cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25573/serc.20060594
title: 2019 Water Level Data, Upper Chesapeake Bay, MarineGEO, Smithsonian Environmental Research Center

description: 2019 water level data collected on CS475A instrumentation at the MarineGEO Upper Chesapeake Bay observatory (Smithsonian Environmental Research Center, Edgewater, MD, USA). Location: 38.886928, -76.540172
DOI: 10.25573/serc.20060594.v2
title: 2019 Water Level Data, Upper Chesapeake Bay, MarineGEO, Smithsonian Environmental Research Center

description: 2019 water level data collected on CS475A instrumentation at the MarineGEO Upper Chesapeake Bay observatory (Smithsonian Environmental Research Center, Edgewater, MD, USA). Location: 38.886928, -76.540172
DOI: 10.25573/serc.20060594.v1
title: 2019 Water Level Data, Upper Chesapeake Bay, MarineGEO, Smithsonian Environmental Research Center

description: 2019 water level data collected on CS475A instrumentation at the MarineGEO Upper Chesapeake Bay observatory (Smithsonian Environmental Research Center, Edgewater, MD, USA). Location: 38.886928, -76.540172
DOI: 10.5281/zenodo.7272503
title: SG - October 2022

description: Measurements of SG (Global) in October 2022 Light Pollution Laboratorie info
DOI: 10.6084/m9.figshare.20399291
title: Life history characteristics and alarm calling in Australian arid-zone birds

description: In prey species, it is often the case that individuals give alarms when they are threatened. In birds, such signals are frequently vocal signals that alert conspecifics to the presence of a threat. The responses to these calls by receivers may include fleeing to cover or approaching to mob the predator. Although most birds do give alarm calls when threatened, not all species do. We used Australian arid-zone bird species (n = 171) to test the hypothesis that alarm calling behaviour is determined by ecological, behavioural, and morphological characteristics. Eighty-nine percent of birds analysed possessed an alarm call, highlighting the prevalence of this behaviour. Our study found three variables – number of food types eaten, mobility, and breeding system – that were associated with predicting alarm calling behaviour in these species. The correspondence of alarm calling with these key life history attributes provides insight into benefits of having alarm calls and the evolutionary processes that have given rise to this behaviour.
DOI: 10.6084/m9.figshare.20399291.v1
title: Life history characteristics and alarm calling in Australian arid-zone birds

description: In prey species, it is often the case that individuals give alarms when they are threatened. In birds, such signals are frequently vocal signals that alert conspecifics to the presence of a threat. The responses to these calls by receivers may include fleeing to cover or approaching to mob the predator. Although most birds do give alarm calls when threatened, not all species do. We used Australian arid-zone bird species (n = 171) to test the hypothesis that alarm calling behaviour is determined by ecological, behavioural, and morphological characteristics. Eighty-nine percent of birds analysed possessed an alarm call, highlighting the prevalence of this behaviour. Our study found three variables – number of food types eaten, mobility, and breeding system – that were associated with predicting alarm calling behaviour in these species. The correspondence of alarm calling with these key life history attributes provides insight into benefits of having alarm calls and the evolutionary processes that have given rise to this behaviour.
DOI: 10.6084/m9.figshare.20399450
title: Revealing migration schedule and potential breeding grounds of Lined Seedeaters using citizen science data

description: The Lined Seedeater (<i>Sporophila lineola</i>) is a small intra-tropical migrant songbird. However, little is known about its breeding and wintering grounds, or migratory habits. To investigate potentially distinct breeding populations and the migratory schedule of Lined Seedeaters we analysed its spatial and temporal distribution using published breeding records, museum vouchers, and data from citizen science projects (eBird and WikiAves). Our findings suggest that there are three main breeding areas: northern Argentina and north-western Paraguay, south-eastern Brazil, and north-eastern Brazil, and that the breeding season seems to be restricted to November through May, with slight differences in timing among these three areas. The species winters in the northern part of South America (mostly in grassland areas) and maybe also in the Amazonia. Moreover, rainfall predicts the latitudinal and longitudinal movements of Lined Seedeaters, with the migratory movements associated with an increase in rainfall. Taken together, these results provide a first comprehensive overview on the migration of Lined Seedeater, calling for further empirical field studies. Understanding intra-tropical migratory patterns is paramount to comprehend the potential impacts of environmental change in tropical ecosystems.
DOI: 10.5281/zenodo.7272502
title: SG - October 2022

description: Measurements of SG (Global) in October 2022 Light Pollution Laboratorie info
DOI: 10.6084/m9.figshare.20722384
title: Dominant factors shaping the gut microbiota of wild birds

description: The role of the gut microbiota in regulating host physiology and health is well established, with effects on host development, behaviour, nutrition, immunity, and reproductive strategy. While mammalian and insect microbiomes have attracted considerable research attention, avian microbiome research is deservedly growing, given the key roles that birds play in ecosystem services and functioning. Here we review recent literature (2008–2021) on the gut microbiome of wild birds, focusing on the role of key drivers that shape gut bacterial communities: diet, environment, and the phylogeny of host species. While most studies on avian gut microbiomes are confined to domestic poultry research, studies of wild birds have been increasing, particularly for the orders Passeriformes, Charadriformes, and Anseriformes. Four bacterial phyla dominate the gut microbiota of wild birds: Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes with varying abundances and compositions across taxa. Diet was reported to be the most influential factor shaping avian gut microbiomes, followed by phylogeny, habitat, the nest environment and seasonal variation. However, the current scarcity and large variation in reported patterns limits a clear understanding about how the ecology and evolution of birds are affected by the gut microbiome. Increased research effort is needed that links microbial diversity to function and avian health, with an expansion of sampling across host phylogenetic lineages, environmental conditions, dietary niches, and life stages. A shift towards field-based experimental approaches will further contribute towards more conclusive findings, which will serve to advance threatened species management in both captivity and in the wild.
DOI: 10.6084/m9.figshare.20399450.v1
title: Revealing migration schedule and potential breeding grounds of Lined Seedeaters using citizen science data

description: The Lined Seedeater (<i>Sporophila lineola</i>) is a small intra-tropical migrant songbird. However, little is known about its breeding and wintering grounds, or migratory habits. To investigate potentially distinct breeding populations and the migratory schedule of Lined Seedeaters we analysed its spatial and temporal distribution using published breeding records, museum vouchers, and data from citizen science projects (eBird and WikiAves). Our findings suggest that there are three main breeding areas: northern Argentina and north-western Paraguay, south-eastern Brazil, and north-eastern Brazil, and that the breeding season seems to be restricted to November through May, with slight differences in timing among these three areas. The species winters in the northern part of South America (mostly in grassland areas) and maybe also in the Amazonia. Moreover, rainfall predicts the latitudinal and longitudinal movements of Lined Seedeaters, with the migratory movements associated with an increase in rainfall. Taken together, these results provide a first comprehensive overview on the migration of Lined Seedeater, calling for further empirical field studies. Understanding intra-tropical migratory patterns is paramount to comprehend the potential impacts of environmental change in tropical ecosystems.
DOI: 10.6084/m9.figshare.20722384.v1
title: Dominant factors shaping the gut microbiota of wild birds

description: The role of the gut microbiota in regulating host physiology and health is well established, with effects on host development, behaviour, nutrition, immunity, and reproductive strategy. While mammalian and insect microbiomes have attracted considerable research attention, avian microbiome research is deservedly growing, given the key roles that birds play in ecosystem services and functioning. Here we review recent literature (2008–2021) on the gut microbiome of wild birds, focusing on the role of key drivers that shape gut bacterial communities: diet, environment, and the phylogeny of host species. While most studies on avian gut microbiomes are confined to domestic poultry research, studies of wild birds have been increasing, particularly for the orders Passeriformes, Charadriformes, and Anseriformes. Four bacterial phyla dominate the gut microbiota of wild birds: Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes with varying abundances and compositions across taxa. Diet was reported to be the most influential factor shaping avian gut microbiomes, followed by phylogeny, habitat, the nest environment and seasonal variation. However, the current scarcity and large variation in reported patterns limits a clear understanding about how the ecology and evolution of birds are affected by the gut microbiome. Increased research effort is needed that links microbial diversity to function and avian health, with an expansion of sampling across host phylogenetic lineages, environmental conditions, dietary niches, and life stages. A shift towards field-based experimental approaches will further contribute towards more conclusive findings, which will serve to advance threatened species management in both captivity and in the wild.
DOI: 10.25573/serc.20060591
title: 2020 Water Level Data, Upper Chesapeake Bay, MarineGEO, Smithsonian Environmental Research Center

description: 2020 water level data collected on CS475A instrumentation at the MarineGEO Upper Chesapeake Bay observatory (Smithsonian Environmental Research Center, Edgewater, MD, USA). Location: 38.886928, -76.540172
DOI: 10.25573/serc.20060591.v2
title: 2020 Water Level Data, Upper Chesapeake Bay, MarineGEO, Smithsonian Environmental Research Center

description: 2020 water level data collected on CS475A instrumentation at the MarineGEO Upper Chesapeake Bay observatory (Smithsonian Environmental Research Center, Edgewater, MD, USA). Location: 38.886928, -76.540172
DOI: 10.25573/serc.20060591.v1
title: 2020 Water Level Data, Upper Chesapeake Bay, MarineGEO, Smithsonian Environmental Research Center

description: 2020 water level data collected on CS475A instrumentation at the MarineGEO Upper Chesapeake Bay observatory (Smithsonian Environmental Research Center, Edgewater, MD, USA). Location: 38.886928, -76.540172
DOI: 10.5281/zenodo.7272505
title: LPL1 - October 2022

description: Measurements of LPL1 (Teide NP) in October 2022 Tenerife Light Pollution Laboratorie info
DOI: 10.5281/zenodo.7272504
title: LPL1 - October 2022

description: Measurements of LPL1 (Teide NP) in October 2022 Tenerife Light Pollution Laboratorie info
DOI: 10.25573/serc.20060588.v2
title: 2021 Water Level Data, Upper Chesapeake Bay, MarineGEO, Smithsonian Environmental Research Center

description: 2021 water level data collected on CS475A instrumentation at the MarineGEO Upper Chesapeake Bay observatory (Smithsonian Environmental Research Center, Edgewater, MD, USA). Location: 38.886928, -76.540172
DOI: 10.25573/serc.20060588.v1
title: 2021 Water Level Data, Upper Chesapeake Bay, MarineGEO, Smithsonian Environmental Research Center

description: 2021 water level data collected on CS475A instrumentation at the MarineGEO Upper Chesapeake Bay observatory (Smithsonian Environmental Research Center, Edgewater, MD, USA). Location: 38.886928, -76.540172
DOI: 10.25573/serc.20060585.v4
title: 2022 Water Level Data, Upper Chesapeake Bay, MarineGEO, Smithsonian Environmental Research Center

description: 2022 water level data collected on CS475A instrumentation at the MarineGEO Upper Chesapeake Bay observatory (Smithsonian Environmental Research Center, Edgewater, MD, USA). Location: 38.886928, -76.540172
DOI: 10.25573/serc.20060585.v3
title: 2022 Water Level Data, Upper Chesapeake Bay, MarineGEO, Smithsonian Environmental Research Center

description: 2022 water level data collected on CS475A instrumentation at the MarineGEO Upper Chesapeake Bay observatory (Smithsonian Environmental Research Center, Edgewater, MD, USA). Location: 38.886928, -76.540172
DOI: 10.5281/zenodo.7272507
title: LPL2 - October 2022

description: Measurements of LPL2 (Caldera de Taburiente NP) in October 2022 La Palma Light Pollution Laboratorie info
DOI: 10.5281/zenodo.7272506
title: LPL2 - October 2022

description: Measurements of LPL2 (Caldera de Taburiente NP) in October 2022 La Palma Light Pollution Laboratorie info
DOI: 10.25573/serc.20054609.v1
title: 2015 Water Quality Data, Upper Chesapeake Bay, MarineGEO, Smithsonian Environmental Research Center

description: 2015 water quality data collected on a YSI EXO2 multiparameter sonde at the MarineGEO Upper Chesapeake Bay observatory (Smithsonian Environmental Research Center, Edgewater, MD, USA). The figshare resource also contains quality control metadata to assist interpretation and analysis. Review the "readme.txt" file for more information. Location: 38.886928, -76.540172
DOI: 10.5281/zenodo.7272509
title: LPL3 - October 2022

description: Measurements of LPL3 (Madeira East Coast) in October 2022 Madeira Light Pollution Laboratorie info
DOI: 10.5281/zenodo.7272508
title: LPL3 - October 2022

description: Measurements of LPL3 (Madeira East Coast) in October 2022 Madeira Light Pollution Laboratorie info
DOI: 10.5281/zenodo.7272511
title: LPL4 - October 2022

description: Measurements of LPL4 (Ilha do Corvo) in October 2022 Corvo Light Pollution Laboratorie info
DOI: 10.5281/zenodo.7272510
title: LPL4 - October 2022

description: Measurements of LPL4 (Ilha do Corvo) in October 2022 Corvo Light Pollution Laboratorie info
DOI: 10.25573/serc.20054606.v1
title: 2016 Water Quality Data, Upper Chesapeake Bay, MarineGEO, Smithsonian Environmental Research Center

description: 2016 water quality data collected on a YSI EXO2 multiparameter sonde at the MarineGEO Upper Chesapeake Bay observatory (Smithsonian Environmental Research Center, Edgewater, MD, USA). The figshare resource also contains quality control metadata to assist interpretation and analysis. Review the "readme.txt" file for more information. Location: 38.886928, -76.540172
DOI: 10.5281/zenodo.7272513
title: LPL5 - October 2022

description: Measurements of LPL5 (Tejeda) in October 2022 Gran Canaria Light Pollution Laboratorie info
DOI: 10.5281/zenodo.7272512
title: LPL5 - October 2022

description: Measurements of LPL5 (Tejeda) in October 2022 Gran Canaria Light Pollution Laboratorie info
DOI: 10.5281/zenodo.7272515
title: LPL7 - October 2022

description: Measurements of LPL7 (Archipielago Chinijo) in October 2022 Archipielago Chinijo Light Pollution Laboratorie info
DOI: 10.5281/zenodo.7272514
title: LPL7 - October 2022

description: Measurements of LPL7 (Archipielago Chinijo) in October 2022 Archipielago Chinijo Light Pollution Laboratorie info
DOI: 10.25573/serc.20054603.v1
title: 2017 Water Quality Data, Upper Chesapeake Bay, MarineGEO, Smithsonian Environmental Research Center

description: 2017 water quality data collected on a YSI EXO2 multiparameter sonde at the MarineGEO Upper Chesapeake Bay observatory (Smithsonian Environmental Research Center, Edgewater, MD, USA). The figshare resource also contains quality control metadata to assist interpretation and analysis. Review the "readme.txt" file for more information. Location: 38.886928, -76.540172
DOI: 10.5281/zenodo.7272517
title: LPL8 - October 2022

description: Measurements of LPL8 (Timanfaya NP) in October 2022 Lanzarote Light Pollution Laboratorie info
DOI: 10.5281/zenodo.7272516
title: LPL8 - October 2022

description: Measurements of LPL8 (Timanfaya NP) in October 2022 Lanzarote Light Pollution Laboratorie info
DOI: 10.5281/zenodo.7272519
title: LPL12 - October 2022

description: Measurements of LPL12 (Ilhas Desertas) in October 2022 Madeira Light Pollution Laboratorie info
DOI: 10.5281/zenodo.7272518
title: LPL12 - October 2022

description: Measurements of LPL12 (Ilhas Desertas) in October 2022 Madeira Light Pollution Laboratorie info
DOI: 10.5281/zenodo.7272521
title: LPL14 - October 2022

description: Measurements of LPL14 (Ilha das Flores) in October 2022 Flores Light Pollution Laboratorie info
DOI: 10.5281/zenodo.7272520
title: LPL14 - October 2022

description: Measurements of LPL14 (Ilha das Flores) in October 2022 Flores Light Pollution Laboratorie info
DOI: 10.25573/serc.20054597.v1
title: 2018 Water Quality Data, Upper Chesapeake Bay, MarineGEO, Smithsonian Environmental Research Center

description: 2018 water quality data collected on a YSI EXO2 multiparameter sonde at the MarineGEO Upper Chesapeake Bay observatory (Smithsonian Environmental Research Center, Edgewater, MD, USA). The figshare resource also contains quality control metadata to assist interpretation and analysis. Review the "readme.txt" file for more information. Location: 38.886928, -76.540172
DOI: 10.25573/serc.20054594.v1
title: 2019 Water Quality Data, Upper Chesapeake Bay, MarineGEO, Smithsonian Environmental Research Center

description: 2019 water quality data collected on a YSI EXO2 multiparameter sonde at the MarineGEO Upper Chesapeake Bay observatory (Smithsonian Environmental Research Center, Edgewater, MD, USA). The figshare resource also contains quality control metadata to assist interpretation and analysis. Review the "readme.txt" file for more information. Location: 38.886928, -76.540172
DOI: 10.25573/serc.20054591.v1
title: 2020 Water Quality Data, Upper Chesapeake Bay, MarineGEO, Smithsonian Environmental Research Center

description: 2020 water quality data collected on a YSI EXO2 multiparameter sonde at the MarineGEO Upper Chesapeake Bay observatory (Smithsonian Environmental Research Center, Edgewater, MD, USA). The figshare resource also contains quality control metadata to assist interpretation and analysis. Review the "readme.txt" file for more information. Location: 38.886928, -76.540172
DOI: 10.25573/serc.20054588.v1
title: 2021 Water Quality Data, Upper Chesapeake Bay, MarineGEO, Smithsonian Environmental Research Center

description: 2021 water quality data collected on a YSI EXO2 multiparameter sonde at the MarineGEO Upper Chesapeake Bay observatory (Smithsonian Environmental Research Center, Edgewater, MD, USA). The figshare resource also contains quality control metadata to assist interpretation and analysis. Review the "readme.txt" file for more information. Location: 38.886928, -76.540172
DOI: 10.25573/serc.20054588.v2
title: 2021 Water Quality Data, Upper Chesapeake Bay, MarineGEO, Smithsonian Environmental Research Center

description: 2021 water quality data collected on a YSI EXO2 multiparameter sonde at the MarineGEO Upper Chesapeake Bay observatory (Smithsonian Environmental Research Center, Edgewater, MD, USA). The figshare resource also contains quality control metadata to assist interpretation and analysis. Review the "readme.txt" file for more information. Location: 38.886928, -76.540172
DOI: 10.5519/qd.0mqq94vg
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.25573/serc.20060768
title: 2019 Meteorological Data, Bocas del Toro, MarineGEO, Smithsonian Tropical Research Institute

description: 2019 meteorological data collected on WXT520 instrumentation at the MarineGEO Bocas del Toro observatory (Bocas del Toro Research Station, Smithsonian Tropical Research Institute, Panama). Location: 9.350822, -82.257853
DOI: 10.25573/serc.20060768.v4
title: 2019 Meteorological Data, Bocas del Toro, MarineGEO, Smithsonian Tropical Research Institute

description: 2019 meteorological data collected on WXT520 instrumentation at the MarineGEO Bocas del Toro observatory (Bocas del Toro Research Station, Smithsonian Tropical Research Institute, Panama). Location: 9.350822, -82.257853
DOI: 10.25573/serc.20060768.v3
title: 2019 Meteorological Data, Bocas del Toro, MarineGEO, Smithsonian Tropical Research Institute

description: 2019 meteorological data collected on WXT520 instrumentation at the MarineGEO Bocas del Toro observatory (Bocas del Toro Research Station, Smithsonian Tropical Research Institute, Panama). Location: 9.350822, -82.257853
DOI: 10.5281/zenodo.7272527
title: LPL15 - October 2022

description: Measurements of LPL15 (Badajoz) in October 2022 Provincia de Badajoz Light Pollution Laboratorie info
DOI: 10.5281/zenodo.7272526
title: LPL15 - October 2022

description: Measurements of LPL15 (Badajoz) in October 2022 Provincia de Badajoz Light Pollution Laboratorie info
DOI: 10.25573/serc.20060765
title: 2020 Meteorological Data, Bocas del Toro, MarineGEO, Smithsonian Tropical Research Institute

description: 2020 meteorological data collected on WXT520 instrumentation at the MarineGEO Bocas del Toro observatory (Bocas del Toro Research Station, Smithsonian Tropical Research Institute, Panama). Location: 9.350822, -82.257853
DOI: 10.25573/serc.20060765.v2
title: 2020 Meteorological Data, Bocas del Toro, MarineGEO, Smithsonian Tropical Research Institute

description: 2020 meteorological data collected on WXT520 instrumentation at the MarineGEO Bocas del Toro observatory (Bocas del Toro Research Station, Smithsonian Tropical Research Institute, Panama). Location: 9.350822, -82.257853
DOI: 10.25573/serc.20060765.v1
title: 2020 Meteorological Data, Bocas del Toro, MarineGEO, Smithsonian Tropical Research Institute

description: 2020 meteorological data collected on WXT520 instrumentation at the MarineGEO Bocas del Toro observatory (Bocas del Toro Research Station, Smithsonian Tropical Research Institute, Panama). Location: 9.350822, -82.257853
DOI: 10.5281/zenodo.7272531
title: LPL16 - October 2022

description: Measurements of LPL16 (Caceres) in October 2022 Provincia de Cáceres Light Pollution Laboratorie info
DOI: 10.5281/zenodo.7272530
title: LPL16 - October 2022

description: Measurements of LPL16 (Caceres) in October 2022 Provincia de Cáceres Light Pollution Laboratorie info
DOI: 10.5281/zenodo.7272533
title: LPL17 - October 2022

description: Measurements of LPL17 (Isla de Tabarca) in October 2022 Alicante Light Pollution Laboratorie info
DOI: 10.5281/zenodo.7272532
title: LPL17 - October 2022

description: Measurements of LPL17 (Isla de Tabarca) in October 2022 Alicante Light Pollution Laboratorie info
DOI: 10.25573/serc.20060762
title: 2021 Meteorological Data, Bocas del Toro, MarineGEO, Smithsonian Tropical Research Institute

description: 2021 meteorological data collected on WXT520 and WXT536 instrumentation at the MarineGEO Bocas del Toro observatory (Bocas del Toro Research Station, Smithsonian Tropical Research Institute, Panama). Location: 9.350822, -82.257853
DOI: 10.25573/serc.20060762.v1
title: 2021 Meteorological Data, Bocas del Toro, MarineGEO, Smithsonian Tropical Research Institute

description: 2021 meteorological data collected on WXT520 and WXT536 instrumentation at the MarineGEO Bocas del Toro observatory (Bocas del Toro Research Station, Smithsonian Tropical Research Institute, Panama). Location: 9.350822, -82.257853