DOI: 10.5061/dryad.c2fqz6179
title: Decline of the brown-throated sloth (Bradypus variegatus Schinz, 1825) in an Atlantic Forest protected area

description: Estimates on demographic parameters altogether with social factors are
 integral and can be very useful to assess the risks that a population may
 face in the future. Rescue operations may provide a unique opportunity to
 gather data on individuals of an area and thus provide population
 information. Animal rescue data provided by the Rio de Janeiro Botanical
 Garden fauna team were used to understand the structure of a population of
 Bradypus variegatus in an urban remnant of the Atlantic Forest (Tijuca
 National Park, PNT). This study aims to provide data on the abundance,
 density estimation, sex ratio, and occurrence of this population in the
 PNT. We rescued 44 sloths, four of whom were dead. The population density
 was estimated at 0.6 ind / ha, a low-density value compared to other urban
 remnants (8.5 to 12.5 ind / ha). Our model suggests a unstable and in
 decline population, which could be a delayed reflection of years of
 deforestation in the Atlantic forest. Although B. variegatus isn’t, yet,
 considered threatened due to their broad distribution, they can be locally
 extirpated due to population unfeasibility in forest remnants of Atlantic
 Forest regions, suggesting we should evaluate its threat levels at
 population level.
description: <b>Data
 Collection</b> 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 &lt; 40 cm; adults, HBL &gt; 40 cm
 (Castro-Vásquez et al. 2010 <i>apud </i>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. <b>Data
 Analysis</b> 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 &gt; 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 <i>seastests</i>, which combines three other tests:
 QS-test, QS-R, and Kruskal-Wallis (Ollech, 2019).
 <b>Population estimation</b> 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 <i>R2jags</i> (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 ̂&lt;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).