The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

In 2024, the number of data compromises in the United States stood at 3,158 cases. Meanwhile, over 1.35 billion individuals were affected in the same year by data compromises, including data breaches, leakage, and exposure. While these are three different events, they have one thing in common. As a result of all three incidents, the sensitive data is accessed by an unauthorized threat actor. Industries most vulnerable to data breaches Some industry sectors usually see more significant cases of private data violations than others. This is determined by the type and volume of the personal information organizations of these sectors store. In 2024 the financial services, healthcare, and professional services were the three industry sectors that recorded most data breaches. Overall, the number of healthcare data breaches in some industry sectors in the United States has gradually increased within the past few years. However, some sectors saw decrease. Largest data exposures worldwide In 2020, an adult streaming website, CAM4, experienced a leakage of nearly 11 billion records. This, by far, is the most extensive reported data leakage. This case, though, is unique because cyber security researchers found the vulnerability before the cyber criminals. The second-largest data breach is the Yahoo data breach, dating back to 2013. The company first reported about one billion exposed records, then later, in 2017, came up with an updated number of leaked records, which was three billion. In March 2018, the third biggest data breach happened, involving India’s national identification database Aadhaar. As a result of this incident, over 1.1 billion records were exposed.

As of March 2025, Google represented 79.1 percent of the global online search engine market on desktop devices. Despite being much ahead of its competitors, this represents the lowest share ever recorded by the search engine in these devices for over two decades. Meanwhile, its long-time competitor Bing accounted for 12.21 percent, as tools like Yahoo and Yandex held shares of over 2.9 percent each. Google and the global search market Ever since the introduction of Google Search in 1997, the company has dominated the search engine market, while the shares of all other tools has been rather lopsided. The majority of Google revenues are generated through advertising. Its parent corporation, Alphabet, was one of the biggest internet companies worldwide as of 2024, with a market capitalization of 2.02 trillion U.S. dollars. The company has also expanded its services to mail, productivity tools, enterprise products, mobile devices, and other ventures. As a result, Google earned one of the highest tech company revenues in 2024 with roughly 348.16 billion U.S. dollars. Search engine usage in different countries Google is the most frequently used search engine worldwide. But in some countries, its alternatives are leading or competing with it to some extent. As of the last quarter of 2023, more than 63 percent of internet users in Russia used Yandex, whereas Google users represented little over 33 percent. Meanwhile, Baidu was the most used search engine in China, despite a strong decrease in the percentage of internet users in the country accessing it. In other countries, like Japan and Mexico, people tend to use Yahoo along with Google. By the end of 2024, nearly half of the respondents in Japan said that they had used Yahoo in the past four weeks. In the same year, over 21 percent of users in Mexico said they used Yahoo.

For more than a decade, open access book platforms have been distributing titles in order to maximise their impact. Each platform offers some form of usage data, showcasing the success of their offering. However, the numbers alone are not sufficient to convey how well a book is actually performing.

Our data set is consists of 18,014 books and chapters. The selected titles have been added to the OAPEN Library collection before 1 January 2022, and the usage data of twelve months (January to December 2022) has been captured. During that period, this collection of books and chapters has been downloaded more than 10 million times. Each title has been linked to one broad subject and the title’s language has been coded as either English, German or other languages.

The titles are rated using the TOANI score.

The acronym stands for Transparent Open Access Normalised Index. The transparency is based on the application of clear regulations, and by making all data used visible. The data is normalised, by using a common scale for the complete collection of an open access book platform. Additionally, there are only three possible values to score the titles: average, less than average and more than average. This index is set up to provide a clear and simple answer to the question whether an open access book has made an impact. It is not meant to give a sense of false accuracy; the complexities surrounding this issue cannot be measured in several decimal places.

The TOANI score is based on the following principles:

Select only titles that have been available for at least 12 months;

Use the usage data of the same 12 months period for the whole collection;

Each title is assigned one – high level – subject;

Each title is assigned one language;

All titles are grouped based on subject and language;

The groups should consists of at least 100 titles;

The following data must be made available for each title:

Platform

Total number of titles in the group

Subject

Language

Period used for the measurement

Minimum value, maximum value, median, first and third quartile of the platform’s usage data

Based on the previous, titles are classified as:

“Less than average” – First quartile; 25 % of the titles

“Average” – Second and third quartile; 50% of the titles

“More than average” – Fourth quartile; 25 % of the titles

Using micro-data on six surveys–the Gallup World Poll 2005–2023, the U.S. Behavioral Risk Factor Surveillance System, 1993–2022, Eurobarometer 1991–2022, the UK Covid Social Survey Panel, 2020–2022, the European Social Survey 2002–2020 and the IPSOS Happiness Survey 2018–2023 –we show individuals’ reports of subjective wellbeing in Europe declined in the Great Recession of 2008/9 and during the Covid pandemic of 2020–2021 on most measures. They also declined in four countries bordering Ukraine after the Russian invasion in 2022. However, the movements are not large and are not apparent everywhere. We also used data from the European Commission’s Business and Consumer Surveys on people’s expectations of life in general, their financial situation and the economic and employment situation in the country. All of these dropped markedly in the Great Recession and during Covid, but bounced back quickly, as did firms’ expectations of the economy and the labor market. Neither the annual data from the United Nation’s Human Development Index (HDI) nor data used in the World Happiness Report from the Gallup World Poll shifted much in response to negative shocks. The HDI has been rising in the last decade reflecting overall improvements in economic and social wellbeing, captured in part by real earnings growth, although it fell slightly after 2020 as life expectancy dipped. This secular improvement is mirrored in life satisfaction which has been rising in the last decade. However, so too have negative affect in Europe and despair in the United States.

Footnotes: 1 Sources: Statistics Canada, Canadian Vital Statistics, Birth, Death and Stillbirth Databases. The table 13-10-0110-01 is an update of table 13-10-0408-01. 2 Infant mortality corresponds to the death of a child under one year of age. Expressed as a rate per 1,000 live births. 3 Perinatal deaths include late fetal deaths (stillbirths with a gestational age of 28 weeks or more) and early neonatal deaths (deaths of infants aged less than one week). 4 Numbers and rates in this table may differ from those found in similar data published by the Vital Statistics program as the data here have been tabulated based on postal codes available for place of residence. 5 2017 data for Yukon are not available. 6 The number of births, stillbirths, and deaths in Ontario for 2016 and 2017 are considered preliminary. 7 Due to improvements in methodology and timeliness, the duration of data collection has been shortened compared to previous years. As a result, there may have been fewer births and stillbirths captured by the time of the release. The 2017 data are therefore considered preliminary. 8 A census metropolitan area (CMA) is an area consisting of one or more adjacent municipalities situated around a major urban core. To form a census metropolitan area, the urban core must have a population of at least 100,000. The CMAs are those defined for the 2016 Census. To form a census agglomeration, the urban core must have a population of at least 10,000. 9 The metropolitan influenced zone (MIZ) classification is an approach to better differentiate areas of Canada outside of census metropolitan areas and census agglomerations. Census subdivisions that lie outside these areas are classified into one of four zones of influence. They are assigned to categories based on the flow of residents travelling to work in an urban area with a population greater than 10,000. Municipalities where more that 30% of the residents commute to work in an urban core are assigned to the strong MIZ category. Municipalities where between 5% and 30% of the residents commute to work in an urban core are assigned to the moderate MIZ category. Municipalities where between 0% and 5% of the residents commute to work in an urban core are assigned to the weak MIZ category. Municipalities where fewer than 40 or none of the residents commute to work in an urban core are assigned to the zero MIZ category. 10 Geographical areas are modified every 5 years to reflect the most recent census definitions, therefore, data are not strictly comparable historically. 11 Counts and rates in this table are based on three consecutive years of data. 12 The 95% confidence interval (CI) illustrates the degree of variability associated with a rate. 13 Wide confidence intervals (CIs) indicate high variability, thus, these rates should be interpreted and compared with due caution. 14 The following standard symbols are used in this Statistics Canada table: (..) for figures not available for a specific reference period, (...) for figures not applicable and (x) for figures suppressed to meet the confidentiality requirements of the Statistics Act. 15 The figures shown in the tables have been subjected to a confidentiality procedure known as controlled rounding to prevent the possibility of associating statistical data with any identifiable individual. Under this method, all figures, including totals and margins, are rounded either up or down to a multiple of 5. Controlled rounding has the advantage over other types of rounding of producing additive tables as well as offering more protection.

Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This case surveillance public use dataset has 12 elements for all COVID-19 cases shared with CDC and includes demographics, any exposure history, disease severity indicators and outcomes, presence of any underlying medical conditions and risk behaviors, and no geographic data.

CDC has three COVID-19 case surveillance datasets:

• COVID-19 Case Surveillance Public Use Data with Geography: Public use, patient-level dataset with clinical data (including symptoms), demographics, and county and state of residence. (19 data elements)
• COVID-19 Case Surveillance Public Use Data: Public use, patient-level dataset with clinical and symptom data and demographics, with no geographic data. (12 data elements)
• COVID-19 Case Surveillance Restricted Access Detailed Data: Restricted access, patient-level dataset with clinical and symptom data, demographics, and state and county of residence. Access requires a registration process and a data use agreement. (33 data elements)

• Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.
• Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers.
• Some data cells are suppressed to protect individual privacy.
• The datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the current datasets. This 14-day lag allows case reporting to be stabilized and ensures that time-dependent outcome data are accurately captured.
• Datasets are updated monthly.
• Datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy.
• For more information about data collection and reporting, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.
• For more information about the COVID-19 case surveillance data, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html
  

Overview

The COVID-19 case surveillance database includes individual-level data reported to U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020, to clarify the interpretation of antigen detection tests and serologic test results within the case classification (Interim-20-ID-02). The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data are collected by jurisdictions and reported voluntarily to CDC.

For more information: NNDSS Supports the COVID-19 Response | CDC.

The deidentified data in the “COVID-19 Case Surveillance Public Use Data” include demographic characteristics, any exposure history, disease severity indicators and outcomes, clinical data, laboratory diagnostic test results, and presence of any underlying medical conditions and risk behaviors. All data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.

COVID-19 Case Reports

COVID-19 case reports have been routinely submitted using nationally standardized case reporting forms. On April 5, 2020, CSTE released an Interim Position Statement with national surveillance case definitions for COVID-19 included. Current versions of these case definitions are available here: https://ndc.services.cdc.gov/case-definitions/coronavirus-disease-2019-2021/.

All cases reported on or after were requested to be shared by public health departments to CDC using the standardized case definitions for laboratory-confirmed or probable cases. On May 5, 2020, the standardized case reporting form was revised. Case reporting using this new form is ongoing among U.S. states and territories.

Data are Considered Provisional

• The COVID-19 case surveillance data are dynamic; case reports can be modified at any time by the jurisdictions sharing COVID-19 data with CDC. CDC may update prior cases shared with CDC based on any updated information from jurisdictions. For instance, as new information is gathered about previously reported cases, health departments provide updated data to CDC. As more information and data become available, analyses might find changes in surveillance data and trends during a previously reported time window. Data may also be shared late with CDC due to the volume of COVID-19 cases.
• Annual finalized data: To create the final NNDSS data used in the annual tables, CDC works carefully with the reporting jurisdictions to reconcile the data received during the year until each state or territorial epidemiologist confirms that the data from their area are correct.
• Access Addressing Gaps in Public Health Reporting of Race and Ethnicity for COVID-19, a report from the Council of State and Territorial Epidemiologists, to better understand the challenges in completing race and ethnicity data for COVID-19 and recommendations for improvement.

Data Limitations

To learn more about the limitations in using case surveillance data, visit FAQ: COVID-19 Data and Surveillance.

Data Quality Assurance Procedures

CDC’s Case Surveillance Section routinely performs data quality assurance procedures (i.e., ongoing corrections and logic checks to address data errors). To date, the following data cleaning steps have been implemented:

• Questions that have been left unanswered (blank) on the case report form are reclassified to a Missing value, if applicable to the question. For example, in the question “Was the individual hospitalized?” where the possible answer choices include “Yes,” “No,” or “Unknown,” the blank value is recoded to Missing because the case report form did not include a response to the question.
• Logic checks are performed for date data. If an illogical date has been provided, CDC reviews the data with the reporting jurisdiction. For example, if a symptom onset date in the future is reported to CDC, this value is set to null until the reporting jurisdiction updates the date appropriately.
• Additional data quality processing to recode free text data is ongoing. Data on symptoms, race and ethnicity, and healthcare worker status have been prioritized.

Data Suppression

To prevent release of data that could be used to identify people, data cells are suppressed for low frequency (<5) records and indirect identifiers (e.g., date of first positive specimen). Suppression includes rare combinations of demographic characteristics (sex, age group, race/ethnicity). Suppressed values are re-coded to the NA answer option; records with data suppression are never removed.

For questions, please contact Ask SRRG (eocevent394@cdc.gov).

Additional COVID-19 Data

COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths by state and by county. These

Abstract

Within the frame of PCBS' efforts in providing official Palestinian statistics in the different life aspects of Palestinian society and because the wide spread of Computer, Internet and Mobile Phone among the Palestinian people, and the important role they may play in spreading knowledge and culture and contribution in formulating the public opinion, PCBS conducted the Household Survey on Information and Communications Technology, 2014.

The main objective of this survey is to provide statistical data on Information and Communication Technology in the Palestine in addition to providing data on the following: - Prevalence of computers and access to the Internet. - Study the penetration and purpose of Technology use.

Geographic coverage

Palestine (West Bank and Gaza Strip), type of locality (urban, rural, refugee camps) and governorate.

Analysis unit

• Household.
• Persons 10 years and over .

Universe

All Palestinian households and individuals whose usual place of residence in Palestine with focus on persons aged 10 years and over in year 2014.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The sampling frame consists of a list of enumeration areas adopted in the Population, Housing and Establishments Census of 2007. Each enumeration area has an average size of about 124 households. These were used in the first phase as Preliminary Sampling Units in the process of selecting the survey sample.

Sample Size The total sample size of the survey was 7,268 households, of which 6,000 responded.

Sample Design The sample is a stratified clustered systematic random sample. The design comprised three phases:

Phase I: Random sample of 240 enumeration areas. Phase II: Selection of 25 households from each enumeration area selected in phase one using systematic random selection. Phase III: Selection of an individual (10 years or more) in the field from the selected households; KISH TABLES were used to ensure indiscriminate selection.

Sample Strata Distribution of the sample was stratified by: 1- Governorate (16 governorates, J1). 2- Type of locality (urban, rural and camps).

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire consists of identification data, quality controls and three main sections: Section I: Data on household members that include identification fields, the characteristics of household members (demographic and social) such as the relationship of individuals to the head of household, sex, date of birth and age.

Section II: Household data include information regarding computer processing, access to the Internet, and possession of various media and computer equipment. This section includes information on topics related to the use of computer and Internet, as well as supervision by households of their children (5-17 years old) while using the computer and Internet, and protective measures taken by the household in the home.

Section III: Data on persons (aged 10 years and over) about computer use, access to the Internet and possession of a mobile phone.

Cleaning operations

Preparation of Data Entry Program: This stage included preparation of the data entry programs using an ACCESS package and defining data entry control rules to avoid errors, plus validation inquiries to examine the data after it had been captured electronically.

Data Entry: The data entry process started on the 8th of May 2014 and ended on the 23rd of June 2014. The data entry took place at the main PCBS office and in field offices using 28 data clerks.

Editing and Cleaning procedures: Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

Response rate

Response Rates: 79%

Sampling error estimates

There are many aspects of the concept of data quality; this includes the initial planning of the survey to the dissemination of the results and how well users understand and use the data. There are three components to the quality of statistics: accuracy, comparability, and quality control procedures.

Checks on data accuracy cover many aspects of the survey and include statistical errors due to the use of a sample, non-statistical errors resulting from field workers or survey tools, and response rates and their effect on estimations. This section includes:

Statistical Errors Data of this survey may be affected by statistical errors due to the use of a sample and not a complete enumeration. Therefore, certain differences can be expected in comparison with the real values obtained through censuses. Variances were calculated for the most important indicators.

Variance calculations revealed that there is no problem in disseminating results nationally or regionally (the West Bank, Gaza Strip), but some indicators show high variance by governorate, as noted in the tables of the main report.

Non-Statistical Errors Non-statistical errors are possible at all stages of the project, during data collection or processing. These are referred to as non-response errors, response errors, interviewing errors and data entry errors. To avoid errors and reduce their effects, strenuous efforts were made to train the field workers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, and practical and theoretical training took place during the training course. Training manuals were provided for each section of the questionnaire, along with practical exercises in class and instructions on how to approach respondents to reduce refused cases. Data entry staff were trained on the data entry program, which was tested before starting the data entry process.

Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

The sources of non-statistical errors can be summarized as: 1. Some of the households were not at home and could not be interviewed, and some households refused to be interviewed. 2. In unique cases, errors occurred due to the way the questions were asked by interviewers and respondents misunderstood some of the questions.

Abstract1. Population cycles have long fascinated ecologists from the time of Charles Elton in the 1920s. The discovery of large population fluctuations in undisturbed ecosystems challenged the idea that pristine nature was in a state of balance. The 10-year cycle of snowshoe hares (Lepus americanus Erxleben) across the boreal forests of Canada and Alaska is a classic cycle, recognized by fur traders for more than 300 years. 2. Since the 1930s ecologists have investigated the mechanisms that might cause these cycles. Proposed causal mechanisms have varied from sunspots to food supplies, parasites, diseases, predation, and social behaviour. Both the birth rate and the death rate change dramatically over the cycle. Social behaviour was eliminated as a possible cause because snowshoe hares are not territorial and do not commit infanticide. 3. Since the 1960s large-scale manipulative experiments have been used to discover the major limiting factors. Food supply and predation quickly became recognized as potential key factors causing the cycle. Experiments adding food and restricting predator access to field populations have been decisive in pinpointing predation as the key mechanism causing these fluctuations. 4. The immediate cause of death of most snowshoe hares is predation by a variety of predators, including the Canada lynx (Lynx canadensis Kerr). The collapse in the reproductive rate is not due to food shortage as was originally thought, but is a result of chronic stress from predator chases. 5. Five major issues remain unresolved. First, what is the nature of the predator-induced memory that results in the prolonged low phase of the cycle? Second, why do hare cycles form a travelling wave, starting in the centre of the boreal forest in Saskatchewan and travelling across western Canada and Alaska? Third, why does the amplitude of the cycle vary greatly from one cycle to the next in the same area? Fourth, do the same mechanisms of population limitation apply to snowshoe hares in eastern North American or in similar ecosystems across Siberia? Finally, what effect will climatic warming have on all the above issues? The answers to these questions remain for future generations of biologists to determine. Usage notes1_Metadata for Kluane Hare GridsDescriptive data for the data given in the following 5 files2_Phases of Hare CycleDescribes the phases of the ten-year cycle of snowshoe hares for the years of study in the Yukon3_Monitoring Data for Small MammalsThe detailed data for the 3 main species discussed in this paper for the years studied, population size and confidence limits.4_Controls Hare Live trap data KluaneThe detailed demographic data for snowshoe hares on the control grids by capture date over all the years of study and all the grids that were controls.5_Feeding Experiment Data Figure 1The data used in Figure 1 for the feeding experiment on hares.6_Fence+Food Hare Capture DataThe detailed demographic data for the 1986-96 fence and food individual snowshoe hares by capture time, for this experiment that has been critical for our understanding.

Original provider: Blue Ocean Institute

Dataset credits: Blue Ocean Institute

Abstract: The oceanic movements of loggerhead turtles (Caretta caretta), green turtles (Chelonia mydas), and a Pacific ridley turtle (Lepidochelys olivacea) were monitored with satellite telemetry between 1996 and 2001 in the Pacific Ocean. During this time, several turtles migrated across the Pacific Ocean, covering more than 11,500 km between Santa Rosalita, Baja California, Mexico (28 degrees 40 minutes N, 114 degrees 14 minutes W), and Sendai Bay, Japan (37 degrees 54 minutes N, 140 degrees 56 minutes E). These findings are consistent with the hypothesis that loggerheads feeding in the eastern Pacific eventually return to nest on western Pacific beaches. Baja California loggerhead turtles have been shown, through molecular genetic analysis and flipper tag returns, to be primarily of Japanese origin.

We conclude that loggerhead turtles are capable of transpacific migrations and propose that the band of water between 25 and 30 degrees north latitude, the Subtropical Frontal Zone, may be an important transpacific migratory corridor. Recent findings indicate that juvenile loggerheads in the North Pacific move westward against weak (0.1 - 0.3 km/hr) eastward geostrophic currents, demonstrating that passive drift may not entirely explain the dispersal of loggerheads.

Purpose: The objective of the study was to monitor the oceanic movement, using satellite telemetry, of a Pacific loggerhead turtle initially captured on feeding grounds along the Baja California coast. Movement data also were examined with respect to oceanographic and meteorological information in an effort to gain insight into the navigational cues that guide adult sea turtles and to identify possible transpacific movement corridors.

Juvenile loggerhead turtles, Caretta caretta, in the 20 - 85 cm straight carapace length (SCL) size range have been observed in the offshore waters along the Pacific coast of California, USA, and the Baja California peninsula, Mexico. It was suggested that these turtles might be of western Pacific origin, migrating 10,000 km and feeding on pelagic red crabs (Pleuroncodes planipes) along the Baja California coast. Subsequently, Pacific loggerheads appear to utilize the entire North Pacific during the course of development in a manner similar to Atlantic loggerheads' use of the Atlantic Ocean. After a period of more than 10 years, mature turtles evidently cross the Pacific Ocean from pelagic waters and foraging areas along the Baja California coast to return to natal beaches, a journey of more than 12,000 km in each direction.

This is the first effort to document pelagic movements of North Pacific loggerheads from feeding grounds to nesting areas using satellite telemetry. Previous telemetry studies of loggerhead turtles have documented post-reproductive movements, pelagic movements, home ranges, navigational abilities and homing behavior. However, few studies of sea turtles have documented pre-nesting movements from feeding grounds to breeding areas. Notably, documented movement of a Kemp's ridley turtle (Lepidochelys kempii) from feeding grounds in Louisiana, United States, to its successful nesting in Rancho Nuevo, Mexico.

A unique opportunity to track the movements of an adult-sized loggerhead turtle, rarely encountered along the Baja California coast, emerged in 1996. The turtle had been raised in captivity and used in the initial genetic analysis of Baja California loggerhead turtles. Its mature size, genetic affinities with Japanese turtles, and the existence of a previous tag return from Japanese waters of a captive-raised, Baja California loggerhead turtle were the deciding factors in choosing this particular turtle for the study. This turtle is included in the dataset as series 7667, named Adelita.

Adelita was monitored following release at Santa Rosalita, Baja California, Mexico (28 degrees 40 minutes N, 114 degrees 14 minutes W). The turtle was first captured in October 1986 by sport fishermen in Bahia de Los Angeles, Baja California, and maintained in captivity at the Centro Regional de Investigaciones Pesqueras, Sea Turtle Research Station (CRIP-STRS). At the time of capture, it had a straight carapace length (SCL) of 29.9 cm and weighed 4 kg. The turtle was used in a study of captive growth rates and in genetic analysis of Pacific loggerhead stocks. Genetic studies suggested that this individual was of Japanese origin. At the time of release, the turtle measured 83.4 cm (SCL) and weighed 95 kg. The tail measured 3.5 cm from the edge of the carapace to the tip.

Sixteen other satellite tagged turtles are included in this dataset. Each turtle is identified by series number, name, species, and life stage. The shortest track consists of 20 locations, and the longest track includes 787 locations. Most of the tracked turtles were adult females, one turtle was an adult male, and the remainder were juveniles.

A model ST-3 backpack transmitter manufactured by Telonics, Inc. (Mesa, Arizona, United States) was programmed with a duty cycle of 6 hours on, 6 hours off. The transmitter was attached to the second vertebral scute (counting from the anterior) of the turtle's carapace using a modified version of the attachment technique. Specifically, we substituted a thin layer (<1 cm) of tinted two-part marine epoxy (Marine-Tex; Montgomeryville, Pennsylvania, United States) for Silicone Elastomer. Epoxy was also used to create a small faring on the leading and trailing edges of the transmitter to reduce drag. Release of the telemetered turtle occurred about 2 km offshore of Santa Rosalita, Baja California, Mexico (28 degrees 40 minutes N, 114 degrees 14 mintes W), on 10 August 1996, 10 years after initial capture. Transmission data were received via the Argos/NOAA satellite-based location and data collection system, which interprets and classifies signal locations in categories called location classes (LC). In addition to the date and location, data included surface time for each 12-hour period, average dive time for each 12-hour period, last dive time and temperature. Only positions with LCs of 0, 1, 2 or 3 were included in the analysis of distances traveled and swim speeds. LCs of 1 or greater have known error factors of <1,000 m and accuracy increases with location class (LC = 2, accuracy within 350 m; LC = 3, within 150 m). Distances and headings were calculated using variations of the Great Circle Equation. Each segment of the track, or distance traveled between quality locations, is presented and swim speeds for these segments are calculated by dividing the distance traveled by the time between locations. The straightness index, or the ratio between the great circle distance (shortest line between the release location and the final location) and the calculated distance traveled, was calculated using endpoints of the track.

Supplemental information: [UPDATE 2013-03-20] The following animals were removed as these are included in Pacific Turtle Tracks: Grupo Tortuguero
1085, 20750, 20780, 21217, 3851, 5524, 7667, 5521

Lower activity threshold studyTo evaluate the lower activity threshold, E. giganteana larvae were collected starting in the first week of April to May 21st, 2023, at the Land Institute, Salina, KS (38.768402, -97.567081). The larvae were collected from outdoor potted S. integrifolium plants. The top six to eight centimeters of soil within each pot was removed and sifted through a 0.635 cm mesh screen to remove all loose soil. Any lepidopteran hibernacula in the remaining debris were removed and placed into a plastic screw-top container with a mesh bottom to allow airflow. All collected hibernacula (and the larvae within) were transported to the USDA-ARS Center for Grain and Animal Health Research (Manhattan, KS, USA) in an insulated ice chest. If they were unable to be transported the same day they were collected, they were instead kept in a refrigerator at 4.4℃. A total of 97 E. giganteana larvae were collected across 10 dates (2 April, 7 April, 8 April, 12 April, 14 April, 3 May, 4 May, 17 May, 19 May, 21 May).Once in the lab all hibernacula were opened and the larvae were counted, and any non-E. giganteana larvae were excluded from the study. Larvae were then sorted into Petri dishes (100 × 15 mm, diameter: height) and labeled according to when they had been collected, the date they were placed in the environmental chambers (Percival Scientific Inc., Perry, IA, USA), and the temperature (7, 12.5, 18, 27.5, and 30 ℃). The Petri dishes were buried in a larger container (300 × 150 × 100 mm length:width:height) containing potting soil to mimic their natural environment, insulation, and humidity. The soil-filled container was watered whenever the soil had dried to mimic the natural moisture cycle. Beginning May 11th, E. giganteana larvae in the chambers were checked every one to three days, newly constructed hibernacula were counted, and dead larvae were documented and removed. No further larvae were placed in the chamber after May 16th as they had all rapidly died.To determine their lower activity threshold (LAT), two E. giganteana larvae from the same chamber were removed from their initial environmental chamber (e.g., 7, 12.5, 18, or 27.5 ℃) and placed in a separate environmental chamber with a different temperature (e.g., 5, 6, 8, 9, 10, 11, 14, 17, and 20℃) as a common garden experiment. This new temperature was intended to mimic a change in temperature that the larvae would experience during the spring in their natural environment. In the chamber, each larva’s s movements were recorded for 30 min in a smaller Petri dish (35 × 15 mm diameter: height) using a Dino-Lite camera (AF4135ZTE, Dino-Lite, VA, USA) attached to a Dino-Lite stand (RK-06A Dino-Lite, VA, USA) using a fully rotating clip before returned to its chamber of origin. Video was streamed live to a nearby laptop and captured with DinoCapture 2.0 (v.1.5.48.A, AnMo Electronics, New Taipei City, Taiwan). Each larva was only used once in each temperature. The selection of larvae for a given temperature was randomized. There was a total of n = 6–24 replicates per common chamber temperature. The range of replicate numbers was due to larval death during the duration of this experiment. Video files were uploaded manually into Ethovision software (v.16.0, Noldus Inc., Leesburg, VA, USA), which was then used to track and quantify the movement of each larva in the recordings (n= 233 total, 6,990 minutes). The total distance moved (cm) and velocity (cm/s) was recorded.Weather data and GDD ModelWeather data was provided by The Land Institute through a weather station positioned on their property (38.80000, -97.60000). Shielded air temperature was measured using a Vantage Pro2 Plus weather station (Davis Instruments, Hayward, CA, USA) that fed its data to WeatherLink. The station has been in continuous operation for more than 10 years. This weather station provided readings of the high and low temperature every 30 minutes. GDD were calculated based on the Baskerville-Emin method (Baskerville and Emin, 1969). Briefly, the temperature diurnal time course in a 24-h period is approximated by a sine wave using the high and low temperature readings from the weather station. The area above the lower activity threshold (from the study above), but below the daily maximum, approximated by the sine wave, was integrated for the resulting GDD. The biofix date for the GDD was set to the 60th day of the year (e.g., March 1 in most years).Phenological DataThere were two sources of phenological data for the GDD model. One was from historical trap capture data from 2019 and 2020 at The Land Institute (Ruiz et al. 2022). Another was from this study, which was conducted in six fields, three each in 2023 and 2024. All fields were located on The Land Institute's property in Salina, Kansas (Table 1 and Table 2). This trapping data was used to pair key milestones of adult E. giganteana development to GDDs. The key milestones we examined included beginning of flight, peak flight, middle of flight, and end of flight. Data from 2019 was used to develop predictions, while data in the three other years (e.g., 2020, 2023, 2024) was used to validate the model.Phenological Data from 2023Field trapping was done according to the methodology in Ruiz et al. (2022). The fields were located in North-Central Kansas at the Land Institute (Table 1). No pesticides were applied to these fields during this experiment. Starting the first week of June, six transects were set out with two in each S. integrifolium field. Each transect contained seven 30.4 cm × 30.4 cm clear sticky card traps (Alpha Scents, Canby, OR, USA) folded in half and affixed to the top of a 1.27 cm diameter, 91.4 cm PVC pole that was hammered into the ground making the top about 80 cm above ground. The cards were affixed using a 271-cm-long sticky card ring holder (Olson Products Inc., Medina, OH, USA) that was bent to a 90° angle then placed inside the PVC pipe. Two large binder clips were also used to anchor the sticky card to its card holder (Figure 1).The sticky traps in each transect were spaced 10 m apart around the perimeter of the field. For each transect, all traps were baited with one of three treatments. Two to three of the seven traps were baited with a control of 50 µl of acetone inside a LDPE 3-mL dropping bottle (Wheaton, DWK Life Sciences, Millville, NJ, USA). The remaining traps were baited with 50 µl of diluted (E)-8-dodecenyl acetate (Alfa Chemistry, Ronkonkoma, NY, USA). The low concentration of (E)-8-dodecenyl acetate was made by diluting 5.75 µl of (E)-8-dodecenyl acetate in 5 ml of acetone. A doubled concentration of (E)-8-dodecenyl acetate was made by diluting 11.5 µl in 5 ml of acetone. In all cases, the baited dropping bottle was placed in the top of the PVC pipe by the base of the sticky card (Figure 1). The sticky cards were collected and replaced biweekly until the first E. giganteana individual was caught, at which time it was changed to weekly. The lures and control bottles were replaced biweekly, and the treatment positions were rotated at that time.When collected, the sticky cards were held in a 7.6 L (=2 gal) labeled Ziploc® bag for transport back to USDA-ARS. All collected sticky traps were placed in a freezer for approximately twenty-four hours prior to counting. The total number of E. giganteana and nontarget Lepidoptera per trap was recorded. Individual E. giganteana and non-target lepidoptera were only counted if more than half of the specimen was remaining on the sticky trap at the time of counting.Phenological Data from 2024Field trapping in 2024 was conducted similarly to that in 2023 with the following modifications. Three different fields located at The Land Institute were used (Table 2). The pesticides (methoxyfenozide and chlorantraniliprole) were applied once during the season directly to one of the fields and adjacent to one of the other fields. Three transects were set out in each of the three fields used. Each transect contained four traps for a total of 36 traps. The traps were assembled similarly as in 2023, however hand-made sticky cards were used instead of manufactured ones, because of a noticeable decrease in efficacy in capturing E. giganteana. These sticky cards were made of a laminated 21.6 × 27.9 cm (=8.5 by 11 in) piece of white cardstock paper (Astrobright, Neenah, WI, USA) coated on both sides with TADⓇ all-weather (Trécé Adhesives Division, Adair, OK, USA). The sticky sides were covered with wax paper for transport. In the field, each sticky card was enclosed in a chicken wire cage (2in mesh) to reduce capture of birds and other large nontarget species. In each transect, one of the traps was baited with a control of 50 µl of acetone inside a 3-mL dropping bottle, the rest were baited with 50 µl diluted (E)-8-dodecenyl acetate at three concentrations. A low concentration (5.75 µl of (E)-8-dodecenyl acetate in 5 ml of acetone), a medium concentration (78.5 µl of (E)-8-dodecenyl acetate in 5 ml of acetone), and at a high concentration (580.4 µl of (E)-8-dodecenyl acetate in 5 ml of acetone). Traps were replaced weekly, and the baited dropping bottles were replaced every two weeks at which point the position of the treatment was rotated. The total number of E. giganteana and the number of nontarget lepidoptera was recorded for each trap. For both years, captures are averaged across baited treatments to generalize phenological events for GDD model.Egg to larvae GDDDuring the summer of 2024, adult E. giganteana moths were collected between the hours of 22:00-24:00 in one of the S. integrifolium fields at The Land Institute (38.769622, -97.598576). All captured E. giganteana were sexed and placed into individual small deli cups. Cups with female E. giganteana moths were checked daily for the presence of eggs. Cups with eggs laid in them were dated and then checked daily for larval emergence. A data logger (HOBO Temperature/Relative Humidity

Eco-geographic rules describe spatial patterns in biological trait variation and shed light on the drivers of such variation. In animals, a consensus is emerging that ‘pioneering’ traits may facilitate range shifts via a set of bold, aggressive, and stress-resilient traits. Many of these same traits are associated with more northern latitudes, and most range shifts in the northern hemisphere indicate northward movement. As a consequence, it is unclear whether pioneering traits are simply corollaries of existing latitudinal variation, or whether they override other well-trodden latitudinal patterning as a unique eco-geographic rule of phenotypic variation. The tree swallow (Tachycineta bicolor) is a songbird undergoing a southward range shift in the eastern United States, in direct opposition of the poleward movement seen in most other native species’ range shifts. Because this organic range shift countervails the typical direction of movement, this case study provides for unique ecological insights on organisms and their ability to thrive in our changing world. We sampled female birds across seven populations, quantifying behavioral, physiological, and morphological traits. We also used GIS and field data to quantify a core set of ecological factors with strong ties to these traits as well as female performance. Females at more southern expansion sites displayed higher maternal aggression, higher baseline corticosterone, and more pronounced elevation of corticosterone following a standardized stressor, contrary to otherwise largely conserved latitudinal patterning in these traits. Microhabitat variation explained some quantitative phenotypic variation, but the expansion and historic ranges did not differ in openness, distance to water, or breeding density. This countervailing range shift therefore suggests that pioneering traits are not simply corollaries of existing latitudinal variation, but rather, they may override other well-trodden latitudinal patterning as a unique eco-geographic rule of phenotypic variation.

Methods Study sites and environmental data Methods were approved by Appalachian State University IACUC #13-15 and US Master Banding Permit #23563. All animals were handled in such a way to reduce stress and avoid physical harm. All adults were released in their home territory. We conducted fieldwork during May-June 2015. Historical sites included Saukville, Wisconsin (43.382 N, 88.023 W), Long Point, Ontario (42.623 N, 80.465 W), and Wolfville, Nova Scotia (45.107 N, 64.378 W). Expansion sites included Bloomington, Indiana (39.142 N, 86.602 W); Ames, Iowa (42.073 N, 93.635 W); Davidson, North Carolina (35.438 N, 80.697 W); and Boone, North Carolina (36.196 N, 81.783 W). We recorded GPS coordinates at each nestbox (Garmin GPSmap 78s). Sites were categorized as either historical or expansion based on prior publications (Lee, 1993; Shutler et al., 2012), bolstered by personal communications with local researchers and data from the Bird Breeding Survey (BBS; 1967 to 2017)(Sauer et al., 2017). Historical sites have abundant tree swallow breeding for >100 years (Winkler et al., 2020), whereas expansion sites have increased abundance since the 1960s. In the first 10 years of the BBS, Nova Scotia, Ontario, and Wisconsin (historic sites) reported an annual average (± SE) of 458 ± 87 breeding tree swallows, whereas Indiana, Iowa and North Carolina (expansion sites) reported only 2 ± 1. In the most recent 10 years, breeding numbers have increased by 40-fold in expansion sites (Figure 2b; visualized at the state-/province-level in Figure S1a). Although BBS data surely underestimate abundance, these trends reflect site-specific data (Lee, 1993; Shutler et al., 2012). Notably, not all expansion sites are at the southernmost range edge (i.e. Tennessee, the Carolinas), and some expansion sites (Davidson and Boone, North Carolina) have more recent histories than others (Iowa, Indiana). All expansion sites are nonetheless beyond the historic core of the species distribution. We quantified three key ecological factors at each site, using measurements in the field as well as satellite data. The purpose of these data was two-fold: First, we sought to test whether the expansion and historic range differed in a core set of parameters with strong ties to tree swallow success. Second, we wanted to assess whether habitat variation might confound any range-related differences in traits. To achieve these goals, we characterized land use/land cover (LULC) using ArcGIS 10.2 (ESRI, Redlands, California). Analyses focused on a typical foraging range of 300m around each nestbox (McCarty and Winkler, 1999). LULC data were obtained from USGS National Land Cover Dataset (2011) or Gouvernement du Canada Land Use (2010), at 30x30m resolution. We selected LULC categories based on their relevance to tree swallows, which require open, wet habitat for aerial foraging of insect prey (Winkler et al., 2020). As such, we focused on the percent of land with open habitat (pasture, open water, or barren), and distance to water (streams, rivers, ponds, wetlands). In the field, we estimated conspecific density as the number of nestboxes within a 50m radius that were defended by other tree swallows; previous studies have linked density with aspects of aggressive behavior (Bentz et al., 2013). Adult capture, morphology, and blood sampling Females were captured between 09:00–16:30h, during incubation or chick-rearing (expansion: 44 incubating and 48 provisioning females; historic: 44 incubating and 36 provisioning). For provisioning females, average chick age was 6.3 days post-hatch (95%CI 5.3–7.3 days). We started a timer as soon as we captured each bird and collected an initial blood sample (~40–80uL) from the wing vein. Samples that took >3min were not used because Cort can elevate rapidly after handling (Schoech et al., 2013). We measured wing length, tail length, and body mass, but excluded wing data due to inadvertent methodological differences among sites. Tail length is correlated with structural size (Bourret and Garant, 2017; Hainstock et al., 2010) and affects flying ability (Norberg, 1990), an important feature for aerial insectivores. We recorded age as “second year of life” (SY, or yearling) or “after second year of life” (ASY, or >1 year) using plumage coloration (Hussell, 1983). All birds were banded with an aluminum leg band, and we marked each female across the breast with a colored marker to facilitate identification in during behavioral assays (Whittingham and Dunn, 2001). After processing, we placed each bird in an opaque paper bag until 30 min post-capture, a standardized restraint protocol to measure Cort elevation, or ΔCort. We collected a second blood sample (~40–80uL) and released the bird shortly thereafter. Assay of maternal aggression Nest defense is a key component of adaptive maternal investment. We measured clutch and brood size and found no difference between expansion and historic sites (linear mixed model, LMM, with population as random effect, clutch: β = 0.38 ±0.32 SE, F1,85=1.46, p=0.23; brood: β = -0.09 ±0.43 SE, F1,77= 0.05, p=0.84), and we therefore focused on a behavioral aspect of maternal investment. Specifically, we assayed maternal aggression against a simulated nest predator ~24h after blood sampling, using an assay modified from (Winkler, 1992). Decoys were commercially-manufactured models of the American crow (Corvus brachyrhynchos), a widespread nest predator. We randomly rotated among six exemplars to limit psuedoreplication. Ahead of time, we affixed a decoy via wire, dangling from a ~0.7m pole. Trials began by visually identifying the female and then deploying the decoy alongside crow calls from the Cornell Lab of Ornithology. The observer quickly slid the pole onto the existing nestbox and pole hardware, rapidly suspending the decoy above the box in a semi-natural flight position. The observer retreated to ~40m and began the behavioral assay. For 5 min, we measured the number of dives towards the model, within 1m of the nestbox. In tree swallows, nest defense is a highly repeatable trait (Betini and Norris, 2012). Hormone assays We quantified plasma Cort levels using an enzyme immunoassay kit that has high accuracy and assay parallelism (Cayman ELISA #500655, see Rosvall et al., 2012). Briefly, we added 10uL plasma to 200uL ultrapure H2O, extracted 3x with ether, dried with N2, and reconstituted with 600uL assay buffer. Each plate contained up to 33 samples in duplicate, an 8-point standard curve, blank, maximum binding, non-specific binding, and total activity controls, as well as 3 additional plasma pools used to calculate variability. Plates were balanced by site, breeding stage, and time point (baseline or 30-min). We read absorbance at 412 nm and interpolated concentration using Gen5 software (v2.09, BioTek, Winooski, VT, USA). Inter-plate variability was 10.2%, and intra-plate ranged from 3.7–12.4% (mean: 7.7%). We did not calculate extraction efficiency at a sample-level; instead, we spiked a separate set of samples (n=10) with 20uL H3-Cort (~2500 CPM), extracted 3x, and found average efficiencies to be 98.1%. Values may therefore underestimate true Cort concentrations, though our data are typical for this species (e.g. Zimmer et al., 2019), suggesting this effect is minimal. Data Analyses Statistical analyses were performed in R (v. 3.4.3, R Core Team, 2017). We report results as mean ± one standard error, and α=0.05. This study included data on 172 birds and the habitat surrounding each bird’s nestbox; however, we did not collect all data from every bird, e.g. because not all birds could be captured or identified during behavioral observations, some could not be bled quickly enough, some did not have full LULC data. Thus, sample sizes vary from 97 to 147 birds (Table 1). To test whether ranges (expansion vs historical) differed in habitat parameters,

As biparental care is crucial for breeding success in Procellariiformes seabirds (i.e., albatrosses and petrels), these species are expected to be choosy during pair formation. However, the choice of partners is limited in small-sized populations, which might lead to random pairing. In Procellariiformes, the consequences of such limitations for mating strategies have been examined in a single species. Here, we studied mate choice in another Procellariiforme, Bulwer’s petrel Bulweria bulwerii, in the Azores (ca 70 breeding pairs), where the species has suffered a dramatic population decline. We based our approach on both a 11-year demographic survey (capture-mark-recapture) and a genetic approach (microsatellites, n = 127 individuals). The genetic data suggest that this small population is not inbred and did not experience a genetic bottleneck. Moreover, pairing occurred randomly with respect to genetic relatedness, we detected no extrapair parentage (n = 35 offspring), and pair fecundity was unrelated to relatedness between partners. From our demographic survey, we detected no assortative mating with respect to body measurements and breeding experience and observed very few divorces, most of which were probably forced. This contrasts with the pattern previously observed in the much larger population from the Selvagens archipelago (assortative mating with respect to bill size and high divorce rate). We suggest that the Bulwer’s petrels from the Azores pair with any available partner and retain it as long as possible despite the fact that reproductive performance did not improve with pair common experience, possibly to avoid skipping breeding years in case of divorce. We recommend determining whether decreased choosiness during mate choice also occurs in reduced populations of other Procellariiform species. This might have implications for the conservation of small threatened seabird populations.

Methods Field work was conducted on Vila islet, Santa Maria island, Azores archipelago, from 2002 to 2012 included. Adults were captured in their nesting burrows each year during incubation, and ringed for identification. Chicks were ringed before fledging. These capture-mark-recapture sessions enabled us to know the life-history of each ringed individual, year after year, that is, the nest it was occupying (nesting cavities were marked with individual numbers), whether or not it was breeding, the outcomes of its breeding attempts, the identity of its social partner(s) and its offspring. Adults were measured (wing length using a stopped ruler to the nearest mm; tarsus length, culmen length and bill depth at the gonys using a vernier calliper to the nearest 0.1 mm).

Blood samples (50-100 µl) were collected from adults upon their first capture in 2002, 2003 and 2004. . Chicks were sampled a few days after hatching. We extracted bird DNA using the QIAmp Tissue Kit (QIAGEN). Eleven microsatellite loci (autosomal loci Bb2, Bb3, Bb7, Bb10, Bb12, Bb20, Bb21, Bb22, Bb23, Bb25, plus the sex-linked Bb11, Molecular Ecology Resources Primer Development Consortium 2010) were amplified by Polymerase Chain Reaction (PCR). Genotypes (number of base pairs at each allele for each locus) were analysed using GeneMapper 4.0 (Applied Biosystems). 118 adults (57 males, 61 females), including those that were genotyped, plus the offspring from 2002 to 2004 included, were sexed using molecular methods (Fridolfsson and Ellegren 1999, cited in our MS). The sex of 48 other adults (18 males, 30 females), including some chicks that later recruited into the breeding population, was inferred from that of their partner for which molecular sexing had been conducted.

To check if the demographic bottleneck experienced by Bulwer’s petrels in the Azores was associated with a genetic bottleneck, we used the BOTTLENECK software, which relies on the method of Cornuet and Luikart (1996, cited in our MS). Relatedness between social partners was estimated using MER (Wang 2002; version 3 downloadable from http://www.zoo.cam.ac.uk/ioz), after excluding the sex-linked locus Bb11.

We tested if there was an assortative mating based on body measurements or structural body size (PC1 scores of a Principal Component Analysis conducted on wing length, tarsus length and culmen length). To do this, we used two methods. First, we considered the pairs that were observed each year and we analysed our study years separately, after conducting Generalized Linear Models (GLMs) or Spearman rank correlations, according to whether or not the conditions for GLMs were met (that is, whether or not model residuals were normally distributed, Kéry and Hatfield 2003, cited in our MS). Second, we considered all the sexed pairs that were observed in our study together. In this situation, however, a given individual could be involved in several pair bonds (after e.g., the death of its former partner and/or a divorce). To overcome this problem, we used the MIXED procedure of SAS (with the Kenward-Roger degrees of freedom method, SAS Institute 2020), an equivalent of Generalized Linear Mixed Models which allows accounting for the correlations between observations concerning the same individual, can use data from individuals for which there are missing observations, allows within-individual effects to consist of continuous variables and to vary for the same individual, and analyses the data in their original form. To do this, we considered female (male) identity as a random effect.

To test whether pairing occurred at random with respect to genetic relatedness, we compared the relatedness of pair mates with that of male-female pairs drawn at random using a resampling procedure implemented in RESAMPLING PROCEDURES Version 1.3 (Howell 2001, cited in our MS), to account for non-independence of individual pairs. The procedure was repeated 5000 times.

To conduct parentage analyses, we compared chick genotypes with those of their social parents, and we excluded paternity (maternity) when the genotype of a chick mismatched that of its social father (mother) at two loci at least. A single mismatch between offspring and parental genotypes was interpreted as a mutation.

Only birds known to have made at least one breeding attempt in the past were used when calculating mate fidelity rates and determining the causes of divorce. Mate fidelity was defined as 1 minus the probability of divorce, the latter parameter being the total number of divorces divided by the total number of pair × years when both previous partners survive from one year to the next during the study period (Black 1996, cited in our MS).

To determine whether (1) reproductive performance (i.e., the probability of fledging chick) increased with pair common experience and (2) whether the probability of divorce depended on pair common experience and previous reproductive performance, we performed logistic regerssions for repeated measures (GENMOD procedure of SAS, binomial distribution, logit link, with the pair as the 'repeated' subject). Results from these logistic regressions were obtained from the models using generalized estimating equations (GEE).

More details are given in the main text of our MS.

For migratory landbird species, large expanses of open water or inhospitable areas provide unique challenges during migration. Research on the strategies that species use to navigate barriers can yield insights into the factors shaping the evolution of migration and facilitate the identification of critical staging areas prior to barrier crossing. One such barrier, the Caribbean Sea, has received little study but must be negotiated by ≈50 migratory landbirds as they fly from South America to North America in spring. Recent discoveries from the Gray-cheeked Thrush (Catharus minimus), which undertakes non-stop flights >3000 km across the Caribbean Sea, raises the possibility that the breadth of potential strategies has been unappreciated thus far. We calculated fuel load and potential flight range in 9985 individuals of 16 species captured over 10 years at two stopover sites in northern Colombia to 1) evaluate the likely migratory strategy of these species as they depart northern Colombia in spring, and 2) evaluate the influence of family, diet, morphology and migratory distance on potential flight range. We found considerable variation in flight ranges and therefore strategies for crossing the Caribbean Sea/Gulf of Mexico barrier complex. In addition to Gray-cheeked Thrush, non-stop flights >2500 km were possible in Yellow-billed Cuckoo (Coccyzus americanus), Yellow Warbler (Setophaga petechia) and Northern Waterthrush (Parkesia noveboracensis). The remaining species were either capable of over-water flights to the Yucatan Peninsula/Cuba (>1800 km) or shorter flights to middle Central America (>1000 km) and likely required one or more stopovers to reach North America. Predicted flight ranges were influenced by morphology but not by distance, diet or taxonomic group, providing a novel insight into the evolution of migratory strategies. Our study confirms the vital role northern Colombia performs in providing energy for migratory birds and highlights the Caribbean as a key migratory barrier for many species. Methods These data were collected as part of the SELVA projects “Crossing the Caribbean” and the “Neotropical Flyways Project”. They represent long term banding/ringing datasets collected in order to better understand stopover behaviour and use by migratory landbirds in strategic regions in northern Colombia. More information can be found here www.neotropicalflyways.com and here http://selva.org.co/en/research-programs/migratory-species/crossing-the-caribbean/

Data collection. Migration monitoring stations were established in two study regions, one in northwest Colombia and one in the Sierra Nevada de Santa Marta (SNSM) in the northeast. In the northwest, one station was established in Finca Las Palmeras (8.529713, -76.102434), while in the SNSM stations were established in Finca La Victoria (11.122652, -74.087351) and Quebrada Valencia (11.235270, -73.797807). Stations were run daily or every other day during spring migration between 2009 and 2018. A migration station consisted of 7 to 10 mist-nets placed to maximize captures/recaptures of migratory birds. Nets were opened at dawn and operated for an average of five hours daily between late-March and mid-May (spring). All captured individuals were marked with individually numbered rings (Porzana Ltd. reporting address www.aselva.co) and we recorded age (following Pyle 1997), fat score (Kaiser 1993), wing chord (mm) and body mass (measured to nearest 0.1 g using an electronic balance). The dataset contains capture information for16 species of Neotropical migratory landbirds selected for this study, including ring number, date, hour of capture, site, species, age, fat, muscle, wing and body mass. In addition, two processed data are included (see below).

Fuel load estimation. To calculate the fuel loads (FL) in the dataset, we first estimated lean body mass (LBM) based on individuals captured during autumn migration with no visible fat deposits (see supplementary material associated with Cano et al. 2020). FL was then calculated as the difference between the mass of birds captured on spring migration and LBM: FL = (Body mass – LBM)/LBM. We expressed FL as a percentage to facilitate comparisons between species.

Lean Body Mass: The lean body masses in the dataset were calculated from linear regressions of body mass against wing length, based on individuals with fat score 0 captured during fall migration in northern Colombia. The raw data are not included here. Lean body mass equations follow the following formula: LBM = a + b*Wing length.

Footnotes: 1 Sources: Statistics Canada, Canadian Vital Statistics, Birth, Death and Stillbirth Databases. The table 13-10-0110-01 is an update of table 13-10-0408-01. 2 Infant mortality corresponds to the death of a child under one year of age. Expressed as a rate per 1,000 live births. 3 Perinatal deaths include late fetal deaths (stillbirths with a gestational age of 28 weeks or more) and early neonatal deaths (deaths of infants aged less than one week). 4 Numbers and rates in this table may differ from those found in similar data published by the Vital Statistics program as the data here have been tabulated based on postal codes available for place of residence. 5 2017 data for Yukon are not available. 6 The number of births, stillbirths, and deaths in Ontario for 2016 and 2017 are considered preliminary. 7 Due to improvements in methodology and timeliness, the duration of data collection has been shortened compared to previous years. As a result, there may have been fewer births and stillbirths captured by the time of the release. The 2017 data are therefore considered preliminary. 8 A census metropolitan area (CMA) is an area consisting of one or more adjacent municipalities situated around a major urban core. To form a census metropolitan area, the urban core must have a population of at least 100,000. The CMAs are those defined for the 2016 Census. To form a census agglomeration, the urban core must have a population of at least 10,000. 9 The metropolitan influenced zone (MIZ) classification is an approach to better differentiate areas of Canada outside of census metropolitan areas and census agglomerations. Census subdivisions that lie outside these areas are classified into one of four zones of influence. They are assigned to categories based on the flow of residents travelling to work in an urban area with a population greater than 10,000. Municipalities where more that 30% of the residents commute to work in an urban core are assigned to the strong MIZ category. Municipalities where between 5% and 30% of the residents commute to work in an urban core are assigned to the moderate MIZ category. Municipalities where between 0% and 5% of the residents commute to work in an urban core are assigned to the weak MIZ category. Municipalities where fewer than 40 or none of the residents commute to work in an urban core are assigned to the zero MIZ category. 10 Geographical areas are modified every 5 years to reflect the most recent census definitions, therefore, data are not strictly comparable historically. 11 Counts and rates in this table are based on three consecutive years of data. 12 The 95% confidence interval (CI) illustrates the degree of variability associated with a rate. 13 Wide confidence intervals (CIs) indicate high variability, thus, these rates should be interpreted and compared with due caution. 14 The following standard symbols are used in this Statistics Canada table: (..) for figures not available for a specific reference period, (...) for figures not applicable and (x) for figures suppressed to meet the confidentiality requirements of the Statistics Act. 15 The figures shown in the tables have been subjected to a confidentiality procedure known as controlled rounding to prevent the possibility of associating statistical data with any identifiable individual. Under this method, all figures, including totals and margins, are rounded either up or down to a multiple of 5. Controlled rounding has the advantage over other types of rounding of producing additive tables as well as offering more protection.

During 2007–10, Canada’s Department of Fisheries and Oceans conducted a series of four midwater-trawl surveys, at meso- and bathypelagic depths, in The Gully – a very large submarine canyon incised into the Scotian Shelf, immediately east of Sable Island, the core of which has been encompassed within a Marine Protected Area since 2004. The surveys followed a fixed-station, depth-stratified design, with replicate sets both in daylight and at night. Most sampling used International Young Gadoid Pelagic Trawls (IYGPTs) –open nets with 60 m² mouth area– worked on double-oblique profiles. From the 2008 survey, the trawls were fitted with rigid (“aquarium”) codends and successfully took a number of delicate specimens in exceptional condition. Full details of the field methodologies have been presented by Kenchington et al. (2009, 2014) , while a report on the cephalopods captured is in preparation.

Anticipating that many rarely-seen species would be captured, a variety of camera systems were taken to sea on the Gully surveys and an attempt was made to capture images of every species taken, while the at-sea data-capture protocols emphasized visual recording of cephalopods – at least 38 species of which were captured. The resulting image collection has been catalogued and lightly edited (removing duplicates and unfocused or otherwise valueless images), while erring on the side of retaining any image that might prove useful in the future. Almost all of those retained are in their original formats and resolutions. The present collection includes the 402 catalogued images of cephalopods from all four surveys and a catalogue of them. Individual images are uniquely numbered (from S07001 to S07130, S08001 to S08014, S09001 to S09088 and S10001 to S10170 – the first two digits in each case indicating the survey year). There are corresponding entries in the catalogue, which presents the contents of each image (typically only a species name, though some entries have more details) and such other image-specific details as are available. Not all images can be linked to particular specimens but, for those which can be, catalogue entries include cross-references to the survey-program’s catch database, which provides further details on the specimens concerned.

All images © His Majesty the King in Right of Canada, 2022.

Amongst the named authors, the surveys were led by Trevor Kenchington, who also catalogued the images and, working with Cam Lirette, presented them here. Elizabeth Shea provided all specimen identifications, captured most of the images and assisted with their cataloguing. Other images were taken by Bill MacEachern, Kevin MacIsaac, Merlin Best or Andrew Cogswell.

Analysis of ‘Netflix Shows’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/netflix-showse on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Background

Netflix in the past 5-10 years has captured a large populate of viewers. With more viewers, there most likely an increase of show variety. However, do people understand the distribution of ratings on Netflix shows?

Netflix Suggestion Engine

Because of the vast amount of time it would take to gather 1,000 shows one by one, the gathering method took advantage of the Netflix’s suggestion engine. The suggestion engine recommends shows similar to the selected show. As part of this data set, I took 4 videos from 4 ratings (totaling 16 unique shows), then pulled 53 suggested shows per video. The ratings include: G, PG, TV-14, TV-MA. I chose not to pull from every rating (e.g. TV-G, TV-Y, etc.).

Source

Access to the study can be found at The Concept Center

This dataset was created by Chase Willden and contains around 1000 samples along with User Rating Score, Rating Description, technical information and other features such as: - Release Year - Title - and more.

How to use this dataset

• Analyze User Rating Size in relation to Rating
• Study the influence of Rating Level on User Rating Score
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Chase Willden

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations.

The following dataset provides facility-level data for hospital utilization aggregated on a weekly basis (Sunday to Saturday). These are derived from reports with facility-level granularity across two main sources: (1) HHS TeleTracking, and (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities.

The hospital population includes all hospitals registered with Centers for Medicare & Medicaid Services (CMS) as of June 1, 2020. It includes non-CMS hospitals that have reported since July 15, 2020. It does not include psychiatric, rehabilitation, Indian Health Service (IHS) facilities, U.S. Department of Veterans Affairs (VA) facilities, Defense Health Agency (DHA) facilities, and religious non-medical facilities.

For a given entry, the term “collection_week” signifies the start of the period that is aggregated. For example, a “collection_week” of 2020-11-15 means the average/sum/coverage of the elements captured from that given facility starting and including Sunday, November 15, 2020, and ending and including reports for Saturday, November 21, 2020.

Reported elements include an append of either “_coverage”, “_sum”, or “_avg”.

• A “_coverage” append denotes how many times the facility reported that element during that collection week.
• A “_sum” append denotes the sum of the reports provided for that facility for that element during that collection week.
• A “_avg” append is the average of the reports provided for that facility for that element during that collection week.

The file will be updated weekly. No statistical analysis is applied to impute non-response. For averages, calculations are based on the number of values collected for a given hospital in that collection week. Suppression is applied to the file for sums and averages less than four (4). In these cases, the field will be replaced with “-999,999”.

A story page was created to display both corrected and raw datasets and can be accessed at this link: https://healthdata.gov/stories/s/nhgk-5gpv

This data is preliminary and subject to change as more data become available. Data is available starting on July 31, 2020.

Sometimes, reports for a given facility will be provided to both HHS TeleTracking and HHS Protect. When this occurs, to ensure that there are not duplicate reports, deduplication is applied according to prioritization rules within HHS Protect.

For influenza fields listed in the file, the current HHS guidance marks these fields as optional. As a result, coverage of these elements are varied.

For recent updates to the dataset, scroll to the bottom of the dataset description.

On May 3, 2021, the following fields have been added to this data set.

• hhs_ids
• previous_day_admission_adult_covid_confirmed_7_day_coverage
• previous_day_admission_pediatric_covid_confirmed_7_day_coverage
• previous_day_admission_adult_covid_suspected_7_day_coverage
• previous_day_admission_pediatric_covid_suspected_7_day_coverage
• previous_week_personnel_covid_vaccinated_doses_administered_7_day_sum
• total_personnel_covid_vaccinated_doses_none_7_day_sum
• total_personnel_covid_vaccinated_doses_one_7_day_sum
• total_personnel_covid_vaccinated_doses_all_7_day_sum
• previous_week_patients_covid_vaccinated_doses_one_7_day_sum
• previous_week_patients_covid_vaccinated_doses_all_7_day_sum

On May 8, 2021, this data set has been converted to a corrected data set. The corrections applied to this data set are to smooth out data anomalies caused by keyed in data errors. To help determine which records have had corrections made to it. An additional Boolean field called is_corrected has been added.

On May 13, 2021 Changed vaccination fields from sum to max or min fields. This reflects the maximum or minimum number reported for that metric in a given week.

On June 7, 2021 Changed vaccination fields from max or min fields to Wednesday reported only. This reflects that the number reported for that metric is only reported on Wednesdays in a given week.

On September 20, 2021, the following has been updated: The use of analytic dataset as a source.

On January 19, 2022, the following fields have been added to this dataset:

• inpatient_beds_used_covid_7_day_avg
• inpatient_beds_used_covid_7_day_sum
• inpatient_beds_used_covid_7_day_coverage

On April 28, 2022, the following pediatric fields have been added to this dataset:

• all_pediatric_inpatient_bed_occupied_7_day_avg
• all_pediatric_inpatient_bed_occupied_7_day_coverage
• all_pediatric_inpatient_bed_occupied_7_day_sum
• all_pediatric_inpatient_beds_7_day_avg
• all_pediatric_inpatient_beds_7_day_coverage
• all_pediatric_inpatient_beds_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum
• staffed_icu_pediatric_patients_confirmed_covid_7_day_avg
• staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage
• staffed_icu_pediatric_patients_confirmed_covid_7_day_sum
• staffed_pediatric_icu_bed_occupancy_7_day_avg
• staffed_pediatric_icu_bed_occupancy_7_day_coverage
• staffed_pediatric_icu_bed_occupancy_7_day_sum
• total_staffed_pediatric_icu_beds_7_day_avg
• total_staffed_pediatric_icu_beds_7_day_coverage
• total_staffed_pediatric_icu_beds_7_day_sum

On October 24, 2022, the data includes more analytical calculations in efforts to provide a cleaner dataset. For a raw version of this dataset, please follow this link: https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/uqq2-txqb

Due to changes in reporting requirements, after June 19, 2023, a collection week is defined as starting on a Sunday and ending on the next Saturday.

Community based citizen science has increased the scope of ecological data collection and monitoring. Despite its growing popularity, citizen science methods are rarely validated. Validation is important to ensure high quality data can be used in scientific studies, monitoring, and management. The Rouge River (Michigan, USA), an Environmental Protection Agency Area of Concern, is considered a highly degraded river, but has benefited from numerous restoration projects. These projects have improved abiotic conditions in the river, but improvements to the biotic communities have not been assessed. Friends of the Rouge, a non-profit, has collected fish assemblage data throughout the river network for 10 years by seining, a sampling method they selected due to concerns including cost, safety, and fit to the organization's volunteer-based monitoring program.

We aimed to evaluate differences between sampling fish assemblages through seining performed by citizen scientists and the electrofishing method recommended for standardized assessments performed by fisheries professionals. We examined data from 48 sites across the Rouge River watershed where both sampling methods were implemented. We compared: a) species captured, b) the relationship between species richness and effort, c) diversity metrics used for standardized evaluation, and d) assemblage similarity between methods across the watershed.

Our results showed that in the wadeable reaches of this urban river, electrofishing and seining were comparable. The majority of species captured within the reaches were shared across sampling methods, although community similarity was lowest and highest in small branches. Differences in species captured were mostly driven by rare and benthic species. Species accumulation curves were not significantly different at the watershed or subwatershed scales (except when non-wadeable reaches were included).

Total species richness, the richness of species tolerant and intolerant to environmental degradation, and Procedure 51 scores used by Michigan agencies to assess the status of fish communities, sometimes differed among branches, but neither method was more effective overall at capturing fish diversity.

Our work demonstrates how citizen science methods can be validated by comparison with standard methods. Validating citizen science data enhances utility for monitoring, assessment, and management decisions.

Ifremer conducts numerous fisheries surveys dedicated to benthic and demersal populations (commercial / non-commercial fishes and invertebrates). For several years, in application of the ecosystem approach, all benthic invertebrate fauna collected in fishing gear has been systematically monitored: megabenthic invertebrates captured have been sorted, identified, counted and weighted. All these surveys are based on fixed or random stratified sampling strategy with varying intensity depending on the covered survey area. These data are stored, in historical access-based databases or for the most recent years in the centralised “Harmonie” database held in the Ifremer Fishery Information Systeme (SIH). The species nomenclature used was standardized using WoRMS database. Taxa caught at least once a year are listed for each monitoring area on the basis of already available data series. In order to facilitate the identification of individuals sampled on board vessels and to improve the training of onboard scientists, the present work aims to define the minimum level of identification for each of them. The analysis identifies taxa that appears recurrently on available historical series or gathers them on less precise taxonomic levels if this is not the case, which may indicate potential identification difficulties. The following procedure was used: all taxa expressed at the species level were first aggregated at genus level if they occurred less 90% of the years over the available time series. For MEDITS, EPIBENGOL and ORHAGO, the occurrence threshold was set to 70% and to only 50% for NOURMONT because the datasets were less than 10 years long. Then to be kept at that taxonomic level, a given genus had to be observed over 90% of the time (for example over at least 9 years if the dataset contains 10 years). Otherwise it was iteratively regrouped into a higher taxonomic level (family, order, class, division) following the same criteria (Foveau et al, 2017). For instance, for the NOURSEINE survey, this resulted into the aggregation of the 103 origin taxa into 35 taxonomic groups. The name of the final taxon after data processing represents the minimum level of identification defined by the analysis. However, these results are very theoretical. This is why they were sent to scientists who embark regularly in order to refine the level of taxonomic identification with field experience. The first dataset is composed of 8 tables relevant to the different vessel surveys. The first column of each table represents the permanent code of the taxon in the Ifremer taxonomic referential, the second the systematic number and the third the species abbreviated code. The other columns are the different taxonomic levels of the taxon. The minimum level of identification at sea defined by the data processing appears in blue. The level determined by feedback of scientist’s field experience, which is the one to use at sea, appears in green. The second dataset summaries the results detailed in the first table and indicates directly for each taxon identified to far, the minimum level of identification required for the benthic invertebrates by-catch of each fisheries surveys studied.