Bayesian estimation of humpback whale (Megaptera novaeangliae) population abundance and movement patterns in southeastern Alaska

2012 ◽  
Vol 69 (11) ◽  
pp. 1783-1797 ◽  
Author(s):  
A.N. Hendrix ◽  
J. Straley ◽  
C.M. Gabriele ◽  
S.M. Gende
Keyword(s):  
Deviance Information Criterion ◽  
Information Criterion ◽  
Megaptera Novaeangliae ◽  
Humpback Whales ◽  
Observation Error ◽  
Movement Model ◽  
Abundance Estimate ◽  
High Area ◽  
Rate Of Increase ◽  
Competing Models

We used a mechanistic movement model within a Bayesian framework to estimate survival, abundance, and rate of increase for a population of humpback whales ( Megaptera novaeangliae ) subject to a long-term photographic capture–recapture effort in southeastern Alaska, USA (SEAK). Multiple competing models were fitted that differed in movement, recapture rates, and observation error using deviance information criterion. The median annual survival probability in the selected model was 0.996 (95% central probability interval (CrI): 0.984, 0.999), which is among the highest reported for this species. Movement among areas was temporally dynamic, although whales exhibited high area fidelity (probability of returning to same area of ≥0.75) throughout the study. Median abundance was 1585 whales in 2008 (95% CrI: 1455, 1644). Incorporating an abundance estimate of 393 (95% confidence interval: 331, 455) whales from 1986, the median rate of increase was 5.1% (95% CrI: 4.4%, 5.9%). Although applied here to cetaceans in SEAK, the framework provides a flexible approach for estimating mortality and movement in populations that move among sampling areas.

scholarly journals Population growth of Australian East coast humpback whales, observed from Cape Byron, 1998 to 2004

2020 ◽  
pp. 261-268
Author(s):  
David A. Paton ◽  
Rric Kniest
Keyword(s):  
Population Growth ◽  
Confidence Intervals ◽  
East Coast ◽  
Megaptera Novaeangliae ◽  
Annual Rate ◽  
Humpback Whales ◽  
Survey Period ◽  
Group V ◽  
Breeding Stock ◽  
Rate Of Increase

Humpback whales (Megaptera novaeangliae) that migrate past the east coast of Australia comprise part of Group V (E(i) breeding stock). From1995 to 2004 an annual 16 day survey was conducted from Cape Byron (28°37’S, 153°38’E), the most easterly point on the Australian mainland,monitoring the peak of the humpback whale northern migration. The annual rate of increase between 1998 and 2004 of humpback whales observedoff Cape Byron is 11.0% (95% CI 2.3–20.5%). This rate of increase is consistent with that recorded from other studies of the humpback whalepopulation off the east coast of Australia. The large confidence intervals associated with this estimate are due to considerable inter-annual variationin counts. The most likely explanation for this being the short survey period, which may not have always coincided with the peak of migration, andin some years a large proportion of whales passed Cape Byron at a greater distance out to sea, making sightability more difficult.

scholarly journals Abundance of East coast Australian humpback whales (Megaptera novaeangliae) in 2005 estimated using multi-point sampling and capture-recapture analysis

2020 ◽  
pp. 253-259
Author(s):  
David A. Paton ◽  
Lyndon Brooks ◽  
Daniel Burns ◽  
Trish Franklin ◽  
Wally Franklin ◽  
...  
Keyword(s):  
East Coast ◽  
Megaptera Novaeangliae ◽  
Humpback Whales ◽  
Migration Route ◽  
Closed Population ◽  
Breeding Stock ◽  
Abundance Estimate ◽  
Abundance Estimates ◽  
Capture Recapture ◽  
Near Extinction

The humpback whales (Megaptera novaeangliae) that migrate along the east coast of Australia were hunted to near extinction during the lastcentury. This remnant population is part of Breeding Stock E. Previous abundance estimates for the east Australian portion of Breeding Stock Ehave been based mainly on land-based counts. Here we present a capture-recapture abundance estimate for this population using photo-identificationdata. These data were collected at three locations on the migration route (Byron Bay – northern migration, Hervey Bay and Ballina – southernmigration) in order to estimate the population of humpback whales that migrated along the east coast of Australia in 2005. The capture-recapturedata were analysed using a variety of closed population models with a model-averaged estimate of 7,041 (95% CI 4,075–10,008) whales.

scholarly journals Absolute and relative abundance estimates of Australian east coast humpback whales (Megaptera novaeangliae)

2020 ◽  
pp. 243-252
Author(s):  
Michael J. Noad ◽  
Rebecca A. Dunlop ◽  
David Paton ◽  
Douglas H. Cato
Keyword(s):  
Data Analysis ◽  
East Coast ◽  
Humpback Whales ◽  
Absolute Abundance ◽  
Abundance Estimate ◽  
Abundance Estimates ◽  
Rate Of Increase ◽  
Migratory Population ◽  
Australian Coastline ◽  
Near Extinction

The humpback whales that migrate along the east coast of Australia were hunted to near-extinction in the 1950s and early 1960s. Two independentseries of land-based surveys conducted over the last 25 years during the whales’ northward migration along the Australian coastline havedemonstrated a rapid increase in the size of the population. In 2004 we conducted a survey of the migratory population as a continuation of theseseries of surveys. Two methods of data analysis were used in line with the previous surveys, both for calculation of absolute and relative abundance.We consider the best estimates for 2004 to be 7,090±660 (95% CI) whales with an annual rate of increase of 10.6±0.5% (95% CI) for 1987–2004.The rate of increase agrees with those previously obtained for this population and demonstrates the continuation of a strong post-exploitationrecovery. While there are still some uncertainties concerning the absolute abundance estimate and structure of this population, the rate of annualincrease should be independent of these and highly robust.

scholarly journals Bayesian analysis of periodic asymmetric power GARCH models

2019 ◽  
Vol 24 (4) ◽  
Author(s):  
Abdelhakim Aknouche ◽  
Nacer Demmouche ◽  
Stefanos Dimitrakopoulos ◽  
Nassim Touche
Keyword(s):  
Value At Risk ◽  
Deviance Information Criterion ◽  
Estimation Method ◽  
Real Data ◽  
Information Criterion ◽  
Tail Behavior ◽  
Risk Forecasting ◽  
Asymmetric Power ◽  
Competing Models ◽  
Set Up

AbstractIn this paper, we set up a generalized periodic asymmetric power GARCH (PAP-GARCH) model whose coefficients, power, and innovation distribution are periodic over time. We first study its properties, such as periodic ergodicity, finiteness of moments and tail behavior of the marginal distributions. Then, we develop an MCMC algorithm, based on the Griddy-Gibbs sampler, under various distributions of the innovation term (Gaussian, Student-t, mixed Gaussian-Student-t). To assess our estimation method we conduct volatility and Value-at-Risk forecasting. Our model is compared against other competing models via the Deviance Information Criterion (DIC). The proposed methodology is applied to simulated and real data.

scholarly journals Abundance estimates of Southern Hemisphere Breeding Stock ‘D’ humpback whales from aerial and land-based surveys off Shark Bay, Western Australia, 2008

2020 ◽  
pp. 209-221
Author(s):  
Sharon L. Hedley ◽  
John L. Bannister ◽  
Rebecca A. Dunlop
Keyword(s):  
Southern Hemisphere ◽  
Western Australia ◽  
Aerial Survey ◽  
Humpback Whales ◽  
Line Transect ◽  
Breeding Stock ◽  
Abundance Estimate ◽  
Shark Bay ◽  
Abundance Estimates ◽  
Rate Of Increase

Single platform aerial line transect and land-based surveys of Southern Hemisphere Breeding Stock ‘D’ humpback whales Megaptera novaeangliaewere undertaken off Shark Bay, Western Australia to provide absolute abundance estimates of animals migrating northward along the westernAustralian coast. The aerial survey flew a total of 28 flights, of which 26 were completed successfully, from 24 June–19 August 2008. The landbased survey was undertaken from Cape Inscription, Dirk Hartog Island, Shark Bay, during the expected peak of the whales’ northward migration,from 8–20 July. During the first week of the land-based survey, some double count effort was undertaken to provide information on the numbersof pods missed from the land station. The assumed period of northward migration was 2 June–7 September. Estimated abundance of northwardmigrating whales during that time is 34,290 (95% CI: (27,340–53,350)), representing an annual rate of increase of 12.9% (CV = 0.20) since anestimate of 11,500 in 1999. This estimate is based on an estimate of relative abundance of surface-available whales of 10,840 (8,640–16,860), andan estimated g(0) of 0.32. There were considerable practical difficulties encountered during the land-based survey which reduced the effectivenessof the dual-survey approach for estimating g(0) for the aerial survey. Furthermore only about 15% of whales were estimated to be within the visualrange of the land-based station. Alternative approaches for estimating g(0) from these data are therefore also presented, resulting in considerablyhigher estimates of around 0.6–0.7, and yielding a conservative abundance estimate of 17,810 (14,210–27,720).

scholarly journals Use of Bayesian Markov Chain Monte Carlo Methods to Model Kuwait Medical Genetic Center Data: An Application to Down Syndrome and Mental Retardation

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 248
Author(s):  
Reem Aljarallah ◽  
Samer A Kharroubi
Keyword(s):  
Monte Carlo ◽  
Down Syndrome ◽  
Mental Retardation ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Univariate Analysis ◽  
Deviance Information Criterion ◽  
Predictive Ability ◽  
Information Criterion ◽  
Medical Genetic

Logit, probit and complementary log-log models are the most widely used models when binary dependent variables are available. Conventionally, these models have been frequentists. This paper aims to demonstrate how such models can be implemented relatively quickly and easily from a Bayesian framework using Gibbs sampling Markov chain Monte Carlo simulation methods in WinBUGS. We focus on the modeling and prediction of Down syndrome (DS) and Mental retardation (MR) data from an observational study at Kuwait Medical Genetic Center over a 30-year time period between 1979 and 2009. Modeling algorithms were used in two distinct ways; firstly, using three different methods at the disease level, including logistic, probit and cloglog models, and, secondly, using bivariate logistic regression to study the association between the two diseases in question. The models are compared in terms of their predictive ability via R2, adjusted R2, root mean square error (RMSE) and Bayesian Deviance Information Criterion (DIC). In the univariate analysis, the logistic model performed best, with R2 (0.1145), adjusted R2 (0.114), RMSE (0.3074) and DIC (7435.98) for DS, and R2 (0.0626), adjusted R2 (0.0621), RMSE (0.4676) and DIC (23120) for MR. In the bivariate case, results revealed that 7 and 8 out of the 10 selected covariates were significantly associated with DS and MR respectively, whilst none were associated with the interaction between the two outcomes. Bayesian methods are more flexible in handling complex non-standard models as well as they allow model fit and complexity to be assessed straightforwardly for non-nested hierarchical models.

Sign in / Sign up

Export Citation Format

Share Document