TY - JOUR
T1 - Alternative method for assessment of southwestern Atlantic humpback whale population status
AU - Bortolotto, Guilherme A.
AU - Thomas, Len
AU - Hammond, Philip Steven
AU - Zerbini, Alexandre
N1 - This work is a result of GAB PhD studies for which the Brazilian National Council for Scientific and Technological Development (https://www.gov.br/cnpq/pt-br) granted scholarship number 208203/2014-1 through the Science without borders programme.
PY - 2021/11/17
Y1 - 2021/11/17
N2 - The population of humpback whales (Megaptera novaeangliae)
wintering off eastern South America was exploited by commercial whaling
almost to the point of extinction in the mid-twentieth century. Since
cessation of whaling in the 1970s it is recovering, but the timing and
level of recovery is uncertain. We implemented a Bayesian population
dynamics model describing the population’s trajectory from 1901 and
projecting it to 2040 to revise a previous population status assessment
that used Sampling-Importance-Resampling in a Bayesian framework. Using
our alternative method for model fitting (Markov chain Monte Carlo),
which is more widely accessible to ecologists, we replicate a “base case
scenario” to verify the effect on model results, and introduce
additional data to update the status assessment. Our approach allowed us
to widen the previous informative prior on carrying capacity to better
reflect scientific uncertainty around historical population levels. The
updated model provided more precise estimates for population sizes over
the period considered (1901–2040) and suggests that carrying capacity (K: median 22,882, mean 22,948, 95% credible interval [CI] 22,711–23,545) and minimum population size (N1958: median 305, mean 319, 95% CI 271–444) might be lower than previously estimated (K: median 24,558, mean 25,110, 95% CI 22,791–31,118; N1958:
median 503, mean 850, 95% CI 159–3,943). However, posterior 95%
credible intervals of parameters in the updated model overlap those of
the previous study. Our approach provides an accessible framework for
investigating the status of depleted animal populations for which
information is available on historical mortality (e.g., catches) and
intermittent estimates of population size and/or trend.
AB - The population of humpback whales (Megaptera novaeangliae)
wintering off eastern South America was exploited by commercial whaling
almost to the point of extinction in the mid-twentieth century. Since
cessation of whaling in the 1970s it is recovering, but the timing and
level of recovery is uncertain. We implemented a Bayesian population
dynamics model describing the population’s trajectory from 1901 and
projecting it to 2040 to revise a previous population status assessment
that used Sampling-Importance-Resampling in a Bayesian framework. Using
our alternative method for model fitting (Markov chain Monte Carlo),
which is more widely accessible to ecologists, we replicate a “base case
scenario” to verify the effect on model results, and introduce
additional data to update the status assessment. Our approach allowed us
to widen the previous informative prior on carrying capacity to better
reflect scientific uncertainty around historical population levels. The
updated model provided more precise estimates for population sizes over
the period considered (1901–2040) and suggests that carrying capacity (K: median 22,882, mean 22,948, 95% credible interval [CI] 22,711–23,545) and minimum population size (N1958: median 305, mean 319, 95% CI 271–444) might be lower than previously estimated (K: median 24,558, mean 25,110, 95% CI 22,791–31,118; N1958:
median 503, mean 850, 95% CI 159–3,943). However, posterior 95%
credible intervals of parameters in the updated model overlap those of
the previous study. Our approach provides an accessible framework for
investigating the status of depleted animal populations for which
information is available on historical mortality (e.g., catches) and
intermittent estimates of population size and/or trend.
UR - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0259541#sec014
U2 - 10.1371/journal.pone.0259541
DO - 10.1371/journal.pone.0259541
M3 - Article
SN - 1932-6203
VL - 16
JO - PLoS ONE
JF - PLoS ONE
IS - 11
M1 - e0259541
ER -