10.5061/DRYAD.C2FQZ6179
Lopes, Gabriel
0000-0003-3924-6481
Universidade Estadual de Santa Cruz
Penido, Gabriel
Federal University of Rio Grande do Sul
Heming, Neander
Universidade Estadual de Santa Cruz
Diele-Viegas, Luisa
University of Maryland, Baltimore
Decline of the brown-throated sloth (Bradypus variegatus Schinz, 1825) in
an Atlantic Forest protected area
Dryad
dataset
2021
Bradypus variegatus
brownt-throated sloth
population estimation
rescue methodology
en
37556 bytes
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
Data Collection 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. Data Analysis 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). Population
estimation 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).