Datasets, conda environments and Softwares for the course "Population Genomics" of Prof Kasper Munch. This course material is maintained by the health data science sandbox. This webpage shows the latest version of the course material.

1. Data.tar.gz Contains the datasets and executable files for some of the softwares
   You can unpack by simply doing
   tar -zxf Data.tar.gz -C ./
   This will create a folder called Data with the uncompressed material inside
2. Course_Env.packed.tar.gz Contains the conda environment used for the course. This needs to be unpacked to adjust all the prefixes (Note this environment is created on Ubuntu 22.10). You do this in the command line by
   1. creating the folder Course_Env: mkdir Course_Env
   2. untar the file: tar -zxf Course_Env.packed.tar.gz -C Course_Env
   3. Activate the environment: conda activate ./Course_Env
   4. Run the unpacking script (it can take quite some time to get it done): conda-unpack
3. Course_Env.unpacked.tar.gz The same environment as above, but will work only if untarred into the folder /usr/Material - so use the version above if you are using it in another folder. This file is mostly to execute the course in our own cloud environment.
4. environment_with_args.yml The file needed to generate the conda environment. Create and activate the environment with the following commands:
   1. conda env create -f environment_with_args.yml -p ./Course_Env
   2. conda activate ./Course_Env

The data is connected to the following repository: https://github.com/hds-sandbox/Popgen_course_aarhus. The original course material from Prof Kasper Munch is at https://github.com/kaspermunch/PopulationGenomicsCourse.

Description

The participants will after the course have detailed knowledge of the methods and applications required to perform a typical population genomic study.

The participants must at the end of the course be able to:

• Identify an experimental platform relevant to a population genomic analysis.
• Apply commonly used population genomic methods.
• Explain the theory behind common population genomic methods.
• Reflect on strengths and limitations of population genomic methods.
• Interpret and analyze results of population genomic inference.
• Formulate population genetics hypotheses based on data

The course introduces key concepts in population genomics from generation of population genetic data sets to the most common population genetic analyses and association studies. The first part of the course focuses on generation of population genetic data sets. The second part introduces the most common population genetic analyses and their theoretical background. Here topics include analysis of demography, population structure, recombination and selection. The last part of the course focus on applications of population genetic data sets for association studies in relation to human health.

Curriculum

The curriculum for each week is listed below. "Coop" refers to a set of lecture notes by Graham Coop that we will use throughout the course.

Course plan

1. Course intro and overview:
   • Coop chapters 1, 2, 3, Paper: Genome Diversity Project
2. Drift and the coalescent:
   • Coop chapter 4; Paper: Platypus
   • Exercise: Read mapping and base calling
3. Recombination:
   • Lecture: Review: Recombination in eukaryotes, Review: Recombination rate estimation
   • Exercise: Phasing and recombination rate
4. Population strucure and incomplete lineage sorting:
   • Lecture: Coop chapter 6, Review: Incomplete lineage sorting
   • Exercise: Working with VCF files
5. Hidden Markov models:
   • Lecture: Durbin chapter 3, Paper: population structure
   • Exercise: Inference of population structure and admixture
6. Ancestral recombination graphs:
   • Lecture: Paper: Approximating the ARG, Paper: Tree inference
   • Exercise: ARG dashboard exercises + Inference of trees along sequence
7. Past population demography:
   • Lecture: Coop chapter 4, Paper: PSMC, revisit Paper: Tree inference
   • Exercise: Inferring historical populations
8. Direct and linked selection:
   • Lecture: Coop chapters 12, 13, revisit Paper: Tree inference
9. Admixture:
   • Lecture: Review: Admixture, Paper: Admixture inference
   • Exercise: Detecting archaic ancestry in modern humans
10. Genome-wide association study (GWAS):
    • Lecture: Coop lecture notes 99-120
    • Exercise: GWAS quality control
11. Heritability:
    • Lecture: Coop Lecture notes Sec. 2.2 (p23-36) + Chap. 7 (p119-142)
    • Exercise: Association testing
12. Evolution and disease:
    • Lecture: Coop Lecture notes Sec. 11.0.1 (p217-221)
    • Exercise: Estimating heritability

Do you know further standard populations?

If you know any further standard populations worth integrating in this dataset, please let me know in the discussion part. I would be happy to integrate further data to make this dataset more useful for everybody.

German "Federal Health Monitoring System" about 'standard populations':

"Standard populations are "artificial populations" with fictitious age structures, that are used in age standardization as uniform basis for the calculation of comparable measures for the respective reference population(s).

Use: Age standardizations based on a standard population are often used at cancer registries to compare morbidity or mortality rates. If there are different age structures in populations of different regions or in a population in one region over time, the comparability of their mortality or morbidity rates is only limited. For interregional or inter-temporal comparisons, therefore, an age standardization is necessary. For this purpose the age structure of a reference population, the so-called standard population, is assumed for the study population. The age specific mortality or morbidity rates of the study population are weighted according to the age structure of the standard population. Selection of a standard population:

Which standard population is used for comparison basically, does not matter. It is important, however, that

1. the demographic structure of the standard population is not too dissimilar to that of the reference population and
2. the comparable rates refer to the same standard."

Aim of this dataset

The aim of this dataset is to provide a variety of the most commonly used 'standard populations'.

Currently, two files with 22 standard populations are provided: - standard_populations_20_age_groups.csv - 20 age groups: '0', '01-04', '05-09', '10-14', '15-19', '20-24', '25-29', '30-34', '35-39', '40-44', '45-49', '50-54', '55-59', '60-64', '65-69', '70-74', '75-79', '80-84', '85-89', '90+' - 7 standard populations: 'Standard population Germany 2011', 'Standard population Germany 1987', 'Standard population of Europe 2013', 'Standard population Old Laender 1987', 'Standard population New Laender 1987', 'New standard population of Europe', 'World standard population' - source: German Federal Health Monitoring System

• standard_populations_19_age_groups.csv
  • 19 age groups: '0', '01-04', '05-09', '10-14', '15-19', '20-24', '25-29', '30-34', '35-39', '40-44', '45-49', '50-54', '55-59', '60-64', '65-69', '70-74', '75-79', '80-84', '85+'
  • 15 standard populations: '1940 U.S. Std Million', '1950 U.S. Std Million', '1960 U.S. Std Million', '1970 U.S. Std Million', '1980 U.S. Std Million', '1990 U.S. Std Million', '1991 Canadian Std Million', '1996 Canadian Std Million', '2000 U.S. Std Million', '2000 U.S. Std Population (Census P25-1130)', '2011 Canadian Standard Population', 'European (EU-27 plus EFTA 2011-2030) Std Million', 'European (Scandinavian 1960) Std Million', 'World (Segi 1960) Std Million', 'World (WHO 2000-2025) Std Million'
  • source: National Institutes of Health, National Cancer Institute, Surveillance, Epidemiology, and End Results Program

Terms of use

No restrictions are known to the author. Standard populations are published by different organisations for public usage.

These transcripts were collected from:2 focus groups conducted in Pontefract, West Yorkshire1 focus group conducted in the Mumbles, Swansea, Wales

The Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 consists of estimates of human population density (number of persons per square kilometer) based on counts consistent with national censuses and population registers, for the years 2000, 2005, 2010, 2015, and 2020. A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative units, was used to assign population counts to 30 arc-second grid cells. The population density rasters were created by dividing the population count raster for a given target year by the land area raster. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution.

Purpose: To provide estimates of population density for the years 2000, 2005, 2010, 2015, and 2020, based on counts consistent with national censuses and population registers, as raster data to facilitate data integration.

Recommended Citation(s)*: Center for International Earth Science Information Network - CIESIN - Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H49C6VHW. Accessed DAY MONTH YEAR.

When I was searching for COVID-19 datasets online, I soon realized that there were no comprehensive datasets of the United States on a county level basis which included social, economic, and demographic factors in addition to the general case information that was already available on several sites. To quench my thirst for clean and relevant data, I proceeded to gather information from several various sources to compile the dataset I was looking for.

I started by looking for a reliable dataset that has general information such as confirmed cases, deaths, etc. I found John Hopkin's COVID-19 dataset to be the best one for this purpose as it is well organized and updated daily. Then, I set out looking for economic factors and population data for each county in the United States. I found a collection of such files compiled by the Economic Research Service branch of the USDA on their website. Finally, I had to find a dataset which had racial and demographic information for each county, which I found on the US Census Bureau's website under a page which was dedicated to county population data by several characteristics. Now that I had all the data I was looking for, I proceeded to find which counties were common in all datasets. After several hours of cleaning each dataset and extracting relevant information, I combined all the information into one CSV file with 2959 counties of clean information - exactly what I was looking for.

I hope that the Kaggle community will use this dataset to answer important questions regarding COVID-19 in the United States and the role that external economic, social, and demographic factors play in the shaping of the pandemic. I know that there are several patterns to be discovered and I sincerely hope that this helps our community understand just a little more about the pandemic than we do right now.

We built an Integrated Population Model (IPM) and fitted parameters with four datasets: (1) population survey data, (2) nest survey data, (3) duckling survival data, and (4) annual band-recovery data. Our study area covers the entire country of the Netherlands, as all data were collected as part of national monitoring programs or citizen science projects. We limited our analyses to the years 2003-2020 due to a limited sampling effort in the years before 2003 for most datasets. Population survey data. Breeding bird populations have been monitored in the Netherlands as part of a national Breeding bird Monitoring Program (BMP) starting in 1984 (Boele et al. 2014). BMP is based on intensive territory mapping in fixed study plots carried out by well-trained volunteers and professionals who follow a standardized protocol. Territory mapping is based on a large, and annually constant, number of field visits (5-10 between March and July depending on species) in which all birds with territorial behavior (e.g., pair bond, display, nests) are recorded on maps. Species-specific interpretation criteria are used to determine the number and exact locations of "territories" at the end of the season. The counts of 1990 constitute the index baseline. Index values for the following years are then calculated as the relative difference between that year's counts and those of the reference year (Sovon 2019). Nest survey data. Nest data for Dutch Mallards were retrieved from the National Nest Record Scheme (Bijlsma et al. 2020). Mallard nests are mainly reported as 'bycatch' during nest surveys of grassland-breeding waders. To calculate clutch size and egg hatch rate, incomplete clutches were excluded by using only successfully hatched nests. In total, there were 4415 observations of clutch size with a range of 149-402 nests observed annually. For egg hatch rate per successful clutch, we calculated for each year the total number of eggs laid and the number of eggs hatched in nests where at least one egg had hatched. In total 2383 nest observations were available, with the number of observations ranging between 54 and 282 nests per year. Duckling survival data. We used data for Mallard females with broods collected through a citizen science project to estimate annual duckling survival rates. Contributors to this project were asked to report brood size and brood age on a mobile application specifically developed for this purpose. From 2018 onward, contributors were asked to make repeated observations of the same broods to allow for the calculation of duckling survival. We therefore used data for the years 2018-2020, as only for these years were there enough repeated observations of broods to estimate duckling survival. The final dataset included 2825 observations of 1212 broods distributed over the three years. Adult band-recovery data. Annual survival of post-fledgling birds was estimated using data for Mallards that were banded between 2003 and 2020 and were later recovered and reported dead. In the Netherlands, Mallards are banded mostly in traditional duck decoys (Karelse 1994). Since the year 2000, 20133 Mallards have been banded, of which 1233 were recovered dead (Buijs and Thomson, 2001, van Noordwijk et al. 2003). We only included Mallards that were banded in the breeding season (March-July) to exclude winter migrants that breed in other countries. This dataset included 2615 Mallards, of which 203 were recovered dead. Europe's highest densities of breeding Mallards (Anas platyrhynchos) are found in the Netherlands, but the breeding population there has declined by ~30% since the 1990s. The exact cause of this decline has remained unclear. Here, we used an integrated population model to jointly analyze Mallard population survey, nest survey, duckling survival and band-recovery data. We used this approach to holistically estimate all relevant vital rates, including duckling survival rates for years for which no explicit data were available. Mean vital rate estimates were high for nest success (0.38 ±0.01) and egg hatch rate (0.96 ±0.001), but relatively low for clutch size (8.2 ±0.05) compared to populations in other regions. Estimates for duckling survival rate for the three years for which explicit data were available were low (0.16-0.27) compared to historical observations, but were comparable to rates reported for other regions with declining populations. Finally, mean survival rate was low for ducklings (0.18 ±0.02), but high and stable for adults (0.71 ±0.03). Population growth rate was only affected by variation in duckling survival, but since this is a predominantly latent state variable, this result should be interpreted with caution. However, it does strongly indicate that none of the other vital rates, all of which were supported by data, was able to sufficiently explain the population decline. Together with a comparison with historic vital rates, these findings point to a reduced duckling survival rate as the li...

The National Survey of College Graduates is a repeated cross-sectional biennial survey that provides data on the nation's college graduates, with a focus on those in the science and engineering workforce. This survey is a unique source for examining the relationship of degree field and occupation in addition to other characteristics of college-educated individuals, including work activities, salary, and demographic information. This dataset includes National Survey of College Graduates assets for 2021.

COVID-19 Dataset for Epidemic Model Development

I combined several data sources to gain an integrated dataset involving country-level COVID-19 confirmed, recovered and fatalities cases which can be used to build some epidemic models such as SIR, SIR with mortality. Adding information regarding population which can be used for calculating incidence rate and prevalence rate. One of my applications based on this dataset is published at https://dylansp.shinyapps.io/COVID19_Visualization_Analysis_Tool/.

Content

My approach is to retrieve cumulative confirmed cases, fatalities and recovered cases since 2020-01-22 onwards from the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) COVID-19 dataset, merged with country code as well as population of each country. For the purpose of building epidemic models, I calculated information regarding daily new confirmed cases, recovered cases, and fatalities, together with remaining confirmed cases which equal to cumulative confirmed cases - cumulative recovered cases - cumulative fatalities. I haven't yet to find creditable data sources regarding probable cases of various countries yet. I'll add them once I found them.

• Date: The date of the record.
• Country_Region: The name of the country/region. -alpha-3_code: country code for that can be used for map visualization.
• Population: The population of the given country/region.
• Total_Confirmed_Cases: Cumulative confirmed cases.
• Total_Fatalities: Cumulative fatalities.
• Total_Recovered_Cases: Cumulative recovered cases.
• New_Confirmed_Cases: Daily new confirmed cases.
• New_Fatalities: Daily new fatalities.
• New_Recovered_Cases: Daily new recovered cases.
• Remaining_Confirmed_Cases: Remaining infected cases which equal to (cumulative confirmed cases - cumulative recovered cases - cumulative fatalities).

Acknowledgements

1. The data source of confirmed cases, recovered cases and deaths is JHU CSSE https://github.com/CSSEGISandData/COVID-19;
2. The data source of the country-level population mainly comes from https://storage.guidotti.dev/covid19/data/ and Worldometer (https://www.worldometers.info/population/).

Inspiration

1. Building up the country-level COVID-19 case track dashboard.
2. Insights regarding the incidence rate, prevalence rate, mortality and recovery rate of various countries.
3. Building up epidemic models for forecasting.

Population models can facilitate assessment of potential impacts of pesticides on populations or species rather than individuals and have been identified as important tools for pesticide risk assessment of non-target species including those listed under the Endangered Species Act (ESA). Few examples of population models developed for this specific purpose are available, however, population models are commonly used in conservation science as a tool to project the viability of populations and the long-term outcomes of management actions. We present a population model for Mead’s milkweed (Asclepias meadii), a species listed as threatened under the ESA throughout its range across the Midwestern U.S. We adapted a published population model based on demographic field data for application in pesticide risk assessment. Exposure and effects were modeled as reductions of sets of vital rates in the transition matrices, simulating both lethal and sublethal effects of herbicides. Two herbicides, atrazine and mesotrione, were used as case study examples to evaluate a range of assumptions about potential exposure-effects relationships. In addition, we assessed buffers (i.e. setback distances of herbicide spray applications from the simulated habitat) as hypothetical mitigation scenarios and evaluated their influence on population-level effects in the model. The model results suggest that buffers can be effective in reducing risk from herbicide drift to plant populations. These case studies demonstrate that existing population models can be adopted and integrated with exposure and effects information for use in pesticide risk assessment.

The Global Human Footprint Data Set of the Last of the Wild Project, Version 2, 2005 (LWP-2) presents the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1-kilometer grid cells created from nine global data layers covering human population pressure (population density population settlements), human land use and infrastructure (built up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). The data set can be downloaded in Band Interleaf (BIL) format. The data set was produced by the Wildlife Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN). The purpose is to provide an upgrade to existing maps of wild areas, which in turn can be used in modeling efforts, wildlife conservation planning, natural resource management, policy-making, biodiversity studies and human-environment interactions.

The Gridded Population of the World, version 3 (GPWv3) depicts the distribution of human population across the globe. The data product renders global population data at the scale and extent required to demonstrate the spatial relationship of human populations and the environment across the globe. The purpose of GPWv3 is to provide a spatially disaggregated population layer that is compatible with data sets from social, economic, and Earth science fields. The gridded data set is constructed from national or subnational input units (usually administrative units) of varying resolutions. The native grid cell resolution is 2.5 arc-minutes, or ~5km at the equator, although aggregates at coarser resolutions are also provided. Separate grids are available for population count and density per grid cell. Population data estimates are provided for 1990, 1995, and 2000, and projected to 2005, 2010, and 2015.

a. Data content (data file/table name, including observation index content)

Data File Name: Liaoning Province Population and Socioeconomic Dataset (2000-2009) Main Content: This dataset mainly includes Liaoning Province's socio-economic, population and labor, agricultural production, finance, tourism, health, education, social security and other data resources. The data type is attribute data, containing 181 Excel tables:

b. Construction purpose

Used for agricultural production and socio-economic research.

c. Service object

Students and researchers engaged in related research, as well as management and teaching personnel.

d. Time range of data

Data collection period from 2000 to 2009

e. The spatial range and projection method of data

Liaoning Province

a. Data content (data file/table name, including observation index content)

The 1:250000 population spatial dataset of Heilongjiang Province (1970) reflects the population distribution of various counties and cities in Heilongjiang Province in 1970, including three indicators: agricultural population, year-end total population, and rural population.

b. Construction purpose

Mainly providing data support for the research on the current situation, characteristics, and evolution patterns of population spatial distribution in Heilongjiang Province.

c. Service object

Students and researchers engaged in research related to population geography.

d. Time range of data

1970

e. The spatial range of data

Heilongjiang Province

This project is integrating scientific research in the Arctic with education and outreach, with a strong central focus on engaging undergraduate students and visiting faculty from groups that have had little involvement in Arctic science to date. Science and society in the United States will be stronger in the long-term if the scientific workforce more closely reflects the racial, ethnic, and cultural diversity of its residents. The Arctic research community currently does not. Of the Principal Investigators funded by NSF's Arctic programs in the past five years, only 1% were African American, Hispanic, Native American, or Alaska Native. This project is catalyzing change in these demographics by engaging faculty from Minority Serving Institutions (MSIs) and a diverse group of undergraduate students in cutting-edge Arctic research and providing them encouragement, mentoring, and opportunities to continue pursuing Arctic studies in subsequent years. The central element of the project is a month-long research expedition to the Yukon River Delta in Alaska. The expedition provides a deep intellectual and cultural immersion in the context of an authentic research experience that is paramount for "hooking" students and keeping them moving along the pipeline to careers as Arctic scientists. The overarching scientific issue that drives the research is the vulnerability and fate of ancient carbon stored in Arctic permafrost (permanently frozen ground). Widespread permafrost thaw is expected to occur this century, but large uncertainties remain in estimating the timing, magnitude, and form of carbon that will be released when thawed. Project participants are working in collaborative research groups to make fundamental scientific discoveries related to the vulnerability of permafrost carbon in the Yukon River Delta and the potential implications of permafrost thaw in this region for the global climate system.

a. Data content (data file/table name, including observation index content)

The 1:250000 population spatial dataset of Liaoning Province (2000) reflects the population distribution of various counties and cities in Liaoning Province in 2000, including three indicators: agricultural population, year-end total population, and rural population.

b. Construction purpose

Mainly providing data support for the research on the current situation, characteristics, and evolution patterns of population spatial distribution in Liaoning Province.

c. Service object

Students and researchers engaged in research related to population geography.

d. Time range of data

2000

e. The spatial range of data

Liaoning Province

The dataset includes data on all fur seals tagged at Macquarie Island since 1989. The dataset includes information on the sex and species of individuals, information on their reproductive histories, resight data and tagging history.

The program began in 1986, but no data are available pre-1989.

The download file consists of a wide-range of files: an access database, a large number of excel spreadsheets, word documents, pdf files and text files. Data are contained in the access database (1994-1997) and excel spreadsheets and text files (all other years). The word documents and pdf files contain a lot of further information about the data collected in each season.

A readme document containing some general information about the datset is also part of the download file - in the top level directory.

The fields in this dataset are: date type ID number tag tagged previous tag weight digit sole width headgaurd muzzle coat belly biopsy blood milk girth length sex birth date weaning date birth mass mass at weaning date of weaning death date comments mother tag breeding father last seen year status territory

2007/2008 Season update A successful field season was undertaken at Macquarie Island during the 07/08 summer. This included maintenance of the annual surveys of pup production (DNA sampling for species identification), pup tagging and resighting of individual seals for assessment of reproductive performance and survival for long-term demographic analyses. Two publications in the journal Molecular Ecology on reproductive success of hybrids and mating strategies to limit hybridisation were produced, and the preparation of a major manuscript on the colonisation, status and trends in abundance of the three fur seal species at Macquarie Island has been completed and will be submitted shortly.

Progress has been made of three main fronts: 1. Completed field season at Macquarie Island and maintenance of the annual surveys of pup production (DNA sampling for species identification), pup tagging and resighting of individual seals for assessment of reproductive performance and survival for long-term demographic analyses. 2. Two publications in the journal Molecular Ecology on reproductive success of hybrids and mating strategies to limit hybridisation, 3. The preparation of a major manuscript on the colonisation, status and trends in abundance of the three fur seal species at Macquarie Island. We plan to make significant developments in demographic database management and analyses over the 08/09.

Taken from the 2008-2009 Progress Report: Project objectives: Background 1986-2008 The 'conservation and management of fur seals in the Antarctic marine ecosystem' research program (hereafter referred to as "the fur seal program") aims to provide key performance measures for recovering fur seal populations, and key Antarctic State of the Environment indicators, to monitor how biological and physical oceanographic change in Southern Ocean ecosystems, effects the reproductive performance of high trophic-level predators such as fur seals. Fur seals were the most heavily exploited of all of the Antarctic marine biota, and populations on both of Australia's subantarctic islands (Macquarie and the Heard and MacDonald Islands, HIMI), have yet to recover to pre-sealing numbers.

Over the last twenty years (1986-2007), research undertaken on this and former programs (managed by Dr Peter Shaughnessy) have aimed to provide information on: - the population status and ecology of recovering fur seal populations on Macquarie and Heard Islands, - species identification and composition, - the extent, trends, processes and implications of hybridisation among fur seals at Macquarie Island, - the impact of commercial sealing on the genetic variation and population structure of southern ocean fur seal populations, - the foraging ecology and lactation strategies of fur seals at Heard and Macquarie Islands, - the trophic linkages between fur seals and commercial fisheries at Macquarie and Heard Islands, and - how physical and biological oceanographic changes affect the reproductive performance of fur seals.

The fur seal program has successfully achieved these aims, and in doing so made significant contributions to implementing the many milestones of Australia's Antarctic Science Strategy (both past and present). In addition, the program has provided important advice on the conservation and management of Southern Ocean fur seal populations and marine systems, including: - providing information to Australian Fisheries Management Authority (AFMA) to assist ecological sustainable development (ESD) of the Patagonian toothfish fisheries around Macquarie and Heard Islands. - proving information to Environment Australia (now DEWR) on the distribution of fur seal foraging effort to assist planning and development of the Macquarie Island Marine Park. - providing specific data on the status of the subantarctic fur seal at Macquarie Island to DEWR, and assisting as a member of the subantarctic fur seal Recovery Team. - providing regular updates on the status of fur seal populations at Macquarie and Heard Islands to the SCAR Expert Group on Seals. - reporting to the Antarctic State of the Environment (Indicator 32).

The fur seal program is now one of the longest standing ongoing biological studies supported by the Australian Antarctic Division, providing an important time-series of population recovery following human exploitation, and most recently (after identification of sensitive demographic responses to small changes in sea surface temperatures), important ecological performance indicators and reference points that provide some of the best examples of how climate change may impact high trophic-level predator populations in the Southern Ocean.

The next five years (2008-2012) Over the next five years, the fur seal program aims to build on the above successes and continue core aspects population monitoring and demography. There will be a continued focus on undertaking research with clear applied management applications and a strong strategic focus targeting specific priorities of Australia's Antarctic Science Program Science Strategy. Applied applications include ESD of fisheries, MPA management and planning, acting on research and management priorities set out in the Department of the Environment and Heritage "The Action Plan for Australian Seals", the Recovery Plans for the Subantarctic fur seal and Antarctic State of the Environment reporting (SEO Indicator No. 32). All of these are in accord with and will help implement Australia's Oceans Policy.

The last five years of the fur seal program have seen considerable advancement in our understanding of the extent, trends and processes that facilitate and limit hybridisation among the three fur seal species at Macquarie Island. We have also identified highly significant relationships between fur seal reproductive success (fecundity and pup growth rates) and small changes to local sea surface temperature (STT) north of Macquarie Island associated with the subantarctic front. We also have a considerable data base on the survival and reproductive success of known-aged animals extending back to the early 1990s, and because of significant progress in developing molecular methods for identification of species and hybrids over the last five years, we now also have detailed genotype data for a large proportion of these seals (approx. 1,300).

With these data sets and knowledge, the focus of the fur seal program over the next five years will be to integrate molecular, demographic and oceanographic data to provide further insights into the how climatic and oceanographic changes in the Southern Ocean affect fur seal population on both annual and lifetime scales. The specific aims of the fur seal program will be to:

1. Maintain the population monitoring programs at Macquarie and Heard Islands
2. Maintain the long-term demographic program at Macquarie Island and analysis of data to determine age-specific survival and fecundity rates for each species and determine the reproductive costs of hybridisation.
3. Calculate annual changes in foraging ecology, survival, recruitment, reproductive rates and pup growth, and relate these to annual changes in regional oceanography.

The scientific relevance of these objectives are detailed below.

Progress against objectives: Progress has been made of three main fronts: 1. Field season at Macquarie Island during the 2008/09 summer has been completed. This included maintenance of the annual surveys of pup production (DNA sampling for species identification), pup tagging and resighting of individual seals for assessment of reproductive performance and survival for long-term demographic analyses. 2. A publication titled: "Fur seals at Macquarie Island: post-sealing colonisation, trends in abundance and hybridisation of three fur seals species" has been accepted for publication in Polar Biology. 3. Some database maintenance has been undertaken on the demographic database.

Taken from the 2010-2011 Progress Report: Public summary of the season progress: A successful field season was undertaken at Macquarie Island during the 10/11 season. This included maintenance of the annual surveys of pup production (DNA sampling for species identification), pup tagging and resighting of individual seals for assessment of reproductive performance and survival for long-term demographic analyses. A total of 255 pups were recorded this season, about an 8% increases since the 2009/10 season and more than any previous year since recolonisation. A new PhD program has commenced this year the focus will be analyses of the 25 year demographic dataset, and the impacts of climate change on population recovery.

This model product contains the source code for the Ecosystem Demography Model (ED version 1.0) as well as model input and output data files for the conterminous United States. The ED is a mechanistic ecosystem model built around established sub-models of leaf level physiology, organic matter decomposition, hydrology, and functional biodiversity. It was used herein to estimate ecosystem carbon stocks and fluxes in the conterminous U.S. at 1.0 degree resolution from 1700 to 1990. Output data of carbon stocks and fluxes are stored in NetCDF format.

To produce the U.S. scenario, ED was run from an estimated state of ecosystems in the year 1700 to an estimated state of ecosystems in the year 1990 for each 1 degree by 1 degree grid cell through time using ISLSCP Initiative I climate and soil data and a gridded land-use history reconstruction as inputs (Hurtt et al., 2002). The land-use history was based on several sources including: spatial distribution of potential vegetation in 1700, spatial patterns of cropland from 1700 to 1990, regional estimates of land use and logging from 1700 to 1990, and U.S. Forest Inventory and Analysis (FIA) data on the current age distribution of forest stands. The Miami Land Use History Model (Miami-LU), a far simpler empirically-based ecosystem model, was used to track the history of disturbance, land use, fire, and ecosystem recovery. The effects of fire suppression were also included. Atmospheric CO2 concentrations and climatic conditions were held constant throughout the runs to focus on the consequences of land-use and fire-management changes on carbon stocks and fluxes.

CHCP Overview:The human behavior and brain are shaped by genetic, environmental and cultural interactions. Recent advances in neuroimaging integrate multimodal imaging data from a large population and start to explore the large-scale structural and functional connectomic architectures of the human brain. One of the major pioneers is the Human Connectome Project (HCP) that developed sophisticated imaging protocols and has built a collection of high-quality multimodal neuroimaging, behavioral and genetic data from US population. A large-scale neuroimaging project parallel to the HCP, but with a focus on the East Asian population, will allow comparisons of brain-behavior associations across different ethnicities and cultures. The Chinese Human Connectome Project (CHCP) is launched in 2017 and led by Professor Jia-Hong GAO at Peking University, Beijing, China. CHCP aims to provide large sets of multimodal neuroimaging, behavioral and genetic data on the Chinese population that are comparable to the data of the HCP. The CHCP protocols were almost identical to those of the HCP, including the procedure for 3T MRI scanning, the data acquisition parameters, and the task paradigms for functional brain imaging. The CHCP also collected behavioral and genetic data that were compatible with the HCP dataset. The first public release of the CHCP dataset is in 2022. CHCP dataset includes high-resolution structural MR images (T1W and T2W), resting-state fMRI (rfMRI), task fMRI (tfMRI), and high angular resolution diffusion MR images (dMRI) of the human brain as well as behavioral data based on Chinese population. The unprocessed "raw" images of CHCP dataset (about 1.85 TB) have been released on the platform and can be downloaded. Considering our current cloud-storage service, sharing full preprocessed images (up to 70 TB) requires further construction. We will be actively cooperating with researchers who contact us for academic request, offering case-by-case solution to access the preprocessed data in a timely manner, such as by mailing hard disks or a third-party trusted cloud-storage service. V2 Release (Date: January 16, 2023):Here, we released the seven major domains task fMRI EVs files, including: 1) visual, motion, somatosensory, and motor systems; 2) category specific representations; 3) working memory/cognitive control systems; 4) language processing (semantic and phonological processing); 5) social cognition (Theory of Mind); 6) relational processing; and 7) emotion processing.V3 Release (Date: January 12, 2024):This version of data release primarily discloses the CHCP raw MRI dataset that underwent “HCP minimal preprocessing pipeline”, located in CHCP_ScienceDB_preproc folder (about 6.90 TB). In this folder, preprocessed MRI data includes T1W, T2W, rfMRI, tfMRI, and dMRI modalities for all young adulthood participants, as well as partial results for middle-aged and older adulthood participants in the CHCP dataset. Following the data sharing strategy of the HCP, we have eliminated some redundant preprocessed data, resulting in a final total size of the preprocessed CHCP dataset is about 6.90 TB in zip files. V4 Release (Date: December 4, 2024):In this update, we have fixed the issue with the corrupted compressed file of preprocessed data for subject 3011, and removed the incorrect preprocessed results for subject 3090. Additionally, we have updated the subject file information list. Additionally, this release includes the update of unprocessed "raw" images of the CHCP dataset in CHCP_ScienceDB_unpreproc folder (about 1.85 TB), addressing the previously insufficient anonymization of T1W and T2W modalities data for some older adulthood participants in versions V1 and V2. For more detailed information, please refer to the data descriptions in versions V1 and V2.CHCP Summary:Subjects:366 healthy adults (Chinese Han)Imaging Scanner:3T MR (Siemens Prisma)Institution:Peking University, Beijing, ChinaFunding Agencies:Beijing Municipal Science & Technology CommissionChinese Institute for Brain Research (Beijing)National Natural Science Foundation of ChinaMinistry of Science and Technology of China CHCP Citations:Papers, book chapters, books, posters, oral presentations, and all other printed and digital presentations of results derived from CHCP data should contain the following wording in the acknowledgments section: "Data were provided [in part] by the Chinese Human Connectome Project (CHCP, PI: Jia-Hong Gao) funded by the Beijing Municipal Science & Technology Commission, Chinese Institute for Brain Research (Beijing), National Natural Science Foundation of China, and the Ministry of Science and Technology of China."

Aim: Many studies use differences among plant populations to infer future plant responses, but these predictions will only provide meaningful insights if plasticity patterns among populations are similar (i.e. in the absence of Population-by-Environment interaction, P × E). In this study, we test whether P × E is considered in climate change studies. We evaluated whether population differentiation varies across environments and is determined by aspects of the study system and experimental design.

Location: Global.

Methods: We conducted a literature search in the Web of Science database to identify studies assessing population differentiation in a climate change context. We quantified P × E and performed a meta-analysis to calculate the percentage of traits showing P × E in the study cases.

Results: We identified 309 study cases (from 237 articles) assessing population differentiation in 172 plant species, of which 64 % included more than one test environment and tested P × E. In 77% of these, P × E was significant for at least one functional trait. The overall proportion of traits showing P × E was 33.4% (CI 27.7-39.3). These results were generally consistent across life forms, ecoregions and type of experiment. Furthermore, population differentiation varied across test environments in 76% of cases. The overall proportion of traits showing environment-dependent population differentiation was 53.7 % (CI 37.9-69.3).

Conclusions: Our findings revealed that differences in phenotypic plasticity among populations are common, but are usually neglected to forecast population responses to climate change. Future studies should assess population differentiation in multiple test environments that realistically reflect future environmental conditions, assessing climate change drivers that are rarely considered (e.g. multifactor experiments). Our review also revealed the predominant focus of population studies on trees from temperate climates, identifying underexplored life forms, phylogenetic groups and ecoregions that should receive more future attention.

The Human Influence Index Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is derived from the LWP-2 Human Footprint Dataset. The gridded data are classified according to their raster value (wild = 0-10; not wild >10). The ten largest polygons of more than 5 square kilometers within each biome by realm are selected and identified. The data set was produced by the Wildlife Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN). The global and continental data are available. The purpose of the data set is to provide an updated map of wild areas in geographic projection which can be used in wildlife conservation planning, natural resource management, and research on human-environment interactions.