DOI: 10.5061/dryad.mpg4f4r3h
title: Promising abundance and composition of forest dwelling anurans in cashew plantations in a tropical semi-evergreen forest landscape

description: Plantation crops in tropical human-modified landscapes provide alternative
 habitat to biodiversity outside protected areas. The Western Ghats of
 India is home to a mosaic of closely spaced habitats, including forests
 and agroecosystems. Cashew is a widely grown plantation crop in the
 northern Western Ghats and is known to provide economic and societal
 benefits. However, its role as a supplementary habitat for
 anurans is not well understood. We assessed the factors that
 influence understorey anuran composition and abundance in cashew
 plantations, forest edges, and forest interiors in Tillari Conservation
 Reserve (TCR), Maharashtra. Species composition of cashew plantations
 differed significantly from forests and was positively influenced by
 understorey and canopy cover. Cashew plantations had near equal abundance
 of anurans as that of forest edges and interiors, which could be due to
 the preponderance of habitat generalists. Understorey positively
 influenced anuran abundance while ambient temperature had a negative
 influence. Reduced understorey and low canopy cover represent habitat
 modifications that occur in cashew plantations. Such structural changes
 could lead to reduced environmental refuges for anurans, thereby exposing
 them to large variation in temperature and moisture. Cashew plantations in
 TCR serve as supplementary habitat for anurans. That said,
 cashew cultivation practices and markets must be understood
 before biodiversity-friendly plantation practices are
 proposed.  
description: <strong>2.1 Study area</strong>
 The study was carried out at Tillari Conservation Reserve (TCR)
 in Sindhudurg District, Maharashtra state, which falls under the Northern
 Western Ghats, India (Figure 1a). The region’s elevation ranges between 50
 m and 1,030 m a.s.l. It receives an average annual rainfall of 3,000 mm.
 Almost all the rainfall is received during the monsoon, which spans from
 mid-June to October. It has tropical semi-evergreen and moist-deciduous
 forest types (Champion and Seth 1968). Besides these natural habitats, TCR
 is part of a human-modified landscape, that includes hydroelectric
 projects, human habitation and secondary evergreen forest. Cash crops
 include: cashew, banana, pineapple, and rubber. Cashew plantations cover
 roughly 537 km<sup>2</sup> in Sindhudurg district, and that
 constituted 31 % of all area under cashew cultivation in the entire state
 of Maharashtra (Sengar, Mohod, and Khandetod 2012). In the landscape
 comprising of forests, plantations, reservoir and human habitations, the
 state owned primary and secondary forests that are part of TCR encompasses
 29.53 km<sup>2</sup> (Patil 2021).  The
 sampling for anurans was conducted across forest interiors, forest edges,
 and cashew plantations. The cashew plantations are typically small land
 holdings, locally owned and predominantly characterized by 2–3 m-high
 cashew trees spaced 15 ft apart. They are interspersed with a few remnant
 native trees such as <em>Terminalia paniculata</em>,
 <em>Mangifera</em> <em>indica</em>, and*
 Artocarpus* <em>heterophyllus</em>, and native trees are
 occasionally logged. Cashew requires sunlight and is therefore grown
 primarily on gentle hill slopes in TCR. The age of the plantations in the
 area ranged between 7 and 10 years. The understorey of the plantations is
 cleared twice a year, in summer and after monsoon. No irrigation is
 provided to the cashew plantation during the dry period. The sampling was
 done in cashew plantations owned by several people of the Hevale village
 and prior permission for this work was obtained from land owners and
 village committee. The forest edge transects were laid at a distance of
 not more than 1.5 km from the bordering cashew plantations. Seven of the
 ten forest edge transects were present in a Sacred Grove. They are
 protected by the local communities and no modification to the understorey
 is made. The remaining three transects were laid in habitats that were not
 sacred groves but were bordering cashew plantations. The transects in
 forest interior habitats were laid about 2 km from the plantation border
 inside TCR The forest interior sites had no recent anthropogenic
 disturbance. However, locals practiced shifting cultivation and selective
 logging about four decades ago. 
 <strong>2.2 Sampling anurans and environmental
 covariates</strong> We laid thirty 50 × 2 m
 transect strips, with ten transects in each of the three habitats.
 Sampling was limited to a maximum of 3 m vertically. Each transect had a
 minimum of 50 m distance between them to avoid double counting. We
 employed a nocturnal Visual Encounter Survey (VES) for sampling
 anurans (Crump and Scott 1994), wherein, two people with headlamps
 searched for terrestrial and arboreal anurans. We sampled only on land and
 avoided aquatic habitats and canopy. Each transect was thoroughly scouted
 for anurans after sundown for 1 hour, typically between 1900–2100 h. Since
 we sampled anurans during the period when they were most active, we made
 the assumption that their passive encounters were representative of their
 relative abundances. Anurans were sampled from August 2020 to October 2020
 during the monsoon. We conducted a pilot survey during the monsoon of 2019
 and made an inventory of anuran species in the study area. Six out of nine
 species observed during this study were taxonomically identified based on
 our 2019 effort. The anurans with ambiguous taxonomic identity were
 carefully examined and identified using DNA barcoding by matching partial
 16S rRNA sequences obtained from oral swabs (Kurabayashi et al. 2005). In
 addition, we recorded microhabitats used by anurans which included, bare
 ground, leaf litter, and bush.  Environmental
 covariates such as elevation, canopy cover, percent understorey, leaf
 litter depth (LLD), and air temperature (T<sub>a</sub>) were
 measured to determine their contribution in influencing the anuran
 composition and abundance across sampled habitat types. Each variable was
 measured at every 10 m from the beginning to the end of the transect and
 then averaged. The elevation was measured in meters above sea level using
 a Garmin-Etrex 10 handheld GPS with an accuracy of 5 m. A vertical
 densitometer was used to measure the percent canopy cover. The understorey
 was estimated by measuring the amount of 1–2 m-high vegetation in a 10 × 2
 m plot inside the transect. Leaf litter depth was measured using a steel
 ruler inserted through the litter until it touched the soil or humus layer
 following Rowley and Alford, 2007, and the air temperature was measured
 using a digital thermometer, Extech™ RH101.
 <strong>2.3 Measuring operative body temperature and prey
 availability</strong> Operative temperature
 (T<sub>e</sub>) is the temperature attained by a
 non-thermo-regulating animal from radiation, conduction, and convection.
 It was measured by placing a physical model or operative temperature model
 (OTM) mimicking the size and shape in the microhabitat typically used by
 the species (Taylor et al. 2020). In this study, the OTM was made of 3%
 agar gel shaped into a frog of 6 cm in size weighing 10–13 g using a
 gypsum frog cast, mimicking the body size of an adult frog and covered by
 a plastic mesh (Figure 1b). The models had a DS1921G Thermochron® iButton
 temperature data logger embedded in them to record hourly temperature data
 in degrees Celsius.  We installed pitfall traps coupled
 with drift fences guiding the pitfalls to measure the prey availability.
 Invertebrates found in the pitfall were identified to order level and prey
 availability was measured by counting the number of insect orders. The
 traps were made of 5 cm diameter cylindrical bottles with an inverted
 conical top to avoid escape of the trapped insects and small holes at the
 bottom to avoid waterlogging. The models and pitfalls were placed from
 1800–0700 h in the three microhabitats: bare ground, leaf litter, and
 bush. We placed the models and pitfalls for two consecutive days in every
 transect for all habitats except the forest edge, where the
 T<sub>e</sub> and prey availability was not measured in one
 microhabitat i.e., leaf litter, as we did not find anurans in the leaf
 litter in this habitat. The model temperature and prey availability values
 were averaged at the transect level. We checked the pitfalls every day to
 avoid water logging or damage by animals. 
 <strong>2.4 Statistical analysis</strong>
 The abundance of anurans was the count of anurans of a species
 encountered in a transect in the three habitats. A Spearman correlation
 test was used to check for correlation among environmental covariates
 variables (Figure S1). We used Moran's I test to check for spatial
 autocorrelation (Kurz et al. 2016). To test for differences in the
 environmental covariates between habitats, we used a Kruskal-Wallis test
 coupled with effect sizes followed by a Dunn’s pairwise comparison test.
 We used non-parametric tests as some variables collected during the study
 (canopy cover, ambient temperature, and prey availability) did not follow
 normal distribution. These tests were performed using
 <em>kruskal_test</em>, <em>kruskal_effsize</em>,
 and <em>dunn_test</em> functions from
 the <em>rstatix</em> package (Kassambara 2020),
 and <em>Mean ± SD</em> was calculated. Generalized linear
 mixed models (GLMMs) with Poisson distribution and log link function were
 used to assess the influence of environmental covariates on anuran
 abundance (Bolker et al. 2009). We used
 the <em>glmer</em> function from
 the <em>lme4</em> package (Bates et al. 2015) to perform this
 analysis. Habitat type, canopy cover, understorey, LLD,
 T<sub>a</sub>, T<sub>e</sub>, and prey
 availability were used as fixed effects, and intercept varying among
 transects was used as a random effect. Elevation was removed for this
 analysis as it was positively correlated with canopy cover
 (<em>ρ</em> &gt; 0.7, P &lt; 0.001). The continuous
 fixed effect variables were scaled using the
 function <em>scale</em> in
 package <em>base</em> by dividing the values by standard
 deviation before performing the GLMM (R Core Team 2019). We used
 the <em>dredge</em> function from the
 <em>MuMIn</em> package (Barton 2020) to run all possible
 models. We performed model averaging instead of a single parsimonious
 model following Burnham and Anderson (2002), by selecting candidate models
 with ΔAIC<sub>c</sub>&lt;2 and using the
 function <em>aictab</em> from
 the <em>AICcmodavgpackage</em> for model averaging. A total of
 128 candidate models were built by
 the <em>dredge</em> function, of which 6 models were selected
 for averaging. Following this, we extracted the model average predictions
 for all variables using the <em>modavgfunction</em>. We used
 Permutational multivariate analysis of variance (PERMANOVA) to assess the
 patterns in anuran species composition across habitat types using
 the <em>adonis2</em> function (Anderson 2001). Bray-Curtis
 dissimilarity measure was computed from the species frequency data matrix
 using the <em>vegdist</em> function. Non-metric
 Multidimensional Scaling (NMDS) was performed using
 the <em>metaMDS</em> function to visualize species composition
 patterns across habitat types (Borcard, Gillet, and Legendre 2011).
 Following NMDS, the <em>envfit</em> function was used to fit
 environmental covariates as vectors to the ordination. Species composition
 analyses were performed using the <em>vegan</em> package in
 R (Oksanen et al. 2020). Further, to characterize the
 value of the habitats, we conducted an indicator species analysis (ISA) to
 identify species that select a particular habitat type (De Cáceres,
 Legendre, and Moretti 2010). The analysis generates an indicator value
 (IV) for every species based on the specificity (A = mean abundance) and
 fidelity (B = relative frequency of occurrence). An IV of a species is a
 predictive value of a species as an indicator for a site or a combination
 of sites (De Cáceres, Legendre, and Moretti 2010). The ISA was carried out
 using a <em>multipatt</em> function with 999 permutations from
 the <em>indicspecies</em> package (De Cáceres, Jansen, and
 Dell 2020). All the analysis was conducted using R version 3.6.2 and
 plotted using the package <em>ggplot2</em> (Wickham
 2016).
description: # Promising abundance and composition of forest dwelling anurans in cashew
 plantations in a tropical semi-evergreen forest landscape Dataset DOI:
 [10.5061/dryad.mpg4f4r3h](https://doi.org/10.5061/dryad.mpg4f4r3h) ##
 Description of the data and file structure We assessed the factors that
 influence understorey anuran composition and abundance in cashew
 plantations, forest edges, and forest interiors in Tillari Conservation
 Reserve, Maharashtra. Species composition of cashew plantations differed
 significantly from forests and was positively influenced by understorey
 and canopy cover. Cashew plantations had a near equal abundance of anurans
 as that of forest edges and interiors, which could be due to the
 preponderance of habitat generalists. Understorey positively influenced
 anuran abundance while ambient temperature had a negative influence.
 Reduced understorey and low canopy cover represent habitat modifications
 that occur in cashew plantations. Cashew plantations in Tillari
 Conservation Reserve serve as supplementary habitats for anurans. ###
 Files and variables Microsoft Excel file containing three sheets with the
 following names: (i) NMDS\_species, (ii) NMDS\_Environmental_variables,
 (iii) GLMM (Generalized linear mixed models (GLMMs) with Poisson
 distribution and log link function were used to assess the influence of
 environmental covariates on anuran abundance) Abbreviations used are:
 **Species** DTM *Duttaphrynus melanostictus* HOT *Hoplobatrachus
 tigerinus* INC *Indirana chiravasi* MVC *Minervarya cepfi* MVG *Minervarya
 gomantaki* PSA *Psuedophilautus amboli* PSW *Psuedophilautus waynadensis*
 UPM *Uperodon mormoratus* SPD *Sphaerotheca dobsoni* **List of abiotic
 factors** veg Understorey vegetation in % elev Elevation in meters ccov
 Canopy cover in % lld Leaf litter depth in cm ta Ambient Temperature in
 celcius tmod Operative Temperature of model in celcius Prey prey
 availability or counts of prey trnsct  Transect identity abund Abundance
 or counts ## Code/software All the analyses were conducted using R version
 3.6.2 and plotted using the package ggplot2 (Wickham, 2016) ## Access
 information Other publicly accessible locations of the data: * Not
 applicable Data was derived from the following sources: * Not applicable