Hierarchical Bayesian model reveals the distributional shifts of Arctic marine mammals

Jussi  Mäkinen; Jarno  Vanhatalo

doi:10.1111/ddi.12776

Hierarchical Bayesian model reveals the distributional shifts of Arctic marine mammals

Julkaisun otsikon käännös: Hierarkkinen Bayes-malli osoittaa Arktisten merinisäkkäiden levinneisyyksien muutokset

Jussi Mäkinen, Jarno Vanhatalo

Tutkimustuotos: Artikkelijulkaisu › Artikkeli › Tieteellinen › vertaisarvioitu

Abstrakti

Kehitimme menetelmän erilaisten avointen aineistojen, kuten julkaistujen artikkelien ja tietokantojen, käyttämiseen analysoidaksemme arktisten merinisäkkäiden levinneisyyksiä. Menetelmän avulla arvioimme ympäristön vaikutusta lajien levinneisyydelle sekä levinneisyyksien mahdollisia muutoksia havaittujen ympäristömuutosten seurauksena. Tutkimus toteutettiin Karan Merellä, joka on yksi arktisista reunameristä.
Etsimme avoimista aineistosta havaintotietoja jääkarhuista (Ursus maritimus), mursuista (Odobenus rosmarus rosmarus) ja norpista (Phoca hispida). Paikansimme havainnot ja analysoimme lajien esiintymistiheytta Poissonin pisteprosessimallilla. Vaihtelevan laatuinen aineisto ei sisältänyt tietoa lähdetutkimusten havaintoprosessista, minkä vuoksi pystyimme mallintamaan ainoastaan lajien esiintymistiheyden suhteessa tuntemattomaan havaintointensiteettiin. Selitimme lajien suhteellista esiintymistiheyttä ympäristömuuttujilla ja satunnaismuuttujilla, joista jälkimmäiset kuvastavat esiintymistiheyden satunnaisvaihtelua ajassa ja tilassa sekä suhteessa satunnaiseen havainnointi-intensiteettiin. Jääkarhujen esiintymistiheyttä selitimme myös norppien ennustetulla esiintymistiheydellä jääkarhujen havaintopisteissä.
Merijään tiheys ja havaintojen etäisyys rannikosta olivat tärkeimpiä selittäviä ympäristömuuttujia jokaisen lajin kohdalla. Hylkeiden esiintymistiheys oli tärkein muuttuja selittämään jääkarhujen esiintymistiheyttä. Merijään tiheyden heikkeneminen 17-vuotisen tutkimusjakson aikana vaikutti lajien esiintymistiheyksiin siten, että mursujen ja jääkarhujen tiheys pysyi vakaana tai heikkeni hieman, kun taas norppien tiheys laski itäisellä ja kasvoi läntisellä Karan Merellä.
Pisteprosessimalli on vakaa menetelmä lajien levinneisyyksien arvioimiseen perustuen vaihtelevan laatuisiin havaintoihin. Menetelmä tarjoaa sijainnista riippumattomasti luotettavaa tietoa ekosysteemeistä ja tarjoaa työkaluja suojelutoimintaan arktisella. Tuloksemme osoittivat, että yksinkertaisessa ravintoverkossa saalistajalajin levinneisyys selittyy paremmin saalislajien levinneisyydellä kuin ympäristömuuttujilla. Heikkenevä merijää on ilmeinen syy merinisäkkäiden levinneisyyksien muutoksiin arktisella alueella.

Julkaisun otsikon käännös	Hierarkkinen Bayes-malli osoittaa Arktisten merinisäkkäiden levinneisyyksien muutokset
Alkuperäiskieli	englanti
Lehti	Diversity and Distributions
Vuosikerta	24
Numero	10
Sivut	1381-1394
Sivumäärä	14
ISSN	1366-9516
DOI - pysyväislinkit	https://doi.org/10.1111/ddi.12776
Tila	Julkaistu - lokak. 2018
OKM-julkaisutyyppi	A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä, vertaisarvioitu

Tieteenalat

1181 Ekologia, evoluutiobiologia
112 Tilastotiede

Pääsy asiakirjaan

10.1111/ddi.12776Lisenssi: CC BY

Makinen_et_al_2018_Hierarchical_bayesian_model_reveals_the_habitat_characteristics_of_marine_mammalsLopullinen julkaistu versio, 0,99 MBLisenssi: CC BY

2 Päättynyt

Applying species distribution modeling in marine spatial planning and fisheries management
Vanhatalo, J., Hartmann, M., Liu, J. & Kaurila, K.
SUOMEN AKATEMIA
01/10/2016 → 30/09/2018
Projekti: Tutkimusprojekti
CEARCTIC – Joint Center of Excellence for Arctic Shipping and Operations (2013-2018)
Vanhatalo, J., Nevalainen, M. K., Kuikka, S., Helle, I. & Mäkinen, J. A.
01/01/2013 → 31/12/2018
Projekti: Tutkimusprojekti

Siteeraa tätä

@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{\textquoteright} 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{\textquoteright} 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{\textquoteright} 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{\textquoteright} distributions compared to a solely environmental model. The decreasing ice cover seems to have led to changes in AMMs{\textquoteright} 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 = oct,

doi = "10.1111/ddi.12776",

language = "English",

volume = "24",

pages = "1381--1394",

journal = "Diversity and Distributions",

issn = "1366-9516",

publisher = "Wiley",

number = "10",

}

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

SN - 1366-9516

VL - 24

SP - 1381

EP - 1394

JO - Diversity and Distributions

JF - Diversity and Distributions

IS - 10

ER -

Hierarchical Bayesian model reveals the distributional shifts of Arctic marine mammals

Abstrakti

Tieteenalat

Pääsy asiakirjaan

Projektit

Applying species distribution modeling in marine spatial planning and fisheries management

CEARCTIC – Joint Center of Excellence for Arctic Shipping and Operations (2013-2018)

Siteeraa tätä