ORI GIN AL PA PER

Opposing responses to ecological gradients structureamphibian and reptile communities across a temperategrassland–savanna–forest landscape

Ralph Grundel • David A. Beamer • Gary A. Glowacki •

Krystalynn J. Frohnapple • Noel B. Pavlovic

Received: 28 March 2014 / Revised: 19 November 2014 / Accepted: 24 November 2014� Springer Science+Business Media Dordrecht (out side the USA) 2014

Abstract Temperate savannas are threatened across the globe. If we prioritize savanna

restoration, we should ask how savanna animal communities differ from communities in

related open habitats and forests. We documented distribution of amphibian and reptile

species across an open-savanna–forest gradient in the Midwest U.S. to determine how fire

history and habitat structure affected herpetofaunal community composition. The transition

from open habitats to forests was a transition from higher reptile abundance to higher

amphibian abundance and the intermediate savanna landscape supported the most species

overall. These differences warn against assuming that amphibian and reptile communities

will have similar ecological responses to habitat structure. Richness and abundance also

often responded in opposite directions to some habitat characteristics, such as cover of bare

ground or litter. Herpetofaunal community species composition changed along a fire

gradient from infrequent and recent fires to frequent but less recent fires. Nearby (200-m)

wetland cover was relatively unimportant in predicting overall herpetofaunal community

composition while fire history and fire-related canopy and ground cover were more

important predictors of composition, diversity, and abundance. Increased developed cover

was negatively related to richness and abundance. This indicates the importance of fire

history and fire related landscape characteristics, and the negative effects of development,

in shaping the upland herpetofaunal community along the native grassland–forest

continuum.

Communicated by Dirk Sven Schmeller.

R. Grundel (&) � K. J. Frohnapple � N. B. PavlovicGreat Lakes Science Center, U.S. Geological Survey, 1100 N. Mineral Springs Rd.,Porter, IN 46304, USAe-mail: rgrundel@usgs.gov

D. A. BeamerNash Community College, Rocky Mount, NC 27804, USA

G. A. GlowackiLake County Forest Preserves, 1899 West Winchester Rd., Libertyville, IL 60048, USA

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Biodivers ConservDOI 10.1007/s10531-014-0844-x

Keywords Ecotone � Fire � Herpetofaunal conservation � Savannas � Wetlands

Introduction

Significant, widespread declines of amphibians and reptiles have been documented

worldwide (Adams et al. 2013; Alford et al. 2001; Hof et al. 2011; Reading et al. 2010;

Stuart et al. 2004). Gardner et al. (2007) concluded that the threat of extinction or decline

for amphibians and reptiles is higher than for birds or mammals but many more studies

have examined the effects of habitat change on birds and mammals than on herpetofauna.

They also noted a relative lack of community studies of reptiles compared to amphibians

and an increasing emphasis on the effects of novel stressors on herpetofaunal species and

communities. Despite this emphasis on novel stressors, Gardner et al. (2007) concluded

that the effects of structural habitat change related to natural regeneration, fire, fragmen-

tation, and disturbance on the herpetofaunal community, are inadequately understood but

are likely the most significant reasons for species’ declines. Nonetheless, they found rel-

atively few studies conducted on the effects of structural habitat change or variation on

herpetofaunal communities. Hof et al. (2011) similarly concluded that pathogens, climate

change, and land use change contribute significantly to amphibian declines, although in

different patterns of intensity across the planet.

The temperate savanna and grassland biome is one of the most threatened major global

terrestrial biomes (Hoekstra et al. 2005). This is evident in North America, where the once

dominant savanna–grassland biome of the mid-continent has been converted, often to

agricultural, residential, and industrial uses, over most of its historic range (Nuzzo 1986).

The decline in this dominant habitat has been exacerbated by succession of grasslands and

open canopy habitats to closed canopy woodlands and forests, a change often related to fire

suppression (Nowacki and Abrams 2008). This decline motivates efforts to restore these

now-rare savanna–grassland landscapes in the Midwest U.S. (Leach and Ross 1995). The

importance of fire in maintaining and restoring temperate grasslands and woodlands

underscores the value of improving our understanding of how fire history affects herpe-

tofaunal communities (Hu et al. 2013). Restoration activities include manipulating woody

vegetation density to convert forests and woodlands to savannas and reducing woody

vegetation encroachment in grasslands (Nielsen et al. 2003). However, the effect on animal

populations of modifying woody vegetation across the open-forest continuum is inade-

quately documented and the value of specific habitats along this continuum to these animal

species is not well established (Temple 1998), calling into question how important res-

toration of specific habitats is for animal species or communities and decreasing our ability

to predict the outcome of restoration actions (Grundel et al. 2010; Grundel and Pavlovic

2007a, 2008).

To address these concerns, we ask here how amphibian and reptile communities vary

along an open-forest habitat gradient in the Midwest U.S. and how fire history, which

reflects the major management activity used to manipulate woody vegetation density,

affects herpetofaunal community composition (Masterson et al. 2008; Perry et al. 2012;

Rochester et al. 2010; Smith and Rissler 2010; Wilgers and Horne 2006). The results will

inform us how savannas are related to biodiversity maintenance and how fire management

is likely to affect herpetofaunal community structure in these habitats.

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Methods

Study area

We surveyed amphibians and reptiles at 25 sites along an open-forest gradient in northwest

Indiana, U.S.A. These sites were used in previously published studies of birds and bees

(Grundel et al. 2010, 2011; Grundel and Pavlovic 2007a, b). Study sites were situated from

0.8 to 80 km inland from the southern shore of Lake Michigan and averaged 1.8 km ± 3.4

(SD) (range 0.08–16.9 km) between nearest neighbor sites. Sites were located at Indiana

Dunes National Lakeshore (41�380N, 87�090W; n = 17 sites; 6,000 ha total park area),

Tefft Savanna Nature Preserve and Jasper-Pulaski Fish and Wildlife Area (41�100N,

86�580W; n = 7 sites; 3,250 ha), and Hoosier Prairie Nature Preserve (41�310N, 87�270W;

n = 1 site; 225 ha) (Grundel and Pavlovic 2007a; Haney et al. 2008). Based on average

densiometer-measured canopy cover percent and shrub density across sites, we classified

sites as open (\20 % canopy cover), savanna (20–50 %), woodland (50–90 %), scrub

[[1,000 woody stems 2.5–10 cm diameter at breast height (dbh) ha-1], or forest ([90 %

canopy cover and [300 woody stems [10 cm dbh ha-1) (Grundel and Pavlovic 2007a).

Five replicates of each habitat type were represented within the 25 sites. The region where

this study was carried out is characterized by a variety of habitat types occurring in close

juxtaposition (Cole and Taylor 1995). Therefore, each of these five habitat types will occur

within a matrix of other upland habitat types and wetlands. Sites were selected to consist of

relatively large blocks of the target habitat. Approximate mean area of the sites was

11.8 ha ± 2.4 (s.e.) (3.8–58.9 ha range).

At each site, we placed two drift fence arrays with funnel traps to capture herpetofauna

(Crosswhite et al. 1999; Jenkins et al. 2003; Todd et al. 2007). The intent of this study was

to assess a suite of vegetation and fire history factors affecting distribution of herpetofauna

across varied upland, terrestrial sites. Therefore, at each of the 25 sites, two drift fence

arrays were placed within upland locations, away from wetlands, separating arrays from

each other by an average of 163 m ± 34 (SD) (range 70–228 m) (n = 25).

Drift fence arrays consisted of three 10-m long fiberglass window screen arms, oriented

120� from each other. The bottom edge of each arm was buried, leaving about 0.7 m of

screen aboveground as a barrier to animal movement. At the end of each arm, away from

the center, we placed two funnel traps parallel to the wall, with openings facing the center

of the array, for a total of six traps per array. The funnel trap was a cylinder made from

window screen about 30-cm wide by 75-cm long, one end of which was stapled to the lip

of a 20-cm wide funnel. We cut off the small end of the funnel, leaving a 3-cm wide

opening for animals to enter and clipped shut the opposite end of the screen cylinder. Many

animals encountering the fence moved down the fence and into the screen cylinder. We

visited traps frequently (mean interval between trap inspections: 3.93 days ± 0.03, range

3.28–4.77, n = 4,892 visits) and removed animals captured in the trap. The traps captured

snakes, lizards, and amphibians whose body diameter was less than the funnel opening. We

occasionally encountered turtles, and other herpetofauna, moving along the fence but not in

the traps. We counted the amphibians or reptiles encountered along the fence but excluded

turtles from analyses because turtles were too large to be trapped. In total, 5.6 % of all

observations were of individuals seen along the fences and 94.4 % were captured in the

funnel traps (n = 8,310). By species, the percentage of individuals observed along the

fences, as opposed to captured in the traps, ranged from 0 to 22.6 %. The highest per-

centages observed outside of the traps were for small frogs, such as the Spring Peeper

(Pseudacris crucifer). Arrays were checked between 0620 to 2048 (n = 5,302). Mean

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times for checking ranged between 1050 to 1307 by site. The fifty upland arrays were open

for complete seasons (late March to early November 2000–2002) for the final 3 years of

the study and one partial season (late June to late October 1999) during the first year of the

study. During these months, temperatures were generally warm enough that animals were

active. Traps were left open continuously during those intervals except for occasional

maintenance. The two sets of upland traps at each of the 25 sites were open for capture for

a mean of 769 ± 1.9 days (s.e.) (range 750–798). Number of trap-days per habitat type

were: 767 ± 5.3 (open), 767 ± 2.9 (savanna), 768 ± 3.8 (woodland), 768.4 ± 3.7

(scrub), and 775 ± 13.8 (forest) (F4,20 = 0.54, p = 0.71, n = 25 sites, no significant

difference in trap-days per site).

Frogs, toads, salamanders, lizards, snakes, and turtles captured in the traps or found

along the fences in the arrays were marked by site but not with an individual number. For

statistical analyses, which we based on total number of individuals captured per array, we

counted each individual once, ignoring recaptures. We occasionally captured many juve-

nile frogs and snakes on a single day in an array—in a few instances, several times more

juveniles in a day than we ever captured adults. We tallied multiple observations of

juvenile animals as a single observation for that species on that day and array.

Habitat assessment

Around the drift fence arrays, we measured environmental variables describing vegetation

structure, land cover, and fire history. We measured habitat variables in six 0.05 ha plots

near each array using methods described in Grundel and Pavlovic (2007a). Maximum

Pearson correlation among all pairs of these untransformed predictors was 0.56. These

predictor variables, and mean values across 50 upland arrays are summarized in Table 1.

Predictor values from the six plots were averaged for each array using inverse weighting in

which the relative contribution of each plot to the average was proportional to 1/d2, where

d is the distance from the plot to the array center (ESRI 2009). Using available maps of

recent fires at the study sites, we also calculated two measures of fire history, Fire Fre-

quency and Fire Age, the average number of years since the most recent fire in that 200-m

area. For Fire Frequency, we summed the total area burned within the 200-m radius over

the 15-year prior period and divided by the area of the 200-m circle. This accounted for

fires that did not cover the entire area or multiple fires within a year across the same circle.

Because the study spanned several years, we calculated the 15-year interval back from

each date on which we checked arrays and averaged Fire Frequency over all such dates.

For Fire Age, we also calculated back from each date on which we checked contents of

arrays and averaged over all such dates. Although our database of fires extended nearly

20 years, some sites did not burn during those 20 years and we assigned a value of 20 years

to those arrays. However, this was a conservative measure of fire age and we expect that

some of those sites did not burn for perhaps more than 50 years.

We also mapped the area within a 200-m radius circle of each array (ESRI 2009).

Mapping was done after creating the categories of wetland and developed land cover listed

below and was accomplished by visiting sites in the field, drawing habitat boundaries on a

map and digitizing the map. Two hundred meters is similar to the distance into uplands

surrounding wetlands that amphibians and reptiles typically use (Semlitsch and Bodie

2003). We included two types of land cover as predictors in analyses here. These two

covers, Wetland Cover and Developed Cover, were not well characterized by the habitat

variables listed in Table 1. Wetland Cover included (a) Wetland Tree habitats dominated

([50 % cover) by aspen (Populus tremuloides), pin and swamp white oak (Quercus

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palustris and bicolor), green ash (Fraxinus pennsylvanica) and other wetland facultative

tree species; (b) Permanent, ephemeral, or ephemeral edge wetlands, including wet

meadows and ponds; (c) Wetland Shrub habitats ([50 % cover) dominated by willow

(Salix spp.) and other wetland facultative woody plants; and (d) Wetland Forb dominated

habitats ([50 % cover) by wetland forbs and grasses. Developed Cover included agri-

cultural landscapes and structures such as home sites and similar developed areas and

roads.

Data structure and analysis

We used principal curves to ordinate community composition (frequencies of captures of

different species at each array) across arrays (De’ath 1999; Walsh 2011). De’ath (1999)

noted that ordination of sites by their species composition can have two goals—elucidating

an ecological gradient that influences species composition and describing similarity of

species composition among sites—but that a given ordination technique is typically better

at achieving only one of those goals. Principal curve ordination emphasizes discovery of

the ecological gradient underlying species composition. Thus, sites with similar PC ordi-

nation scores should share similar key ecological characteristics that are strongly related to

species composition. If we mapped sites as points in a multi-dimensional space whose axes

were defined by abundances of species present at the sites, a principal curve would be a

smooth one-dimensional curve that passed through this cloud of site points in a manner that

Table 1 Environmental variables, describing vegetation and fire history, used to predict distribution ofamphibians and reptiles in northwest Indiana, USA

Variablename

Descriptor (transformation used) Mean ± standarderror

Measurementmethod

Bareground

% cover of bare ground (H) 30.9 % ± 3.0 Six 0.05 ha plots

Litter % cover of litter 7.6 % ± 1.3 Six 0.05 ha plots

Downedlogs

% cover of downed logs (H) 1.0 % ± 0.2 Six 0.05 ha plots

Vegetation % Herbaceous and woody cover within 0.3–1 m ofground (log ? 1)

58.0 % ± 3.0 Six 0.05 ha plots

Canopycover

% Canopy cover measured by densiometer 56.2 % ± 4.6 Six 0.05 ha plots

Stems2510 Density 2.5–10 cm dbh trees, saplings, or shrubs(H)

454 ± 478 stemsha-1

Six 0.05 ha plots

Wetlandcover

% Wetland cover within 200 m of array center (H) 19.0 % ± 2.0 Mapped with200 m of arraycenter

Developedcover

% Developed cover within 200 m of array center(H)

5.9 % ± 1.0 Mapped with200 m of arraycenter

Firefrequency

Total area burned within 200-m radius of arraycenter over 15-year prior period divided by area ofthe 200-m circle (log)

2.5 fires15-y-1 ± 0.3

Local fire historymaps

Fire age Average # years since most recent fire within 200 mradius of array center (log)

2.7 yrs. ± 0.4 Local fire historymaps

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minimized the distance from the points to the curve. For ordinations in this paper, the

principal curve was scaled to a length of 1, with each site given a score between 0 and 1.

Arrays’ locations on the principal curve reflected their relative location on an underlying

ecological gradient that helped predict the composition of the herpetofaunal community.

We ordinated three communities across the 50 arrays—all herpetofauna except turtles,

amphibians, and reptiles except for turtles. Species counts were square root transformed to

decrease effects of the most abundant species on the ordination results (McCune and Grace

2002). For community analyses involving the two upland arrays at each site, we used

species that were captured a minimum of ten times (Fig. 1). The direction of principal

curve ordination scores is arbitrary so, for a given ordination, the assignment of the

ordination scores on the 0–1 scale can be reversed (e.g., sites with a score of 0 could be

assigned a score of 1 and sites with a score of 1 could be assigned a score of 0) and the

interpretation of the ordination would not be affected, although the sign of correlations

between scores and environmental predictors would reverse. Therefore, the sign of the

correlation between principal curve scores and a given environmental variable should not

be compared between amphibians and reptiles when those groups are analyzed in separate

Fig. 1 a Average within-habitat herpetofaunal community similarity based on Chao’s estimator (correctedfor unseen species) of Sørensen abundance-based similarity (Chao et al. 2005) ± standard error for fivehabitat types. b Species density (# species captured per sampled area) as a function of number of arrayssampled. Species density should be compared at the same sampling effort (e.g., 10 arrays) across habitattypes. c Species richness (# species captured) as a function of number of individuals captured. Speciesdensity should be compared at the same number of individual captured (e.g., 820 individuals) across habitattypes. d Mean number of amphibian (black bars) and reptile (white) individuals captured ± standard errorfor five habitat types. Single captures per individual were tallied. In a and d bars followed by same letter arenot significantly different (p \ 0.05; Tukey’s multiple comparisons test). In d comparisons are withinamphibians and within reptiles separately

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ordinations, unless the ordination scores are arranged in a comparable manner, such as

higher ordination scores being associated with higher abundance. However, differences in

signs among multiple predictors are meaningful. For example, two predictors might have

correlations of opposite signs with amphibian ordination scores while they have the same

sign for reptiles. Comparisons of importance of different predictors would not be affected.

Principal curves were calculated using the R program ‘pcurve’ calculated from square root

transformed abundances of the herpetofaunal species (Walsh 2011). Curves were evaluated

for fit following the protocol described by De’ath (1999).

We used permutational multivariate analysis of variance (perMANOVA), based on

Sørenson distance, to test whether community species composition (square root trans-

formed) differed significantly among the five habitat types along the open-forest gradient

(Anderson 2001; McCune and Mefford 2011). F values were an indicator of effect size for

perMANOVA and were reported along with p values to indicate how different habitats

were from each other in herpetofaunal community composition. We adjusted p values for

multiple tests using the Benjamini–Hochberg adjustment (Benjamini and Hochberg 1995;

R Core Team 2014).

To assess how habitat characteristics were related to the ecological gradient described

by the principal curve scores and to community richness and abundance, we used averaged

ordinary least squared (OLS) models, implemented in SAM software (Rangel et al. 2010)

as a method of multimodel inference (Burnham and Anderson 2002). Individual OLS

models (n = 2047 models; 211 - 1, where 11 is the number of predictors) were weighted

by Akaike Information Criteria (AICc) weights to produce a weighted average of the

regression coefficient of each predictor across those 2047 models. Importance of each

predictor was evaluated as the sum of the AICc weights for the subset of OLS models in

which a particular predictor was included. Relative importance of each predictor was also

indicated by the absolute value of the standardized regression coefficient. Because spatial

autocorrelation might affect these results (Lichstein et al. 2002), we used an eigenvector

based spatial filtering method (SEVM, spatial eigenvector mapping) to help account for

spatial trends in the dependent variable in the OLS regressions (De Marco et al. 2008;

Rangel et al. 2010). SEVM produces a series of spatial filters that describe spatial rela-

tionships among the study sites at different scales across the complete area studied. Those

filters that were most highly correlated with a particular response (r2 [ 0.1, p \ 0.05) were

linearly combined into a single filter that was entered as a predictor along with the ten

environmental predictors. This combined filter helps account for the effect of spatial

autocorrelation from the relationship between the environmental predictors and herpe-

tofaunal responses in the averaged OLS model. We also performed a partial regression

analysis of the effect of the SEVM filter versus the effect of the group of the ten envi-

ronmental predictors on explaining variance in the responses (Rangel et al. 2010). We

present the amount of variance that can be explained by the spatial filter and by the

environmental variables as another way of expressing the possible role of spatial pattern in

determining the relationship between the responses and predictors. Predictors were

examined for deviations from normality and were transformed to improve fit to a normal

distribution as needed (SPSS 2004).

Because species richness will be affected by sampling intensity (Gotelli and Colwell

2001), including number of individuals collected, we assessed differences in species

richness among habitat types not only by simple species counts, but also by rarefaction

analysis (Colwell 2009) and by extrapolation (Hortal et al. 2006) using the incidence based

cover estimator (ICE) of richness (Colwell et al. 2012). Within-habitat composition sim-

ilarity was estimated using Chao’s abundance based modified Sørensen similarity index,

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which took into account species potentially not encountered at an array due to chance or

insufficient sampling effort (Chao et al. 2005).

Results

We captured 9 frog and toad, 5 salamander, 2 lizard, 11 snake, and 4 turtle species

(Appendix). Based on our personal observations and published accounts (Minton 2001), we

recognize about nineteen other herpetofaunal species that might have historically occurred

at these study sites but have either likely been extirpated from these sites, are aquatic

species or turtles not likely to be sampled in this study, or are potentially present at our

sites but not captured and likely uncommon. This last group of species would likely not

have been captured the minimum of ten times for use in our community analyses

(Appendix).

Of these 31 species, 24 non-turtle species (8 frog and toad, 4 salamander, 2 lizard, and

10 snake) were captured at least ten times, not including recaptures (Appendix). For this

herpetofaunal community of 24 species, significant compositional differences were found

between habitat types for six of ten habitat comparisons and indicated that herpetofaunal

communities in scrub dominated areas and in forests differed significantly from commu-

nities in other habitat types and from each other (Table 2). Amphibian communities had

three significant differences between habitat pairs; reptile communities had seven differ-

ences. Amphibian communities were different between forest habitats and other habitats,

except savannas. Reptile communities in scrub dominated areas and in forests differed

significantly from communities in other habitat types and from each other. Community

composition was most similar among forested sites and least similar among open sites

(Fig. 1a; F4,220 = 8.5, p \ 0.001).

Principal curve ordinations accounted for 46.4, 75.8, and 86.4 % of species variation in

overall herpetofaunal, amphibian, and reptile communities, respectively (Table 3). Eastern

Hog-nosed Snakes (rs = 0.59), North American Racers (snake) (rs = 0.57), Common

Gartersnakes (rs = -0.40), Northern Leopard Frogs (rs = -0.49), and Dekay’s Brown-

snakes (rs = -0.78) had the highest significant (p \ 0.01) positive and negative Spearman

rank correlations with the overall herpetofaunal principal curve ordination scores.

Table 2 Significance of compo-sitional differences in herpetofa-unal communities betweenhabitat types, based on permuta-tional multivariate analysis ofvariance (perMANOVA)(McCune and Mefford 2011)

Species capture counts squareroot transformed. Table entriesare F values (* p \ 0.05,** p \ 0.01, *** p \ 0.001,adjusted for multiple tests)

Habitat 1 Habitat 2 All Amphibians Reptiles

Open Savanna 1.15 0.97 1.35

Open Woodland 1.35 1.05 1.45

Open Scrub 1.90*** 1.22 2.25***

Open Forest 2.19*** 1.79* 2.54***

Savanna Woodland 1.06 0.87 1.09

Savanna Scrub 1.57* 1.36 1.68*

Savanna Forest 1.44 1.22 1.71*

Woodland Scrub 1.84* 1.18 2.31**

Woodland Forest 2.34*** 1.85* 2.40**

Scrub Forest 2.45*** 2.17** 2.35***

Overall 3.08*** 1.96* 3.74***

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Biodivers Conserv

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Biodivers Conserv

123

The averaged OLS model, with spatial filters included, explained 64.6 % of the vari-

ation in the ecological gradient underlying composition of the entire herpetological

community, as represented by the principal curve scores, and 30 % of the species variation

in the herpetological community across the 50 arrays (30 % = 64.6 % of the 46.4 % of

deviance explained by the PC curve) (Table 3). The averaged model accounted for 54.8 %

of variation in amphibian community compositional variation and 47.6 % of reptile

community compositional variation. For the overall herpetofaunal community, Bare

Ground, Downed Logs, Fire Frequency, and Fire Age were the strongest environmental

predictors (highest importance value, highest absolute standardized regression coefficients)

of principal curve ordination scores (Table 3). Generally, the ecological gradient under-

lying overall species composition was one in which higher ordination scores were asso-

ciated with more bare ground, more downed logs, more frequent fires, and longer time

since most recent fire. In the complete OLS model containing all ten environmental pre-

dictors plus the SEVM spatial filter, the ten environmental predictors by themselves

uniquely accounted for 18.5 % of the PC score variation, the SEVM spatial filter uniquely

accounted for about 12.5 %, and the eleven predictors shared about 36.4 % of the total

67.4 % of variation in PC scores explained by the complete model. Thus, the environ-

mental predictors accounted, uniquely and shared, for about 54.9 % of the PC score

variation. These predictors accounted for between 37.5 and 67.6 % of the variation in

principal curve scores, richness, and abundance of the overall herpetofauna, amphibian,

and reptile communities (Table 3).

Higher amphibian principal curve scores were most strongly associated with increasing

cover of Litter, Downed Logs, Vegetation, and decreasing Developed cover and the

environmental predictors accounted for about 49.2 % of the variation in amphibian PC

scores (Table 3). Higher amphibian ordination scores were associated with higher

amphibian abundance (r = 0.70, p \ 0.001) and richness (r = 0.68, p \ 0.001). For

reptiles, higher principal curve scores occurred with lower Bare Ground, Canopy Cover,

and Developed Cover and the environmental predictors accounted for about 58.9 % of the

variation in amphibian PC scores. Higher reptile ordination scores were associated with

higher reptile abundance (r = 0.42, p = 0.003) and richness (r = 0.32, p = 0.03).

When species accumulation curves are expressed as a function of sample units, or

sampling intensity, the resulting curve represents a species density (species per unit area)

while species accumulation curves expressed as a function of individuals collected rep-

resents a species richness. When habitats were compared at the same number of samples

(10 arrays) or individuals captured (ca. 820), overall herpetofaunal species density and

richness, respectively, were highest in savanna habitats and lowest in scrub habitats

(Fig. 1b, c).

Higher overall herpetofaunal richness was associated with higher Litter, Downed Logs,

Fire Frequency and Fire Age and lower Canopy Cover and Developed Cover (Table 3).

Amphibian richness increased as Litter, Downed Logs, and Fire Frequency increased and

as Canopy Cover decreased. Reptile richness increased as Litter, Downed Logs, Fire

Frequency and Fire Age increased and Canopy Cover, Wetland Cover, and Developed

Cover decreased.

Amphibian abundance increased as Litter and Fire Age increased and as Bare Ground,

Stem Density, and Developed Cover decreased. Reptile abundance increased as Bare

Ground and Wetland Cover increased and as Stem Density, Developed Cover, and Fire

Frequency decreased. Amphibian and reptile abundances differed significantly among

habitat types, being highest in forests for amphibians and in open areas for reptiles

(Fig. 1d; F4,45 = 2.9, p = 0.03 for amphibians; F4,45 = 7.4, p = 0.0001 for reptiles).

Biodivers Conserv

123

Tab

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Biodivers Conserv

123

Table 4 summarizes the general relationship, positive or negative, between the environ-

mental predictors included in the single best OLS model as determined by AICc scores,

and the responses of principal curve scores, richness, and abundance.

Overall wetland cover was not one of the most important predictors of community

composition or amphibian richness or abundance (Table 3). Spearman rank correlations

(rs) between cover, within 200 m of arrays, of individual wetland classes and PC scores,

richness, or abundance of amphibians and reptiles were generally low. For the six wetland

classes (Wetland Tree, Permanent, Ephemeral, or Ephemeral Edge wetlands, Wetland

Shrub, and Wetland Forb), no correlations (rs) were [0.5 between wetland cover and PC

score or richness for amphibians or reptiles. There was a significant (p \ 0.05) negative

correlation between amphibian abundance and wetland forb cover (rs = -0.51) and a

significant positive correlation between reptile abundance and ephemeral edge wetland

cover (rs = 0.56).

Discussion

The Midwest U.S. can be characterized as a terrestrial ecological transition zone in which

grasslands to the west and temperate deciduous forests to the east meet, yielding a mixture

of habitat types that can be differentiated, in part, by woody vegetation density (Anderson

and Bowles 1999). Successional shifts among the habitats along this woody vegetation

gradient as a function of moisture and fire, and existence of ecotonal habitats such as

savanna that combine characteristics of grasslands and forests, characterize this Midwest

landscape and similar grassland–savanna–forest transitions around the world (Lehmann

et al. 2014; Staver et al. 2011). The habitats with lower canopy cover in this mix, prairies

and savannas, are critically diminished globally over their historic range (Hoekstra et al.

2005; Nuzzo 1986). Maintenance and restoration management of these open habitats in the

Midwest U.S. and worldwide often depend on frequent fire, perhaps as frequent as yearly

(Bowles and Jones 2013; Considine et al. 2013). Given this vegetation structural depen-

dency on frequent fire, how do amphibian and reptile distributions relate to vegetation

structure and how do distributions of amphibians and reptiles relate to fire history across

the gradient of woody vegetation spanning the grassland–forest continuum in the transition

zone of the Midwest U.S. (Anderson and Bowles 1999)?

Vegetation structure, land cover, and fire history accounted for 38–68 % of the variation

observed in amphibian and reptile ecological gradient (principal curve) scores, richness,

and abundance, suggesting that these are important determinants of amphibian and reptile

distribution but that other important determinants might be added to model herpetofaunal

habitat requirements more fully. For example, Huang et al. (2014) showed that changes in

forest cover affected forest microclimate and microclimate affected distribution of a

mountain lizard species in Taiwan, suggesting a critical role for microclimate in reptile

distribution. In particular, our models performed most poorly describing amphibian rich-

ness, suggesting that factors beyond vegetation, land cover, and fire were important for

determining number of amphibian species using an area.

Across the grassland–forest continuum in our study sites in northwest Indiana, the

herpetofaunal community is broadly divided into closed canopy and more open canopy

assemblages. The transition from open habitats to forests is a transition from higher reptile

abundance to higher amphibian abundance and the intermediate savanna landscape sup-

ports the most species overall. This is one example we documented of opposing trends,

between amphibians and reptiles, in factors affecting richness, abundance, and distribution.

Biodivers Conserv

123

For example, increasing bare ground was associated with decreasing reptile richness but

increasing reptile abundance and decreasing amphibian abundance. Basking in open areas,

well exposed to the sun, is a common thermoregulatory activity of reptiles, but one that is

tempered by threats associated with exposure such as predation or dehydration. This

illustrates why opposing trends between reptile abundance and richness may arise, as some

species seek out these open areas and others avoid them (Bovo et al. 2012). Toft (1985)

noted that resource partitioning patterns in amphibians and reptiles are most strongly

affected by aspects of interspecific interactions, such as competition and predation, and by

factors that act independently of interspecific interactions, such as physiological constraints

that have to be accommodated by habitat characteristics. Differences between reptiles and

amphibians in habitat use or differences between abundance and diversity patterns there-

fore likely reflect differences in competition, predation, and factors such as physiological

constraints. For example, opposing trends between reptile abundance and richness in areas

with different cover of bare ground might arise because of relative dominance of a few

species in areas with high cover of bare ground. Several such opposing trends were noted.

Reptile and amphibian richnesses increased as fire frequency increased but reptile abun-

dance declined. Developed land cover in the vicinity of the study areas was consistently

strongly related to amphibian and reptile community composition and negatively related to

richness and abundance of amphibians and reptiles in our study sites, yet was not a strong

predictor of overall herpetofaunal community composition. Similarly, higher litter cover

was associated with higher amphibian and reptile species richness and higher amphibian

abundance and was a strong predictor of amphibian community composition but not reptile

community composition. Such opposing trends are consistent with the diversity of life

history traits within and between reptile and amphibian communities and support the

caution raised against management planning that assumes too much ecological similarity

between these classes (Gardner et al. 2007; Gibbons et al. 2000). Overall, at our study sites,

amphibian community composition varied most along a gradient characterized at one end

by high litter, downed logs, low vegetation density, and low developed land cover. That

end of the gradient was associated with higher abundance and richness. Reptile community

composition varied along a gradient at one end of low bare ground cover, canopy cover,

and developed land cover. That end of the reptile gradient was also associated with higher

abundance and richness. Therefore, the main landscape traits associated with the two

classes did not overlap, except for a response to developed land cover, which was nega-

tively associated with amphibian and reptile richness and abundance. The fact that

abundance and richness of amphibians or reptiles at times respond in opposite directions to

factors such as bare ground, wetland cover, and fire frequency suggests that some species

respond strongly negatively and others strongly positively to those factors and the balance

can be fewer but abundant species or more but less abundant species. Santos and Poquet

(2010), for example, upon examining a Mediterranean reptile community’s response to

fire, noted some lizards responding positively, and some negatively, to long fire return

intervals, while snakes seemed much less affected by fire regime.

While fire history often affects reptile or amphibian community composition (Perry

et al. 2012; Rochester et al. 2010), a lack of significant change in diversity with a dif-

ference in fire frequency is also often observed (Renken 2006). Because many upland areas

are managed using prescribed burning, understanding the relationship between fire regimes

and herpetofaunal abundance can help set fire management goals (Masterson et al. 2008;

Perry et al. 2012; Rochester et al. 2010; Smith and Rissler 2010; Wilgers and Horne 2006).

Here, fire frequency and interval since the most recent fire over a 15 year interval were

important predictors of overall herpetofaunal community composition and were associated

Biodivers Conserv

123

with changes in richness of amphibians and reptiles, but in a potentially unexpected way.

Overall herpetofaunal community composition was related to fire regime along a gradient

from more frequent and less recent to less frequent and more recent fires. For both

amphibians and reptiles, richness increased as fire frequency increased and as time since

most recent fire also increased. Amphibian abundance increased as time since last fire

increased while reptile abundance increased as fire frequency decreased. While we might

expect areas with more frequent fires to have had more recent fires, the results suggest that

relationship might not be most favorable to amphibian and reptile community richness in

northwest Indiana. This may reflect short term negative effects of fire coupled with longer

term positive effects on abundance and richness. Such opposing temporal trends are known

for amphibian and reptile communities (Pilliod et al. 2003; Renken 2006; Russell et al.

1999). To take advantage of these short term and longer term effects, mosaic burning

patterns that provide areas of more frequent burns and other areas of longer intervals since

the most recent fire might be appropriate if maintenance of relatively high herpetofaunal

diversity is the management goal. However, abundance of some species may be negatively

affected by frequent burning, as seen by the negative relationship between fire frequency

and reptile abundance and may alter the desired goal.

Opposing trends in abundance between amphibians and reptiles, with amphibian

abundance being highest in forested habitat and reptiles in open habitat suggest why

overall richness of the herpetofaunal community was highest in savannas, which represent

an intermediate state of canopy cover along the open-forest continuum. Indeed, capture

frequencies of amphibians and reptiles were most similar in savannas, among the five

habitats surveyed, and intermediate in magnitude between open and forest habitats

(Fig. 1d). Oak savannas in the Midwest U.S. are a critically threatened habitat (Hoekstra

et al. 2005; Nuzzo 1986). In previous studies examining how bird and bee distribution

varied along this open-forest woody vegetation gradient at these study sites (Grundel et al.

2010; Grundel and Pavlovic 2007a), a similar pattern was suggested—species rich sav-

annas inhabited by species that were not strong savanna obligates and stronger species

affinities for habitat extremes suggesting that savannas share a mixture of species present

in open and forest habitats. Indeed, savanna herpetofaunal communities are not signifi-

cantly different from herpetofaunal, amphibian, and reptile communities in nine of twelve

comparisons with other habitat types in this study. The same general lack of community

differentiation was observed for bees but not for birds in this area (Grundel et al. 2010;

Grundel and Pavlovic 2007a) reinforcing the notion that savannas, as defined by canopy

cover, are often ecotonal in nature for resident animals with the savanna animal com-

munities not significantly differentiated compositionally from open or forest animal

communities, even if open habitats are significantly different from forests in animal

composition, which we observed here for amphibians and reptiles.

Amphibians and reptile species are often associated with wetlands. Along the woody

vegetation gradient studied, however, overall percentage of wetlands within 200 m of sites

was only an important predictor of reptile community richness (negatively related) and

abundance (positively related), not on overall herpetofaunal or amphibian community

characteristics. There was a negative correlation between amphibian abundance and wet-

land forb cover and a positive correlation between reptile abundance and ephemeral edge

wetland cover. Others have noted a lack of effect of wetland proximity on upland her-

petofauna diversity (Loehle et al. 2005) possibly related to differences in how far different

amphibian species move from wetlands (Rittenhouse and Semlitsch 2007). Within the

200-m zone we assessed, no strong trends in wetland effects on amphibians or the overall

herpetofaunal community emerged.

Biodivers Conserv

123

The relative lack of importance of nearby (200-m) wetland cover in predicting overall

upland herpetofauna community composition, combined with the observed importance of

fire history and fire-related canopy, bare ground cover, and litter cover on richness and

abundance, paints a picture of the importance of fire history and fire related landscape

characteristics in shaping the upland herpetofaunal community along the native open-forest

continuum. However, composition of these communities is consistently sensitive to pre-

sence of nearby human related disturbance suggesting an overriding influence of residential

and agricultural development on this community. For savanna conservation, the results

indicate that many herpetofaunal species use savannas, suggesting these savannas are

valuable for conservation of the overall herpetofauna. However, because habitat extremes

(forests, open canopy habitats) are occupied differently by the amphibians and reptiles, the

savannas for the overall herpetofaunal community are likely an ecotonal compromise

between preferred landscapes for the amphibians and reptiles separately.

Acknowledgments We thank G. Dulin, E. Garza, J. LaPlante, and V. Price for assistance in herpetofaunaldata collection and R. Deering, L. Forste, R. Phillips, J. Taylor, M. Pryzdia, and A. Zammit for vegetationdata collection, two anonymous reviewers and Alan Resetar for review of the manuscript. Research wasconducted with permission and assistance of the National Park Service and the Indiana Department ofNatural Resources Division of Nature Preserves. Funding was provided by a U.S. Geological Survey–National Park Service technician support grant and by the USGS Grasslands Research Funding Initiative.Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement bythe U.S. Government. This article is Contribution 1892 of the USGS Great Lakes Science Center.

Appendix

See Table 5.

Table 5 Herpetofaunal species captured at fifty drift fence arrays in northwest Indiana, USA (top) andspecies possibly historically occurring, or occurring at present, but not captured (bottom)

Name Common name (* indicates species not used for community analyses)

Species captured

Anaxyrus americanus American Toad

Anaxyrus fowleri Fowler’s Toad

Hyla versicolor Eastern Gray Treefrog

Pseudacris crucifer Spring Peeper

Pseudacris triseriata Midland Chorus Frog

Lithobates catesbeianus Bullfrog

Lithobates clamitans Bronze Frog

Lithobates pipiens Northern Leopard Frog

Lithobates sylvaticus Wood Frog*

Ambystoma jeffersonianum Jefferson Salamander*

Ambystoma laterale Bluespotted Salamander

Ambystoma tigrinum Eastern Tiger Salamander

Plethodon cinereus Northern Redback Salamander

Notophthalmus viridescens Eastern Newt

Coluber constrictor Eastern Racer

Lampropeltis triangulum Eastern Milk Snake

Biodivers Conserv

123

References

Adams MJ et al (2013) Trends in amphibian occupancy in the United States. PLoS One 8:e64347. doi:10.1371/journal.pone.0064347

Table 5 continued

Name Common name (* indicates species not used for community analyses)

Pituophis catenifer Bullsnake

Diadophis punctatus Ringneck Snake*

Heterodon platirhinos Eastern Hognose Snake

Nerodia sipedon Northern Water Snake

Storeria dekayi Brown Snake

Storeria occipitomaculata Redbelly Snake

Thamnophis proximus Western Ribbon Snake

Thamnophis sauritus Eastern Ribbon Snake

Thamnophis sirtalis Common Garter Snake

Ophisaurus attenuatus Slender Glass Lizard

Aspidoscelis sexlineata Sixlined Racerunner

Chelydra serpentina Common Snapping Turtle*

Chrysemys picta Northern Painted Turtle*

Terrapene carolina Eastern Box Turtle*

Sternotherus odoratus Common Musk Turtle*

Name Common name Possible reason for not being captured

Species not captured and possibly present in area currently or historically

Siren intermedia Lesser Siren Aquatic

Necturus maculosus Common Mudpuppy Aquatic

Clemmys guttata Spotted Turtle Turtles

Emydoidea blandingii Blanding’s Turtle Turtles

Terrapene ornata Ornate Box Turtle Turtles

Plethodon glutinosus Northern Slimy Salamander Not found since 1960

Ambystoma opacum Marbled Salamander Not found since 1960

Ambystoma maculatum Spotted Salamander Not found since 1960

Rana palustris Pickerel Frog Not found since 1960

Plestiodon fasciatus Five-lined Skink Not found since 1960

Pantherophis alleghaniensis Eastern Ratsnake Not found since 1960

Clonophis kirtlandii Kirtland’s Snake Not found since 1960

Acris crepitans Northern Cricket Frog Not found since 1960

Hemidactylium scutatum Four-toed Salamander May be present but uncommon

Sistrurus catenatus Eastern Massasauga Rattlesnake May be present but uncommon

Regina septemvittata Queen Snake May be present but uncommon

Pantherophis vulpinus Fox Snake May be present but uncommon

Opheodrys vernalis Smooth Green Snake May be present but uncommon

Thamnophis radix Plains Garter Snake May be present but uncommon

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