Estimating Regions of Oceanographic Importance for Seabirds Using A-Spatial Data
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{"title"=>"Estimating regions of oceanographic importance for seabirds using a-spatial data", "type"=>"journal", "authors"=>[{"first_name"=>"Grant Richard Woodrow", "last_name"=>"Humphries", "scopus_author_id"=>"37063078700"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84947466024", "doi"=>"10.1371/journal.pone.0137241", "pui"=>"606946154", "pmid"=>"26331957", "scopus"=>"2-s2.0-84947466024", "issn"=>"19326203"}, "id"=>"214b365e-a30e-3a0b-97d5-c5d0f2fcdde2", "abstract"=>"Advances in GPS tracking technologies have allowed for rapid assessment of important oceanographic regions for seabirds. This allows us to understand seabird distributions, and the characteristics which determine the success of populations. In many cases, quality GPS tracking data may not be available; however, long term population monitoring data may exist. In this study, a method to infer important oceanographic regions for seabirds will be presented using breeding sooty shearwaters as a case study. This method combines a popular machine learning algorithm (generalized boosted regression modeling), geographic information systems, long-term ecological data and open access oceanographic datasets. Time series of chick size and harvest index data derived from a long term dataset of Maori 'muttonbirder' diaries were obtained and used as response variables in a gridded spatial model. It was found that areas of the sub-Antarctic water region best capture the variation in the chick size data. Oceanographic features including wind speed and charnock (a derived variable representing ocean surface roughness) came out as top predictor variables in these models. Previously collected GPS data demonstrates that these regions are used as \"flyways\" by sooty shearwaters during the breeding season. It is therefore likely that wind speeds in these flyways affect the ability of sooty shearwaters to provision for their chicks due to changes in flight dynamics. This approach was designed to utilize machine learning methodology but can also be implemented with other statistical algorithms. Furthermore, these methods can be applied to any long term time series of population data to identify important regions for a species of interest.", "link"=>"http://www.mendeley.com/research/estimating-regions-oceanographic-importance-seabirds-using-aspatial-data", "reader_count"=>34, "reader_count_by_academic_status"=>{"Unspecified"=>4, "Professor > Associate Professor"=>2, "Researcher"=>3, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>5, "Student > Postgraduate"=>2, "Student > Master"=>7, "Other"=>3, "Student > Bachelor"=>3, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>4, "Professor > Associate Professor"=>2, "Researcher"=>3, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>5, "Student > Postgraduate"=>2, "Student > Master"=>7, "Other"=>3, "Student > Bachelor"=>3, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Engineering"=>1, "Environmental Science"=>5, "Mathematics"=>1, "Agricultural and Biological Sciences"=>19, "Computer Science"=>1, "Earth and Planetary Sciences"=>2}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>19}, "Computer Science"=>{"Computer Science"=>1}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>5}, "Environmental Science"=>{"Environmental Science"=>5}}, "reader_count_by_country"=>{"United States"=>2, "Denmark"=>1, "Australia"=>1, "Chile"=>1, "Germany"=>1}, "group_count"=>6}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2250854"], "description"=>"<p>European Center for Medium Range Weather Forecasting (ECMWF; <a href=\"https://apps.ecmwf.int/datasets\" target=\"_blank\">https://apps.ecmwf.int/datasets</a>) data downloaded for use in modelling exercises.</p>", "links"=>[], "tags"=>["population data", "case study", "seabird distributions", "Oceanographic features", "harvest index data", "chick size data", "flight dynamics", "method", "predictor variables", "shearwater", "wind speed", "region", "time series", "ocean surface roughness", "wind speeds", "term population monitoring data", "chick size", "quality GPS", "GPS data", "Oceanographic Importance", "response variables", "information systems", "term dataset", "term time series"], "article_id"=>1533776, "categories"=>["Uncategorised"], "users"=>["Grant Richard Woodrow Humphries"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0137241.t001", "stats"=>{"downloads"=>1, "page_views"=>25, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_European_Center_for_Medium_Range_Weather_Forecasting_ECMWF_https_apps_ecmwf_int_datasets_data_downloaded_for_use_in_modelling_exercises_/1533776", "title"=>"European Center for Medium Range Weather Forecasting (ECMWF; https://apps.ecmwf.int/datasets) data downloaded for use in modelling exercises.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-09-02 03:20:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/2250855"], "description"=>"<p>* Spearman correlation is significant with bonferroni corrected <i>p</i> < 0.0045</p><p>Negative directionality in a relationship is shown by a minus sign in front of the correlation coefficient.</p>", "links"=>[], "tags"=>["population data", "case study", "seabird distributions", "Oceanographic features", "harvest index data", "chick size data", "flight dynamics", "method", "predictor variables", "shearwater", "wind speed", "region", "time series", "ocean surface roughness", "wind speeds", "term population monitoring data", "chick size", "quality GPS", "GPS data", "Oceanographic Importance", "response variables", "information systems", "term dataset", "term time series"], "article_id"=>1533777, "categories"=>["Uncategorised"], "users"=>["Grant Richard Woodrow Humphries"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0137241.t002", "stats"=>{"downloads"=>1, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spearman_correlations_for_March_mean_values_of_oceanographic_variables_from_1979_8211_2010_versus_three_harvest_indices_within_each_of_the_identified_oceanographic_regions_that_are_important_for_sooty_shearwaters_/1533777", "title"=>"Spearman correlations for March mean values of oceanographic variables from 1979–2010 versus three harvest indices within each of the identified oceanographic regions that are important for sooty shearwaters.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-09-02 03:20:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/2250856"], "description"=>"<div><p>Advances in GPS tracking technologies have allowed for rapid assessment of important oceanographic regions for seabirds. This allows us to understand seabird distributions, and the characteristics which determine the success of populations. In many cases, quality GPS tracking data may not be available; however, long term population monitoring data may exist. In this study, a method to infer important oceanographic regions for seabirds will be presented using breeding sooty shearwaters as a case study. This method combines a popular machine learning algorithm (generalized boosted regression modeling), geographic information systems, long-term ecological data and open access oceanographic datasets. Time series of chick size and harvest index data derived from a long term dataset of Maori ‘muttonbirder’ diaries were obtained and used as response variables in a gridded spatial model. It was found that areas of the sub-Antarctic water region best capture the variation in the chick size data. Oceanographic features including wind speed and charnock (a derived variable representing ocean surface roughness) came out as top predictor variables in these models. Previously collected GPS data demonstrates that these regions are used as “flyways” by sooty shearwaters during the breeding season. It is therefore likely that wind speeds in these flyways affect the ability of sooty shearwaters to provision for their chicks due to changes in flight dynamics. This approach was designed to utilize machine learning methodology but can also be implemented with other statistical algorithms. Furthermore, these methods can be applied to any long term time series of population data to identify important regions for a species of interest.</p></div>", "links"=>[], "tags"=>["population data", "case study", "seabird distributions", "Oceanographic features", "harvest index data", "chick size data", "flight dynamics", "method", "predictor variables", "shearwater", "wind speed", "region", "time series", "ocean surface roughness", "wind speeds", "term population monitoring data", "chick size", "quality GPS", "GPS data", "Oceanographic Importance", "response variables", "information systems", "term dataset", "term time series"], "article_id"=>1533778, "categories"=>["Uncategorised"], "users"=>["Grant Richard Woodrow Humphries"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0137241", "stats"=>{"downloads"=>3, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Estimating_Regions_of_Oceanographic_Importance_for_Seabirds_Using_A_Spatial_Data_/1533778", "title"=>"Estimating Regions of Oceanographic Importance for Seabirds Using A-Spatial Data", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-02 03:20:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/2250849"], "description"=>"<p>The 95% kernel density polygon for all data is represented by the largest polygon with a white background, while monthly 50% kernel densities for the offshore regions (offshore core foraging areas), and the 50% kernel density polygon for the nearshore region are represented by blue hues. The sub-Tropical front (STF), sub-Antarctic front (SAF), and Polar front (PF) are also represented on the map. The grid in the background represents the resolution of the environmental data used for modeling.</p>", "links"=>[], "tags"=>["population data", "case study", "seabird distributions", "Oceanographic features", "harvest index data", "chick size data", "flight dynamics", "method", "predictor variables", "shearwater", "wind speed", "region", "time series", "ocean surface roughness", "wind speeds", "term population monitoring data", "chick size", "quality GPS", "GPS data", "Oceanographic Importance", "response variables", "information systems", "term dataset", "term time series"], "article_id"=>1533771, "categories"=>["Uncategorised"], "users"=>["Grant Richard Woodrow Humphries"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0137241.g001", "stats"=>{"downloads"=>4, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Map_showing_GLS_data_from_Shaffer_et_al_2006_for_GLS_birds_tracked_from_Whenua_Hou_Codfish_Island_starred_on_the_map_from_January_2005_to_March_2006_/1533771", "title"=>"Map showing GLS data from Shaffer et al. (2006) for GLS birds tracked from Whenua Hou/Codfish Island (starred on the map) from January 2005 to March 2006.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-02 03:20:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/2250851"], "description"=>"<p>Areas with the lowest root mean squared error represent regions where oceanographic factors for the month of March from 1979–2010 best capture the variability in the chicksize (a), nanao (b), and rama (c) indices from Humphries [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137241#pone.0137241.ref025\" target=\"_blank\">25</a>]. Frontal regions are depicted to demonstrate the boundaries of Southern Ocean zones.</p>", "links"=>[], "tags"=>["population data", "case study", "seabird distributions", "Oceanographic features", "harvest index data", "chick size data", "flight dynamics", "method", "predictor variables", "shearwater", "wind speed", "region", "time series", "ocean surface roughness", "wind speeds", "term population monitoring data", "chick size", "quality GPS", "GPS data", "Oceanographic Importance", "response variables", "information systems", "term dataset", "term time series"], "article_id"=>1533773, "categories"=>["Uncategorised"], "users"=>["Grant Richard Woodrow Humphries"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0137241.g002", "stats"=>{"downloads"=>1, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mapped_root_mean_squared_error_for_generalized_boosted_regression_models_in_the_study_area_/1533773", "title"=>"Mapped root mean squared error for generalized boosted regression models in the study area.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-02 03:20:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/2250853"], "description"=>"<p>Linear relationships with oceanographic variables significantly correlated with the chick size index in the sub-Antarctic water region as per <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137241#pone.0137241.t002\" target=\"_blank\">Table 2</a>.</p>", "links"=>[], "tags"=>["population data", "case study", "seabird distributions", "Oceanographic features", "harvest index data", "chick size data", "flight dynamics", "method", "predictor variables", "shearwater", "wind speed", "region", "time series", "ocean surface roughness", "wind speeds", "term population monitoring data", "chick size", "quality GPS", "GPS data", "Oceanographic Importance", "response variables", "information systems", "term dataset", "term time series"], "article_id"=>1533775, "categories"=>["Uncategorised"], "users"=>["Grant Richard Woodrow Humphries"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0137241.g003", "stats"=>{"downloads"=>1, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Linear_relationships_with_oceanographic_variables_significantly_correlated_with_the_chick_size_index_in_the_sub_Antarctic_water_region_as_per_Table_2_/1533775", "title"=>"Linear relationships with oceanographic variables significantly correlated with the chick size index in the sub-Antarctic water region as per Table 2.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-02 03:20:04"}

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Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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