Improved abundance and trend estimates for sperm whales in the eastern North Pacific from Bayesian hierarchical modeling

Moore, Jeffrey E.; Barlow, Jay

doi:10.3354/esr00633

Cited by 29 publications

(19 citation statements)

References 26 publications

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“…Hierarchical models enable the joint estimation of an ecological process submodel that describes the ecological processes of interest (such as population size and distribution), and an observation submodel that describes the relationship between unobserved ecological state variables and the observed data (Royle & Dorazio, ). Moore and Barlow (, , ) designed a series of BHMs with ecological submodels for population dynamics to estimate population size and trends for fin whales ( Balaenoptera physalus ), beaked whales (family Ziphiidae ) and sperm whales ( Physeter macrocephalus ) (see also Nadeem, Moore, Zhang, & Chipman, ). BHMs designed to estimate distribution patterns as a function of habitat covariates have generally built on the multinomial N ‐mixture model developed by Royle, Dawson, and Bates () (e.g., Chelgren, Samora, Adams, & McCreary, ; Gerrodette & Eguchi, ; Oedekoven et al., ).…”

Section: Introductionmentioning

confidence: 99%

Estimation of population size and trends for highly mobile species with dynamic spatial distributions

Boyd

Barlow

Becker

et al. 2017

Diversity and Distributions

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Aim: To develop a more ecologically realistic approach for estimating the population size of cetaceans and other highly mobile species with dynamic spatial distributions.Location: California Current Ecosystem, USA. Methods:Conventional spatial density models assume a constant relationship between densities and habitat covariates over some time period, typically a survey season. The estimated population size must change whenever total habitat availability changes. For highly mobile long-lived species, however, density-habitat relationships likely adjust more rapidly than population size. We developed an integrated populationredistribution model based on a more ecologically plausible alternative hypothesis: (1) population size is effectively constant over each survey season; (2) if habitat availability changes, then the population redistributes itself following an ideal free distribution process. Thus, the estimated relationship between densities and habitat covariates adjusts rather than population size. We constructed Bayesian hierarchical models corresponding to the conventional and alternative hypotheses and applied them to distance sampling data for Dall's porpoise (Phocoenoides dalli), a highly mobile cetacean with distribution patterns closely tied to cool sea-surface temperatures.Results: The Dall's porpoise data provided strong support for the hypothesis based on an ideal free redistribution process. Our results indicate that the population size of Dall's porpoise within the survey region was relatively stable over each summer/fall survey season, but the distribution expanded and contracted with the extent of suitable habitat. Over multiple survey seasons, the model partitioned variation in observed densities among three sources: variation in population size, the density-habitat relationship and measurement error, leading to lower and more ecologically plausible estimates of interannual variation in population size. Main conclusions:We conclude that the integrated population-redistribution model (IPRM) presented here represents an ecologically plausible model for use in future assessments of the population size and dynamics of cetaceans and other highly mobile long-lived species with variable spatial distributions.

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Section: Introductionmentioning

confidence: 99%

Estimation of population size and trends for highly mobile species with dynamic spatial distributions

Boyd

Barlow

Becker

et al. 2017

Diversity and Distributions

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“…Also, the Bayesian philosophy acknowledges the non-experimental nature of ecological studies, allows for the inclusion of previous knowledge on the parameters, and provides their estimations in terms of probabilities, instead of fixed quantities [ 75 – 79 ]. This type of approach has served recently to estimate trends in abundance of various cetacean species with improved accuracy [ 80 – 83 ], but this is the first time it is used for the inference of their spatial distribution.…”

Section: Introductionmentioning

confidence: 99%

Inferring Cetacean Population Densities from the Absolute Dynamic Topography of the Ocean in a Hierarchical Bayesian Framework

et al. 2015

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We inferred the population densities of blue whales (Balaenoptera musculus) and short-beaked common dolphins (Delphinus delphis) in the Northeast Pacific Ocean as functions of the water-column’s physical structure by implementing hierarchical models in a Bayesian framework. This approach allowed us to propagate the uncertainty of the field observations into the inference of species-habitat relationships and to generate spatially explicit population density predictions with reduced effects of sampling heterogeneity. Our hypothesis was that the large-scale spatial distributions of these two cetacean species respond primarily to ecological processes resulting from shoaling and outcropping of the pycnocline in regions of wind-forced upwelling and eddy-like circulation. Physically, these processes affect the thermodynamic balance of the water column, decreasing its volume and thus the height of the absolute dynamic topography (ADT). Biologically, they lead to elevated primary productivity and persistent aggregation of low-trophic-level prey. Unlike other remotely sensed variables, ADT provides information about the structure of the entire water column and it is also routinely measured at high spatial-temporal resolution by satellite altimeters with uniform global coverage. Our models provide spatially explicit population density predictions for both species, even in areas where the pycnocline shoals but does not outcrop (e.g. the Costa Rica Dome and the North Equatorial Countercurrent thermocline ridge). Interannual variations in distribution during El Niño anomalies suggest that the population density of both species decreases dramatically in the Equatorial Cold Tongue and the Costa Rica Dome, and that their distributions retract to particular areas that remain productive, such as the more oceanic waters in the central California Current System, the northern Gulf of California, the North Equatorial Countercurrent thermocline ridge, and the more southern portion of the Humboldt Current System. We posit that such reductions in available foraging habitats during climatic disturbances could incur high energetic costs on these populations, ultimately affecting individual fitness and survival.

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“…, Nadeem and Lele ). Improved frameworks have been developed (e.g., Moore and Barlow , , ); but we have taken this work further by developing a comprehensive state‐space modeling approach that integrates distance sampling with a spatially explicit population process. By incorporating spatial variability in population growth within a unified framework (inference on SPD process model and the distance sampling observation model obtained simultaneously), ecologists can better infer key population characteristics such as presence of density regulation and spatial variability in a population's intrinsic growth potential.…”

Section: Discussionmentioning

confidence: 99%

“…The Southwest Fisheries Science Center, part of the National Marine Fisheries Service, systematically conducted seven ship‐based line‐transect surveys for cetaceans in waters off the U.S. west coast during late summer and autumn between 1991 and 2014. Data through 2008 (the first six surveys) have been used to estimate abundance trends for fin whales ( B. physalus ), beaked whales (family Ziphiidae), and sperm whales ( Physeter macrocephalus ) from a simpler hierarchical distance sampling model (exponential growth process model, no spatial covariance in the observation model) (Moore and Barlow , , ). The current analysis provides updated abundance and trend estimates for fin whales based on the newer model, new estimates of g (0), and inclusion of the 2014 survey data.…”

Section: Methodsmentioning

confidence: 99%

Integrating population dynamics models and distance sampling data: a spatial hierarchical state‐space approach

Nadeem

Moore

Zhang

et al. 2016

Ecology

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Stochastic versions of Gompertz, Ricker, and various other dynamics models play a fundamental role in quantifying strength of density dependence and studying long-term dynamics of wildlife populations. These models are frequently estimated using time series of abundance estimates that are inevitably subject to observation error and missing data. This issue can be addressed with a state-space modeling framework that jointly estimates the observed data model and the underlying stochastic population dynamics (SPD) model. In cases where abundance data are from multiple locations with a smaller spatial resolution (e.g., from mark-recapture and distance sampling studies), models are conventionally fitted to spatially pooled estimates of yearly abundances. Here, we demonstrate that a spatial version of SPD models can be directly estimated from short time series of spatially referenced distance sampling data in a unified hierarchical state-space modeling framework that also allows for spatial variance (covariance) in population growth. We also show that a full range of likelihood based inference, including estimability diagnostics and model selection, is feasible in this class of models using a data cloning algorithm. We further show through simulation experiments that the hierarchical state-space framework introduced herein efficiently captures the underlying dynamical parameters and spatial abundance distribution. We apply our methodology by analyzing a time series of line-transect distance sampling data for fin whales (Balaenoptera physalus) off the U.S. west coast. Although there were only seven surveys conducted during the study time frame, 1991-2014, our analysis detected presence of strong density regulation and provided reliable estimates of fin whale densities. In summary, we show that the integrative framework developed herein allows ecologists to better infer key population characteristics such as presence of density regulation and spatial variability in a population's intrinsic growth potential.

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Improved abundance and trend estimates for sperm whales in the eastern North Pacific from Bayesian hierarchical modeling

Cited by 29 publications

References 26 publications

Estimation of population size and trends for highly mobile species with dynamic spatial distributions

Estimation of population size and trends for highly mobile species with dynamic spatial distributions

Inferring Cetacean Population Densities from the Absolute Dynamic Topography of the Ocean in a Hierarchical Bayesian Framework

Integrating population dynamics models and distance sampling data: a spatial hierarchical state‐space approach

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