Data_Sheet_1_Use of an INLA Latent Gaussian Modeling Approach to Assess Bird Population Changes Due to the Development of Offshore Wind Farms.pdf

The utilization of marine renewable energies such as offshore wind farming leads to globally expanding human activities in marine habitats. While knowledge on the responses to offshore wind farms and associated shipping traffic is accumulating now at a fast pace, it becomes important to assess the p...

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Main Authors: Raul Vilela, Claudia Burger, Ansgar Diederichs, Fabian E. Bachl, Lesley Szostek, Anika Freund, Alexander Braasch, Jochen Bellebaum, Brian Beckers, Werner Piper, Georg Nehls
Format: Dataset
Language:unknown
Published: 2021
Subjects:
Online Access:https://doi.org/10.3389/fmars.2021.701332.s001
https://figshare.com/articles/dataset/Data_Sheet_1_Use_of_an_INLA_Latent_Gaussian_Modeling_Approach_to_Assess_Bird_Population_Changes_Due_to_the_Development_of_Offshore_Wind_Farms_pdf/14931423
id ftfrontimediafig:oai:figshare.com:article/14931423
record_format openpolar
spelling ftfrontimediafig:oai:figshare.com:article/14931423 2023-05-15T16:19:34+02:00 Data_Sheet_1_Use of an INLA Latent Gaussian Modeling Approach to Assess Bird Population Changes Due to the Development of Offshore Wind Farms.pdf Raul Vilela Claudia Burger Ansgar Diederichs Fabian E. Bachl Lesley Szostek Anika Freund Alexander Braasch Jochen Bellebaum Brian Beckers Werner Piper Georg Nehls 2021-07-08T06:06:02Z https://doi.org/10.3389/fmars.2021.701332.s001 https://figshare.com/articles/dataset/Data_Sheet_1_Use_of_an_INLA_Latent_Gaussian_Modeling_Approach_to_Assess_Bird_Population_Changes_Due_to_the_Development_of_Offshore_Wind_Farms_pdf/14931423 unknown doi:10.3389/fmars.2021.701332.s001 https://figshare.com/articles/dataset/Data_Sheet_1_Use_of_an_INLA_Latent_Gaussian_Modeling_Approach_to_Assess_Bird_Population_Changes_Due_to_the_Development_of_Offshore_Wind_Farms_pdf/14931423 CC BY 4.0 CC-BY Oceanography Marine Biology Marine Geoscience Biological Oceanography Chemical Oceanography Physical Oceanography Marine Engineering Red-throated loons Gavia sp population trend German North Sea Bayesian model INLA SPDE latent Gaussian model Dataset 2021 ftfrontimediafig https://doi.org/10.3389/fmars.2021.701332.s001 2021-07-14T23:00:45Z The utilization of marine renewable energies such as offshore wind farming leads to globally expanding human activities in marine habitats. While knowledge on the responses to offshore wind farms and associated shipping traffic is accumulating now at a fast pace, it becomes important to assess the population impacts on species affected by those activities. In the North Sea, the protected diver species Red-throated Diver (Gavia stellata) and Black-throated Diver (Gavia arctica) widely avoid offshore wind farms. We used an explicit spatio-temporal Bayesian model to get a robust estimate of the diver population during the spring season between 2001 and 2018, based on a set of aerial surveys from long-term monitoring programs within the German North Sea. Despite the erection of 20 offshore wind farms in the study area and marked responses of divers to wind farms, model results indicated that there was no population decline, and overall numbers fluctuated around 16,600 individuals, with average annual 95% CI ranging between 13,400 and 21,360 individuals. Although, avoidance behavior due to wind farm development led to a more narrowly focused spatial distribution of the birds centered in the persistent high concentration zone in the Eastern German Bight Special Protection Area, the results provide no indication of negative fitness consequences on these long-lived species. However, more research is needed on habitat use and food availability in this regard. Dataset Gavia arctica Frontiers: Figshare
institution Open Polar
collection Frontiers: Figshare
op_collection_id ftfrontimediafig
language unknown
topic Oceanography
Marine Biology
Marine Geoscience
Biological Oceanography
Chemical Oceanography
Physical Oceanography
Marine Engineering
Red-throated loons
Gavia sp
population trend
German North Sea
Bayesian model
INLA
SPDE
latent Gaussian model
spellingShingle Oceanography
Marine Biology
Marine Geoscience
Biological Oceanography
Chemical Oceanography
Physical Oceanography
Marine Engineering
Red-throated loons
Gavia sp
population trend
German North Sea
Bayesian model
INLA
SPDE
latent Gaussian model
Raul Vilela
Claudia Burger
Ansgar Diederichs
Fabian E. Bachl
Lesley Szostek
Anika Freund
Alexander Braasch
Jochen Bellebaum
Brian Beckers
Werner Piper
Georg Nehls
Data_Sheet_1_Use of an INLA Latent Gaussian Modeling Approach to Assess Bird Population Changes Due to the Development of Offshore Wind Farms.pdf
topic_facet Oceanography
Marine Biology
Marine Geoscience
Biological Oceanography
Chemical Oceanography
Physical Oceanography
Marine Engineering
Red-throated loons
Gavia sp
population trend
German North Sea
Bayesian model
INLA
SPDE
latent Gaussian model
description The utilization of marine renewable energies such as offshore wind farming leads to globally expanding human activities in marine habitats. While knowledge on the responses to offshore wind farms and associated shipping traffic is accumulating now at a fast pace, it becomes important to assess the population impacts on species affected by those activities. In the North Sea, the protected diver species Red-throated Diver (Gavia stellata) and Black-throated Diver (Gavia arctica) widely avoid offshore wind farms. We used an explicit spatio-temporal Bayesian model to get a robust estimate of the diver population during the spring season between 2001 and 2018, based on a set of aerial surveys from long-term monitoring programs within the German North Sea. Despite the erection of 20 offshore wind farms in the study area and marked responses of divers to wind farms, model results indicated that there was no population decline, and overall numbers fluctuated around 16,600 individuals, with average annual 95% CI ranging between 13,400 and 21,360 individuals. Although, avoidance behavior due to wind farm development led to a more narrowly focused spatial distribution of the birds centered in the persistent high concentration zone in the Eastern German Bight Special Protection Area, the results provide no indication of negative fitness consequences on these long-lived species. However, more research is needed on habitat use and food availability in this regard.
format Dataset
author Raul Vilela
Claudia Burger
Ansgar Diederichs
Fabian E. Bachl
Lesley Szostek
Anika Freund
Alexander Braasch
Jochen Bellebaum
Brian Beckers
Werner Piper
Georg Nehls
author_facet Raul Vilela
Claudia Burger
Ansgar Diederichs
Fabian E. Bachl
Lesley Szostek
Anika Freund
Alexander Braasch
Jochen Bellebaum
Brian Beckers
Werner Piper
Georg Nehls
author_sort Raul Vilela
title Data_Sheet_1_Use of an INLA Latent Gaussian Modeling Approach to Assess Bird Population Changes Due to the Development of Offshore Wind Farms.pdf
title_short Data_Sheet_1_Use of an INLA Latent Gaussian Modeling Approach to Assess Bird Population Changes Due to the Development of Offshore Wind Farms.pdf
title_full Data_Sheet_1_Use of an INLA Latent Gaussian Modeling Approach to Assess Bird Population Changes Due to the Development of Offshore Wind Farms.pdf
title_fullStr Data_Sheet_1_Use of an INLA Latent Gaussian Modeling Approach to Assess Bird Population Changes Due to the Development of Offshore Wind Farms.pdf
title_full_unstemmed Data_Sheet_1_Use of an INLA Latent Gaussian Modeling Approach to Assess Bird Population Changes Due to the Development of Offshore Wind Farms.pdf
title_sort data_sheet_1_use of an inla latent gaussian modeling approach to assess bird population changes due to the development of offshore wind farms.pdf
publishDate 2021
url https://doi.org/10.3389/fmars.2021.701332.s001
https://figshare.com/articles/dataset/Data_Sheet_1_Use_of_an_INLA_Latent_Gaussian_Modeling_Approach_to_Assess_Bird_Population_Changes_Due_to_the_Development_of_Offshore_Wind_Farms_pdf/14931423
genre Gavia arctica
genre_facet Gavia arctica
op_relation doi:10.3389/fmars.2021.701332.s001
https://figshare.com/articles/dataset/Data_Sheet_1_Use_of_an_INLA_Latent_Gaussian_Modeling_Approach_to_Assess_Bird_Population_Changes_Due_to_the_Development_of_Offshore_Wind_Farms_pdf/14931423
op_rights CC BY 4.0
op_rightsnorm CC-BY
op_doi https://doi.org/10.3389/fmars.2021.701332.s001
_version_ 1766005960294268928