A collection of population life tables covering a multitude of countries and many years. Most of the HLD life tables are life tables for national populations, which have been officially published by national statistical offices. Some of the HLD life tables refer to certain regional or ethnic sub-populations within countries. Parts of the HLD life tables are non-official life tables produced by researchers. Life tables describe the extent to which a generation of people (i.e. life table cohort) dies off with age. Life tables are the most ancient and important tool in demography. They are widely used for descriptive and analytical purposes in demography, public health, epidemiology, population geography, biology and many other branches of science. HLD includes the following types of data: * complete life tables in text format; * abridged life tables in text format; * references to statistical publications and other data sources; * scanned copies of the original life tables as they were published. Three scientific institutions are jointly developing the HLD: the Max Planck Institute for Demographic Research (MPIDR) in Rostock, Germany, the Department of Demography at the University of California at Berkeley, USA and the Institut national d''��tudes d��mographiques (INED) in Paris, France. The MPIDR is responsible for maintaining the database.

Life table data, and derived quantities, for Equilibrium Conditions in the Evolution of Senescence (Bahry, 2022, MSc thesis); adapted from the supplementary data of (Jones et al., 2014). Life table data for human (Japan 2009), human (Aché hunter-gatherer), fruit fly, Soay sheep, freshwater hydra, and desert tortoise.

Basic life table quantities: age interval ((X)); survival function ((l_X)); and age-specific interval fecundity ((m_X)). Derived quantities include interval average force of mortality; reproductive value; residual reproductive value; Hamilton's indicators of the age-specific forces of selection; and actual age-specific mortality vs. predicted age-specific mortality based on models treated in (Bahry, 2022).

In the original life tables of Jones et al. (2014), desert tortoises negatively senesce over the range of observed ages, but had a final observed cut-off age of 74; this causes reproductive value to artifactually fall to 0 as age-approached the cutoff. To get around this, I also used an extrapolated desert tortoise life table, assuming the age-74 mortality and fecundity rates remained constant until age 1000, then using the extrapolated life table to calculate reproductive value (and Hamilton's indicators) up to the cutoff age 74.

References

Bahry, D. (2022). Equilibrium Conditions in the Evolution of Senescence [Master's thesis, Carleton University].

Jones, O. R. et al. (2014). Diversity of ageing across the tree of life. Nature 505: 169–174. https://doi.org/10.1038/nature12789

Life table data for "Bounce backs amid continued losses: Life expectancy changes since COVID-19"

cc-by Jonas Schöley, José Manuel Aburto, Ilya Kashnitsky, Maxi S. Kniffka, Luyin Zhang, Hannaliis Jaadla, Jennifer B. Dowd, and Ridhi Kashyap. "Bounce backs amid continued losses: Life expectancy changes since COVID-19".

These are CSV files of life tables over the years 2015 through 2021 across 29 countries analyzed in the paper "Bounce backs amid continued losses: Life expectancy changes since COVID-19".

40-lifetables.csv

Life table statistics 2015 through 2021 by sex, region and quarter with uncertainty quantiles based on Poisson replication of death counts. Actual life tables and expected life tables (under the assumption of pre-COVID mortality trend continuation) are provided.

30-lt_input.csv

Life table input data.

id: unique row identifier

region_iso: iso3166-2 region codes

sex: Male, Female, Total

year: iso year

age_start: start of age group

age_width: width of age group, Inf for age_start 100, otherwise 1

nweeks_year: number of weeks in that year, 52 or 53

death_total: number of deaths by any cause

population_py: person-years of exposure (adjusted for leap-weeks and missing weeks in input data on all cause deaths)

death_total_nweeksmiss: number of weeks in the raw input data with at least one missing death count for this region-sex-year stratum. missings are counted when the week is implicitly missing from the input data or if any NAs are encounted in this week or if age groups are implicitly missing for this week in the input data (e.g. 40-45, 50-55)

death_total_minnageraw: the minimum number of age-groups in the raw input data within this region-sex-year stratum

death_total_maxnageraw: the maximum number of age-groups in the raw input data within this region-sex-year stratum

death_total_minopenageraw: the minimum age at the start of the open age group in the raw input data within this region-sex-year stratum

death_total_maxopenageraw: the maximum age at the start of the open age group in the raw input data within this region-sex-year stratum

death_total_source: source of the all-cause death data

death_total_prop_q1: observed proportion of deaths in first quarter of year

death_total_prop_q2: observed proportion of deaths in second quarter of year

death_total_prop_q3: observed proportion of deaths in third quarter of year

death_total_prop_q4: observed proportion of deaths in fourth quarter of year

death_expected_prop_q1: expected proportion of deaths in first quarter of year

death_expected_prop_q2: expected proportion of deaths in second quarter of year

death_expected_prop_q3: expected proportion of deaths in third quarter of year

death_expected_prop_q4: expected proportion of deaths in fourth quarter of year

population_midyear: midyear population (July 1st)

population_source: source of the population count/exposure data

death_covid: number of deaths due to covid

death_covid_date: number of deaths due to covid as of

death_covid_nageraw: the number of age groups in the covid input data

ex_wpp_estimate: life expectancy estimates from the World Population prospects for a five year period, merged at the midpoint year

ex_hmd_estimate: life expectancy estimates from the Human Mortality Database

nmx_hmd_estimate: death rate estimates from the Human Mortality Database

nmx_cntfc: Lee-Carter death rate projections based on trend in the years 2015 through 2019

Deaths

source:

STMF input data series (https://www.mortality.org/Public/STMF/Outputs/stmf.csv)

ONS for GB-EAW pre 2020

CDC for US pre 2020

STMF:

harmonized to single ages via pclm

pclm iterates over country, sex, year, and within-year age grouping pattern and converts irregular age groupings, which may vary by country, year and week into a regular age grouping of 0:110

smoothing parameters estimated via BIC grid search seperately for every pclm iteration

last age group set to [110,111)

ages 100:110+ are then summed into 100+ to be consistent with mid-year population information

deaths in unknown weeks are considered; deaths in unknown ages are not considered

ONS:

data already in single ages

ages 100:105+ are summed into 100+ to be consistent with mid-year population information

PCLM smoothing applied to for consistency reasons

CDC:

The CDC data comes in single ages 0:100 for the US. For 2020 we only have the STMF data in a much coarser age grouping, i.e. (0, 1, 5, 15, 25, 35, 45, 55, 65, 75, 85+). In order to calculate life-tables in a manner consistent with 2020, we summarise the pre 2020 US death counts into the 2020 age grouping and then apply the pclm ungrouping into single year ages, mirroring the approach to the 2020 data

Population

source:

for years 2000 to 2019: World Population Prospects 2019 single year-age population estimates 1950-2019

for year 2020: World Population Prospects 2019 single year-age population projections 2020-2100

mid-year population

mid-year population translated into exposures:

if a region reports annual deaths using the Gregorian calendar definition of a year (365 or 366 days long) set exposures equal to mid year population estimates

if a region reports annual deaths using the iso-week-year definition of a year (364 or 371 days long), and if there is a leap-week in that year, set exposures equal to 371/364*mid_year_population to account for the longer reporting period. in years without leap-weeks set exposures equal to mid year population estimates. further multiply by fraction of observed weeks on all weeks in a year.

COVID deaths

source: COVerAGE-DB (https://osf.io/mpwjq/)

the data base reports cumulative numbers of COVID deaths over days of a year, we extract the most up to date yearly total

External life expectancy estimates

source:

World Population Prospects (https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/CSV_FILES/WPP2019_Life_Table_Medium.csv), estimates for the five year period 2015-2019

Human Mortality Database (https://mortality.org/), single year and age tables

Life Table Data: Field-based, partial life table data for immature stages of Bemisia tabaci on cotton in Maricopa, Arizona, USA. Data were generated on approximately 200 individual insects per cohort with 2-5 cohorts per year for a total of 44 cohorts between 1997 and 2010. Data provide the marginal, stage-specific rates of mortality for eggs, and 1st, 2nd, 3rd, and 4th instar nymphs. Mortality is characterized as caused by inviability (eggs only), dislodgement, predation, parasitism and unknown. Detailed methods can be found in Naranjo and Ellsworth 2005 (Entomologia Experimentalis et Applicata 116(2): 93-108). The method takes advantage of the sessile nature of immature stages of this insect. Briefly, an observer follows individual eggs or settled first instar nymphs from natural populations on the underside of cotton leaves in the field with a hand lens and determines causes of death for each individual over time. Approximately 200 individual eggs and nymphs are observed for each cohort. Separately, densities of eggs and nymphs are monitored with standard methods (Naranjo and Flint 1994, Environmental Entomology 23: 254-266; Naranjo and Flint 1995, Environmental Entomology 24: 261-270) on a weekly basis. Matrix Model Data: Life table data were used to provide parameters for population matrix models. Matrix models contain information about stage-specific rates for development, survival and reproduction. The model can be used to estimate overall population growth rate and can also be analyzed to determine which life stages contribute the most to changes in growth rates. Resources in this dataset:Resource Title: Matrix model data from Naranjo, S.E. (2017) Retrospective analysis of a classical biological control program. Journal of Applied Ecology. File Name: MatrixModelData.xlsxResource Description: Life table data were used to provide parameters for population matrix models. Matrix models contain information about stage-specific rates for development, survival and reproduction. The model can be used to estimate overall population growth rate and can also be analyzed to determine which life stages contribute the most to changes in growth rates. Resource Title: Data Dictionary: Life table data. File Name: DataDictionary_LifeTableData.csvResource Title: Life table data from Naranjo, S.E. (2017) Retrospective analysis of a classical biological control program. Journal of Applied Ecology. File Name: LifeTableData.xlsxResource Description: Field-based, partial life table data for immature stages of Bemisia tabaci on cotton in Maricopa, Arizona, USA. Data were generated on approximately 200 individual insects per cohort with 2-5 cohorts per years for a total of 44 cohorts between 1997 and 2010. Data provide the marginal, stage-specific rates of mortality for eggs, and 1st, 2nd, 3rd, and 4th instar nymphs. Mortality is characterized as caused by inviability (eggs only), dislodgement, predation, parasitism and unknown. Detailed methods can be found in Naranjo and Ellsworth 2005 (Entomologia, Experimentalis et Applicata 116: 93-108). The method takes advantage of the sessile nature of immature stages of this insect. Briefly, an observer follows individual eggs or settled first instar nymphs from natural populations on the underside of cotton leaves in the field with a hand lens and determines causes of death for each individual over time. Approximately 200 individual eggs and nymphs are observed for each cohort. Separately, densities of eggs and nymphs are monitored with standard methods (Naranjo and Flint 1994, Environmental Entomology 23: 254-266; Naranjo and Flint 1995, Environmental Entomology 24: 261-270) on a weekly basis. Resource Title: Life table data from Naranjo, S.E. (2017) Retrospective analysis of a classical biological control program. Journal of Applied Ecology. File Name: LifeTableData.csvResource Description: CSV version of the data. Field-based, partial life table data for immature stages of Bemisia tabaci on cotton in Maricopa, Arizona, USA. Data were generated on approximately 200 individual insects per cohort with 2-5 cohorts per years for a total of 44 cohorts between 1997 and 2010. Data provide the marginal, stage-specific rates of mortality for eggs, and 1st, 2nd, 3rd, and 4th instar nymphs. Mortality is characterized as caused by inviability (eggs only), dislodgement, predation, parasitism and unknown. Detailed methods can be found in Naranjo and Ellsworth 2005 (Entomologia, Experimentalis et Applicata 116: 93-108). The method takes advantage of the sessile nature of immature stages of this insect. Briefly, an observer follows individual eggs or settled first instar nymphs from natural populations on the underside of cotton leaves in the field with a hand lens and determines causes of death for each individual over time. Approximately 200 individual eggs and nymphs are observed for each cohort. Separately, densities of eggs and nymphs are monitored with standard methods (Naranjo and Flint 1994, Environmental Entomology 23: 254-266; Naranjo and Flint 1995, Environmental Entomology 24: 261-270) on a weekly basis.

ABSTRACT Life tables have been elaborated throughout much of human history. However, the first life table to use actuarial concepts was only constructed in 1815 by Milne for the city of Carlisle in England. Since then, numerous tables have been elaborated for different regions and countries, due to their crucial importance for analyzing various types of problems covering a vast range of possibilities, from actuarial studies to forecasting and evaluating demands in order to define public policies. The most common problem nowadays in an actuarial calculation is choosing a suitable table for a given population. Brazil has few specific tables for the pensions market and has been using imported tables that refer to other countries, with different cultures and different mortality experiences. Using data from the Integrated Human Resource Administration System, this table constructs life tables for Executive branch federal civil servants for the period from 1993 to 2014, disaggregated for sex, age, and educational level (high school and university). The international literature has recognized differences in mortality due to sex, socioeconomic differences, and occupation. The creation of the Complementary Pension Foundation for Federal Public Servants in 2013 requires specific mortality tables for this population to support actuarial studies, healthcare, and personnel policies. A mathematical equation is fitted to the data. This equation can be broken down into infant mortality (not present in the data), mortality from external causes, and mortality from senescence. Recent results acknowledging an upper limit for old age mortality are incorporated into the adjusted probabilities of death. Assuming a binomial distribution for deaths, the deviance was used as a figure of merit to evaluate the goodness of fit of the observed data both to a set of tables used by the insurance/pensions market and to the adjusted tables.

Following the publication of the period and cohort life expectancy tables ONS prepares databases for the UK and each of the constituent countries containing mortality data used in the calculation of historic and projected life tables. Published for the first time in this release are tables of historic and projected qx (probability of dying at each age) and lx values (numbers of people surviving at each age) for the UK, on a period and cohort basis for each year 1951 to 2060.

Source agency: Office for National Statistics

Designation: Official Statistics not designated as National Statistics

Language: English

Alternative title: qx and lx tables

Field-based, partial life table data for immature stages of silverleaf whitefly, Bemisia argentifolii, on 6 host plants including alfalfa, broccoli, spring and fall cantaloupe, cotton, ornamental lantana, and several species of annual weeds in Maricopa, Marana and Yuma Arizona, USA. Data were collected on a total of 73 individual cohorts (each replicated 4 times) from November 2000 to April 2003 at all three study sites. For each cohort, data were generated on approximately 400 individual insects (200 eggs and 200 first instar nymphs). Data provide the marginal, cause-specific mortality for eggs, and 1st, 2nd, 3rd, and 4th instar nymphs collectively and stage-specific marginal mortality for each stage over all causes. Mortality was characterized as caused by inviability (eggs only), dislodgement, predation, parasitism (nymphs only), desiccation, and unknown. Detailed methods can be found in Naranjo and Ellsworth 2005 (Entomologia Experimentalis et Applicata 116(2): 93-108; https://doi.org/10.1111/j.1570-7458.2005.00297.x ; and Naranjo and Ellsworth 2017 (Journal of Visualized Experiments, 129; https://doi.org/10.3791/56150). The method takes advantage of the sessile nature of immature stages of this insect. Briefly, an observer follows individual eggs or settled first instar nymphs from natural populations on the underside of host plant leaves in the field with a hand lens and determines causes of death for each individual over time. Weather data was monitored using the University of Arizona AzMet system. Note that these life table data do not include adult mortality or reproduction. The life table data were used to generate survivorship curves for each cohort on each host plant based on a physiological time scale of accumulated degree-days above 10C from the initiation of each cohort. Resources in this dataset:Resource Title: Mortality causes over all immature life stages. File Name: MortalityCause.xlsxResource Description: Data of mortality causes over all immature life stages.Resource Title: Mortality of each immature life stage over all causes. File Name: MortalityStage.xlsxResource Description: Data for mortality of each immature life stage over all causes.Resource Title: Data for pest survivorship curves on each host plant. File Name: SurvivalCurves.xlsxResource Description: Data to generate survivorship curves for pest on each host plant.

The U.S. Small-area Life Expectancy Estimates Project (USALEEP) is a partnership of NCHS, the Robert Wood Johnson Foundation (RWJF)External, and the National Association for Public Health Statistics and Information Systems (NAPHSIS)External to produce a new measure of health for where you live. The USALEEP project produced estimates of life expectancy at birth—the average number of years a person can expect to live—for most of the census tracts in the United States for the period 2010-2015. These estimates were published in September, 2018."A growing body of research is recognizing the importance of measuring mortality outcomes in small geographic areas, such as U.S. census tracts, to identify health disparities within a population. The indicator most widely identified as the ideal measure of a population’s mortality experience is life expectancy at birth. The concept of life expectancy is intuitive and easily understood by both policymakers and the lay public. Life expectancy is estimated for national populations by most developed countries, including the United States, which has produced the estimate annually since 1945 and decennially since 1900. However, its calculation is relatively complex compared with that of other summary mortality measures, because it entails the calculation of six distinct functions and requires a minimum number of age groups and total population size, below\ which the estimates become unstable and unreliable." - USALEEP Methodology Summary The methodology used to calculate the U.S. censustract abridged life tables consisted of several stages. First, through a collaboration between the National Vital Statistics System registration areas and the National Center for Health Statistics, death records of U.S. residents (excluding residents of Maine and Wisconsin) for deaths occurring in 2010 through 2015 were geocoded using decedents’ residential addresses to identify and code census tracts. Second, population estimates were produced based on the 2010 decennial census and the 2011–2015 American Community Survey 5-year survey. Third, a methodology that combined standard demographic techniques and statistical modeling was developed to address challenges posed by small population sizes and small and missing age-specific death counts. Last, standard, abridged life table methods were adjusted to account for error introduced by population estimates based on sample data. To review the full methodology, please use the following link: https://www.cdc.gov/nchs/data/series/sr_02/sr02_181.pdf

age1results of first life table experimentage2results of second life table experimentage3results of third life table experimentage4results of hybrid experiment, also labelled experiment 4

BackgroundThe human immunodeficiency virus (HIV) has caused a lot of havoc since the early 1970s, affecting 37.6 million people worldwide. The 90-90-90 treatment policy was adopted in Ghana in 2015 with the overall aim to end new infections by 2030, and to improve the life expectancy of HIV seropositive individuals. With the scale-up of Highly Active Antiretroviral Therapy, the lifespan of People Living with HIV (PLWH) on antiretrovirals (ARVs) is expected to improve. In rural districts in Ghana, little is known about the survival probabilities of PLWH on ARVs. Hence, this study was conducted to estimate the survival trends of PLWH on ARVs.MethodsA retrospective evaluation of data gathered across ARV centres within Tatale and Zabzugu districts in Ghana from 2016 to 2020 among PLWH on ARVs. A total of 261 participants were recruited for the study. The data was analyzed using STATA software version 16.0. Lifetable analysis and Kaplan-Meier graph were used to assess the survival probabilities. “Stptime” per 1000 person-years and the competing risk regression were used to evaluate mortality rates and risk.ResultsThe cumulative survival probability was 0.8847 (95% CI: 0.8334–0.9209). The overall mortality rate was 51.89 (95% CI: 36.89–72.97) per 1000 person-years. WHO stage III and IV [AHR: 4.25 (95%CI: 1.6–9.71) p = 0.001] as well as age group (50+ years) [AHR: 5.02 (95% CI: 1.78–14.13) p = 0.002] were associated with mortality.ConclusionSurvival probabilities were high among the population of PLWH in Tatale and Zabzugu with declining mortality rates. Clinicians should provide critical attention and care to patients at HIV WHO stages III and IV and intensify HIV screening at all entry points since early diagnosis is associated with high survival probabilities.

Life tables

Zooplankton have evolved several mechanisms to deal with environmental threats, such as ultraviolet radiation (UVR), and in order to identify strategies inherent to organisms exposed to different UVR environments, we here examine life-history traits of two lineages of Daphnia pulex. The lineages differed in the UVR dose they had received at their place of origin from extremely high UVR stress at high-altitude Bolivian lakes to low UVR stress near the sea level in temperate Sweden. Nine life-history variables of each lineage were analyzed in laboratory experiments in the presence and the absence of sub-lethal doses of UVR (UV-A band), and we identified trade-offs among variables through structural equation modeling (SEM). The UVR treatment was detrimental to almost all life-history variables of both lineages; however, the Daphnia historically exposed to higher doses of UVR (HighUV) showed a higher overall fecundity than those historically exposed to lower doses of UVR (LowUV). The total offspring and ephippia production, as well as the number of clutches and number of offspring at first reproduction, was directly affected by UVR in both lineages. Main differences between lineages involved indirect effects that affected offspring production as the age at first reproduction. We here show that organisms within the same species have developed different strategies as responses to UVR, although no increased physiological tolerance or plasticity was shown by the HighUV lineage. In addition to known tolerance strategies to UVR, including avoidance, prevention, or repairing of damages, we here propose a population strategy that includes early reproduction and high fertility, which we show compensated for the fitness loss imposed by UVR stress.

BackgroundWhile combination antiretroviral therapy (cART) has significantly improved survival times for persons diagnosed with HIV, estimation of life expectancy (LE) for this cohort remains a challenge, as mortality rates are a function of both time since diagnosis and age, and mortality rates for the oldest age groups may not be available.MethodsA validated case-finding algorithm for HIV was used to update the cohort of HIV-positive adults who had entered care in Ontario, Canada as of 2012. The Chiang II abridged life table algorithm was modified to use mortality rates stratified by time since entering the cohort and to include various methods for extrapolation of the excess HIV mortality rates to older age groups.ResultsAs of 2012, there were approximately 15,000 adults in care for HIV in Ontario. The crude all-cause mortality rate declined from 2.6% (95%CI 2.3, 2.9) per year in 2000 to 1.3% (1.2, 1.5) in 2012. Mortality rates were elevated for the first year of care compared to subsequent years (rate ratio of 2.6 (95% CI 2.3, 3.1)). LE for a 20-year old living in Ontario was 62 years (expected age at death is 82), while LE for a 20-year old with HIV was estimated to be reduced to 47 years, for a loss of 15 years of life. Ignoring the higher mortality rates among new cases introduced a modest bias of 1.5 additional years of life lost. In comparison, using 55+ as the open-ended age group was a major source of bias, adding 11 years to the calculated LE.ConclusionsUse of age limits less than the expected age at death for the open-ended age group significantly overstates the estimated LE and is not recommended. The Chiang II method easily accommodated input of stratified mortality rates and extrapolation of excess mortality rates.

File Name: MatrixModelData.xlsxResource Description: Life table data were used to provide parameters for population matrix models. Matrix models contain information about stage-specific rates for development, survival and reproduction. The model can be used to estimate overall population growth rate and can also be analyzed to determine which life stages contribute the most to changes in growth rates. Resource Title: Data Dictionary: Life table data. File Name: DataDictionary_LifeTableData.csvResource Title: Life table data from Naranjo, S.E. (2017) Retrospective analysis of a classical biological control program. Journal of Applied Ecology. File Name: LifeTableData.xlsxResource Description: Field-based, partial life table data for immature stages of Bemisia tabaci on cotton in Maricopa, Arizona, USA. Data were generated on approximately 200 individual insects per cohort with 2-5 cohorts per years for a total of 44 cohorts between 1997 and 2010. Data provide the marginal, stage-specific rates of mortality for eggs, and 1st, 2nd, 3rd, and 4th instar nymphs. Mortality is characterized as caused by inviability (eggs only), dislodgement, predation, parasitism and unknown. Detailed methods can be found in Naranjo and Ellsworth 2005 (Entomologia, Experimentalis et Applicata 116: 93-108). The method takes advantage of the sessile nature of immature stages of this insect. Briefly, an observer follows individual eggs or settled first instar nymphs from natural populations on the underside of cotton leaves in the field with a hand lens and determines causes of death for each individual over time. Approximately 200 individual eggs and nymphs are observed for each cohort. Separately, densities of eggs and nymphs are monitored with standard methods (Naranjo and Flint 1994, Environmental Entomology 23: 254-266; Naranjo and Flint 1995, Environmental Entomology 24: 261-270) on a weekly basis. Resource Title: Life table data from Naranjo, S.E. (2017) Retrospective analysis of a classical biological control program. Journal of Applied Ecology. File Name: LifeTableData.csvResource Description: CSV version of the data. Field-based, partial life table data for immature stages of Bemisia tabaci on cotton in Maricopa, Arizona, USA. Data were generated on approximately 200 individual insects per cohort with 2-5 cohorts per years for a total of 44 cohorts between 1997 and 2010. Data provide the marginal, stage-specific rates of mortality for eggs, and 1st, 2nd, 3rd, and 4th instar nymphs. Mortality is characterized as caused by inviability (eggs only), dislodgement, predation, parasitism and unknown. Detailed methods can be found in Naranjo and Ellsworth 2005 (Entomologia, Experimentalis et Applicata 116: 93-108). The method takes advantage of the sessile nature of immature stages of this insect. Briefly, an observer follows individual eggs or settled first instar nymphs from natural populations on the underside of cotton leaves in the field with a hand lens and determines causes of death for each individual over time. Approximately 200 individual eggs and nymphs are observed for each cohort. Separately, densities of eggs and nymphs are monitored with standard methods (Naranjo and Flint 1994, Environmental Entomology 23: 254-266; Naranjo and Flint 1995, Environmental Entomology 24: 261-270) on a weekly basis.

Global changes in temperature and rainfall are provoking increasingly prolonged periods of drought stress, which results in forest dieback. Potential mechanisms of plant mortality under drought include damages to the hydraulic system and the depletion of nonstructural carbohydrates (NSC). We designed an experiment on seedlings of 12 tree species to assess the mechanisms behind plan mortality under drought. The experiment consisted in a first phase of light manipulation where individual plants were alternatively placed under light or shade aiming to manipulate carbohydrate contents. Subsequently, we started a drought phase until full mortality was reached. Plant water potential (predawn and midday) was measured weekly during the experiment. NSC contents were measured in five different harvest campaigns along the experiment. The database is made of four different tables. The first table (‘Mortality_Percentage’) contains data of plant mortality over time. Mortality was visually assessed by the percentage of dead leaves per plant, evaluated by observing changes in leaf coloration. The second table (‘Mortality_Kaplan_Meier’) contains the same plant mortality data but organized for a Kaplan Meier regression, including the number of elapsed days between the start of the experiment and the plant death. The third table (‘NSC_Data’) contains nonstructural carbohydrate contents, including soluble sugars (fructose, glucose, sucrose) and starch, measured on three different organs (leaves, stem, root) at five different campaigns during the experiment. The fourth table (‘Water_Potential_Database’) contains weekly measurements of predawn and midday water potentials across the drought experiment. All tables depict individuals from two treatments (light, shade) from the light acclimation phase.

Our study on saplings was conducted in six forested sites in three southern Michigan counties: Ingham Co. (three sites), Gratiot Co. (two sites), and Shiawassee Co. (one site), with 10 to 60 km between sites.Data set one - on the fate and density of emerald ash borer larvae and associated parasitoids on ash saplings from both biocontrol-release and non-release control plots in southern Michigan during the three-year study (2013–2015). Data set one was used for calculations and associated analyses for of the parameters presented in Figure 1, 2, 3, and 4.Data set two - on ash tree abundance (per 100 m2) and healthy conditions (or crown classes) at the six study sites in southern Michigan observed in summer 2015. Data set two was used for estimation of tree density (Figure 5) and healthy condition (or crown classes).Resources in this dataset:Resource Title: Emerald ash borer biocontrol in ash saplings: the potential for early stage recovery of North American ash trees. File Name: Sapling Data 2013-2015 FINAL.xlsx Resource Description: Data set one - on fate and density of emerald ash borer larvae and/or pupae and associated mortality factors (parasitoids, predators, and undetermined diseases/plant resistance /competition)Resource Title: Emerald ash borer biocontrol in ash saplings: the potential for early stage recovery of North American ash trees. File Name: MI Ash Transect 2015 - All trees.xlsx Resource Description: Data on ash abundance and healthy conditions from transect surveyResource Title: Data Dictionary - EAB biocontrol in ash saplings. File Name: EAB_data_dictionary.csvResource Title: 2013-2014 data sorted. File Name: 2013-2014_data_sorted_EAB.csv Resource Description: Data set one - on fate and density of emerald ash borer larvae and/or pupae and associated mortality factors (parasitoids, predators, and undetermined diseases/plant resistance /competition)Resource Title: 2014-2015 data sorted. File Name: 2014-2015_data_sorted_EAB.csv Resource Description: Data set one - on fate and density of emerald ash borer larvae and/or pupae and associated mortality factors (parasitoids, predators, and undetermined diseases/plant resistance /competition)Resource Title: 2015-2016 data sorted. File Name: 2015-2016_data_sorted_EAB.csv Resource Description: Data set one - on fate and density of emerald ash borer larvae and/or pupae and associated mortality factors (parasitoids, predators, and undetermined diseases/plant resistance /competition)Resource Title: Combined: Emerald ash borer biocontrol in ash saplings: the potential for early stage recovery of North American ash trees. File Name: Emerald ash borer biocontrol in ash saplings the potential for early stage recovery of North American ash trees.csv Resource Description: Data set one - on fate and density of emerald ash borer larvae and/or pupae and associated mortality factors (parasitoids, predators, and undetermined diseases/plant resistance /competition) All 3 sets (2013-2016) combined into a CSV for visualization purposesResource Title: Emerald ash borer biocontrol in ash saplings: the potential for early stage recovery of North American ash trees. File Name: MI Ash Transect 2015 - All trees.csv Resource Description: Data on ash abundance and healthy conditions from transect survey (CSV version for data visualization)Resource Title: Estimates of the net population growth rate of emerald ash borer on saplings from life tables constructed from Dataset One. File Name: DUAN J Data on EAB Life Tables Calculation for Saplings 2013-2015.xlsx Resource Description: This life table of emerald ash borer on saplings was constructed from Dataset One and used to estimate the next population growth rate according to method described in Duan et al. (2014, 2017)Resource Title: Estimates of the net population growth rate of emerald ash borer on saplings from life tables constructed from Dataset One. File Name: EAB_Life_Tables_Calculation_for_Saplings_2013-2015.csv Resource Description: CSV version of the data - This life table of emerald ash borer on saplings was constructed from Dataset One and used to estimate the next population growth rate according to method described in Duan et al. (2014, 2017)

Belgian National Burden of Disease Study

Estimates of the fatal burden of disease

Causes of death

Our estimates are based on the official causes of death database compiled by Statbel. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 131 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person.

Years of Life Lost

In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live.

More information

For additional background on BeBOD, please visit https://www.sciensano.be/en/projects/belgian-national-burden-disease-study.

Explore the estimates via https://burden.sciensano.be/shiny/mortality.

Number of perinatal deaths (late fetal deaths and early neonatal deaths) and perinatal mortality rate, 1991 to most recent year.

Biological invasions are increasingly being considered important spatial processes that drive global changes, threatening biodiversity, regional economies, and ecosystem functions. A unifying conceptual model of the invasion dynamics could serve as a useful tool for comparison and classification of invasion processes involving different species across large geographic ranges. By dividing these geographic ranges that are subject to invasions into discrete spatial units we here conceptualize the invasion process as the transition from pristine to invaded spatial units. We use California cities as the spatial units and a long-term database of invasive tropical tephritids to characterize the invasion patterns. A new life-table method based on insect demography, including the progression model of invasion stage transition and the species-specific partitioning model of multispecies invasions, was developed to analyze the invasion patterns. The progression model allows us to estimate the probability and rate of transition, for individual cities, from pristine to infested stages and subsequently differentiate first year of detection from detection recurrences. Importantly, we show that the interval of invasive tephritid recurrence in a city declines with increasing invasion stages of the city. The species-specific partitioning model revealed profound difference in invasion outcome depending on which tephritid species was first detected (and then locally eradicated) in the early stage of invasion. Taken together, we discuss how these two life-table invasion models can cast new light on existing invasion concepts; in particular, on formulating invasion dynamics as the state transition of cities and partitioning species-specific role during multispecies invasions. These models provide a new set of tools for predicting the spatiotemporal progression of invasion and providing early warnings of recurrent invasions for efficient management.

Contents: database of oyster growth (i.e., the changes in mass over time) and mortality along French coasts since 1993. To build this database, we took advantage of the Pacific oyster production monitoring network coordinated by IFREMER (the French Research Institute for the Exploitation of the Sea). This network monitors the growth and mortality of spat (less than one-year-old individuals) and half-grown (between one and two-year-old individuals) Crassostrea gigas oysters since 1993. As the number of sites monitored over the years varied, we focused on 13 sites that were almost continuously monitored during this period. For these locations, we modeled growth and cumulative mortality for spat and half-grown oysters as a function of time, to cope with changes in data acquisition frequency, and produced standardized growth and cumulative mortality indicators to improve data usability. Code to reproduce these analyses are archived here, as well as figures included in the companion data paper: "A 26-year time series of mortality and growth of the Pacific oyster C. gigas recorded along French coasts".

Sampling protocol: in the oyster production monitoring network, oysters were mainly reared in plastic meshed bags fixed on iron tables, mimicking the oyster farmers practices. After their deployment at the beginning of the campaign (seeding dates from February to April depending on the year), growth and mortality were longitudinally monitored yearly. At each sampling date, local operators carefully emptied each bag in separate baskets, counted the dead individuals and alive ones, and removed the dead individuals. Then local operators weighed all alive individuals in each basket (mass taken at the bag level, protocol mainly used between 1993 and 1998 and since 2004) and/or collected 30 individuals to individually weigh them in the laboratory (mass taken at the individual level, protocol used between 1995 and 2010 for spat and since 1996 for half-grown oysters).

Data:

AllDataresco. csv is a csv file containing the raw observations of oyster growth and mortality recorded within the REMORA, RESCO and ECOSCOPA programs. This data set is a modified extraction (carried out on 2021-07-20) of the RESCO REMORA Database (https://doi.org/10.17882/53007) available in SEANOE, an academic publisher or marine research data. The table contains 571101 rows and 18 columns. Description of columns:

program: the name of the program. Blank cells indicate that this information was not available.

mnemonic_site: the mnemonic is a unique identifier of the site and is constructed as follows: code of the marine area - P (for monitoring point) - order number of the monitoring location in the marine area. For example, 014-P-055.

site: the name of the site.

class_age: the age class of the oyster: N0 (spat), J1 (half-grown) or A2 (commercial size). Blank cells indicate that this information was not available.

ploidy: the ploidy of the oysters: diploïdes or triploïdes (in English: diploid or triploid). Blank cells indicate that this information was not available.

date: the date of data collection (format DD/MM/YYYY).

mnemonic_date: mnemonic of the visit. The name of the quarterly operation (P0, P1, P2, P3 or RF: last data collection). For intermediate operations, we use the previous name of the operation followed by an underscore and the number of the week. For example, data collection on 2019-05-06 corresponds to P1_S19. Biométrie initiale (in English: initial biometrics) is equivalent to P0 (first data collected during the campaign).

param: the name of the measured parameter: Nombre d'individus morts, Nombre d'individus vivants, Poids de l'individu or Poids total des individus vivants (in English: number of dead oysters, number of alive oysters, mass of the individual and total mass of alive individuals).

code_param: code of the measured parameter. INDVVIVNB = number of alive oysters, INDVMORNB = number of dead oysters, INDVPOID = mass of the individual, TOTVIVPOI = total mass of alive individuals (i.e., the mass of the bag).

unit_measure: the unit of measurement: Gramme or Unité de dénombrement (d'individus, de cellules, ...)

fraction: either the measure was made at the bag level on which case the fraction is "Sans objet" = Not applicable or the measure was made at the individual level (code_param = INDVPOID), in which case the fraction indicates the part of the oyster that was measured: Chair totale égouttée or coquille (in English: total flesh drained or shell).

method: the method used to obtain the data. For the number of alive and dead oysters (code_param = INDVVIVNB and INDMORNB), the method is comptage macroscopique (in English: macroscopic count). For mass taken at the individual level (code_param = INDVPOID), the method is Pesée après lyophilisation or Pesée simple sans préparation (in English: weighing without preparation or weighing after lyophilization).

id_ind: the id of the individual oyster when code_param is INDVPOI or the id of the bag when code_param is INDVVIVNB, INDVPOID and TOTVIVPOI.

value: numeric value of the measurement.

mnemonic_sampling: This is a concatenated field. Its coding is not consistent throughout the dataset. Indeed, it is sometimes composed of the first letter of the program name attached to 2 numbers indicating the year of data collection and the age class (gj: spat, ga: half-grown or commercial size oysters) - 2 letters indicating the region attached to a 4-character site identifier- mnemonic passage. For example, R05gj-NOBV02-P0 corresponds to data collected in the program REMORA in 2005 on gigas spat (gj) in Normandy (NO) in the site Géfosse 02 (BV02) in the 1st quarter (P0). Other times the mnemonic_prelevement is composed of the first two letters of the program name attached to 2 numbers indicating the year of data collection _ the age class (GJ: spat, GA18: half-grown, GA30: commercial size oysters) attached to the origin of the initial spat group (this information is not always indicated) (CN + number: identifier of wild-caught site, ET + character: identifier of the hatchery, NSI: Argenton hatchery via a standardized protocol) _ a 4-character identifier for the site. For example, RE12_GJET2_BV02 corresponds to data collected in the program REMORA in 2012 (RE12) on gigas spat born in hatchery 2 (GJET2) in the site Géfosse 02 (BV02). Finally, mnemonic_prelevement is sometimes: Biométrie initiale (initial biometrics), Biométrie initiale 6 mois (initial biometrics of spat), Biométrie initiale 18 mois and Biométrie initiale adulte (both correspond to initial biometrics of half-grown oysters), Biométrie initiale 30 mois (initial biometrics of commercial size oysters), Biométrie initiale NSI (initial biometrics of spat batch produced in Argenton Ifremer hatchery via a standardized protocol).

long: The longitudinal coordinate of the site given in decimal in the WGS 84 system.

lat: The latitudinal coordinate of the site given in decimal in the WGS 84 system.

pop_init_batch: this is a concatenated field. It is composed of the two first letters of the name of the program name attached to 2 numbers indicating the year of data collection _ the age class code (GJ: gigas spat, GA: gigas half-grown, GA30: gigas commercial size) _ the origin of the initial spat group (CN: wild-caught, ET: hatchery, NSI: Argenton hatchery) attached to two numbers indicating the year of birth of the initial spat group _ the birth place of the initial spat group (this one is optional). For example, RE00_GJ_CN99_AR corresponds to data collected in the program REMORA in 2000 (RE00) on spat oysters (GJ) born in 1999 and wild-caught (CN99) in the Bay of Arcachon (AR). Blank cells indicate that this information was not available.

sites.csv is a csv file of 7 columns and 13 rows containing information about the 13 sites. Description of the columns found in the data set:

num: a unique identifier for each site. Ranges between 1 and 13.

site: the abbreviated name of the site.

Name: the full name of the site.

zone_fr: the French name of the zone where data collection took place.

zone_en: the English name of the zone where data collection took place.

lat: the latitudinal coordinate of the site given in decimal in the WGS 84 system.

long: the longitudinal coordinate of the site given in decimal in the WGS 84 system.

DataResco_clean.csv is the curated data set of oyster growth and mortality (csv file). The table contains 5178 rows and 13 columns. Each row corresponds to the mean cumulative mortality and mean mass of oysters for a specific date x site x age class combination. This is the data set we used to fit logistic and Gompertz models to describe mean mass and cumulative mortality at time t. Description of the columns found in the data set:

num, site, name, zone_en, lat, long: see the description above for the data set sites.csv.

campaign: the year of data collection. Ranges between 1993 and 2018.

class_age: the age class of the oyster (i.e. spat: N0 or half-grown: J1).

batch: the identifier of the batch (group of oysters born from the same reproductive event, having experienced strictly the same zootechnical route). It is a field that concatenates the campaign, the age class of oysters (spat: N0 or half-grown: J1), the origin of the initial spatgroup (wild-caught: CAPT or Ifremer hatchery: ECLO), ploidy (diploid: 2n) and birthplace of the original spatgroup (AR: Bay of Arcachon or E4: Ifremer hatchery of Argenton).

date: the day of data collection (format YYYY-MM-DD).

DOY: the day of the year (count of days since the beginning of the year). It ranges between 46 and 354.

mean_CM: the mean cumulative mortality of oysters. It ranges between 0 and 0.956 (i.e., between 0–95.6%). We first calculated the cumulative mortality for each bag x date x site x age class combination according to the