DOI: 10.5061/dryad.gb5mkkwqv
title: Species abundances and functional traits data of frog assemblages in Amazonian forests (Tapajós FLONA and Alter do Chão village, Brazil)

description: We sampled frogs along an edaphic and vegetation-structure gradient in the
 Brazilian Amazon. We sampled 33 plots organized in four modules (with 10,
 6, 8 and 9 plots). Environmental data are composed of are litter
 depht(cm), distance to water bodies (m), temperature (ºC), vegetation
 structure (PCA axis), soil sand and silt content (g-kg), and proportion of
 the area deforested. Additionally, we measured snout-vent length (SVL) and
 leg length relative to SVL and evaluated the effects of environmental
 variables on thirteen binary traits used with presence or absence.
description: We sampled 33 plots organized in four modules (with 10, 6, 8 and
 9 plots) of Tapajós National Forest and Alter do Chão Village, Pará state,
 Brazil. We used visual and acoustic search from day to twilight
 (16h30min–18h00min), and night (19h00min–23h00min pm) for frogs detection
 during the rainy season (January–March) in 2018 and 2019. We identified
 species based on the literature (e.g. Lima et al. 2006, Oliveira et al.
 2017, Carvalho et al. 2020) and comparisons with specimens deposited in
 the herpetological section of the zoological collection of Instituto
 Nacional de Pesquisas da Amazônia (INPA, Manaus, Brazil).
 We measured litter depth with a Marimon-Hay litter collector
 (Marimon-Junior & Hay 2008) at six points separated by 50-m
 intervals in each plot and used mean values in the analyses. We using a
 set of variables quantifying forest structure (data in
 https://search.dataone.org/view/PPBioAmOc.577.2 ) summarized with the
 first axis of a principal component analysis (PCA) that represent a 59% of
 the variance in the original variables - the metrics are maximum forest
 height, average forest height, canopy opening ratio, canopy gaps computed
 at 5 m, canopy gaps computed at 10 m, canopy gaps computed at 15 m, leaf
 area index in the vertical profile, leaf area index in the vertical
 profile 0–15 m, leaf area index in the vertical profile above 15 m,
 collected by a portable Light Detection and Ranging (LIDAR) device. We
 measured temperature at the beginning of day and night sampling, using
 AK172-AKSO® data loggers, and used mean values for each plot in the
 inferential models. We measured the distances from the coordinates of the
 beginning of each plot to the nearest water body using the linear-distance
 matrix and Qchainage tools of the QGis 3.16.2 software, applied over
 combined hydrography shapefiles from public repositories (MMA 2006),
 following Venticinque et al. (2016). We used SoilGrids data to obtain sand
 and silt content 0–5 m deep, extracted sand- and silt-content values from
 centroid coordinates of each plot using the raster R-package. Finally, we
 used a 30 x 30 m² satellite-based raster layer summarizing deforestation
 until 2019 from the MapBiomas project (Souza et al. 2020, see full
 description in http://mapbiomas.org) to extract proportions of deforested
 areas in 500 m-buffers using the raster R-package. Analyzes were performed
 using the statistical program R (R Development Core Team 2019).