Projects per year
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.
Translated title of the contribution | Hierarkkinen Bayes-malli osoittaa Arktisten merinisäkkäiden levinneisyyksien muutokset |
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Original language | English |
Journal | Diversity and Distributions |
Volume | 24 |
Issue number | 10 |
Pages (from-to) | 1381-1394 |
Number of pages | 14 |
ISSN | 1366-9516 |
DOIs | |
Publication status | Published - Oct 2018 |
MoE publication type | A1 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
Projects
- 2 Finished
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Applying species distribution modeling in marine spatial planning and fisheries management
Vanhatalo, J. (Project manager), Hartmann, M. (Participant), Liu, J. (Project manager) & Kaurila, K. (Participant)
01/10/2016 → 30/09/2018
Project: Research project
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CEARCTIC – Joint Center of Excellence for Arctic Shipping and Operations (2013-2018)
Vanhatalo, J. (Principal Investigator), Nevalainen, M. K. (Participant), Kuikka, S. (Project manager), Helle, I. (Participant) & Mäkinen, J.A.-E. (Participant)
01/01/2013 → 31/12/2018
Project: Research project