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Biodiversity and Conservation
https://doi.org/10.1007/s10531-021-02302-8
1 3
ORIGINAL PAPER
Habitat selection ofcave‑restricted fauna inanew hotspot
ofsubterranean biodiversity inNeotropics
MarconiSouza‑Silva1,2 · RobertaFernandaVenturaCerqueira1,2 ·
ThaisGiovanniniPellegrini2,3 · RodrigoLopesFerreira2
Received: 19 September 2020 / Revised: 19 September 2021 / Accepted: 1 October 2021
© The Author(s), under exclusive licence to Springer Nature B.V. 2021
Abstract
Environmental stability and oligotrophy are considered the main drivers of species distri-
bution within caves due to physiological and nutritional requirements presented by many
cave dwellers. However, such patterns are poorly evaluated in tropical caves, especially
with regard to habitat selection and interspecific competition between invertebrate groups.
Considering that troglobitic species are usually highly specialized, presenting specific
requirements for environmental conditions, we hypothesize that troglobitic species will be
preferentially associated with deeper areas inside the cave. These areas are stable and pre-
sent trophic and physical constraints, which may favors the troglobites in competitive inter-
actions with non-troglobitic species. The study carried out in the Águas Claras Cave Sys-
tem revealed a new hotspot of subterranean biodiversity, represented by 30 cave-restricted
species (29 invertebrates and 1 fish species), being 73.3% terrestrial, 16.7% amphibian,
and 10% aquatic. The richness of troglobitic species did not respond to physical attributes
or resources availability as postulated, but increased with temperature, humidity content
and with non-troglobitic species richness. The similarity of the troglobitic species along
the cave was determined by the moisture content. Furthermore, the richness of troglobites
was higher in those areas with greatest taxonomic distinctness of non-troglobitic species
and higher values of the TB/nTB species richness ratio. The habitats requirements of the
troglobitic species were not coincident, thus indicating that such species avoid niche over-
lapping. We highlighted the studied cave system as a singular subterranean habitat that
contributes to both local and regional biodiversity. Additionally, the condition of high tem-
perature and humidity seems to be key factors that are favoring the existence of a high
number of endemic species. Unfortunately, this cave system is devoid of any official pro-
tection, thus deserving urgent actions to ensure its conservation.
Keywords Cave conservation· Habitat heterogeneity· Troglobites· Sampling methods·
Niche
Communicated by B.D. Hoffmann.
* Marconi Souza-Silva
marconisilva@ufla.br
Extended author information available on the last page of the article
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Biodiversity and Conservation
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Introduction
Understanding how the local and regional species pool is related to the physical, trophic
and microclimatic traits of a given habitat is one of the most important paradigms in com-
munity ecology (Hutchinson 1959; Rosenzweig 1981; Benedetti-Cecchi etal. 1997; Amar-
asekare 2003; Bregović and Zagmajster 2016; Foster etal. 2019). Based on the theory of
niche overlap, many authors have used habitat structure or heterogeneity to predict species
richness in a given area (Amarasekare and Nisbet 2001; Cornell 2010; Yang etal. 2015;
Stein etal. 2015; Vargas-Mena etal. 2020). In those cases, the differential use of micro-
habitats is one of the main determinants of the coexistence of many species (MacArthur
and Levins 1967; Tilman 1982; Chesson 2000a; Mehrabi etal. 2014). However, the dif-
ferential use of habitats/microhabitats and resources by different species depends not only
on their availability but also on the presence of competing species (Chesson 2000b; Amar-
asekare 2003; Amarasekare etal. 2004). Although habitat heterogeneity is a good predictor
of abundance and diversity, the relationship between these synecological components is
dependent on the spatial and temporal scale under analysis (González-Megías etal. 2007;
Mehrabi etal. 2014).
Despite being considered as simplified and stable environments when compared to the
surface, caves may present many types of microhabitats and organic resources, such as
small cracks and interstices, rocks of different sizes, speleothems, gravel, sand, clay, lentic
and lotic water bodies, biofilms, trunks, leaves, fine vegetable debris, roots, guano and car-
casses (Moseley 2008; Souza-Silva etal. 2011b; Du Preez etal. 2015; Lunghi etal. 2017;
Mammola and Isaia 2017; Mammola 2019; Lunghi and Manenti 2020; Mammola etal.
2020). Furthermore, environmental changes from the entrance to deeper cave locations
promote a gradient of conditions and resources that provide distinct microhabitats for fauna
(Moseley 2009; Tobin etal. 2013; Lunghi et al. 2014; Prous etal. 2015; Mammola and
Isaia 2018; Lunghi and Manenti 2020). The occurrence of zonation in the conditions of
light, temperature, humidity, and organic resources availability allows a species exchange
that can give rise to distinct areas or zones with singular composition and richness (Tobin
etal. 2013; Prous etal. 2004; Kozel etal. 2019; Mammola etal. 2017; Lunghi and Manenti
2020).
Species inhabiting zones close to the entrances may experience most pronounced daily
and seasonal fluctuations in environmental conditions, being able to tolerate microclimatic
changes to survive (Prous etal. 2004; Lunghi et al. 2014; Prous etal. 2015; Mammola
etal. 2017; Mammola and Isaia 2017). On the other hand, higher stability in temperature
and humidity and scarcity of organic resources occurs in the deepest cave locations (Tobin
etal. 2013; Moseley 2008; Ficetola etal. 2018; Mammola 2019). Exceptionally, such traits
typically observed in subterranean environments can be altered because of disturbances,
caused both by human activities, such as tourism (Pellegrini and Ferreira 2016), and natu-
rally, such as bat colonies that can alter the temperature and humidity conditions of a given
cave (Ladle etal. 2012) and/or promote trophic enrichment by guano deposition (Ferreira
2019). Furthermore, rivers can transport organic resources and change trophic and micro-
climatic conditions (Souza-Silva etal. 2011b; Lobo etal. 2015; Simões etal. 2015; Souza-
Silva etal. 2020), as well as roots of external vegetation that can access the caves (Du
Preez etal. 2015; Souza-Silva etal. 2011b).
Organisms that live in caves are classified according to its ecological-evolutionary char-
acteristics in three main categories: troglobites, troglophiles and trogloxens, proposed first by
Schiner in 1854 and modified by Racovitza in 1907. Troglobites are unable to establish viable
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Biodiversity and Conservation
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populations in external environments. The remaining groups alternatively use caves, as a shel-
ter or as a residence (Gibert and Deharveng 2002; Sket 2008; Culver and Pipan 2013). The
troglophiles are frequently found in caves and can complete their life cycle in both external
and subterranean environments. The trogloxens are also frequently found in caves, but must
periodically come out to the external environment to complete their life cycle. The dynam-
ics of the spatial and temporal distribution of these categories of animals in caves are usu-
ally determined by variations in physical, microclimatic and trophic conditions, which can
occur seasonally (Tobin etal. 2013; Ferreira etal. 2015; Bento etal. 2016; Lunghi etal. 2017;
Kozel etal. 2019; Lunghi and Manenti 2020) or discreetly from the entrance to deeper regions
(Novak etal. 2012).
The main drivers of species distributional patterns include abiotic traits (e.g. temperature,
moisture and altitude), quality and availability of trophic resources and the presence of supe-
rior competitors (Cisneros et al. 2014). In subterranean habitats, interspecific competition
patterns are influenced by the ecological-evolutionary category of the species with the cave.
The low organic supply may favors troglobitic species in competitive interactions with non-
troglobitic species, since the former demand low energy cost to survive (Sket 1999). Some
studies carried out in temperate regions have shown that troglobites are mainly distributed
in more stable areas inside caves, although some species can also occur in areas closer to
entrances (Novak etal. 2012; Tobin et al. 2013; Manenti etal. 2015; Mammola and Isaia
2018; Kozel etal. 2019). Studies carried out in tropical caves have shown that several factors
can act in structuring communities of troglobitic invertebrates in different scales. Therefore,
such communities can be influenced by the cave lithology, the presence of water bodies, the
cave size, the number and size of entrances, by the structure and availability of microhabitats
and by trophic attributes (Souza-Silva etal. 2011a; Jaffé et al. 2016; Pellegrini etal. 2016;
Jaffé etal. 2018). Different patterns for niche size are expected for temperate and tropical
regions, in which it is predicted a more specialized niche use at low latitudes (Klopfer and
MacArthur 1960). However, specific studies aiming to understand the role of habitat structure
(e.g. temperature, humidity, substrate diversity), available resources (guano, vegetable debris,
carcasses) and interespecific competition in structuring assemblages of troglobitic species
throughout a cave or cave system have not yet been carried out in the tropics.
Thus, the main goal of this study was to identify the variables determining the spatial dis-
tribution of troglobites along with a new hotspot of subterranean biodiversity in the Neotropi-
cal region. Considering that troglobites are highly specialized fauna with specific requirements
for environmental conditions, we used variables describing the physical, trophic, and microcli-
matic attributes on the cave and the richness of non-troglobitic species to test three hypothesis:
(i) the troglobitic species will be preferably associated with deepest areas of the cave, which
presents higher temperature and humidity stability; (ii) variations in habitat components and
habitat heterogeneity on the cave floor should be the main drivers of troglobites distribution,
instead of competition with non-troglobitic species and (iii) the competitive exclusion would
not allow a long-term coexistence between troglobitic species, thus resulting in low niche
overlapping among those species.
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Biodiversity and Conservation
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Methods
Study area
To identify the main variables driving the spatial distribution of troglobitic invertebrates
in the cave substrates, we investigated the Água Clara cave system (ACCS), located in
the karst region of Serra do Ramalho, municipality of Carinhanha, Bahia state, Bra-
zil (Fig.1). The ACCS has approximately 24km, and is composed of four limestone
caves (Table1) trespassed by an intermittent stream, active during the austral summer
(October until March). The local climate is “Aw”, according to Köppen’s climate clas-
sification system, with dry winter and an average annual rainfall of 640 mm3 (Alvares
etal. 2013). The Serra do Ramalho region is inserted in the Caatinga domain (the only
Brazilian semiarid biome), with transitional areas to the Cerrado (Brazilian Savanna)
(Cole 1960). Due to the heavy tropical rains that occur in the region during the summer,
safe access to the system is only possible in dry periods of the year (March to October).
Fig. 1 Location of the study area in the municipality of Carinhanha, Bahia state, Brazil (A and B). Spatial
distribution of the caves that form the Água Clara cave system (ACCS) system (C). Bambui Speleological
group provides the cave maps (https:// bambu iespe leo. wordp ress. com/)
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Biodiversity and Conservation
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Sampling design
Habitat structure parameters, as well as the richness and composition of cave invertebrates,
were determined along 59 transects (10 × 3 m2 each) equally distributed on the caves’ floor,
from the entrances to the deeper regions of the system. Quadrats (1 × 1 m2) were inserted in
triplicates at the limits of each transects (Fig.2), totalizing 177 quadrats. Table1 shows the
number of transects and quadrats distributed in the system’s caves. The distance between
the sample units was about 150m (Supplementary Material I). Each sample unit was col-
lected only once in October 2017.
Measuring habitat structure
The survey of the habitat structure parameters in the transects was performed using the
methodology modified from Pellegrini etal. (2016). Each transect was subdivided into 10
Table 1 Caves sampled in the Água Clara System (ACCS), with respective values of linear development
(DL), the number of transects (NT), sampled extension (EA), deepest part reached in the cave (PA), the
distance between transects (DT), average temperature (T), average humidity (U) and richness of the cave-
restricted species (S)
Caves DL (m) NT EA (m) PA (m) DT (m) T (°C) U (%) S
Gruna Água Clara 13.880 39 390 6.240 150 24 83 23
Gruna dos Índios 510 4 40 320 70 24 72 4
Lapa dos Peixes I 7.020 9 90 1.440 150 25 66 19
Lapa dos Peixes II 2.100 7 70 1.120 150 225.5 86 17
Total 23.510 59 590 8.876 − − − 30
Fig. 2 An infographic showing methodology for biotic and abiotic data sampling in the Água Clara cave
system (ACCS), using quadrats and transects as standardized sampling areas
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Biodiversity and Conservation
1 3
1 × 3m sections (Fig.2). In each section, the surface area occupied by different organic and
inorganic substrates (Supplementary Material I) was visually quantified. After the meas-
urement of all 10 sections, a sum was made to obtain the area occupied by each substrate
throughout the entire transect. Temperature and humidity values were obtained from a dig-
ital thermo-hygrometer positioned inside the median portion of the transects, at ground
level.
Invertebrate sampling
Invertebrate sampling was carried out by visual search and exhaustive manual collection
with the aid of tweezers and brushes along the transects and quadrats (Sharratt etal. 2000;
Wynne etal. 2019). The additional use of quadrats, which comprises small-scale sampling,
allows the detection of small-size and low mobility species, which can then be thoroughly
searched in the remaining transect if detected. The invertebrates sampling was firstly per-
formed in the quadrats and later in the respective transect, always by two collectors, and
was only completed when all the invertebrates had been sampled and/or accounted. Given
the structural distinction between the different sampling areas along with the caves (due
to the presence/absence of crevices, rocks, and ledges) the searching time varied among
each sampling unit (harratt etal. 2000). Furthermore, to maximize the detection of cave-
restricted species, direct intuitive search techniques were also applied outside the men-
tioned sample units for better coverage of all microhabitats inside the caves (Wynne etal.
2019).
Invertebrate identification
All sampled invertebrates (troglobites—TB and non-troglobites—nTB) were identified to
the lowest accessible taxonomic level and grouped into morphotypes (Oliver and Beattie
1996). The determination of potentially troglobitic species was carried out based on the
presence of troglomorphic traits (for instance eyes and pigmentation reduction, append-
ages elongation) (Christiansen 1962). Specialists in different taxa were also consulted to
assist in the detection of specific troglomorphisms (specialists are acknowledged further
on). Voucher specimens were deposited in the Collection of Subterranean Invertebrates of
Lavras (ISLA), linked to the Center for Studies in Subterranean Biology from the Federal
University of Lavras (www. biolo giasu bterr anea. com. br).
Data analysis
We performed a data analysis sequence as recommended by Shmueli (2010), firstly con-
ducting an exploratory data analysis and secondly an explanatory data analysis. To ana-
lyze the eventual existence of spatial autocorrelated data we used generalized linear models
(
glms
), with the troglobitic species richness as a response variable and the distance among
the transects as an explanatory variable. This distance was obtained from an Euclidean
distance matrix between the transects built through the ‘
dist
’ function of the ‘
stats
’ pack-
age using the geographical coordinates of each sample unit, which were obtained from the
plotting of the transects on the map of each cave, and subsequently projected in the surface.
Then, the first column of the matrix was used as the distance vector, which corresponds to
the distances in relation to the AC-1 point, which was used as an explanatory variable in
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Biodiversity and Conservation
1 3
the
glm
. We performed ‘
shapiro.test
’ function from the ‘
stats
’ package, which revealed
non-normal distribution of the data. We used quasi-poisson error distribution, since the
‘
dispersiontest
’ function from the ‘
aer
’ package revealed overdispersion for the poisson
family. The
glm
was constructed using the ‘
glm
’ function from the ‘
stats
’ package. We
assumed that there is no spatial auto-correlation in the data since there was no significant
relationship between the variables.
Abiotic attributes onthecave floor
All the physical, trophic and microclimatic characteristics of the transects were evaluated
and classified in the following classes: retraction cracks—GRE, hardpan—HRP, silt—
SIL (0.2 < diameter ≤ 0.05 mm), fine gravel—GRAF (2−16 mm), coarse gravel—GRAC
(17−63 mm), rock blocks—BLO (64−250 mm), matrix rock—ROC, water ponds—PON,
temperature—TEMP, humidity—MOT, actinomycetes biofilms—ACT, wood—WOO,
guano—GUA, fine particulate organic matter—FOM, roots—ROO. Based on such classes
we obtained the physical features of the caves that included the distance from the entrance,
the substrate diversity (calculated considering all the classes aforementioned) using Shan-
non-Weaver index (Buttigieg and Ramette 2014), and the availability of shelter for the
invertebrates (calculated by the sum of GRE, GRAC, BLO, and WOO in each transect).
We considered as trophic resources GUA, OM (comprising FOM and ROO) and WOO.
Finally, the microclimatic variables, TEMP and MOT were also considered separately.
To explore the differences in trophic, microclimatic, and physical attributes of the four
caves, we evaluated the existence of differences in the averages of temperature, humid-
ity, substrate diversity, availability of shelter, and trophic resources of each transect among
caves using Wilcoxon-Mann-Whitney U test (Sprent and Smeeton 2000). In sequence, we
checked if such trophic, microclimatic, and physical attributes varied depending on the dis-
tance from the nearest entrance using a distance-based linear model (DistLM) with Euclid-
ean distance. We used the stepwise procedure and the AICc model selection criterion,
in which models with smaller values of AICc are the better ones, after 999 permutations
(Anderson etal. 2008).
Biotic attributes onthecave floor
The abundance and richness of cave-restricted species for each transect were accounted.
Exploratory data analysis was performed using species composition similarities among
the four caves from ACCS with a Bray-Curtis distance index after the
sqroot
transforma-
tion, using the transects as the sampling units (Buttigieg and Ramette 2014; Clarke etal.
2014). To access differences on average in cave-restricted species richness among caves
and transects it was performed a Wilcoxon−Mann−Whitney U test. All the significance
was regarded at p < 0.05. To account for the number of undetected troglobitic species,
it was calculated the extrapolated richness for the transects dataset using non-parametric
richness estimators (Jackknife 1 and 2) (Buttigieg and Ramette 2014). The level of ‘com-
pleteness’ of the sampling effort was achieved by dividing the observed number of taxa by
the estimated values calculated by Jacknife 1 estimator (Àvila etal. 2019).
The average taxonomic distinctness, the richness of non-troglobitic species and the
ratio between troglobitic with non-troglobitic species were used as a proxy of interspecific
competition with the troglobitic species. The average taxonomic distinctness (∆+) analysis
was conducted using non-troglobitic species, being Class (weight 100), Order (weight 75),
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Biodiversity and Conservation
1 3
Family (weight 50), and morphotypes (weight 25) as info variables for a matrix of morpho-
types distribution among transects (Anderson etal. 2008).
Finally, we presented a visual representation of the biodata matrix according to the spa-
tial distribution of the transects along the ACCS (after it has been sqroot transformed), in
a shaded plot. The species composition and sample distribution have been re-ordered in a
cluster analysis utilizing Whittaker’s Index of Association (Whittaker 1952; Clarke etal.
2014).
Relationship betweenhabitat structure andinterspecific competition
withcave‑restricted fauna
To analyze the effects of physical, trophic, microclimatic features and interspecific compe-
tition influence on cave-restricted species richness, we performed four independent
glm’s
.
In the
glm’s
, the error distribution with the best fit was the Quasi-Poisson family. Then we
used the ‘
anova
’ function from ‘
vegan
’ package to compare the
glm’s
results with the null
model. For those significantly different from the null model, we used an information-theo-
retic approach based on the AICc to rank the models, which indicates the most parsimoni-
ous model (Burnham etal. 2011). Further, we used the ‘
dredge
’ function from the ‘
mumin
’
package to test all possible combinations of the variables included in the full model and
ranked them by the AICc-based model weight. Since the best fit was for the quasi-poisson
family, we used the steps proposed by Bolker (2020) for extracting the AICc or quasi-mod-
els. Finally, all possible models were ranked, and we considered only those models that had
ΔAICc lower than two to be strongly supported. To obtain the adjusted r2 values we used
the ‘
rsq
’ function from the ‘
rsq
’ package.
Before running the
glm’s
analysis, we tested them for multicollinearity among explana-
tory variables to prevent variance inflation factors using the ‘
chart.correlation’
function
from the ‘
performanceanalytics
’ package. Since none of the explanatory variables from
physical, trophic and microclimate features showed correlation values higher than 65%, all
variables were included in the models. For interspecific competition variables, we found a
strong correlation between non-troglobitic species richness with the troglobitic and non-
troglobitic species ratio. In this case, we run two
glm
analyses alternating the insertion of
the correlated variables. After obtaining the AIC values following Bolker`s method (2020),
we applied the ‘
model.sel
’ function found in the ‘
mumin
’ package to obtain the best model.
In order to evaluate if the distance from entrance affects the ratio between troglobitic
and non-troglobitic species richness, we also performed
glm
analyses. We checked troglo-
bitic/non-troglobitic ratio data normality using the
shapiro.test
from
stats
package. Since
the response variable had a non-normal distribution, we also built the model using the
Quasi-Poisson family, which showed the best fit for the error distribution. We performed
the
glm’s
analyses in the
r
software, version 3.6.2 (R Core Team 2019).
To explain the eventual relationships between troglobitic species composition with
physical, trophic, and microclimatic variables, we applied a Distance-based linear model
(DistLM) with Bray-Curtis index for faunal composition, excluding singletons and dou-
bletons. We used the stepwise procedure and the AICc model selection criterion, in which
models with smaller values of AICc are the better ones, after 999 permutations (Anderson
etal. 2008). We performed the distance-based redundancy analysis (dbRDA) to ordinate
and visualize the strength of all predictor variables on species composition from the Dis-
tLm result (Buttigieg and Ramette 2014; Clarke etal. 2014).
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Biodiversity and Conservation
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Habitat selection
We evaluated the habitat selection using a multivariate approach based on the niche con-
cept, considering the presence of the ten most frequent troglobitic species—which occurred
in at least five transects. We performed Outlying Marginality Index (
omi
) analyses, which
measures the distance between the mean used for each species and the mean available val-
ues for each environmental condition—including the physical, trophic and microclimatic
variables—of the total sampled area (Dolédec et al. 2000). Then the
omi
analysis plot
each species niche in relation to one reference species, that is the most tolerant to the gen-
eral habitat condition.
omi
analysis fits well for strong driving forces such as gradients; in
the present study the gradient is represented by the environmental factors changing from
entrance to deep cave. The given results provide the variability of each species decom-
posed into three components: (i)
omi—
index of marginality (distance of each species from
an uniform distribution); (ii)
tol—
index of tolerance or niche breadth; and (iii)
rtol—
residual tolerance (determines the confiability of the determined niche).
To run the
omi
analyses, first, we summarized the patterns of covariation among physi-
cal, trophic, and microclimatic variables by performing a principal component analysis
(
pca
). Then the niche from each of the ten selected species was calculated and plotted in
the environmental niche. The analyses were performed in the R program (Development
Core Team 2019) utilizing the ‘
ade4’
package (Dray and Dufour2007). The Monte Carlo
test with 999 permutations was used to evaluate the significance of niche marginality and
the average marginality of each species (Dolédec etal. 2000).
Results
Habitat structure
The values for temperature, humidity, substrate diversity, availability of shelter, and trophic
resources are shown in Spplementary Material I. Only temperature and humidity showed
significant differences between the caves. The substrates diversity presented a negative
relationship with the distance from the nearest entrance (AICc = – 109.11, R2 = 0.14, p =
0.005), while the humidity showed a positive relationship with the nearest entrance (AICc
= 247.03, R2 = 0.21, p = 0.001).
Richness andcomposition ofcave‑restricted fauna
Considering the whole ACCS (including the sampling in the transects, quadrats, and other
habitats), a total of 621 specimens with troglomorphic traits were recorded, comprising
30 troglobitic species distributed in Hexapoda (9 spp.), Arachnida (7 spp.), Crustacea (6
spp.), Myriapoda (4 spp.), Gastropoda (2 spp.), Turbellaria (1 sp.) and Siluriformes (1 sp.)
(Fig.3). Terrestrial species were predominant (22 spp.), followed by amphibious (5 spp.),
and aquatic species (3 spp.) (Table2). Illustrative pictures of some troglobitic species
found in the ACCS are shown in Fig.4. The species composition and richness distribution
of troglobitic species are shown in Supplementary Materials II and III.
In both the quadrats and transects, 25 species of terrestrial troglobites were registered, dis-
tributed in Hexapoda (8 spp.), Arachnida (7 spp.), Crustacea (5 spp.), Myriapoda (4 spp.), and
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Biodiversity and Conservation
1 3
Gastropoda (1 sp.). Hence, amphibian and aquatic troglobites/stygobites were only registered
in habitats not incorporated within transects. In the transects, 22 troglobitic species were reg-
istered (5 spp., exclusively found in such units), while in the quadrats 18 species were found (2
spp., exclusively found in such units) (Table3) (Figs.5 and 6).
Considering the whole ACCS (including the sampling in the transects, quadrats, and other
habitats), a total of 6783 specimens with non-troglomorphic traits were recorded, compris-
ing 142 species distributed in Hexapoda (85 spp.), Arachnida (43 spp.), Myriapoda (5 spp.),
Annelida (3 spp.), Mollusca (3 spp.), Turbellaria (2 spp.), and Nematoda (1 sp.). The number
of non-troglobitic species by transect is shown in Supplementary Material II.
Considering the whole ACCS (including the sampling in the transects, quadrats, and other
habitats), Gruna da Água Clara cave presented the highest richness of troglobitic species (23
spp.), with four species exclusively observed in this cave (Symphypleona sp2, Rhagidiidae
sp1, Caponiidae sp.1, Trichorhina sp.1). Lapa dos Peixes I cave had 19 species, while Lapa
dos Peixes II cave had 17 species and Gruna dos Índios cave only four species (Table1). Caves
with the largest number of shared species were Gruna da Água Clara and Lapa dos Peixes I
cave (16 spp.), both located at the “extremes” of the ACCS (presenting approximately 3km of
the linear distance between the nearest entrances). Only two species (Chelodesmidae sp.1 and
Endecous sp.1) were distributed along all caves in the system (Table2).
The estimated troglobitic species richness suggests that the sampling effort achieved
adequate levels of completeness, as the observed richness (25 spp.) corresponded to over
78% of the estimated richness. No significant differences were observed in the average
troglobitic richness (per square meter) between the caves of the ACCS. The average rich-
ness was 3 spp./30 m2 (sd = 2) in the Gruna da Água Clara cave, 2 spp./30m2 (sd = 2)
in the Gruna dos Índios cave, 3 spp./30 m2 (sd = 1) in the Lapa dos Peixes I cave and 3
spp./30m2 (sd = 1) in the Lapa dos Peixes II cave.
Relationship betweenhabitat structure andinterspecific competition
withcave‑restricted fauna
The models selected for physical and trophic variables showed no significant difference
from the null model to explain troglobitic species richness. Only the microclimate and
Fig. 3 Richness distribution of the cave-restricted species according to the sampling unit in the Água Clara
cave system (ACCS). 29 species of invertebrates and one vertebrate (Siluriformes) were sampled
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Biodiversity and Conservation
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Table 2 Distribution of 30 cave-restricted species to the Águas Claras System, Bahia state, Brazil. Cave (Ca), corresponding to the surveys outside the standardized sam-
pleareas, transects (Tr), quadrats (Qu), terrestrial habitat (T), aquatic habitat (A)
Taxons Species and morphotypes Gruna da Água Clara Gruna dos Índios Lapa dos peixes II Lapa dos peixes I Habitat
Ca Qu Tr Ca Qu Tr Ca Qu Tr Ca Qu Tr
Tricladida Girardia spelaea + + T
Acari Rhagidiidae sp.1 + T
Amblypygi Charinus troglobius + + T
Araneae Caponiidae sp.1 + T
Ochyroceratidae sp.1 + + + + + + + + + T
Opiliones Giupponia chagasi + + + + + T
Palpigradi Eukoenenia sp.1 +++ ++++++T
Pseudoscorpiones Chthoniidae sp.1 + + + T
Collembola Sminthuridae sp.2 + + + T
Sminthuridae sp.1 + T
Entomobryomorpha sp.4 + + + + T
Entomobryomorpha sp.7 + + + T
Symphypleona sp.2 + T
Blattodea Blattodea sp.1 + + + + T
Dermaptera Mesodiplatys falcifer + T
Ensifera Endecous sp. + + + + + + + + T
Hymenoptera Nylanderia sp.1 + + + + + T
Isopoda Pectenoniscus. carinhanhensis + + + + T/A
Styloniscidae sp.2 + + + + T/A
Styloniscidae sp.3 + + + T/A
Styloniscidae p.4 + T/A
Trichorhina sp.1 + + + T
Xangoniscus aganju + + + + T/A
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Table 2 (continued)
Taxons Species and morphotypes Gruna da Água Clara Gruna dos Índios Lapa dos peixes II Lapa dos peixes I Habitat
Ca Qu Tr Ca Qu Tr Ca Qu Tr Ca Qu Tr
Geophilomorpha Geophilomorpha sp.1 + T
Polydesmida Chelodesmidae sp.1 + + + + + + + + + + T
Trichopolydesmidae sp. + + + + T
Pyrgodesmidae sp.1 + + + + + + + T
Gastropoda Spiripockia punctata + + A
Pulmonata sp.1 + + + A
Siluriformes Trichomycterus rubbioli + + A
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interspecific competition variables presented models that differed from the null model.
The most parsimonious model to explain the cave-restricted fauna richness among
microclimate variables showed a positive effect of temperature and humidity (Table3;
Fig.7A and B). For the models selected including interspecific competition variables,
troglobitic species richness also increased with the TB/nTB species richness ratio and
non-troglobitic taxonomic distinctness (∆+) (Tables3 and 4; Fig.7C and D). Models
including the non-troglobitic richness were not different from the null model. The TB/
nTB species richness ratio increased from the regions close to entrances to deeper por-
tions of the caves (r2 = 0.172, p = 0.001, Figs.6 and 8). Thus, troglobitic species tend to
be more frequent than non-troglobitic species in deeper zones of the ACCS.
Fig. 4 Some of the species restricted to the Água Clara cave system (ACCS), Brazil. Xangoniscus aganju
(A), Styloniscidae sp.2 (B), Pectenoniscus carinhanhensis (C), Trichorhina sp.1 (D), Blattodea sp.1 (E),
Mesodiplatys falcifer (F), Nylanderia sp.1 (G), Endecous sp (H), Sminthuridae sp.2 (I), Ochyroceratidae
sp.1 (J), Eukoenenia sp.1 (K), Chthoniidae sp.1 (L), Giupponia chagasi (M), Charinus troglobius (N),
Chelodesmidae sp.1 (O), Pyrgodesmidae sp.1 (P), Trichopolydesmidae sp. (Q), Geophilomorpha sp.1 (R),
Spiripockia punctata (S), Trichomycterus rubbioli (T)
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The variable that explained the variation in troglobitic composition throughout the
ACCS was humidity (AICc = 490.71; R2= 0.06, p = 0.001). The first two axes of the
distance-based redundancy analysis (dbRDA) using all the substrate variables sampled in
transects explained 12.7% of the total variation of the biodata cloud (Fig.9).
The ten troglobitic species included in the OMI analysis were three isopods
(Trichorhina sp.1, Pectenoniscus carinhanhensis and Styloniscidae sp.2), the two
springtails (Sminthuridae sp.2 and Entomobryomorpha sp.1), the harvestman Giupponia
Table 3 Results of (Q)AICc-
Based Model Selection for
the two groups of variables
(microclimate and interspecific
competition) with explanation
models of troglobitic species
richness different from the null
model
We show the models with Δ QAICc lower than 2 and the next model
in the rank. D.F.: degrees of freedom used; QAICc: AICc calculated
for quasi-correction; Δ: QAICc differences; Akaike weights (ω); and
%DE: percentage of deviance explained by the model with Δ lower
than two. Error distribution of models was quasi-poisson
TB troglobitic species, TEMP temperature, HUM humidity, TB/nTB
ratio between troglobitic species richness with non- troglobitic species
richness, TX(Δ+) average non-troglobitic species taxonomic distinct-
ness
Model ranks Model D.F. QAIC Δ ω %DE
TB richness Microclimate models
TEMP + HUM 3 168.0 0.00 0.902 0.22
TEMP 2 173.2 5.19 0.067
TB richness Interspecific competition models
TB/nTB + TX(Δ+) 3 133.7 0.00 0.948 0.27
TB/nTB 2 139.7 5.96 0.048
Fig. 5 Shade plot showing the distribution of the 25 trogobites species at Gruna da Água Clara (AC), Gruna
dos Índos (IN), Lapa dos Peixes I (LPI) e Lapa dos Peixes II (LPII). Numbers from 70 to 3300 are the dis-
tances of the transects concerning the entrance (meters). The species list and sample units were grouped
with a cluster analysis utilizing Whittaker’s Index of Association (displayed left and up). Abundance was
sqroot transformed
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Fig. 6 Spots of the richness of the cave-restricted species from the entrance to deeper parts in the Água
Clara cave system (ACCS). The distance among sample units (colored bubbles) is approximately 150m
(except Gruna dos Índios; 75m). The bubble size represent troglobitic species richness and the color gradi-
ent represent the proportion between troglobitic and non-troglobitic species in each site (log transformed)
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chagasi Pérez & Kury, 2002; the spider Ochyroceratidae sp.1; the microwhip scorpion
Eukoenenia sp.1; the millipede Chelodesmidae sp.1; and finally, the most widespread
troglobitic species found in the system, the cricket Endecous sp.1 (Table 2; Fig. 10).
From those species, the most specialized, with higher values of niche marginality were
Trichorhina sp.1, which was more associated with the presence of wood, and Sminthu-
ridae sp.2, preferably associated with the deeper regions of the caves. Among the most
generalist species, with lower values of niche marginality and consequently higher envi-
ronmental tolerance, stood out Chelodesmidae sp.1, Ochyroceratidae sp.1, Entomobryo-
morpha sp.4 and Eukoenenia sp.1. The other species represented a subtle deviation from
the overall average habitat. Styloniscidae sp.2 was preferably associated with warmer
regions; Giupponia chagasi with deeper regions of the caves; Pectenoniscus carinhan-
hensis and Endecous sp.1 with drier regions with wood. The overall
omi
analysis was
able to explain significantly (p = 0.05) the distances between the habitat conditions and
the habitat used by the evaluated species (Fig.10; Table4).
Fig. 7 Relationship between the cave-restricted species richness with temperature (°C) (A), humidity (mois-
ture content %) (B), troglobite/non-troglobitic species richness (C) and non-troglobitic species taxonomic
distinctness (D)
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Discussion
The results showed that among the tested cave features, only the microclimatic and inter-
specific competition affected cave-restricted species richness. More specifically, troglobitic
species richness was positively related with temperature, humidity and with non-troglobitic
species richness and taxonomic distinctness. This finding suggests that organic resources
and the habitat physical structure are not the main drivers of cave-restricted species rich-
ness distribution in this cave system. Troglobitic species tend to be relatively more fre-
quent than non-troglobitic species in deeper zones of the ACCS. Similarly, the microcli-
matic conditions are the most determinant attribute for the variation in troglobitic species
Table 4 Results of the Outlying
Mean Index (omi) analysis
for the ten most widespread
troglobitic species in ACCS that
occupy the environmental niche
according to the physical, trophic
and microclimate characteristics
of each transect
Inertia; OMI: Mean marginality index; Tol: tolerance index; Rtol:
residual tolerance index and p value were calculated for the troglobitic
species in their respective environments occurrence
Troglobitic species Inertia OMI Tol Rtol P value
Trichorhina sp1 13.81 11.34 1.77 0.71 0.04
Styloniscidae sp2 7.07 2.03 1.65 3.38 0.17
Sminthuridae sp2 5.78 3.85 0.99 0.94 0.05
Giupponia chagasi 6.89 1.85 0.48 4.57 0.06
Pectenoniscus carinhanhensis 9.90 2.51 3.06 4.33 0.05
Entomobryomorpha sp4 5.96 1.00 0.81 4.14 0.85
Chelodesmidae sp1 5.88 0.50 0.58 4.80 0.79
Eukoenenia sp1 6.53 1.21 0.89 4.43 0.39
Ochyroceratidae sp1 5.92 0.78 0.17 4.97 0.38
Endecous sp1 7.44 1.49 2.46 3.49 0.04
Global OMI 0.02
Fig. 8 Relationship between the
troglogite/non-troglobite species
richness proportion in relation
to the distance from the cave
entrance (meters)
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composition. Species-habitat preferences were different for each troglobitic species ana-
lyzed (10 species).
Responses of troglobitic species to environmental conditions are still poorly explored
around the world. Although the temperate and tropical regions are quite distinct in many
environmental attributes, caves share some physical, microclimatic, and trophic character-
istics that drive similar patterns for troglobitic communities. The high environmental stabil-
ity has shaped similar environmental requirements and ecological traits of terrestrial inver-
tebrate species highly specialized to moist conditions and to oligotrophic environments.
Fig. 9 Explanatory analysis
using dbRDA with physical,
microclimatic, and trophic
variables at the cave floor against
troglofauna composition. The
biggest Bray-Curtis similarity
values are circled. Actinomycetes
(ACT), wood (WOO), guano
(GUA), fine particulate organic
matter (FOM), roots (ROO),
substrate diversity (DIVS),
availability of shelter (DIVSH),
temperature (TEMP), humid-
ity (MOT), distance from the
entrance (DIST)
Fig. 10 Outlying Marginality Index (
omi
) showing species habitat selecting according to cave attributes on
the floor (physical, trophic, and microclimatic). Organic matter (om), temperature (temp), moisture (most),
distance from the entrance (entr_dist), substrate diversity (subs. H’)
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However, troglobitic species can be specialized to distinct habitats, as already shown for
the species composition among shallow and deep subterranean habitats, which can be con-
siderably distinct (Novak etal. 2012; Kozel etal. 2019).
The cuticle thinning (which in turn leads to greater intolerance to desiccation) and
reduced metabolic rates are among the most observed adaptations in cave-restricted spe-
cies. However, such traits often limit the distribution of these species to highly humid
places that also tend to present narrow temperature ranges (Tobin etal. 2013; Lunghi
et al. 2014 and 2017; Kozel etal. 2019). Thus, troglobites tend to occupy more stable
areas inside caves, even if these conditions also occur closer to the entrances (Novak etal.
2012; Tobin etal. 2013; Lunghi etal. 2017; Mammola and Isaia 2018). Furthermore, we
observed a gradual reduction in the richness of non-troglobitic species from the entrance
to deeper cave sites, suggesting the existence of ecotones between the entrance regions and
the innermost areas, which are climatically more stable. Thus, in the regions closest to the
entrances, troglobitic and non-troglobitic species can coexist, while in the inner portions of
the caves TB/nTB rate favors the troglobites (Novak etal. 2012; Tobin etal. 2013; Kozel
etal. 2019).
The increase in troglobitic species richness related to the rise of temperature and humid-
ity is probably related to their high degree of specialization, which makes them select more
stable areas in caves, which are generally located in deeper areas, and are usually oligo-
trophic (Novak etal. 2012; Tobin etal. 2013). Corroborating this hypothesis, Deharveng
and Bedos (2000) observed that troglobitic invertebrates become more frequent in areas
far from trophic resources since these organisms avoid the coexistence with non-troglo-
bitic competitors. Moreover, Sket (1999) hypothesized that the comparatively high number
of stygobitic crustaceans in limited and oligotrophic subterranean habitats in the Dinaric
karst, seems to be related to the lack of competitors (mainly insects), spatial and ecological
habitat partition, and favorable temperatures.
The reduced metabolic rates and/or changes in life history towards the K strategy allows
the troglobites to survive during long periods of starvation (Hüppop 2005). However, an
increase in the organic resources input can be harmful to these organisms, which in such
circumstances tend to be excluded by more energetically demanding and competitively
superior non-troglobitic species (Sket 1999). Despite this, the presumably high competition
for scarce resources in deep subterranean environments leads to specializations in specific
habitats, preventing a high niche overlapping. Thus, the usually limited occurrence range
observed for troglobites must be caused by their high ecological specialization concern-
ing specific habitat traits. Both resource scarcity and reduced substrate diversity decrease
the potential number of competing species capable of establishing populations in subter-
ranean environments (Sket 1999). Thus, the contrasting distribution of non-troglobitic and
the richness of troglobites may indicate a tendency towards a reduction in the interspecific
spatial overlap between these two ecological-evolutionary categories.
The analysis performed in ACCS showed that the most widespread troglobitic spe-
cies can use areas with different microhabitat traits, thus avoiding niche overlapping, what
allows their coexistence. Furthermore, the different microhabitat preferences showed by
the troglobites may be the reason why no negative significant relationship among their
richness and the richness of non-troglobitic species was observed, as initially hypothesized.
Thus, since each troglobitic species tends to respond differently to distinct microhabitats,
the richness, in this case, ends up not being a good predictor variable. In addition, even
though it has not been evaluated, it is likely that microhabitat preferences also occurs in
non-troglobitic species, so that simply comparing the number of species between these cat-
egories can be innocuous when addressing this kind of hypothesis.
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The niche determined for the most frequent species revealed some patterns. Although
it was possible to observe that predator species represented by Ochyroceratidae sp 1,
Eukoenenia sp 1 and Giupponia chagassi showed a tendency towards occupying deeper
portions of the caves, these species showed a very low level of niche specialization. Preda-
tors are known to be more related to prey distribution than to environmental conditions
(Bogan and Lytle 2007), which explains their occurrence associated with the average hab-
itat conditions available in the studied system. This pattern can be expected to be even
more pronounced in the case of oligotrophic systems as caves. The lack of prey availability
makes the active foraging strategy more advantageous for predators, instead of sit-and-wait
strategies. The generalist behavior was also observed for Chelodesmidae sp 1 and Ende-
cous sp 1, two generalist scavenger species (Barker 2004; Paixão etal. 2017) for which
high levels of habitat tolerance are expected.
Another interesting pattern found is that Trichorrina sp 1 was by far the most specialized
species evaluated, and together with the other isopods species, showed preference for habi-
tats with wood availability. This finding brings conservation insights, evidencing the need
of preserving the surrounding environment for the continuous inputs of wood resource and
the consequent maintenance of such isopod populations. The springtails showed distinct
patterns: while the Entomobryomorpha sp 1 was more generalist, the Sminthuridae showed
a strong preference for the deep parts of the cave. The more generalist habit exhibited by
the Entomobryomorpha sp 1 is compatible with its detritivorous habit. This characteristic
allows a small part of the troglobitic springtails to be able to reach very wide geographic
distributions, including most of Europe (Dányi 2011). In the other hand, more sensitive
species, such as Sminthuridae, can be closely related to conditions only found in deep
caves, with more stable environmental conditions and high humidity as observed in the
present study. Springtails are among the few animals able to reach major depths in caves,
as the case of Schaefferia profundissima Jordana & Baquero 2012, recorded at -1600m
and Plutomurus ortobalaganensis Jordana & Baquero 2012, recorded at -1980m, both at
the Krubera-Voronja cave (Sendra & Reboleira 2012). This ability of springtails to colo-
nize very deep regions of caves can be advantageous in a cave system colonized by so
many troglobitic species, allowing them to avoid interspecific competition.
The Água Clara cave system asahotspot ofsubterranean biodiversity
The high richness of troglobites (30 cave-restricted species), makes the Água Clara cave
system a new hotspot of subterranean biodiversity in South America (Souza-Silva and Fer-
reira 2016), being currently the richest system in troglobitic/stygobitic species in Brazil. In
the world, there are 38 hotspots of subterranean biodiversity, which occur on all continents,
except for Antarctica and Africa (Pipan etal. 2020), although the Wynberg cave system, in
South Africa, with 19 cave-restricted species, has a huge potential to soon become the first
hotspot of subterranean biodiversity in Africa (Ferreira etal. 2020).
Most hotspots are located in Europe and North America and tropical hotspots of sub-
terranean biodiversity are restricted to Australia, Brazil and Indonesia. It may be specu-
lated that this pattern stems from the fact that most karst areas of the world are located in
temperate regions, thus a higher richness of cave-restricted species can be expected, since
such habitats served as a refuge during past climate changes (Bar 1968; Romero 2009).
However, the high number of new troglomorphic species recently discovered in Brazilian
caves (Souza-Silva etal. 2011a; Souza-Silva and Ferreira 2016; Souza-Silva etal. 2020;
Cardoso etal. 2021) indicates that events of climatic changes in the Neotropics, could also
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Biodiversity and Conservation
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have led to the isolation of subterranean lineages (Souza-Silva and Ferreira 2016). Further-
more, other mechanisms of isolation (for instance, parapatric speciation, oceanic introgres-
sions and regressions) might have led to the evolution of many lineages of subterranean
fauna in Brazil (Fišer etal. 2013; Leal-Zanchet etal. 2014; Benítez-Álvarez etal. 2020;
Castro-Souza etal. 2020). Moreover, most of the caves, particularly in tropical areas, have
not been thoroughly explored (Deharveng and Bedos 2012). Currently, only few caves in
Brazil present more than 10 troglomorphic species (Souza-Silva etal. 2011a; Rabelo etal.
2018; Cardoso etal. 2021) and only two are considered hotspots (Souza-Silva and Ferreira
2016).
High richness of cave-restricted species is generally associated with oversized caves,
high productivity and/or the presence of water bodies isolated from the surface (Culver
and Pipan 2009). In addition to those previously mentioned conditions, the high richness
observed in the ACCS may be related to the Brazilian Tropical Dry Forest (more specifi-
cally theCaatingadomain) in which it is located. Many caves within the Caatinga, espe-
cially those with the presence of perennial water, present an outstanding diversity and end-
emism of cave-restricted fauna (Prevorčnik etal. 2012; Bento etal. 2016; Souza-Silva and
Ferreira 2016; Cardoso etal. 2021). This fact may be related to the current xeric conditions
of the region associated to the historical changes undergone by this biome. Many areas
currently covered by the Caatinga formation presented a moist tropical forest in the Last
Glacial Maximum (LGM) (Collevatti etal. 2013), which may have sheltered the ancestors
of many troglobitic species, which may have been posteriorly “trapped” inside caves after
the retraction of the moist forests (Wang etal. 2004; Polhemus and Ferreira 2018).
Preserving thebiodiversity oftheÁgua Clara cave system
An important action for preserving the ACCS would be the description of the troglomor-
phic species that this system shelters since non-described species are often ignored in
conservation actions (Cardoso et al. 2011). Most cave-restricted species in tropical and
subtropical areas are not formally described (Souza-Silva and Ferreira 2016; Pipan etal.
2020). Furthermore, ignoring undescribed species can artificially indicate that caves are
poor in such species (Pipan etal. 2020). However, consultation with specialists associ-
ated with the use of troglomorphic traits increases the chances of more accurate diagnoses
about the status of these species.
As an example, among the 30 cave-restricted species observed in the ACCS, only 8
(~26%) are formally described: Girardia spelaea (Tricladida), Spiripockia punctata
(Gastropoda: Pomatiopsidae), Giupponia chagasi (Opiliones, Gonyleptidae), Charinus
troglobius (Amblypygi: Charinidae), Xangoniscus aganju (Isopoda, Styloniscidae), Pect-
enoniscus carinhanhensis (Isopoda, Styloniscidae), Mesodiplatys falcifer (Dermaptera:
Diplatyidae) and Trichomycterus rubbioli (Siluriformes: Trichomycteridae). Most of those
species are already inserted in the Brazilian list of threatened species, categorized as criti-
cally endangered (ICMBio 2018). A similar proportion of described species was observed
for the Toca do Gonçalo cave (22 troglobitic species, with only 22% of them described)
(Souza-Silva and Ferreira 2016). This fact unveils the lack of Brazilian taxonomists inter-
ested in cave fauna and the difficulties faced by foreign taxonomists to study these organ-
isms (especially due to legal hindrances). Financial support calls focused in the descrip-
tion of subterranean taxa in Brazil resulted in relatively few species descriptions in the
last years, indicating urgent needs of rethinking strategies for describing and protecting
this singular, endemic and threatened fauna. It is important to highlight that only described
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Biodiversity and Conservation
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species are evaluated according to the IUCN criteria. Hence, around 74% of the cave-
restricted species form the ACCS are legally unprotected. This is an extremely disturbing
fact since anthropic changes as deforestation and soil disturbance persist around the caves
that make up the system.
The Serra do Ramalho region underwent rural settlement programs by the Brazil-
ian government in the 1970s, which established small agricultural villages in the region
(Moura andBichuette 2008). According to Moura and Bichuette (2008), deforestation in
areas close to the ACCS occurs mainly due to charcoal production and the establishment
of monocultures. The removal of epigean vegetation in the areas that directly influence the
caves can affect the supply of organic and inorganic sediments to the caves (Gilieson 1996;
Culver 1982; Souza-Silva etal. 2011b) and, in turn, the food resources available for the
hypogean fauna. Cave communities associated with systems with low availability of guano
and carcasses, as the ACCS, may be more dependent on allochthonous food resources
(Souza-Silva etal. 2011b).
According to Auler etal. (2001), the silting up of the adjacent rivers, as well as some
of the cave conducts, has increased in the last years. Despite the stream’s ability to trans-
port organic matter and to keep high humidity conditions in the ACCS, the degradation of
surface environments ends up transforming them into vehicles of environmental impacts,
thus potentially reducing the cave fauna. In addition, Gonçalves etal. (2018) reported the
vulnerability of the Serra do Ramalho´s aquifer due to deficiencies in basic sanitation in
the area. Thus, protecting the ACCS requires actions to both encourage taxonomic and
ecological research and manage the area considering social, environmental, and economic
contexts (Ford 2005; Brinkmann and Garren 2011; Van Beynen and Van Beynen 2011;
Culver and Pipan 2013; Osborne 2019).
However, efforts must be directed to ensure a consensus between the multiple environ-
mental and cultural facets involved in the creation of conservation areas. Usually, the com-
munities involved or affected by the delimitation of these areas, depending on the process,
can be a source of cooperation or conflicts (Rylands and Brandon 2005). In this sense,
the creation of conservation units, with the community involvement, can be an important
strategy to protect the Água Clara Cave System. Sugai etal. (2015) found that 11.6% of
the 13,816 registered caves in Brazil are within protected areas, whereas almost half of
those caves are at risk to be affected by mining expansion. Historically, the main justifica-
tion for creating protected areas for the conservation of Brazilian speleological heritage has
been based on scenic beauty and geological, paleontological, and archaeological attributes
(Souza-Silva etal. 2015). However, this scenario has changed, and the cave fauna has been
considered when establishing priority areas for the creation of conservation units. The first
Brazilian conservation unit that considered cave biodiversity as an important attribute for
its creation was the National Park of Furna Feia, located in Rio Grande do Norte state (Bra-
zil 2012). Thus, we hope that the uniqueness of the cave fauna from the ACCS will con-
tribute to the creation of a conservation unit that will ensure its maintenance along time.
Supplementary Information The online version contains supplementary material available at https:// doi.
org/ 10. 1007/ s10531- 021- 02302-8.
Acknowledgements Roberta Fernanda Ventura Cerqueira was sponsored by FAPEMIG (Fundação de
Amparo à Pesquisa do Estado de Minas Gerais) with 12 months of grants. The data used in this paper
was part of her master thesis on Ecology at Universidade Federal de São João del-Rei. We thank Joaquim
Ferreira and family, residents, who provide accommodation in Agrovila 23. Joaquim also was part of the
research team. We are thankful to the Bambuí Speleological group for providing the cave maps. The CEBS
team that helped in field work (Dayvison, Denizar, Ellen, Gabrielle, and Gilson). We also thank Leopoldo
F. O. Bernardi (Acari), Rafaela Bastos (Isopoda), Maysa F. V. R. Souza (Palpigradi) and Rodrigo Souza
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Biodiversity and Conservation
1 3
(Orthoptera) for their help in fauna identification. We also thank the VALE/SA company for all support
provided to CEBS/UFLA. RLF is grateful to CNPq for support for Research No. 308334/2018-3. Thais Gio-
vannini Pellegrini is grateful to Fundação de Amparo à Pesquisa do Estado de Minas Gerais – FAPEMIG
and VALE/SA for financial support.
Author contributions RLF formulated the idea and contributed to the literature review. RLF, MSS, RFVC
built the sample design collected the data and identified invertebrates. TGP and MSS performed the statisti-
cal analysis. MSS wrote the manuscript with collaboration from all co-authors. RLF and TGP reviewed the
manuscript and contributed to the discussion of the results.
Data availability The data used in this work are available in the Supplementary Material I, II and III.
Declarations
Conflict of interest The authors declare that they have no conflict of interest.
Ethical approval Not applicable.
Consent to participate All authors consent to participate in the work.
Consent to publish All authors consent to publish the work.
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Authors and Aliations
MarconiSouza‑Silva1,2 · RobertaFernandaVenturaCerqueira1,2 ·
ThaisGiovanniniPellegrini2,3 · RodrigoLopesFerreira2
1 Programa de Pós-Graduação em Ecologia, Universidade Federal de São João del-Rei,
SãoJoãodel-Rei, MG36307-352, Brazil
2 Centro de Estudos em Biologia Subterrânea, Setor de Biodiversidade Subterrânea, Departamento
de Ecologia e Conservação, Instituto de Ciências Naturais, Universidade Federal de Lavras, Cx
Postal 3037, Campus Universitário, Lavras, MGCEP37200-900, Brazil
3 Programa de Pós-Graduação em Entomologia, Departamento de Entomologia, Universidade
Federal de Lavras, Lavras, MGCEP37200-900, Brazil
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