DOI: 10.5061/dryad.wm37pvms1
title: Big family, warm home, and lots of friends: Pteronotus large

description: Roosts are essential for the survival of most animals. Due to homothermic
 requirements, mammals are particularly dependent on roost quality and
 availability. Bats select their roosts in a species-specific way, likely
 related to species´ different physiological and adaptive needs. Unlike
 species whose individuals roost solitarily, roost selection is critical
 for bats forming large colonies due to the requirements for maintaining
 thousands of individuals in a single shelter. This is the case of
 Pteronotus (Mormoopidae), whose colonies reach hundreds of thousands of
 bats. Using captures, bioacoustics, and automated censuses, we evaluated
 how cave size, ceiling characteristics, environmental stability,
 temperature, and humidity influence the formation of exceptionally large
 colonies, species richness and composition in caves in north-eastern
 Brazil. We expected that colonies would be positively related to cave size
 and stability and internal cave selection would be species-specific, but
 larger and more environmentally stable caves would have higher richness.
 Pteronotus colonies were positively related to cave size, stability, and
 ceiling characteristics, and their presence strongly influenced cave
 temperature variation. Species richness was positively correlated to a
 cave stability index. Species other than Pteronotus preferred different
 climatic and ceiling characteristics. We detected an indirect influence of
 the large colonies of Pteronotus on the species richness and occupation
 inside caves. On the other hand, such caves favour species coexistence, as
 they offer a range of microenvironments, reducing niche overlap in their
 interior. P. gymnonotus and P. personatus are both key- and
 umbrella-species for cave ecosystems, stressing the need for specific
 conservation strategies in Brazil.
description: 1.1  Study area 
 We sampled eleven caves
 distributed in the states of Sergipe (2 caves), Pernambuco (3), Rio Grande
 do Norte (3), and Ceará (3), in the Northeast Region of Brazil (Figure 1).
 The caves differed in formation, having limestone or sandstone origin
 (Table 1). The caves of Sergipe are located in the ecotone
 between the Atlantic Forest and Caatinga biomes, where the Tropical
 Megathermal climate predominates, with dry season during summer and
 temperatures varying between 23 and 27°C. Average annual rainfall in
 Sergipe ranges from 1,000 and 1,400 mm, usually concentrated from April to
 August (Alvares et al., 2013; Aragão et al., 2013). The other caves (n =
 9) are located in the Brazilian semi-arid region, which is part of the
 Caatinga biome characterized as a seasonally dry tropical forest (Silva et
 al., 2017). The region has a hot semi-arid climate, with temperatures
 ranging between 25 and 30°C and average annual rainfall varying
 spatiotemporally between 600 and 1,200 mm usually concentrated from March
 to July (Andrade et al., 2017). Caves in Sergipe, Rio Grande do Norte, and
 Ceará were sampled in July 2019, whereas caves in Pernambuco were sampled
 in February 2021. Although samplings were carried out in different
 periods, the precipitation and temperature conditions at the time of both
 sampling were similar (INMET, 2019; 2021).
 1.2  Abiotic data collection 
 Cave size ranged from 44 to
 707 m in horizontal projection (Table 1). We obtained data for cave size
 from the Brazilian National Registry of Speleological Information - CANIE
 (CECAV 2019) and estimated the size of unregistered caves through
 topographic mapping (Cavalcanti, 1996).
 The assessment of the
 availability of microhabitats for bats inside caves was carried out by
 characterizing the ceiling of each cave, considering the structures that
 species use. Thus, we counted the number of crevices, domes, and holes
 (Table 1). Crevices were considered cracks found in rocks.
 Domes and holes are
 concavities present in the ceiling and were distinguished by their
 dimensions: domes were defined as being larger in diameter than depth,
 whereas holes are more deep than wider.
 We assessed the environmental
 stability of caves using the Environmental Stability Index – ESI (e.g.,
 Ferreira, 2004; Bento et al., 2016; Pellegrini et al., 2016; Barros et
 al., 2020). The index (ESI) reflects cave stability in relation to the
 influences of the external environment, considering the cave size (HP),
 the number of entrances (NE), the average distance between entrances (DE -
 if there is more than one), and the entrance width (EW), according to the
 formulas: ESI = ln (HP/EW), for caves bearing a single entrance; and
 ESI = ln [HP x (HP/∑EW) /
 (NE*DE)], for caves
 bearing more than one entrance. Environmental stability means that the influence
 of the daily and annual climatic variations from the external environments
 are minimized. Thus, larger caves with a single entrance tend to have a
 higher ESI, and probably will have less influence from the external
 variation of the climatic conditions, than smaller caves and caves bearing
 multiple entrances (Ferreira, 2004). The ESI of the sampled caves
 ranged from 2.18 to 6.27 (Table 1).
 We calculated the average and
 the range of variation of temperature and humidity inside caves to assess
 microclimatic conditions. We took ten equidistant measurements inside each
 cave, considering its total horizontal projection (from the entrance to
 the deepest chamber), following Barros et al. (2020). The average
 temperature inside the different caves ranged from 27.1 to 36.3°C, with a
 variation range between 1.9 and 9.8°C. The relative humidity recorded
 ranged from 55.8 to 94.4%, with a variation range between 8 and 29%
 (Table
 1). 1.3  Biotic data collection 
 We captured bats inside the
 cave using hand nets during the day to get an estimate of species richness
 at each cave. Captured bats were identified to species in the field using
 identification keys and then released (Gardner, 2008; Diaz et al., 2016).
 Bat capture was authorized by the SISBIO license 68992-1 and carried out
 in accordance with the standards of the American Society of Mammalogists
 (Sikes et al., 2019), with the permit # 114/2019 of the Ethics Committee
 for the Animal Use of the Federal University of Pernambuco – CEUA/ UFPE.
 To confirm identification of species difficult to capture (e.g.,
 insectivores), we carried out acoustic recordings with ultrasound
 detectors (model SM4 – Wildlife Acoustics, Massachusetts, USA) positioned
 at the cave entrance simultaneously with infrared filming. Recorders were
 configured to capture the frequency range up to 384 kHz and 16-bit depth
 of audio. The echolocation signals recorded were analysed using the Raven
 Pro 1.5 software (Cornel Lab of Ornithology, Ithaca, USA) and identified
 at the lowest taxonomic level possible, following Arias-Aguilar et al.
 (2018). We used the non-invasive technique of population
 census based on thermal infrared imaging to estimate bat population in
 each cave (e.g., Otálora-Ardila et al., 2019). A thermal camera (FLIR E60)
 was installed at the cave entrance for three hours during emergence, then,
 using a specially developed algorithm for counting (Rodrigues et al.,
 2016), bats identified in the images were counted. 
 1.4  Data analysis 
 First, we tested data for
 normality. We used Spearman’s correlation analysis to assess the
 relationship between species abundance and other variables (richness,
 stability, horizontal projection, temperature, and humidity). We opted to
 use correlation because, for some variables (e.g., temperature and
 humidity), abundance can be an independent variable, and for others (e.g.,
 cave size and stability), abundance acts as the dependent
 variable. To evaluate the effect of all cave
 characteristics on richness, we performed linear regressions with the
 variables: horizontal projection (HP), environmental stability index
 (ESI), and temperature and humidity range of variation. We opted to use
 linear regressions because of the number of caves sampled and the low
 number of variables. We performed a multivariate analysis of
 abundance based on generalized linear models (Wang et al., 2012) to assess
 the relationship between cave characteristics and species composition, and
 cave characteristics and individual species. Models were created combining
 the following variables: horizontal projection, environmental stability
 index, average and range of temperature and humidity, holes, domes, and
 crevices (see Supporting Information). This analysis was performed in the
 <em>mvabund</em> package, using the
 <em>manyglm</em> function (Wang et al., 2012), in R version
 4.0.3 (R Core Team, 2020). Although the analysis evaluates abundance, it
 is possible to use presence/absence data through the
 <em>manyglm</em> function. As the abundance values obtained from
 the thermal camera counts are for all bats in the cave without
 differentiating species, we used presence and absence data in this
 analysis, with the “binomial” distribution family specified in each model.
 We carried out diagnostic tests using the graphics of the residual
 distribution to ensure that the assumption inferred in each model was
 correct (Figure S1–S6). We used the <em>anova.manyglm</em> function to assess the significance of
 predictor variables on species composition. We applied univariate tests
 using the unadjusted <em>p</em>-value to determine the
 individual response by each species. For this analysis, we considered only
 species that occurred in at least three caves, as the use of rare species
 could generate biased results.
description: We used presence and absence
 data from the bat species to analyze, using <em>mvabund</em>
 package, the relationship with the caves characteristics. The data is
 organized in a spreadsheet containing in each column the values for the
 caves features, followed by the data of presence/absence of each species.
 The file “mvabund” was used as an input in R on the script described
 below.