Global assessment of marine plastic exposure risk for oceanic birds

Plastic pollution is distributed patchily around the world’s oceans. Likewise, marine organisms that are vulnerable to plastic ingestion or entanglement have uneven distributions. Understanding where wildlife encounters plastic is crucial for targeting research and mitigation. Oceanic seabirds, particularly petrels, frequently ingest plastic, are highly threatened, and cover vast distances during foraging and migration. However, the spatial overlap between petrels and plastics is poorly understood. Here we combine marine plastic density estimates with individual movement data for 7137 birds of 77 petrel species to estimate relative exposure risk. We identify high exposure risk areas in the Mediterranean and Black seas, and the northeast Pacific, northwest Pacific, South Atlantic and southwest Indian oceans. Plastic exposure risk varies greatly among species and populations, and between breeding and nonbreeding seasons. Exposure risk is disproportionately high for Threatened species. Outside the Mediterranean and Black seas, exposure risk is highest in the high seas and Exclusive Economic Zones (EEZs) of the USA, Japan, and the UK. Birds generally had higher plastic exposure risk outside the EEZ of the country where they breed. We identify conservation and research priorities, and highlight that international collaboration is key to addressing the impacts of marine plastic on wide-ranging speciespublishedVersio

Global assessment of marine plastic exposure risk for oceanic birds

Plastic pollution is distributed patchily around the world’s oceans. Likewise, marine organisms that are vulnerable to plastic ingestion or entanglement have uneven distributions. Understanding where wildlife encounters plastic is crucial for targeting research and mitigation. Oceanic seabirds, particularly petrels, frequently ingest plastic, are highly threatened, and cover vast distances during foraging and migration. However, the spatial overlap between petrels and plastics is poorly understood. Here we combine marine plastic density estimates with individual movement data for 7137 birds of 77 petrel species to estimate relative exposure risk. We identify high exposure risk areas in the Mediterranean and Black seas, and the northeast Pacific, northwest Pacific, South Atlantic and southwest Indian oceans. Plastic exposure risk varies greatly among species and populations, and between breeding and non-breeding seasons. Exposure risk is disproportionately high for Threatened species. Outside the Mediterranean and Black seas, exposure risk is highest in the high seas and Exclusive Economic Zones (EEZs) of the USA, Japan, and the UK. Birds generally had higher plastic exposure risk outside the EEZ of the country where they breed. We identify conservation and research priorities, and highlight that international collaboration is key to addressing the impacts of marine plastic on wide-ranging species

Global assessment of marine plastic exposure risk for oceanic birds

Plastic pollution is distributed patchily around the world's oceans. Likewise, marine organisms that are vulnerable to plastic ingestion or entanglement have uneven distributions. Understanding where wildlife encounters plastic is crucial for targeting research and mitigation. Oceanic seabirds, particularly petrels, frequently ingest plastic, are highly threatened, and cover vast distances during foraging and migration. However, the spatial overlap between petrels and plastics is poorly understood. Here we combine marine plastic density estimates with individual movement data for 7137 birds of 77 petrel species to estimate relative exposure risk. We identify high exposure risk areas in the Mediterranean and Black seas, and the northeast Pacific, northwest Pacific, South Atlantic and southwest Indian oceans. Plastic exposure risk varies greatly among species and populations, and between breeding and non-breeding seasons. Exposure risk is disproportionately high for Threatened species. Outside the Mediterranean and Black seas, exposure risk is highest in the high seas and Exclusive Economic Zones (EEZs) of the USA, Japan, and the UK. Birds generally had higher plastic exposure risk outside the EEZ of the country where they breed. We identify conservation and research priorities, and highlight that international collaboration is key to addressing the impacts of marine plastic on wide-ranging species.B.L.C., C.H., and A.M. were funded by the Cambridge Conservation Initiative’s Collaborative Fund sponsored by the Prince Albert II of Monaco Foundation. E.J.P. was supported by the Natural Environment Research Council C-CLEAR doctoral training programme (Grant no. NE/S007164/1). We are grateful to all those who assisted with the collection and curation of tracking data. Further details are provided in the Supplementary Acknowledgements. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.Peer reviewe

Global assessment of marine plastic exposure risk for oceanic birds

Plastic pollution is distributed patchily around the world’s oceans. Likewise, marine organisms that are vulnerable to plastic ingestion or entanglement have uneven distributions. Understanding where wildlife encounters plastic is crucial for targeting research and mitigation. Oceanic seabirds, particularly petrels, frequently ingest plastic, are highly threatened, and cover vast distances during foraging and migration. However, the spatial overlap between petrels and plastics is poorly understood. Here we combine marine plastic density estimates with individual movement data for 7137 birds of 77 petrel species to estimate relative exposure risk. We identify high exposure risk areas in the Mediterranean and Black seas, and the northeast Pacific, northwest Pacific, South Atlantic and southwest Indian oceans. Plastic exposure risk varies greatly among species and populations, and between breeding and non-breeding seasons. Exposure risk is disproportionately high for Threatened species. Outside the Mediterranean and Black seas, exposure risk is highest in the high seas and Exclusive Economic Zones (EEZs) of the USA, Japan, and the UK. Birds generally had higher plastic exposure risk outside the EEZ of the country where they breed. We identify conservation and research priorities, and highlight that international collaboration is key to addressing the impacts of marine plastic on wide-ranging species

Database Of Weeds In Cultivation Fields Of France And Uk, With Ecological And Biogeographical Information

datatsetThe database includes a list of 1577 weed plant taxa found in cultivated fields of France and UK, along with basic ecological and biogeographical information.The database is a CSV file in which the columns are separated with comma, and the decimal sign is ".".It can be imported in R with the command "tax.discoweed <- read.csv("tax.discoweed_18Dec2017_zenodo.csv", header=T, sep=",", dec=".", stringsAsFactors = F)"Taxonomic information is based on TaxRef v10 (Gargominy et al. 2016),- 'taxref10.CD_REF' = code of the accepted name of the taxon in TaxRef,- 'binome.discoweed' = corresponding latine name,- 'family' = family name (following APG III),- 'taxo' = taxonomic rank of the taxon, either 'binome' (species level) or 'infra' (infraspecific level),- 'binome.discoweed.noinfra' = latine name of the superior taxon at species level (different from 'binome.discoweed' for infrataxa),- 'taxref10.CD_REF.noinfra' = code of the accepted name of the superior taxon at species level.The presence of each taxon in one or several of the following data sources is reported:- Species list from a reference flora (observations in cultivated fields over the long term, without sampling protocol),* 'jauzein' = national and comprehensive flora in France (Jauzein 1995),- Species lists from plot-based inventories in cultivated fields,* 'za' = regional survey in 'Zone Atelier Plaine & Val de Sèvre' in SW France (Gaba et al. 2010),* 'biovigilance' = national survey of cultivated fields in France (Biovigilance, Fried et al. 2008),* 'fse' = Farm Scale Evaluations in England and Scotland, UK (Perry, Rothery, Clark et al., 2003),* 'farmbio' = Farm4Bio survey, farms in south east and south west of England, UK (Holland et al., 2013)- Reference list of segetal species (species specialist of arable fields),* 'cambacedes' = reference list in France (Cambacedes et al. 2002)Life form information is extracted from Julve (2014) and provided in the column 'lifeform'.The classification follows a simplified Raunkiaer classification (therophyte, hemicryptophyte, geophyte, phanerophyte-chamaephyte and liana). Regularly biannual plants are included in hemicryptophytes, while plants that can be both annual and biannual are assigned to therophytes.Biogeographic zones are also extracted from Julve (2014) and provided in the column 'biogeo'.The main categories are 'atlantic', 'circumboreal', 'cosmopolitan, 'Eurasian', 'European', 'holarctic', 'introduced', 'Mediterranean', 'orophyte' and 'subtropical'.In some cases, a precision is included within brackets after the category name. For instance, 'introduced(North America)' indicates that the taxon is introduced from North America.In addition, some taxa are local endemics ('Aquitanian', 'Catalan', 'Corsican', 'corso-sard', 'ligure', 'Provencal').A single taxon is classified 'arctic-alpine'.Red list status of weed taxa is derived for France and UK:- 'red.FR' is the status following the assessment of the French National Museum of Natural History (2012),- 'red.UK' is based on the Red List of vascular plants of Cheffings and Farrell (2005), last updated in 2006.The categories are coded following the IUCN nomenclature.A habitat index is provided in column 'module', derived from a network-based analysis of plant communities in open herbaceous vegetation in France (Divgrass database, Violle et al. 2015, Carboni et al. 2016).The main habitat categories of weeds are coded following the Divgrass classification,- 1 = Dry calcareous grasslands- 3 = Mesic grasslands- 5 = Ruderal and trampled grasslands- 9 = Mesophilous and nitrophilous fringes (hedgerows, forest edges...)Taxa belonging to other habitats in Divgrass are coded 99, while the taxa absent from Divgrass have a 'NA' value.Two indexes of ecological specialization are provided based on the frequency of weed taxa in different habitats of the Divgrass database.The indexes are network-based metrics proposed by Guimera and Amaral (2005),- c = coefficient of participation, i.e., the propensity of taxa to be present in diverse habitats, from 0 (specialist, present in a single habitat) to 1 (generalist equally represented in all habitats),- z = within-module degree, i.e., a standardized measure of the frequency of a taxon in its habitat; it is negatve when the taxon is less frequent than average in this habitat, and positive otherwise; the index scales as a number of standard deviations from the mean.The database includes a list of 1577 weed plant taxa found in cultivated fields of France and UK, along with basic ecological and biogeographical information.The database is a CSV file in which the columns are separated with comma, and the decimal sign is ".".It can be imported in R with the command "tax.discoweed <- read.csv("tax.discoweed_18Dec2017_zenodo.csv", header=T, sep=",", dec=".", stringsAsFactors = F)"Taxonomic information is based on TaxRef v10 (Gargominy et al. 2016),- 'taxref10.CD_REF' = code of the accepted name of the taxon in TaxRef,- 'binome.discoweed' = corresponding latine name,- 'family' = family name (following APG III),- 'taxo' = taxonomic rank of the taxon, either 'binome' (species level) or 'infra' (infraspecific level),- 'binome.discoweed.noinfra' = latine name of the superior taxon at species level (different from 'binome.discoweed' for infrataxa),- 'taxref10.CD_REF.noinfra' = code of the accepted name of the superior taxon at species level.The presence of each taxon in one or several of the following data sources is reported:- Species list from a reference flora (observations in cultivated fields over the long term, without sampling protocol),* 'jauzein' = national and comprehensive flora in France (Jauzein 1995),- Species lists from plot-based inventories in cultivated fields,* 'za' = regional survey in 'Zone Atelier Plaine & Val de Sèvre' in SW France (Gaba et al. 2010),* 'biovigilance' = national survey of cultivated fields in France (Biovigilance, Fried et al. 2008),* 'fse' = Farm Scale Evaluations in England and Scotland, UK (Perry, Rothery, Clark et al., 2003),* 'farmbio' = Farm4Bio survey, farms in south east and south west of England, UK (Holland et al., 2013)- Reference list of segetal species (species specialist of arable fields),* 'cambacedes' = reference list in France (Cambacedes et al. 2002)Life form information is extracted from Julve (2014) and provided in the column 'lifeform'.The classification follows a simplified Raunkiaer classification (therophyte, hemicryptophyte, geophyte, phanerophyte-chamaephyte and liana). Regularly biannual plants are included in hemicryptophytes, while plants that can be both annual and biannual are assigned to therophytes.Biogeographic zones are also extracted from Julve (2014) and provided in the column 'biogeo'.The main categories are 'atlantic', 'circumboreal', 'cosmopolitan, 'Eurasian', 'European', 'holarctic', 'introduced', 'Mediterranean', 'orophyte' and 'subtropical'.In some cases, a precision is included within brackets after the category name. For instance, 'introduced(North America)' indicates that the taxon is introduced from North America.In addition, some taxa are local endemics ('Aquitanian', 'Catalan', 'Corsican', 'corso-sard', 'ligure', 'Provencal').A single taxon is classified 'arctic-alpine'.Red list status of weed taxa is derived for France and UK:- 'red.FR' is the status following the assessment of the French National Museum of Natural History (2012),- 'red.UK' is based on the Red List of vascular plants of Cheffings and Farrell (2005), last updated in 2006.The categories are coded following the IUCN nomenclature.A habitat index is provided in column 'module', derived from a network-based analysis of plant communities in open herbaceous vegetation in France (Divgrass database, Violle et al. 2015, Carboni et al. 2016).The main habitat categories of weeds are coded following the Divgrass classification,- 1 = Dry calcareous grasslands- 3 = Mesic grasslands- 5 = Ruderal and trampled grasslands- 9 = Mesophilous and nitrophilous fringes (hedgerows, forest edges...)Taxa belonging to other habitats in Divgrass are coded 99, while the taxa absent from Divgrass have a 'NA' value.Two indexes of ecological specialization are provided based on the frequency of weed taxa in different habitats of the Divgrass database.The indexes are network-based metrics proposed by Guimera and Amaral (2005),- c = coefficient of participation, i.e., the propensity of taxa to be present in diverse habitats, from 0 (specialist, present in a single habitat) to 1 (generalist equally represented in all habitats),- z = within-module degree, i.e., a standardized measure of the frequency of a taxon in its habitat; it is negatve when the taxon is less frequent than average in this habitat, and positive otherwise; the index scales as a number of standard deviations from the mean

A multidisciplinary modelling approach to analyse and predict the effects of landscape dynamics on biodiversity

International audienceOver the last 40 years, agricultural extension and intensification of land use have induced profound changes in distribution and dynamics of farmland biodiversity and in the functioning of European agroecosystems. Agroecosystems are mainly private properties, whose dynamics need to be better understood in order to preserve their biodiversity. Several French research teams have recently joined their skills in a multi-disciplinary project, BiodivAgriM, whose main goal is to test, validate, and predict the consequences of different scenarii of landscape changes on the distribution, abundance and persistence of biodiversity in agroecosystems. A central goal of this project is to generate a multi-purpose modelling platform which makes it possible to couple different spatially explicit models toward the same objective, and gather rather similar models toward the same generic object (i.e., the landscape). Such a modelling approach is a real challenge. The main knowledge provided by this project was that the disciplines involved were in various maturation stages, with respect to the modelling approach, to understand the impacts of agricultural practices on biodiversity. Yet, a large panel of models is today available to address more specific questions, between human drivers and landscape, global incentives and landscape, or landscape and species. All of them are presently coupled or/and compared in order to qualify less ambitious yet relevant processes related to the landscape

Meta-analysis of multidecadal biodiversity trends in Europe

Local biodiversity trends over time are likely to be decoupled from global trends, as local processes may compensate or counteract global change. We analyze 161 long-term biological time series (15–91 years) collected across Europe, using a comprehensive dataset comprising ~6,200 marine, freshwater and terrestrial taxa. We test whether (i) local long-term biodiversity trends are consistent among biogeoregions, realms and taxonomic groups, and (ii) changes in biodiversity correlate with regional climate and local conditions. Our results reveal that local trends of abundance, richness and diversity differ among biogeoregions, realms and taxonomic groups, demonstrating that biodiversity changes at local scale are often complex and cannot be easily generalized. However, we find increases in richness and abundance With increasing temperature and naturalness as well as a clear spatial pattern in changes in community composition (i.e. temporal taxonomic turnover) in most biogeoregions of Northern and Eastern Europe