Hierarchical Bayesian model reveals the distributional shifts of Arctic marine mammals

Research output: Contribution to journalArticleScientificpeer-review

Abstract


Aim

Our aim involved developing a method to analyse spatiotemporal distributions of Arctic marine mammals (AMMs) using heterogeneous open source data, such as scientific papers and open repositories. Another aim was to quantitatively estimate the effects of environmental covariates on AMMs’ distributions and to analyse whether their distributions have shifted along with environmental changes.
Location

Arctic shelf area. The Kara Sea.
Methods

Our literature search focused on survey data regarding polar bears (Ursus maritimus), Atlantic walruses (Odobenus rosmarus rosmarus) and ringed seals (Phoca hispida). We mapped the data on a grid and built a hierarchical Poisson point process model to analyse species’ densities. The heterogeneous data lacked information on survey intensity and we could model only the relative density of each species. We explained relative densities with environmental covariates and random effects reflecting excess spatiotemporal variation and the unknown, varying sampling effort. The relative density of polar bears was explained also by the relative density of seals.
Results

The most important covariates explaining AMMs’ relative densities were ice concentration and distance to the coast, and regarding polar bears, also the relative density of seals. The results suggest that due to the decrease in the average ice concentration, the relative densities of polar bears and walruses slightly decreased or stayed constant during the 17‐year‐long study period, whereas seals shifted their distribution from the Eastern to the Western Kara Sea.
Main conclusions

Point process modelling is a robust methodology to estimate distributions from heterogeneous observations, providing spatially explicit information about ecosystems and thus serves advances for conservation efforts in the Arctic. In a simple trophic system, a distribution model of a top predator benefits from utilizing prey species’ distributions compared to a solely environmental model. The decreasing ice cover seems to have led to changes in AMMs’ distributions in the marginal Arctic region.
Original languageEnglish
JournalDiversity and Distributions
Volume24
Issue number10
Pages (from-to)1381-1394
Number of pages14
ISSN1366-9516
DOIs
Publication statusPublished - Oct 2018
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 1181 Ecology, evolutionary biology
  • 112 Statistics and probability
  • Arctic marine mammals
  • data integration
  • extensive transect survey
  • hierarchical Bayesian modelling
  • Poisson point process
  • species distribution
  • PRESENCE-ONLY DATA
  • SPECIES DISTRIBUTION MODELS
  • BEARS URSUS-MARITIMUS
  • POINT PROCESS MODELS
  • CHANGING SEA-ICE
  • POLAR BEARS
  • CLIMATE-CHANGE
  • HABITAT SELECTION
  • PHOCA-HISPIDA
  • RINGED SEAL
  • Arctic marine mammals
  • data integration
  • extensive transect survey
  • hierarchical Bayesian modelling
  • Poisson point process
  • species distribution
  • PRESENCE-ONLY DATA
  • SPECIES DISTRIBUTION MODELS
  • BEARS URSUS-MARITIMUS
  • POINT PROCESS MODELS
  • CHANGING SEA-ICE
  • POLAR BEARS
  • CLIMATE-CHANGE
  • HABITAT SELECTION
  • PHOCA-HISPIDA
  • RINGED SEAL

Cite this

@article{d564325d6a3e41969b1ca81e1a732f6c,
title = "Hierarchical Bayesian model reveals the distributional shifts of Arctic marine mammals",
abstract = "AimOur aim involved developing a method to analyse spatiotemporal distributions of Arctic marine mammals (AMMs) using heterogeneous open source data, such as scientific papers and open repositories. Another aim was to quantitatively estimate the effects of environmental covariates on AMMs’ distributions and to analyse whether their distributions have shifted along with environmental changes.LocationArctic shelf area. The Kara Sea.MethodsOur literature search focused on survey data regarding polar bears (Ursus maritimus), Atlantic walruses (Odobenus rosmarus rosmarus) and ringed seals (Phoca hispida). We mapped the data on a grid and built a hierarchical Poisson point process model to analyse species’ densities. The heterogeneous data lacked information on survey intensity and we could model only the relative density of each species. We explained relative densities with environmental covariates and random effects reflecting excess spatiotemporal variation and the unknown, varying sampling effort. The relative density of polar bears was explained also by the relative density of seals.ResultsThe most important covariates explaining AMMs’ relative densities were ice concentration and distance to the coast, and regarding polar bears, also the relative density of seals. The results suggest that due to the decrease in the average ice concentration, the relative densities of polar bears and walruses slightly decreased or stayed constant during the 17‐year‐long study period, whereas seals shifted their distribution from the Eastern to the Western Kara Sea.Main conclusionsPoint process modelling is a robust methodology to estimate distributions from heterogeneous observations, providing spatially explicit information about ecosystems and thus serves advances for conservation efforts in the Arctic. In a simple trophic system, a distribution model of a top predator benefits from utilizing prey species’ distributions compared to a solely environmental model. The decreasing ice cover seems to have led to changes in AMMs’ distributions in the marginal Arctic region.",
keywords = "1181 Ecology, evolutionary biology, 112 Statistics and probability, Arctic marine mammals, data integration, extensive transect survey, hierarchical Bayesian modelling, Poisson point process, species distribution, PRESENCE-ONLY DATA, SPECIES DISTRIBUTION MODELS, BEARS URSUS-MARITIMUS, POINT PROCESS MODELS, CHANGING SEA-ICE, POLAR BEARS, CLIMATE-CHANGE, HABITAT SELECTION, PHOCA-HISPIDA, RINGED SEAL, Arctic marine mammals, data integration, extensive transect survey, hierarchical Bayesian modelling, Poisson point process, species distribution, PRESENCE-ONLY DATA, SPECIES DISTRIBUTION MODELS, BEARS URSUS-MARITIMUS, POINT PROCESS MODELS, CHANGING SEA-ICE, POLAR BEARS, CLIMATE-CHANGE, HABITAT SELECTION, PHOCA-HISPIDA, RINGED SEAL",
author = "Jussi M{\"a}kinen and Jarno Vanhatalo",
year = "2018",
month = "10",
doi = "10.1111/ddi.12776",
language = "English",
volume = "24",
pages = "1381--1394",
journal = "Diversity and Distributions",
issn = "1366-9516",
publisher = "Wiley",
number = "10",

}

Hierarchical Bayesian model reveals the distributional shifts of Arctic marine mammals. / Mäkinen, Jussi ; Vanhatalo, Jarno .

In: Diversity and Distributions, Vol. 24, No. 10, 10.2018, p. 1381-1394.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Hierarchical Bayesian model reveals the distributional shifts of Arctic marine mammals

AU - Mäkinen, Jussi

AU - Vanhatalo, Jarno

PY - 2018/10

Y1 - 2018/10

N2 - AimOur aim involved developing a method to analyse spatiotemporal distributions of Arctic marine mammals (AMMs) using heterogeneous open source data, such as scientific papers and open repositories. Another aim was to quantitatively estimate the effects of environmental covariates on AMMs’ distributions and to analyse whether their distributions have shifted along with environmental changes.LocationArctic shelf area. The Kara Sea.MethodsOur literature search focused on survey data regarding polar bears (Ursus maritimus), Atlantic walruses (Odobenus rosmarus rosmarus) and ringed seals (Phoca hispida). We mapped the data on a grid and built a hierarchical Poisson point process model to analyse species’ densities. The heterogeneous data lacked information on survey intensity and we could model only the relative density of each species. We explained relative densities with environmental covariates and random effects reflecting excess spatiotemporal variation and the unknown, varying sampling effort. The relative density of polar bears was explained also by the relative density of seals.ResultsThe most important covariates explaining AMMs’ relative densities were ice concentration and distance to the coast, and regarding polar bears, also the relative density of seals. The results suggest that due to the decrease in the average ice concentration, the relative densities of polar bears and walruses slightly decreased or stayed constant during the 17‐year‐long study period, whereas seals shifted their distribution from the Eastern to the Western Kara Sea.Main conclusionsPoint process modelling is a robust methodology to estimate distributions from heterogeneous observations, providing spatially explicit information about ecosystems and thus serves advances for conservation efforts in the Arctic. In a simple trophic system, a distribution model of a top predator benefits from utilizing prey species’ distributions compared to a solely environmental model. The decreasing ice cover seems to have led to changes in AMMs’ distributions in the marginal Arctic region.

AB - AimOur aim involved developing a method to analyse spatiotemporal distributions of Arctic marine mammals (AMMs) using heterogeneous open source data, such as scientific papers and open repositories. Another aim was to quantitatively estimate the effects of environmental covariates on AMMs’ distributions and to analyse whether their distributions have shifted along with environmental changes.LocationArctic shelf area. The Kara Sea.MethodsOur literature search focused on survey data regarding polar bears (Ursus maritimus), Atlantic walruses (Odobenus rosmarus rosmarus) and ringed seals (Phoca hispida). We mapped the data on a grid and built a hierarchical Poisson point process model to analyse species’ densities. The heterogeneous data lacked information on survey intensity and we could model only the relative density of each species. We explained relative densities with environmental covariates and random effects reflecting excess spatiotemporal variation and the unknown, varying sampling effort. The relative density of polar bears was explained also by the relative density of seals.ResultsThe most important covariates explaining AMMs’ relative densities were ice concentration and distance to the coast, and regarding polar bears, also the relative density of seals. The results suggest that due to the decrease in the average ice concentration, the relative densities of polar bears and walruses slightly decreased or stayed constant during the 17‐year‐long study period, whereas seals shifted their distribution from the Eastern to the Western Kara Sea.Main conclusionsPoint process modelling is a robust methodology to estimate distributions from heterogeneous observations, providing spatially explicit information about ecosystems and thus serves advances for conservation efforts in the Arctic. In a simple trophic system, a distribution model of a top predator benefits from utilizing prey species’ distributions compared to a solely environmental model. The decreasing ice cover seems to have led to changes in AMMs’ distributions in the marginal Arctic region.

KW - 1181 Ecology, evolutionary biology

KW - 112 Statistics and probability

KW - Arctic marine mammals

KW - data integration

KW - extensive transect survey

KW - hierarchical Bayesian modelling

KW - Poisson point process

KW - species distribution

KW - PRESENCE-ONLY DATA

KW - SPECIES DISTRIBUTION MODELS

KW - BEARS URSUS-MARITIMUS

KW - POINT PROCESS MODELS

KW - CHANGING SEA-ICE

KW - POLAR BEARS

KW - CLIMATE-CHANGE

KW - HABITAT SELECTION

KW - PHOCA-HISPIDA

KW - RINGED SEAL

KW - Arctic marine mammals

KW - data integration

KW - extensive transect survey

KW - hierarchical Bayesian modelling

KW - Poisson point process

KW - species distribution

KW - PRESENCE-ONLY DATA

KW - SPECIES DISTRIBUTION MODELS

KW - BEARS URSUS-MARITIMUS

KW - POINT PROCESS MODELS

KW - CHANGING SEA-ICE

KW - POLAR BEARS

KW - CLIMATE-CHANGE

KW - HABITAT SELECTION

KW - PHOCA-HISPIDA

KW - RINGED SEAL

U2 - 10.1111/ddi.12776

DO - 10.1111/ddi.12776

M3 - Article

VL - 24

SP - 1381

EP - 1394

JO - Diversity and Distributions

JF - Diversity and Distributions

SN - 1366-9516

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ER -