This DATSETNAMEreadme.txt file was generated on YYYY-MM-DD by NAME GENERAL INFORMATION 1. Title of Dataset: 2. Author Information A. Principal Investigator Contact Information Name: Gabriel Lopes Institution: State University of Santa Cruz Address: Campus Soane Nazaré de Andrade, Rod. Jorge Amado, Km 16 - Salobrinho, Ilhéus - BA, 45662-900 - Brazil Email: gabrielfrenner@gmail.com B. Associate or Co-investigator Contact Information Name: Gabriel Penido Institution: Federal University of Rio Grande do Sul Address: Campus do Vale - Agronomia, Porto Alegre - RS, 90650-001 - Brazil Email: penido.ga@gmail.com 3. Date of data collection (single date, range, approximate date): from 2015-07 to 2019-02 4. Geographic location of data collection : Rio de Janeiro, Rio de Janeiro - Brazil (22°58'02.5"S / 43°13'30.1"W) SHARING/ACCESS INFORMATION 1. Licenses/restrictions placed on the data: None 2. Was data derived from another source? No DATA & FILE OVERVIEW 1. File List: a. "B_variegatus-raw.xlsx" - raw data collected on individuals, biometrics, coordinates and place of rescue and also dates b. "biometric.R" - analysis on biometris carried out on R environment c. "seasonality.R" - analysis carried out on R environment to indentify seasonality in our data d. "model.txt" - model of population estimation constructed on R environment 2. Relationship between files, if important: all analysis were made using information present in "B_variegatus-raw.xlsx" METHODOLOGICAL INFORMATION 1. Description of methods used for collection/generation of data: The RJBG Wildlife conservation team provided us with all data used in this study, and there were no active captures. The team works in technical cooperation with the Brazilian Institute of the Environment and Renewable Natural Resources (IBAMA), assisting the RJBG local vertebrate fauna when it is needed. They operate mostly in the park's common areas to protect both the animals and visitors when there is a call for injured animals or other situations that could offer risk. Rescued animals are measured and, except birds, receive a microchip for identification (Transponder Partners PA120 - PA140, Microchip Partners - Switzerland), being posteriorly relocated to the forest areas. The animals were handled following Cassano et al. (2011) and Falconi et al. (2015) methods. We identified the sex of the individuals based on the presence/absence of the dorsal speculum (Hayssen, 2010). To classify their age classes, we considered the individuals’ HBL (head-body length) as follows: juveniles, HBL < 40 cm; adults, HBL > 40 cm (Castro-Vásquez et al. 2010 apud Emmons & Feer, 1990 and Plese & Moreno, 2005). We used a 1.5 m measuring tape to measure the individuals’ body length, and a 300mm digital caliper (100.178BI Digimess, Brazil) to measure other individuals’ morphometry (e.g., size of the claws, tail, femur, humerus). We weighted the individuals using a digital weight scale – 15 kg (Economic line next – digital scale, Balmak, Brazil). Individuals captured for the first time received a subcutaneous microchip associated with an ID number, as mentioned before. Sloth monitoring and individuals’ identification were performed through a remote reading of the animal’s ID microchip using a bamboo stalk, when needed, with a microchip reader attached to its extremity. After all procedures, the animals were released in a forest belt closer to TNP, known here as Atl. Forest – Cacti. 2. Methods for processing the data: We carried out our analyses in the software environment R 3.6.1 (R Development Core Team, 2019). We evaluated the differences in weight and HBL between sexes through the Mann-Whitney test since our data was not normally distributed (Shapiro-Wilk test, p > 0,05). We used a one-way analysis of variance (ANOVA One Way) to evaluate variations in weight per age class and correlation and polynomial regression tests to visualize the relationship between size and weight better. To evaluate seasonality, we applied the WO-Test (Webel-Ollech overall seasonality test) from the package seastests, which combines three other tests: QS-test, QS-R, and Kruskal-Wallis (Ollech, 2019). We constructed a simple model (appendix) for the estimative of population size (N) based on a variation on detection probabilities (p) with time (similar to Model Mt, Otis et al. 1978). We then estimated the survival and recruitment rates between occasions to infer the population abundance. Hence, we implemented an open population model since a closed population is unlikely due to the survey's long timespan. We ran the model using the Bayesian MCMC framework (Kéry & Schaub, 2011) in which we could limit the posterior distribution of p (through prior definition) to lower values since we had few recaptures (appendix). We applied Data augmentation (Royle et al., 2007) for the estimation of N by transforming a closed population model into an occupancy model (Kéry & Schaub, 2011), which allowed us to increase the accuracy of the posterior distribution of the population size (N). Consequently, we run the occupancy model to estimate detection probability (p) and the probability of the inclusion of a member of the data augmented individual (Ω) to the population size (N) (Kéry & Schaub, 2011) (Table 1). This model was implemented in software R3.1.2 (R Development Core Team, 2014), with the package R2jags (Plummer, 2012), which estimates the posterior distribution of the variables by performing Markov Chain Monte Carlo (MCMC) iterations. We run three chains with 15,000 iterations each, discarding the first 5,000 as burn-in. The convergence of the model for all chains was checked visually and with the Gelman-Rubin statistic (r ̂), in which values sr ̂<1.1 suggests convergence (Kéry, 2010). We also performed a model fitness using a Bayesian P-value (Zipkin et al., 2010), described in the supplementary material. Finally, we estimated the sexual ratio by the number of males per female (Soares & Carneiro, 2002). 3. Instrument- or software-specific information needed to interpret the data: R 3.6.1 (R Development Core Team, 2019) for all analysis except popualtion estimation, which was carried out in 3.1.2 version of R. All packages used are cited above. 7. People involved with sample collection, processing, analysis and/or submission: Sample collection: Gabriel Lopes Analysis: Gabriel Lopes and Gabriel Penido Submission: Gabriel Lopes DATA-SPECIFIC INFORMATION FOR: "B_variegatus-raw.xlsx" in sheet "BIOMETRICS" 1. Number of variables: 2 2. Number of cases/rows: 6 (we only used 4 variables out of 6) 3. Variable List: HBL = body size + tale size (in cm) MASS = weight of animal (in kg) CLASS = Age class of animal - JV juvenile, AD adult SEX = F female M male DATA-SPECIFIC INFORMATION FOR: "B_variegatus-raw.xlsx" in sheet "SEASONALITY" 1. Number of variables: 1 (number of captures) 2. Number of cases/rows: 2 (month and number of capture) DATA-SPECIFIC INFORMATION FOR: "B_variegatus-raw.xlsx" in sheet "HISTORY OF CAPTURES" 1. Number of variables: 3 2. Number of cases/rows: 44 (all months of data collection since 07/2015 to 02/2019) 3. Variable List: for each indivuduals there could be 3 values: 1 - means capture/recapture 0 - means the individual was not captured in the specific month labeled in row title . - means no capture was made (no field occured)