’s rank correlation coefficients are reported for the correlation between abundance of each speciesand burrow density

Taxa Scientific name Common name Correlation

Frogs Anaxyrus terrestris Southern toad -0.11

Eleutherodactylus planirostris Greenhouse frog 0.10

Gastrophryne carolinensis Eastern narrow-mouthed toad -0.46

Hyla cinerea Green treefrog -0.18

Hyla gratiosa Barking treefrog 0.39

Scaphiopus holbrooki holbrooki Eastern spadefoot -0.32

Lizards Anolis carolinensis carolinensis Green anole 0.25

Aspidoscelis sexlineatus sexlineatus Six-lined racerunner -0.04

Plestiodon egregius onocrepis Peninsula mole skink -0.08

Plestiodon inexpectatus Southeastern five-lined skink 0.21

Sceloporus undulatus Eastern fence lizard 0.13

Snakes Coluber constrictor priapus Southern black racer 0.04

Coluber flagellum flagellum Eastern coachwhip 0.39

Heterodon platyrhinos Eastern hog-nosed snake -0.33

Sistrurus miliarius barbouri Dusky pigmy rattlesnake -0.06

Thamnophis sirtalis sirtalis Eastern garter snake -0.08

Mammals Scalopus aquaticus Eastern mole -0.28

Blarina carolinensis Southern short-tailed shrew 0.25

Peromyscus gossypinus Cotton mouse 0.01

Podomys floridanus Florida mouse 0.39

Sigmodon hispidus Hispid cotton rat 0.20

Geomys pinetis Southeastern pocket gopher -0.23

Table 2 Akaike’s information criterion and model probability (xi) for two functional relationships (linearand nonlinear) and a model with no effect (None; intercept only)

Functional relationship Diversity Evenness Richness

AICc xi AICc xi AICc xi

None 67.31 0.00 6.19 0.04 4.42 0.88

Linear 52.36 0.80 0.38 0.82 79.18 0.00

Nonlinear 55.13 0.20 4.02 0.13 8.37 0.12

Each model is the relationship between burrow density and Shannon exponential diversity (D), evenness (J),

and richness SChao2

� �

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explaining a little under half of the variation; b1 = 0.021, SE = 0.006, R2 = 0.43 (Fig. 3).

Burrow density explained less than 15 % of the variation in richness; b1 = 0.104,

SE = 0.070, R2 = 0.14 (Fig. 4). All regression models satisfied assumptions of normality

(Shapiro–Wilk test P � 0.05) and homogeneity of variance (Brown-Forsythe test

P � 0.05). Lack-of-fit tests (P � 0.05) confirmed linear functions were good fits to the

diversity and evenness data (Kutner et al. 2005).

Next, we determined whether fire regime influenced the relationship between burrow

density and diversity by comparing an additive model with both terms (AICc = 4.68;

xi = 0.89) to a model with an interaction between them (AICc = 8.97; xi = 0.11). Based

on the evidence ratio between the models (E = 8.52) it was almost nine times more likely

that fire regime did not affect the relationship between burrow density and diversity than

the alternative hypothesis. Therefore, in both fire regime classes (1–3 and 4–7 years), the

slopes of the relationships were linear and approximately parallel (Fig. 5). However, after

accounting for burrow density (b1 = 0.171, SE = 0.030), there was some effect of fire

regime on diversity (b2 = 0.453, SE = 0.239); species diversity was higher in the

4–7 years burn interval than the 1–3 years interval (least square mean = 4.44 compared to

3.54 respectively, SE = 0.34).

Last, we performed AICc model ranking of a priori multiple regression models to

disentangle the relative contributions of different factors to species diversity. The sum of

Fig. 2 Ordinary least squaresregression (n = 16) of speciesdiversity [exp(Shannon entropy)]versus burrow density (ha-1):regression equation: diversity(D) = 2.05 ? 0.18*BurrowDensity ? ei. Gray dots arediversity estimates, solid line isthe mean predicted function, anddotted lines are the upper andlower 95 % confidence intervals

Fig. 3 Ordinary least squaresregression (n = 16) of speciesevenness versus burrow density(ha-1): regression equation:evenness(J) = 0.38 ? 0.02*BurrowDensity ? ei. Gray dots arediversity estimates, solid line isthe mean predicted function, anddotted lines are the upper andlower 95 % confidence intervals

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the model probabilities of the top two models exceeds 99 % (x1 = 0.79, x2 = 0.20;

Table 3), each of which includes burrow density as a parameter. The five models without

burrow density combine to account for less than 1 % of model probability. The best

model in the analysis was an additive model that included three variables: burrow

density (BD), fire frequency (FF), and CWD volume. This model explained 79.3 % (Adj.

R2 = 0.793) of the variation in diversity and the evidence ratio (Eij = 4.02; xi = 0.72,

xj = 0.18) demonstrated that this model was four times more likely than the next best

model to be the best in the set. No other model had a probability greater than 0.1.

Because this model was far superior to any other model in the set, and the second best

model was simply a subset of the first, we performed multiple regression analysis on the

first model to estimate standardized coefficients of the parameters. The standardized

parameter estimate for burrow density (b10 = 0.793, SE = 0.124) was much greater than

either fire frequency (b20 = -0.227, SE = 0.1234) or CWD (b3

0 = 0.263, SE = 0.121).

Partial regression plots (Fig. 6) show the relationships between each predictor and di-

versity after accounting for the effect of the other predictors. Moran’s I correlograms

constructed from model residuals indicate an absence of unexplained spatial variation in

diversity across the study area.

Fig. 4 Plot of species richness

SChao2

� �versus burrow density

(ha-1), n = 16

Fig. 5 The relationship betweenspecies diversity [exp(Shannonentropy)] and burrow density(ha-1) in two fire frequencyclasses (1–3 and 4–7 years)

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Table 3 Summary of a priori multiple regression models of species diversity versus habitat and environ-mental variables: burrow density (BD), fire frequency (FF), coarse woody debris volume (CWD), % canopycover (CC), ground cover functional diversity (FD), and distance from water (H2O)

Model K Adj. R2 Di LðgijxÞ xi

BD ? CWD ? FF 4 0.79 0.00 1.00 0.79

BD 2 0.65 2.78 0.25 0.20

BD ? GCD ? CWD ? CC 5 0.73 8.48 0.01 0.01

BD ? H2O ? BD*H2O 4 0.60 10.44 0.01 4.26E-03

FF 2 0.20 16.07 3.24E-04 2.56E-04

Null 1 0.00 17.73 1.42E-04 1.12E-04

FF ? H2O 3 0.14 19.70 5.29E-05 4.16E-05

FF ? CWD 3 0.16 19.42 6.05E-05 4.77E-05

CC ? GCD 3 0.01 21.92 1.74E-05 1.37E-05

The null is an intercept only model. The following statistics are reported: the number of parameters plus 1for r2(K), the multiple correlation coefficient corrected for number of variables (Adj. R2), the difference inAICc between the ith model and the best model ðDiÞ, the likelihood of model i, given the data ½LðgijxÞ�, andthe model probability (xi)

Fig. 6 Partial regression plots showing the effect of each predictor variable [burrow density (ha-1) (BD),fire frequency (FF), and downed coarse woody debris (cm3) (CWD)] on species diversity [exp(Shannonentropy)] after accounting for the effect of the other variables in the model

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Discussion

Our results demonstrate for the first time the relationships between gopher tortoise

burrow density and species diversity, richness, and evenness. Tortoise burrow density,

but not the proportion of active burrows, was associated with an increase in diversity.

The lack of effect of burrow activity status is consistent with a prior study in another

sandhill habitat that demonstrated mammal, amphibian, and snake abundances were not

affected by burrow activity status, although lizards were more common in active burrows

(Witz et al. 1991). In our study, the positive influence on local diversity was most

strongly linked to increased evenness of species’ relative abundances, but not species

richness. Tortoise burrow creation and grazing modifies vegetation composition and

increases habitat heterogeneity (Kaczor and Hartnett 1990). The ‘habitat heterogeneity

hypothesis’ posits that diversity increases because heterogeneity promotes species

coexistence as niche opportunities increase (Simpson 1949; MacArthur and Wilson 1967;

Tews et al. 2004). Therefore, it is likely that increased habitat complexity from tortoise

burrows and the use of burrows by commensal species for refugia, nesting, foraging, and

breeding sites (Landers and Speake 1980; Eisenberg 1983; Lips 1991) may alter species

interactions and their relative abundances; thus contributing to the increased evenness

and diversity we observed. Small mammals, especially known obligate commensal P.

floridanus, were among the taxa most consistently positively associated with increased

burrow density. Other known burrow associates, such as Coluber flagellum flagellum,

also displayed positive associations, but overall reptile and amphibian responses were

idiosyncratic.

Development of functional relationships to evaluate keystone effects (H1)

Analysis of functional relationships is ideal to evaluate variation in potential keystone

effects because the importance of a species to its community can vary with abundance or

density (Menge et al. 1994; Kotliar 2000). The functional relationship we developed

between burrow density and diversity shows that vertebrate diversity increased linearly

with burrow density. Because gopher tortoises make up a relatively small amount of the

proportional biomass in this ecosystem, this strong association supports previous hy-

potheses (Eisenberg 1983; Guyer and Bailey 1993) that the gopher tortoise can exert

keystone effects. This effect was largest when burrow density was highest, consistent

with definitions of an ecosystem engineer (Jones et al. 1994). Therefore, the gopher

tortoise can act as a keystone through an ecosystem engineering mechanism (Wright and

Jones 2006).

The role of disturbance in modifying tortoise effects (H2)

The functional form and magnitude of keystone relationships, however, can be altered by

disturbance (Menge et al. 1994; Kotliar 2000). The positive linear relationship between

burrow density and diversity was the same in both frequent fire return intervals (1–3 years)

and less frequent intervals (4–7 years). Therefore, fire frequency does not appear to affect

the positive influence of the gopher tortoise on diversity in our study. The consistency of

the relationship between diversity and burrow density across different disturbance regimes

suggests that their effect on vertebrate diversity is robust within prescribed burn fre-

quencies represented in this study (1–7 years).

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Simultaneous effects of multiple drivers on diversity (H3)

Although it is well demonstrated that alteration of habitat structure by ecosystem engineers is

a strong mechanism influencing diversity (Jones et al. 1994; Wright et al. 2002), other habitat

and environmental variables are also important drivers of local diversity patterns. Generally

absent from studies of keystone or ecosystem engineering effects are the relative contribu-

tions of other mechanisms to observed diversity patterns. We simultaneously assessed the

effects of other variables demonstrated to be important predictors of local diversity in sandhill

habitats. The substantially higher effect of burrow density in top models, and the extremely

low probabilities of models without burrow density, provides strong confirmatory evidence

that burrow density is the most important predictor of local diversity. This finding supports

keystone effects exhibited by gopher tortoises. Our results show that in addition to burrow

density, fire frequency and CWD volume were the next most important variables explaining

vertebrate diversity in our study. This is consistent with previous studies that demonstrate the

importance of downed trees (Freedman et al. 1996; Harmon et al. 2004) and effects of fire

disturbance (Pastro et al. 2011) to animal diversity. Even after accounting for the variation in

diversity explained by these variables, the relationship between diversity and burrow density

remained positive and linear (Fig. 6).

Future directions

Keystone relationships are context dependent and future research should test our inter-

pretations in different sandhill sites and other systems that gopher tortoises occupy (e.g.,

scrub, flatwoods, etc.) because each habitat type has unique attributes and different re-

gional species pools. We predict the relationship between gopher tortoise density and

diversity will vary somewhat over the geographic range of the tortoise with overall trends

remaining consistent with our results. Ultimately, context will lead to variation in the

expression of keystone effects (Fauth 1999). In addition, our study highlights broad pat-

terns of community change that could emerge from complex direct and indirect interac-

tions cascading from tortoise impacts. Because our analysis is correlational, we cannot

definitively test the causal mechanisms driving this keystone relationship. Future studies

would benefit from experimental approaches to determine the direct and indirect effects of

gopher tortoises on variation in species’ relative abundances. Reintroduction (restocking)

of tortoises is a management approach that offers a relatively natural experiment to follow

the temporal response of diversity to burrow dynamics. Also, we focused on non-volant

vertebrate assemblages because they are the most conspicuous users of tortoise burrows,

well studied, and often targets of management. However, hundreds of invertebrate species

have also been observed using tortoise burrows (Jackson and Milstrey 1989). Future

studies should incorporate tortoise effects on invertebrate assemblages to more completely

appreciate their role on community structure and dynamics (see Woodruff 1982 and

Folkerts et al. 1993).

Conclusion

We argue that investigations of keystone species benefit from developing functional re-

lationships to understand variation in effects they exert on their communities or ecosys-

tems. Parameters derived from functional relationships are necessary to build predictive

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models that can be used in various approaches to forecast ecological change and develop

management strategies (Catano et al. 2015). We demonstrated in this study that applying

such an approach to evaluate keystone effects of a threatened species uncovered the

magnitude and functional form of their effect on diversity, and the relative contribution of

this effect compared to other local diversity structuring mechanisms. We confirm the

gopher tortoise’s role as an ecosystem engineer modifying habitat structure is a likely

mechanism altering niche opportunities, increasing species coexistence, and structuring

diversity via a burrow cascade (Kinlaw and Grasmueck 2012).

Considering their non-redundant role as an ecosystem engineer and keystone impacts,

re-establishing populations and their habitat should be a focus of ecological restoration

across their range. Their burrows are expected to become increasingly vital for animals to

persist through more variable and extreme temperatures expected under climate change

(Pike and Mitchell 2013). In addition, the gopher tortoise is one of five related species of

burrowing tortoises (e.g., G. agassizii, G. morafkai, G. flavomarginatus, and G. ber-

landieri) that may serve similar functions across a range of habitat types in North America.

Therefore, continued research, monitoring, management, and policy should be devoted to

the conservation of these and potentially other burrowing tortoises.

Acknowledgments We thank Laura Bhatti Catano and Jesse Abelson for assistance with field work, andstaff at WSSP, including Alice Bard and Paul Lammardo. We are very grateful to Craig Guyer, Eric Stolen,Dave Leonard, and two anonymous reviewers for the helpful comments on this manuscript. The welfare ofall animals was ensured in accordance with the University of Central Florida Institutional Animal Care andUse Committee (approval # 10-39 W), Florida Department of Environmental Protection (permit #04071113), and Florida Fish and Wildlife Conservation Commission (permit # LSSC-11-00033). This workwas made possible by funds from the Gopher Tortoise Council and a SEED grant from the National Centerfor Airborne Laser Mapping (doi: 10.5069/G94M92GW).

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