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403 results for “seabird”
Рис. 2. РаспреΑеΛение трубконосых птиц (А — темноспинный аΛьбатрос, Б — гΛупыш, В — тонкокΛювый буревестник, Г — сизая качурка) в Охотском море и сопреΑеΛьных воΑах Тихого океана и Японского моря по резуΛьтатам суΑовых учетов в февраΛе — мае 2020 г. (особей/км2 на 10-минутных трансектах). СпΛошными Λиниями показаны учетные трансекты, пунктиром — 200-метровая изобата Fig. 2. Distribution of tubenoses — (А) Laysan albatross, (Б) Northern fulmar, (В) shorttailed shearwater, (Г) fork-tailed storm-petrel — in the Sea of Okhotsk and adjacent waters of the Pacific Ocean and the Sea of Japan in February–May 2020 (birds/km2 on 10-minute transects). Solid lines indicate transects; dotted line indicates a 200 m isobath in Population of seabirds in the Sea of Okhotsk and adjacent waters of the Pacific Ocean and the Sea of Japan during the winter-spring period of 2020
Рис. 2. РаспреΑеΛение трубконосых птиц (А — темноспинный аΛьбатрос, Б — гΛупыш, В — тонкокΛювый буревестник, Г — сизая качурка) в Охотском море и сопреΑеΛьных воΑах Тихого океана и Японского моря по резуΛьтатам суΑовых учетов в февраΛе — мае 2020 г. (особей/км2 на 10-минутных трансектах). СпΛошными Λиниями показаны учетные трансекты, пунктиром — 200-метровая изобата Fig. 2. Distribution of tubenoses — (А) Laysan albatross, (Б) Northern fulmar, (В) shorttailed shearwater, (Г) fork-tailed storm-petrel — in the Sea of Okhotsk and adjacent waters of the Pacific Ocean and the Sea of Japan in February–May 2020 (birds/km2 on 10-minute transects). Solid lines indicate transects; dotted line indicates a 200 m isobath
Рис. 1. Размещение трансект (спΛошные черные Λинии) и Αаты провеΑения учетов в Охотском море и сопреΑеΛьных воΑах Тихого океана и Японского моря в февраΛе — мае 2020 г. РыбоΛовные районы: 05.1 — Северо-Охотоморская поΑзона; 05.2 — ЗапаΑно-Камчатская поΑзона; 05.3 — Восточно-СахаΛинская поΑзона; 05.4 — Камчатско-КуриΛьская поΑзона; 03 — Северо-КуриΛьская зона; 04 — Южно-КуриΛьская зона; 06 — зона Японское море. Пунктиром показана 200-метровая изобата Fig. 1. Transect locations (solid black lines) and dates of surveys in the Sea of Okhotsk and adjacent waters of the Pacific Ocean and the Sea of Japan in February–May 2020. Codes of the fishery areas are as follows: 05.1 — Northern Sea of Okhotsk Subzone; 05.2 — West Kamchatka Subzone; 05.3 — East Sakhalin Subzone; 05.4 — Kamchatka-Kuril Subzone; 03 — North Kuril Zone; 04 — South Kuril Zone; 06 — Sea of Japan Zone. Dotted line indicates a 200 m isobath in Population of seabirds in the Sea of Okhotsk and adjacent waters of the Pacific Ocean and the Sea of Japan during the winter-spring period of 2020
Рис. 1. Размещение трансект (спΛошные черные Λинии) и Αаты провеΑения учетов в Охотском море и сопреΑеΛьных воΑах Тихого океана и Японского моря в февраΛе — мае 2020 г. РыбоΛовные районы: 05.1 — Северо-Охотоморская поΑзона; 05.2 — ЗапаΑно-Камчатская поΑзона; 05.3 — Восточно-СахаΛинская поΑзона; 05.4 — Камчатско-КуриΛьская поΑзона; 03 — Северо-КуриΛьская зона; 04 — Южно-КуриΛьская зона; 06 — зона Японское море. Пунктиром показана 200-метровая изобата Fig. 1. Transect locations (solid black lines) and dates of surveys in the Sea of Okhotsk and adjacent waters of the Pacific Ocean and the Sea of Japan in February–May 2020. Codes of the fishery areas are as follows: 05.1 — Northern Sea of Okhotsk Subzone; 05.2 — West Kamchatka Subzone; 05.3 — East Sakhalin Subzone; 05.4 — Kamchatka-Kuril Subzone; 03 — North Kuril Zone; 04 — South Kuril Zone; 06 — Sea of Japan Zone. Dotted line indicates a 200 m isobath
Рис. 4. РаспреΑеΛение чистиковых птиц (А — тонкокΛювая и тоΛстокΛювая кайры, Б — боΛьшая конюга, В — конюга-крошка, Г — топорок) в Охотском море и сопреΑеΛьных воΑах Тихого океана и Японского моря по резуΛьтатам суΑовых учетов в февраΛе — мае 2020 г. (особей/км2 на 10-минутных трансектах). СпΛошными Λиниями показаны учетные трансекты, пунктиром — 200-метровая изобата Fig. 4. Distribution of alcids — (А) common and thick-billed murres, (Б) crested auklet, (В) least auklet, (Г) tufted puffin — in the Sea of Okhotsk and adjacent waters of the Pacific Ocean and the Sea of Japan in February–May 2020 (birds/km2 on 10-minute transects). Solid lines indicate transects, dotted line indicates a 200 m isobath in Population of seabirds in the Sea of Okhotsk and adjacent waters of the Pacific Ocean and the Sea of Japan during the winter-spring period of 2020
Рис. 4. РаспреΑеΛение чистиковых птиц (А — тонкокΛювая и тоΛстокΛювая кайры, Б — боΛьшая конюга, В — конюга-крошка, Г — топорок) в Охотском море и сопреΑеΛьных воΑах Тихого океана и Японского моря по резуΛьтатам суΑовых учетов в февраΛе — мае 2020 г. (особей/км2 на 10-минутных трансектах). СпΛошными Λиниями показаны учетные трансекты, пунктиром — 200-метровая изобата Fig. 4. Distribution of alcids — (А) common and thick-billed murres, (Б) crested auklet, (В) least auklet, (Г) tufted puffin — in the Sea of Okhotsk and adjacent waters of the Pacific Ocean and the Sea of Japan in February–May 2020 (birds/km2 on 10-minute transects). Solid lines indicate transects, dotted line indicates a 200 m isobath
Data for: Environment-dependent relationships between corticosterone and energy expenditure during reproduction: insights from seabirds in the context of climate change
<p>We studied the relationship between baseline levels of the steroid hormone corticosterone and daily energy expenditure (DEE) in the little auk (<em>Alle alle</em>), an Arctic sea bird that is experiencing mounting energetic challenges due to climate change. We specifically investigated the hypothesis that there might be environment-dependent relationships between baseline corticosterone, DEE, time activity budgets, diving behavior and fitness-related traits (chick provisioning rate, adult body condition). Furthermore, we also examined whether mercury (Hg) contamination might interfere with corticosterone production, and hence potentially the capacity to upregulate DEE. In addition, we performed a phylogenetically controlled analysis across breeding seabird species to assess the relationship between baseline corticosterone and DEE, which we estimated via <span>a model derived from a phylogenetically controlled meta-analysis, </span><span>available within a <span>web-based app (‘Seabird FMR Calculator’, </span></span><span><a href="https://ruthedunn.shinyapps.io/seabird_fmr_calculator/"><span>https://ruthedunn.shinyapps.io/seabird_fmr_calculator/</span></a></span><span>) (Dunn et al. 2018). These datasets contain information on corticosterone levels, DEE, TABs and Hg in little auks, and the data used in our phylogenetically controlled analysis. Please see the READ me file for details.</span></p>
It is good to be average: Ecological correlates of breeding phenology in an Arctic seabird, Alle alle (Dovekie)
<p>Recognising importance of deviation from a population mean in an animal's behavior is not only necessary to understand the evolution and stability of the whole system but also to predict the future of a population in an altering environment. Arctic seabirds are expected to exhibit high synchronization in timing of breeding at the population level, due to highly seasonal and harsh environmental conditions. Nevertheless, even in such a highly synchronized system, there are always some earlier and later breeders, and what causes this inter-pair variation remains an intriguing question. Using a set of eight years of data on the dovekie (<em>Alle alle</em>), a small Arctic seabird, we examined potential drivers of the observed distribution of breeding phenology. We found that dovekie pairs were quite repeatable in their phenology, and preserved their phenological status, with their chicks hatching consistently before, during, or after the population median date for hatching, despite that calendar position of the median shifted between years. This would suggest that the timing of breeding is associated with some pair characteristics, either via properties of the nest or/and some partners traits. However, breeding phenology of the pair was not dependent on nest location, neither pair bond duration nor morphological similarity of the partners. Importantly, timing of breeding was negatively associated with chick growth rate, indicating fitness consequences of phenology. Our simulation further suggests that chance of fledgling survival in the context of predation may be the highest for the chicks that hatched during the peak of hatching period. While our results suggest that the timing of the breeding is important for the reproductive outcome, further research is required to determine the drivers of the repeatability within the dovekie pairs.</p>
Data for: Activity of predators in seabird colonies decreases during the darkest compared to the brightest phase of the diel cycle below, but not above, the Arctic Circle
<p>The description of the data and how they were collected is in the associated open access publication: Huffeldt, N.P., F.M. van Beest, H.L. Kenyon, J. Danielsen, and T. Guilford. (2024) Activity of predators in seabird colonies decreases during the darkest compared to the brightest phase of the diel cycle below, but not above, the Arctic Circle. <em>Arctic, Antarctic, and Alpine Research</em> 56: 2367262. https://doi.org/10.1080/15230430.2024.2367262</p>
Fig. 5 in Population structure of the soft tick Ornithodoros maritimus and its associated infectious agents within a colony of its seabird host Larus michahellis
Fig. 5. Spatial autocorrelation in the total tick number of counted nests, measured as Moran's I, across three distance classes: a, 1st visit; b, 2nd visit; c, 3rd visit; d, 4th visit; e, 5th visit; f, 6th visit. Circles indicate the autocorrelation coefficients. The same results were obtained with female count numbers.
Fig. 2 in Population structure of the soft tick Ornithodoros maritimus and its associated infectious agents within a colony of its seabird host Larus michahellis
Fig. 2. Histogram presenting the mean number of ticks observed in all nests over time. Bars represent mean standard errors of the total number of ticks.
Fig. 3 in Population structure of the soft tick Ornithodoros maritimus and its associated infectious agents within a colony of its seabird host Larus michahellis
Fig. 3. Boxplot representations of tick numbers in counted and collected nests over time: a, females only; b, males only; c, nymphs. The box shows the median as a line across the middle and the quartiles (25th and 75th percentiles) at either end. Extremities represent the minimal and maximal values and circles represent outliers.
Fig. 1 in Population structure of the soft tick Ornithodoros maritimus and its associated infectious agents within a colony of its seabird host Larus michahellis
Fig. 1. Map showing the position of the 30 tracked nests on Carteau Island, in the Camargue region of France (represented by the red point on the bottom right map). Orange points represent the 15 nests in which ticks were counted and released. The green points are those nests where all ticks were counted and collected. Stars within the points represent the nests in which ticks were used for the screening of infectious agents. Boxes indicate the number of ticks screened and the detected infectious agents: Ana: Anaplasma spp.; Bab: Babesia spp.; Bar: Bartonella spp.; Bor: Borrelia spp.; Cox: Coxiella-like symbiont; Fra: Francisella-like symbiont; Ri: Rickettsia helvetica; Ri-like: Rickettsia-like symbiont. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 4 in Population structure of the soft tick Ornithodoros maritimus and its associated infectious agents within a colony of its seabird host Larus michahellis
Fig. 4. Spatial autocorrelation in total tick number estimated by Moran's I (Sokal and Oden, 1978). Data are from the first visit in the colony and include nests of both treatments. Ten distance classes representing 10 m between marked nests have been defined. No index value was significantly different from zero. The same results were obtained using female count data only (results not shown).
Linked collectors and determiners for: A new species of tick (Acari: Ixodidae) from seabirds in New Zealand and Australia, previously misidentified as Ixodes eudyptidis.
Natural history specimen data linked to collectors and determiners held within, "A new species of tick (Acari: Ixodidae) from seabirds in New Zealand and Australia, previously misidentified as Ixodes eudyptidis". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/714c988e-9abd-4ba0-b0f2-27508e713840">https://bionomia.net/dataset/714c988e-9abd-4ba0-b0f2-27508e713840</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/714c988e-9abd-4ba0-b0f2-27508e713840">https://gbif.org/dataset/714c988e-9abd-4ba0-b0f2-27508e713840</a>. Formatted as a Frictionless Data package.
Data from: When the "selfish herd" becomes the "frozen herd": spatial dynamics and population persistence in a colonial seabird
Aggregations are common in ecological systems at a range of scales and may be driven by exogenous constraints such as environmental heterogeneity and resource availability or by 'self-organizing' interactions among individuals. One mechanism leading to self-organized animal aggregations is captured by Hamilton's 'selfish herd' hypothesis, which suggests that aggregations may be driven by an individual's effort to minimize their risk of predation by surrounding themselves with conspecifics. We demonstrate that aggregations observed in Adélie penguin (Pygoscelis adeliae) colonies are a convolution of both self-organized dynamics and external forcing arising from landscape terrain. In fluid, highly mobile aggregations, individuals are constantly moving in response to changing environmental conditions, the locations of predators, or the movements of conspecifics. However, when the ability to rearrange is limited and spatial reconfiguration occurs on slower time scales than changes in population size, systems may become trapped in sub-optimal arrangements. We use simulated annealing to demonstrate that Adélie penguin colonies are frozen in sub-optimal spatial arrangements, and employ an individual-based modelling approach to demonstrate that this sub-optimal spatial configuration is driven by a convolution of nest site fidelity and stochastic events at the level of individual nests. The resulting spatial dynamics are responsible for a hysteretic response to long-term changes in abundance. We find that declining abundance leads to fragmentation even in a homogeneous environment, which has population-level consequences for reproductive success because predation is biased towards colony edges. Strong edge effects from heterogeneous predation coupled with fragmentation in response to population declines creates a positive feedback cycle that can accelerate population decline. This work provides a mechanistic understanding of complex spatial structuring in penguin colonies, provides a link between current spatial patterning and past dynamics, and suggests the possibility of critical collapse in seabird populations.
Double-tagging scores of seabirds reveals that light-level geolocator accuracy is limited by species idiosyncrasies and equatorial solar profiles
<p>Light-level geolocators are popular bio-logging tools, with advantageous sizes, longevity, and affordability. Biologists tracking seabirds often presume geolocator spatial accuracies between 186-202 km from previously-innovative, yet taxonomically, spatially, and computationally limited, studies. Using recently developed methods, we investigated whether assumed uncertainty norms held across a larger-scale, multispecies study.</p> <p>We field-tested geolocator spatial accuracy by synchronously deploying these with GPS loggers on scores of seabirds across five species and 11 Mediterranean Sea, East Atlantic and South Pacific breeding colonies. We first interpolated geolocations using the geolocation package FLightR without prior knowledge of GPS tracked routes. We likewise applied another package, probGLS, additionally testing whether sea-surface temperatures could improve route accuracy.</p> <p>Geolocator spatial accuracy was lower than the ~200km often assumed. probGLS produced the best accuracy (mean ± SD = 304 ± 413 km, <i>n</i> = 185 deployments) with 84.5% of GPS-derived latitudes and 88.8% of longitudes falling within resulting uncertainty estimates. FLightR produced lower spatial accuracy (408 ± 473 km, <i>n</i> = 171 deployments) with 38.6% of GPS-derived latitudes and 27% of longitudes within package-specific uncertainty estimates. Expected inter-twilight period (from GPS position and date) was the strongest predictor of accuracy, with increasingly equatorial solar profiles (i.e., closer temporally to equinoxes and/or spatially to the Equator) inducing more error. Individuals, species and geolocator model also significantly affected accuracy, while the impact of distance travelled between successive twilights depended on the geolocation package.</p> <p>Geolocation accuracy is not uniform among seabird species and can be considerably lower than assumed. Individual idiosyncrasies and spatiotemporal dynamics (i.e., shallower inter-twilight shifts by date and latitude) mean that practitioners should exercise greater caution in interpreting geolocator data and avoid universal uncertainty estimates. We provide a function capable of estimating relative accuracy of positions based on geolocator-observed inter-twilight period.</p>
Airflow modelling predicts seabird breeding habitat across islands
<p>Wind is fundamentally related to shelter and flight performance: two factors that are critical for birds at their nest sites. Despite this, airflows have never been fully integrated into models of breeding habitat selection, even for well-studied seabirds. Here we use computational fluid dynamics to provide the first assessment of whether flow characteristics (including wind speed and turbulence) predict the distribution of seabird colonies, taking common guillemots (<em>Uria aalge</em>) breeding on Skomer island as our study system. This demonstrates that occupancy is driven by the need to shelter from both wind and rain/ wave action, rather than airflow characteristics alone. Models of airflows and cliff orientation both performed well in predicting high quality habitat in our study site, identifying 80% of colonies and 93% of avoided sites, as well as 73% of the largest colonies on a neighbouring island. This suggests generality in the mechanisms driving breeding distributions, and provides an approach for identifying habitat for seabird reintroductions considering current and projected wind speeds and directions.</p>
Data from Lamb et al.: "Hanging out at the club: breeding status and territoriality affect individual space use, multi-species overlap, and pathogen transmission risk at a seabird colony"
<p>This dataset consists of two files:</p> <p><strong>ams_sku_all provides</strong> GPS locations from tracked skuas.</p> <p><strong>Skua_GPS_Metadata</strong> provides information on tracked skuas. The file consists of two workseets, the data table ("skua_gps_metadata", and a key providing descriptions of the column names and values ("Key")</p>
Shifting environmental predictors of phenotypes under climate change: A case study of growth in high latitude seabirds
<p>Climate change is altering species' traits across the globe. To predict future trait changes and understand the consequences of those changes, we need to know the environmental drivers of phenotypic change. In the present study, we use multi-decadal long datasets to determine periods of within-year environmental variation that predict growth of three seabird species. We evaluate whether these periods changed over time and use them to predict future growth under climate change. We find that predictions of trait change could be improved by considering that 1) the timing of environmental factors used to predict traits (predictive-environmental features) can change over time, and 2) the type of predictive-environmental features can change over time. We find evidence of changes in the timing of environmental predictors in all populations studied and evidence for a change in the type of predictor in the studied Arctic murre population. Environmental models of growth predict that warming conditions will decrease growth rates and bird body sizes in two species (black-legged kittiwakem <em>Rissa</em> <em>tridactyla</em>, and glaucous-winged gullm <em>Larus</em> <em>glaucescens</em>), but not the third (thick-billed murrem <em>Uria</em> <em>lomvia</em>). Consequently, climate change is likely to decrease fledging rates in the gulls and kittiwakes. Further, we find that ice-cover historically predicted murre chick growth well, but no longer does – instead air temperature is now a better predictor of murre growth. Our study highlights a need to investigate whether environmental determinants of trait variation commonly shift in a changing climate and whether such changes have implications for adaptation to novel environments.</p>
Data and scripts for: Quantifying annual spatial consistency in chick-rearing seabirds to inform important site identification
<p>Data derivates and analysis scripts (in R) used for the paper "Quantifying annual spatial consistency in chick-rearing seabirds to inform important site identification", published in Biological Conservation, on analyzing annual spatial overlap of 25 seabird populations across 23 species to assess variability and inform global efforts to improve spatial conservation measures.</p>
Data from: How do resource distribution and taxonomy affect the use of dual foraging in seabirds?: A review
<p>In many seabird species, parents feeding young switch between short and long foraging excursions in a strategy known as "dual foraging". To investigate whether habitat quality near breeding colonies drives the use of dual foraging, we conducted a systematic review of the seabird literature, compiling the results of 103 studies which identified dual-foraging in 50 species across nine families from all six seabird orders. We estimated the mean distance from the colony of each species' short and long foraging trips and obtained remote-sensed data on chlorophyll concentrations within the radius of both short and long trips around each colony. We then assessed, for each seabird family, the relationship between the use of dual foraging strategies and the difference in the quality of foraging locations between short- and long-distance foraging trips. We found that the probability of dual foraging grew with increasing difference in the quality of foraging locations available during short- and long-distance trips. We also found that when controlling for differences in habitat quality, albatrosses and penguins were less likely to use dual foraging than Procellariidae, which in turn were less likely to use dual foraging than Sulids. This study helps clarify how environmental conditions and taxon-specific characteristics influence seabird foraging behaviour. Keywords: seabirds, dual foraging, habitat quality, central-place foraging, interspecific differences.</p>
Large scale seabird community stucture along oceanographic gradients in the Scotia Sea and northern Antarctic Peninsula
<p>Dataset and associated code for publication submitted to Frontiers in Marine Science. The data represent strip transect data collection efforts of seabirds aboard 2 tourist ships during the 2019-2020 Antarctic summer season throughout the Scotia Sea and Antarctic Peninsula. Data were analysed using R.</p>
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Allen Brain Atlas
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