Multiple factors contribute to explaining the composition and structure of communities and the niche is still the most used theory to describe how organisms interact with such factors to generate biological communities’ patterns. Both biotic and abiotic aspects of the environment along with the geographic space interact in complex ways with the behavior and physiological tolerance of organisms to structure a community. In this study, we tested the potential effects of the environment and space that may determine the distribution of tadpoles in aquatic habitats located at Emas National Park. To this end, we sampled 23 aquatic habitats, described their environmental characteristics, recorded their geographic location, and carried out a survey of the tadpoles found. Later, we evaluated the effect of environmental and spatial variables on the distribution of species, through a Partial Redundancy analysis, using matrices of environmental descriptors and geographic location. We found that both environmental and spatial factors drive the distribution and the species composition of the studied assemblages. Also, when we analyzed the pattern of co-occurrence, we observed that the number of co-occurrence pairs of species in the Emas National Park is less frequent than expected by chance, suggesting that besides the effect of abiotic components (environmental and spatial factors), competition is a co-structuring factor in the assemblages of tadpoles.

Study area

Sampling was carried out at Emas National Park (PNE) (17°49', 18°28'S and 52°39', 53°10'W) which has approximately 7.300 km2 and is located in the midwest region of Brazil. It is one of the largest and most important protected areas of the Cerrado, due to its diversified flora and fauna (IBDF 1981). The average annual temperature ranges from 22 to 24°C and precipitation ranges from 1,500 to 1,700 mm per year, with rainfall mostly concentrated between October and March. The vegetation of PNE consists of different Cerrado types comprising of open savanna gradient (68.1%), dense savanna (Cerrado sensu stricto; 25.1%), wet fields (4.9%), and riparian and mesophytic forests (1.2%) (Ramos-Neto and Pivello 2000).

Data sampling

We sampled 23 aquatic habitats within and around the park. We conducted the fieldwork at the beginning and at the end of three rainy seasons (October to March) from 2012/2013, 2014/2015, and 2017/2018. Therefore, each aquatic habitat was sampled six times. We described the structural characteristics of each sampled pool based on the following variables: % of each vegetation type around and inside the puddle (herbaceous, shrub, floating vegetation), % substrate type (rock, stones, gravel, coarse, sand, clay, mud and leaf litter), % margin type (ravine, flat, inclined and excavated), % of land use type around the puddle (short- or long-term cultures, regenerated forest, continuous forest, and grassland), and dimensions (maximum length, maximum height, and maximum depth). To define the quantities in percent of each of the structural characteristics, the same researcher (IBFP) evaluated the selected traits of each descriptor according to a semi-quantitative scale (0%; 1-20%; 21-40%; 41-60%; 61-80%; 81-100%). Later, we summed the percent of each trait to generate the environment descriptor, using the following conversion rule: 0% = 0; 1-20% = 0.1; 21- 40% = 0.3; 41-60% = 0.4; 61-80% = 0.8; 81-100% = 0.9. Percentages of each descriptor could be higher than 100%. For example, when calculating the % of each vegetation type inside the puddle, the researcher evaluated and applied a value in the semi quantitative scale for each trait (herbaceous = 21-40%, shrub = 1-20%, floating vegetation = 61=80%) and summed its values following the conversion rule (0.3+0.1+0.8=1.2). Values in the raw data represent the mean values of the six samples.

We sampled the margins of aquatic habitats for an hour or until completing a walk along their entire perimeter (Heyer 1994). The sampling was done using a conical dip net (30 cm diameter and a 3mm² mesh). Then, we anesthetized tadpoles in a 5% benzocaine solution and fixed them in 10% formalin. Afterward, we identified each larva with the aid of the identification key from Rossa-Feres and Nomura (2006) and specialized literature that characterized the larvae of anurans. Nomenclature followed Frost (2020). The specimens used in the study are deposited at the Zoological Collection of the Federal University of Goiás (ZUFG).

The datasets are the raw data for local environmental descriptors and the output for intermediary steps of the analyses in our article. Our intention is to give transparency and allow replication of our statistical procedure. Raw data of species distribution and abundance can be found in the published article.

Abbreviations for the Paixao-et-al-2022-Raw-environmental-data.txt as follows: