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15 results for “Chinstrap penguins”
Predicting foraging dive outcomes in chinstrap penguins using biologging and animal-borne cameras
<p>Direct observation of foraging behavior is not always possible, especially for marine species that hunt below the surface. However, biologging and tracking devices in particular have provided very detailed information about how various species use their habitat. From these indirect observations, researchers have tried to infer foraging and prey catching events for a more accurate definition of these species' ecological niches. In this study, we deployed video cameras in addition to GPS and time-depth recorders on chinstrap penguins during the brood phase of the 2018-19 breeding season at various colonies on the Gourlay peninsula (South Orkney Islands). More than 57 hours of footage from 16 birds covering 770 dives were scrutinized by two independent observers. The outcome of each dive was classified as unsuccessful, individual krill encounter or krill swarm encounter. In addition, the number of prey items caught was recorded for successful dives. We then used various predicting variables derived from the other logging devices or from the environment to train a machine-learning algorithm to predict the outcome of each dive. Our results show that despite some limitations, the data collected from the footage was reliable as there was a high agreement from both annotators. We also demonstrate that it was possible to accurately predict the outcome of each dive from basic dive patterns and horizontal movement characteristics that have not been used for penguins previously. Finally, we discuss how video footage can help build more accurate habitat models and gain wider knowledge about predator behavior or prey distribution.</p>
Nesting chinstrap penguins accrue large quantities of sleep through seconds-long microsleeps
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Predicting foraging dive outcomes in chinstrap penguins using biologging and animal-borne cameras
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Structural size measurements and isotopic signatures of foraging among adult male and female Chinstrap penguins (Pygoscelis antarcticus) nesting along the Palmer Archipelago near Palmer Station, 2007-2009
Sexual segregation in vertebrate foraging niche is often associated with sexual size dimorphism (SSD), i.e., ecological sexual dimorphism. We examined ecological sexual dimorphism among sympatric nesting Pygoscelis penguins near Palmer Station, Antarctica, asking whether environmental variability in the form of winter sea ice is associated with differences in male and female pre-breeding foraging niche. Each season, study nests, where pairs of adults were present, were individually marked and chosen before the onset of egg-laying, and consistently monitored. When study nests were found at the one-egg stage, both adults were captured to obtain blood samples used for molecular sexing and stable isotope analyses, and measurements of structural size and body mass. At the time of capture, each adult penguin was quickly blood sampled (~1 ml) from the brachial vein. After handling, individuals at study nests were further monitored to ensure the pair reached clutch completion, i.e., two eggs. Molecular analyses were conducted at Simon Fraser University following standard PCR protocols, and stable isotope analyses were conducted at the Stable Isotope Facility at the University of California, Davis using an elemental analyzer interfaced with an isotope ratio mass spectrometer
Harmony Point Chinstrap penguin GPS and Time-Depth-Recorder processed data
<p>Tracking data from Chinstrap Penguins (<em>Pygoscelis antarcticus</em>) tracked with axy-trek marine loggers in Harmony Point (Nelson Island, Maritime Antarctic Peninsula) in the breeding seasons of 2019/20 and 2021/22.</p> <p>The zip file is composed of two folders containing outputs from the R script named "GPS_TDR_processing.R", which process raw data from Axy-trek marine loggers producing 3 outputs that are within each of the folders. The outputs are: (1) 5-min resampled foraging trips (with trip ID identified), (2) dive statistics and (3) foraging trip summaries.</p> <p> </p> <p>Each file is named as the TagID, type of data (GPS or TDR), period (IB for late incubation / brooding - from late december to early january; CR for Chick-Rearing, from early to mid January) and season (2019/20 as 19 and 2021/22 as 21).</p> <p> </p> <p>GPS data is composed of the fields: Date (YYYY-mm-dd HH:MM:SS), distances, trip number and geographical coordinates in "+proj=longlat +datum=WGS84 +no_defs".</p> <p>TDR data is composed of outputs from dive statistics summarized using the "diveMove" R package. </p>
Comment on Krüger (2023): Decreasing Trends of Chinstrap Penguin Breeding Colonies in a Region of Major and Ongoing Rapid Environmental Changes Suggest Population Level Vulnerability. Diversity 2023, 15, 327
<p>Data and analyis scripts for:</p> <p><strong>Comment on Krüger (2023): Decreasing Trends of Chinstrap Penguin Breeding Colonies in a Region of Major and Ongoing Rapid Environmental Changes Suggest Population Level Vulnerability. Diversity 2023, 15, 327</strong></p> <p>W. Chris Oosthuizen, Murray Christian, Mzabalazo Ngwenya</p> <p>Centre for Statistics in Ecology, Environment and Conservation, Department of Statistical Sciences, University of Cape Town, Cape Town, 7701, South Africa</p> <p><strong>Abstract</strong></p> <p>Historical data on chinstrap penguin (<em>Pygoscelis antarctica</em>) breeding population sizes are sparse and sometimes highly uncertain, making it hard to estimate true population trajectories. Yet, information on population trends is desirable as changes in population size can help inform conservation assessments. Krüger (2023) (<em>Diversity</em> 2023, 15, 327) used chinstrap penguin nest count data to predict breeding colony size trends between 1960 and 2020, to estimate whether the level of population change within three generations exceeded IUCN Red List Criteria for "Vulnerable" populations. Chinstrap penguin population trends are an important research topic, but we caution that Krüger (2023)’s statistical analyses (intended to form the foundation for drawing valid, evidence-based inferences from sparse data) contain fundamental errors that invalidate that paper's findings. We discuss these oversights to help others detect and avoid some of the pitfalls associated with estimating population trends with mixed models. While we do not address all challenges, we also show through reanalysis that improved statistical modelling can yield better predictions of chinstrap penguin population trends, at least within the range of observed data. This case study highlights (1) the profound influence that seemingly minor differences in modelling procedures (both unintentional errors and other decisions) can have on predictions of population trends, and (2) the substantial inherent uncertainty in population trend predictions derived from sparse, heterogenous data.</p> <p> Keywords: Antarctic Peninsula, IUCN red list criteria, Mapping Application for Penguin Populations and Projected Dynamics (MAPPPD), population assessment, population trend, <em>Pygoscelis antarctica, </em>reproducible research</p>
Kopaitic Island Chinstrap penguin GPS tracking data 2018-2019
<p>These represent the speed filtered GPS datasets for individual Chinstrap penguins instrumented between November 2018 and February 2019 at Kopiaitic Island approximately 500m offshore from the Chilean Bernardo O'Higgins Antarctic Base on the western Antarctic Peninsula. They are stored as trip objects within standard .RData Workspaces #NOTE# the tags were set to record data at VERY fine temporal scales (every few seconds) thus the files are large and unwieldy in R . The objects are unprojected, but adhere to the CRS projection " +proj=lonlat +ellps=WGS84 +datum=WGS84" Individuals were also instrumented with dive loggers and accelerometer / magnetometer tags, and a subset were also instrumented with HD video cameras. These additional datasets will be published that are linked by unique animal #ID and deployment round number.</p>
Deception Island Chinstrap penguin GPS tracking data 2018-2019 RAW
<p>These represent the speed filtered GPS dataset and associated CRAWL models (R package crawl, v2.2.1) for 83 individual Chinstrap penguins instrumented between November 2018 and February 2019 at Deception Island at Bailey Head and Macaroni Point. Each individual penguin has a unique identifier (D_Rxx_Pxx) The data is tidy (one row = 1 observation = 1 location) and clean (no NAs; no duplicated lines, tracks cut off by deployment date + 24h and recovery date; points less than 2 min apart were removed for D_R4_P10). Each track is matched to the individual penguin metadata (ID, instrument type, deployment, recovery dates, original file, breeding status) The data is stored as a tibble in a R environment, contains 84,550 GPS locations and 24 columns. The GPS locations are available unprojected (lat lon &#34; +proj=lonlat +ellps=WGS84 +datum=WGS84&#34;; EPGS 4326) and projected in Polar stereographic (+proj=stere +lat_0=-90 +lat_ts=-71 +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs; EPSG:3031). Also included are pdf images of individual tracks, and the shapefile (polygon) of Deception Island used as a mask. </p>
WAP penguins_Nelson_Chinstrap_animalbornevideo_2018_2019
<p>Animal-borne video data and associated dive logger / accelerometer / magnetometer tag data from 4 chinstrap penguins.</p> <p> </p>
Environmental data for evaluation of Chinstrap Penguin foraging behaviour in Harmony Point, Nelson Island, during two years of contrasting conditions.
<p>This dataset includes:</p> <p>1 - a time series of environmental variables subsetted for the Area 48.1 (Antarctic Peninsula);</p> <p>2 - Monthly averaged data on chlorophyll-a concentration CHL, photosynthetically available/active radiation PAR (NASA OB.DAAC 2018a,b), fractional sea ice cover SIC and surface wind speed SWS (GMAO 2015) for a 75km radius around Harmony Point, Nelson Island, maritime Antarctic Peninsula;</p> <p>3 - Antarctic Krill (Euphausia superba) NASC (Nautical Area Scattering Coefficient, m2 nmi−2) acoustic estimated density for december/january 2019/20 and 2021/22, acoustic surveys were carried out in the research vessel boat "<em>RS Karpuj</em>" within the area used by penguins;</p> <p>3 - Foraging trip -level summarized data from 27 chinstrap penguins tracked with Axy-trek marine loggers (40 x 20 x 8 mm, 14g, GPS logger, time depth recorder TDR and accelerometer) during late incubation and early chick-rearing, december/january 2019/20 and 2021/22;</p> <p>4 - Colony-level breeding success estimated as chicks raised per nest in d2019/20 and 2021/22 breeding seasons.</p> <p>Data collection was supported by Áreas Marinas Protegidas program of the Instituto Antártico Chileno (AMP 24 03 052).</p>
Kopaitic Island chinstrap penguins (complete synoptic survey dataset)
<p>These represent the unprocessed, "fresh out the instrument" datasets for chinstrap penguins instrumented at Kopaitic Island, northwestern Antarctic Peninsula during the austral summer 2018-2019. Instrumentation was using Technosmart Axy-trek and AGM devices, with proprietary X-Manager software used to extract and save the data. </p>
Deception Island Chinstrap penguin GPS tracking data 2018-2019 RAW
<p>These represent the speed filtered GPS dataset for 83 individual Chinstrap penguins instrumented between November 2018 and February 2019 at Deception Island at Bailey Head and Macaroni Point.</p> <p>Each individual penguin has a unique identifier (D_Rxx_Pxx)</p> <p>The data is tidy (one row = 1 observation = 1 location) and clean (no NAs; no duplicated lines, tracks cut off by deployment date + 24h and recovery date; points less than 2 min apart were removed for D_R4_P10). </p> <p>Each track is matched to the individual penguin metadata (ID, instrument type, deployment, recovery dates, original file, breeding status)</p> <p>The data is stored as a tibble in a R environment, contains 84,550 GPS locations and 24 columns. The GPS locations are available unprojected (lat lon " +proj=lonlat +ellps=WGS84 +datum=WGS84"; EPGS 4326) and projected in Polar stereographic (+proj=stere +lat_0=-90 +lat_ts=-71 +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs; EPSG:3031). </p> <p> </p>
WAP penguins_Nelson_Chinstrap_GPS_csv_2018_2019
<p>WAP penguins_Nelson_Chinstrap_GPS_csv_2018_2019</p>
WAP penguins_Nelson_Kopaitic_Chinstrap_Gentoo_AGM_csv_2018_2019
<p>WAP penguins_Nelson_Kopaitic_Chinstrap_Gentoo_AGM_csv_2018_2019</p>
WAP penguins_Kopaitic_Gentoo_Chinstrap_GPS_csv_2018_2019
<p>WAP penguins_Kopaitic_Gentoo_GPS_csv_2018_2019</p> <p>WAP penguins_Kopaitic_Chinstrap_GPS_csv_2018_2019</p>
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