SummaryThe repository includes the data and R script for performing an analysis of among- and within-individual differences in the timing of first nesting attempts of the year in natal and pre-breeding environmental conditions (see reference). The data come from a long-term study of the demography of Savannah sparrows (Passerculus sandwichensis) breeding on Kent Island, New Brunswick, Canada (44.58°N, 66.76°W). Climate data were taken from an Environment and Climate Change Canada weather station at the airport in Saint John, NB (45.32°N, 65.89°W; https://www.climate.weather.gc.ca)Datasets(1) SAVS_all_nests_samp.csv: contains summary information for all nest attempts observed for all females included in the analysis (i.e., including both first-of-year and subsequent lay dates).(2) SAVS_first_nest_per_year_samp.csv: contains detailed information on the first nesting attempt by each female Savannah sparrow monitored in the population over the course of the study (1987-2019, excluding the years 2005-2007; see Methods: Study site and field sampling in reference).(3) mean_daily_temperature.csv: contains mean daily temperature records from the ECCC weather station at Saint John, NB (see above). These mean daily temperatures were used in a climate sensitivity analysis to determine the optimum pre-breeding window on Kent Island.(4) SAVS_annual_summary.csv: contains annual summaries of average lay dates, breeding density, reproductive output, etc.Variables- female.id = factor; unique aluminum band number (USGS or Canadian Wildlife Service) assigned to each female- rain.categorical = binary (0 = low rainfall; 1 = high rainfall); groups females into low (81-171 mm) and high (172-378 mm) natal rainfall groups, based on the natal environmental conditions observed in each year (see Methods: Statistical analysis in reference)- year = integer (1987-2019); study year. The population on Savannah sparrows on Kent Island has been monitored since 1987 (excluding three years, 2005-2007)- nest.id = factor; an alpha-numeric code assigned to each nest; unique within years (the combination of year and nest.id would create a unique identifier for each nest)- fledglings = integer; number of offspring fledged from a nest- total.fledglings = integer; the total number of fledglings reared by a given female over the course of her lifetime- nest.attempts = integer; the total number of nest attempts per female (the number of nests over which the total number of fledglings is divided; includes both successful and unsuccessful clutches)hatch.yday = integer; day of the year on which the first egg hatched in a given nestlay.ydate = integer; day of the year on which the first egg was laid in a given nestlay.caldate = date (dd/mm/yyyy); calendar date on which the first egg in a given nest was laidnestling.year = integer; the year in which the female/mother of a given nest was born- nestling.density = integer; the density of adult breeders in the year in which a given female (associated with a particular nest) was born- total.nestling.rain = numeric; cumulative rainfall (in mm) experienced by a female during the nestling period in her natal year of life (01 June to 31 July; see Methods: Temperature and precipitation data in reference)- years.experience = integer; number of previous breeding years per female in a particular year- density.total = integer; total number of adult breeders in the study site in a particular year- MCfden = numeric; mean-centred female density- MCbfden = numeric; mean-centred between-female density- MCwfden = numeric; mean-centred within-female density- mean.t.window = numeric; mean temperature during the identified pre-breeding window (03 May to 26 May; see Methods: Climate sensitivity analysis in reference)- MCtemp = numeric; mean-centred temperature during the optimal pre-breeding window- MCbtemp = numeric; mean-centred between-female temperature during the optimal pre-breeding window- MCwtemp = numeric; mean-centred within-female temperature during the optimal pre-breeding window- female.age = integer; age (in years) of a given female in a given year- MCage = numeric; mean-centred female age- MCbage = numeric; mean-centred between-female age- MCwage = numeric; mean-centred within-female age- mean_temp_c = numeric; mean daily temperature in °C- meanLD = numeric; mean lay date (in days of the year) across all first nest attempts in a given year- sdLD = numeric; standard deviation in lay date (in days of the year) across all first nest attempts in a given year- seLD = numeric; standard error n lay date (in days of the year) across all first nest attempts in a given year- meanTEMP = numeric; mean temperature (in °C) during the breeding period in a given year- records = integer; number of first nest attempts from each year included in the analysis- total.nestling.precip = numeric; total rainfall (in mm) during the nestling period (01 June to 31 July) in a given year- total.breeding.precip = numeric; total rainfall (in mm) during the breeding period (15 April to 31 July) in a given year- density.total = integer; total density of adult breeders on the study site in a given year- total.fledglings = integer; total number of offspring fledged by all breeders in the study site on a given year- cohort.fecundity = numeric; average number of offspring per breeder in a given yearCodecode for Burant et al. - SAVS lay date plasticity analysis.RThe R script provided includes all the code required to import the data and perform the statistical analyses presented in the manuscript. These include:- t-tests investigating the effects of natal conditions (rain.categorical) on female age, nest attempts, and reproductive success- linear models of changes in temperature, precipitation, reproductive success, and population density over time, and lay dates in response to female age, density, etc.- a climate sensing analysis to identify the optimal pre-breeding window on Kent Island- mixed effects models investigating how lay dates respond to changes in within- and between-female age, density, and temperaturesee readme.rtf for a list of datasets and variables.

Despite increasing recognition of the important ecological role large carnivores fulfil and their ability to generate income for protected areas, they remain amongst the most threatened species on Earth. Most large carnivore species have exhibited substantial population declines and geographic range contractions during the past two centuries. Key to reversing this trend is devising cost-effective monitoring methods that produce reliable estimates of abundance or density over timeframes that allow for the success or failure of conservation interventions to be measured. As both scavengers and apex predators, spotted hyaenas (Crocuta crocuta) play extremely important ecological roles, and it has been suggested that they are keystone predators and key indicators of ecosystem health. Although the IUCN Red List of Threatened Species lists spotted hyaenas as “Least Concern”, the overall population trend is decreasing and regional declines have been observed in some areas, such as the northern KwaZulu-Natal province of South Africa. Habitat loss and direct persecution are causing spotted hyaenas to become increasingly reliant on protected areas. In my study, I analysed hyaena by-catch data from camera trap surveys that were conducted in 2019 to monitor leopards (Panthera pardus) in two protected areas in northern KwaZulu-Natal, Mun-Ya-Wana Conservancy and the uMkhuze section of iSimangaliso Wetland Park. I used spatially explicit capture-recapture (SECR) models to estimate the population density of spotted hyaenas in both protected areas. The density of spotted hyaenas in Mun-Ya-Wana Conservancy was estimated to be 5.86 ± 1.12 individuals per 100 km2, based on 30 identified individuals in a sample area of 3122 km2. The density of spotted hyaenas in the uMkhuze section of iSimangaliso Wetland Park was estimated to be 2.97 ± 0.79 individuals per 100 km2, based on 26 identified individuals in a sample area of 2828 km2. These results confirm both the importance of new protected areas (Mun-Ya-Wana Conservancy) in reversing population declines while simultaneously showing that long established protected areas (uMkhuze section of iSimangaliso Wetland Park) may be failing to protect spotted hyaena and presumably other large carnivores. Understanding the drivers of these differences between protected areas is essential to provide regionally stable spotted hyaena populations. Routine camera trap surveys combined with SECR models provide a cost-effective way to monitor spotted hyaena populations and produce reliable estimates of population density. Once more accurate, long-term data on the size and trends of spotted hyaena subpopulations both within and outside protected areas have been collected, the status of spotted hyaenas should be reassessed.

Proximate cues for animal dispersal are complex and varied. Multiple cues may provide information about different aspects of habitat quality, and these aspects may interact with each other, as well as with population density in different ways. We examined how individuals incorporate multiple cues in their decisions to emigrate and immigrate in the colonial orb-weaving spider, Cyrtophora citricola. We manipulated maternal feeding as a cue for prey abundance and measured the size of the maternal web, which provides a limited space for philopatric offspring and a second potential dispersal cue. In addition, we recorded all immigration events to determine dispersal distances and the cues juveniles may use in settlement. Dispersal increased when mothers were poorly fed, web sizes were small, and clutch sizes were large. In addition to these overall effects, maternal feeding also interacted with web size, indicating that offspring from well-fed mothers were more tolerant of high sibling densities. We also detected a threshold for the effect of clutch size on dispersal for the first egg sac: below 20 offspring, there was no effect of clutch size, but dispersal increased with clutch size for larger clutches. Dispersal distances were often short, and immigrants preferred sheltered trees and those occupied by adult females. Dispersal not only depended on multiple cues, but these cues interacted, and the importance of web size suggested that saturation of the natal web might force dispersal, at least for spiders with poorly-fed mothers. How one aspect of habitat quality influences dispersal can therefore depend on the state of other aspects of habitat quality. In particular, some natal resources, such as a nest or territory, may become saturated and limit group size, but this limit will also depend on other factors, such as prey availability.

Population ecology and social organization of dusky-footed woodrats, Neotoma fuscies

Mt. Graham red squirrel natal dispersal distanceDispersal distance (m) of radio-collared juvenile Mt. Graham red squirrels. an.id = unique animal id; sex = m,f; dist.moved = distance moved (m); year = 2010-2013; long.dist = where dispersal distance > 150 m males, 100 m femalesdispersal.distance.csvMerrick_Koprowski2016_Dispersal_correlatesData set used to model dispersal distance and probability of non-philopatric distance dispersal in juvenile Mt. Graham red squirrels from 2010 - 2013 as a function of intrinsic and extrinsic factors.

Data description for “Early predictors of female lifetime reproductive success in a solitary hibernator: an evidence for “silver spoon” effect”

Nina A. Vasilieva, Andrey V. Tchabovsky

The table contains data for lifetime reproductive success of yellow ground squirrel females (Spermophilus fulvus), observed in the natural colony in Saratovskaya oblast’, Russia, in the vicinity of village Dyakovka (50°43′88″N, 46°46′04″E).

COLUMNS DESCRIPTION:

Female ID – female identity.

Litter ID – the female’s natal litter identity.

Lifetime reproductive success predictors:

Date of natal emergence – date of the first emergence of the focal female from the natal burrow.

Weaning weight, g – female body weight at the first emergence from the natal burrow.

N days between weightings – number of days between the female weightings.

Weight gain rate, g/day – early growth rate, the difference between consecutive weightings divided by the number of days between them (Ndays ≥15; the first weighing was the weaning weight).

Natal litter size – size of the female natal litter at the first emergence from the natal burrow.

Mean distance to burrows of 20 nearest juveniles, m – average distance from the female natal burrow to natal burrows of 20 nearest juveniles, including the littermates; used as a measurement of juvenile local density.

Distance to the village, m - the shortest distance (m) from the female natal burrow to the nearest garden fence in the village adjacent to the colony; used as a correlate of the potential negative human effect and disturbance.

Adult population density, ind/ha - the density of adult animals (N animals per 1 ha); used as a proxy for total population density within the colony in the year of female birth

Amount of rain in May-June, mm - the total precipitation during May and June in the year of female birth.

Year - the year of female birth.

Lifetime reproductive success components:

Survival to adulthood (0-no, 1-yes) – we considered the female survived to adulthood if it was registered in May after its first hibernation of later, i.e. it could potentially wean at least one litter.

Lifespan - the number of full years between the female’s natal emergence and disappearance from the colony.

N weaned litters – the lifetime number of weaned litters.

N weaned offspring – the lifetime number of weaned offspring (weanlings).

N yearling offspring – the lifetime number of offspring survived to maturation, i.e. the total number of offspring survived the first hibernation.

As of 2023, South Africa's population increased and counted approximately 62.3 million inhabitants in total, of which the majority inhabited Gauteng, KwaZulu-Natal, and the Western-Eastern Cape. Gauteng (includes Johannesburg) is the smallest province in South Africa, though highly urbanized with a population of over 16 million people according to the estimates. Cape Town, on the other hand, is the largest city in South Africa with nearly 3.43 million inhabitants in the same year, whereas Durban counted 3.12 million citizens. However, looking at cities including municipalities, Johannesburg ranks first. High rate of young population South Africa has a substantial population of young people. In 2024, approximately 34.3 percent of the people were aged 19 years or younger. Those aged 60 or older, on the other hand, made-up over 10 percent of the total population. Distributing South African citizens by marital status, approximately half of the males and females were classified as single in 2021. Furthermore, 29.1 percent of the men were registered as married, whereas nearly 27 percent of the women walked down the aisle. Youth unemployment Youth unemployment fluctuated heavily between 2003 and 2022. In 2003, the unemployment rate stood at 36 percent, followed by a significant increase to 45.5 percent in 2010. However, it fluctuated again and as of 2022, over 51 percent of the youth were registered as unemployed. Furthermore, based on a survey conducted on the worries of South Africans, some 64 percent reported being worried about employment and the job market situation.

The life-history theories of aging predict lifetime trade-offs between early reproductive allocation and late-life survival, reproduction, or both components of fitness. Recent studies in wild populations have found evidence for these early-late life trade-offs, but rarely have they been found across multiple traits while exploring the additional effects of variation in environmental conditions and individual quality. Benefiting from longitudinal data on adult female mountain goats (Oreamnos americanus), we investigated the influence of age at first reproduction (AFR) and early reproductive success (ERS) on longevity, late reproductive success, and senescence rates while accounting for the influence of natal environmental conditions and individual quality. Contrary to predictions, we did not find evidence for early-late life trade-offs. Instead, an earlier AFR and a greater ERS had positive but weak direct effects on late reproductive success. Natal population density, however, was the ...

1. Our understanding of demographic processes is mainly based on analyses of traits from the adult component of populations. Early-life demographic traits are poorly known mainly for methodological reasons. Yet, survival of juvenile and immature individuals is critical for the recruitment into the population and thus for the whole population dynamic, especially for long-lived species. This bias currently restrains our ability to fully understand population dynamics of long-lived species and life-history theory. 2. The goal of this study was to estimate the early-life demographic parameters of a long-lived species with a long immature period (9–10 years), to test for sex and age effects on these parameters and to identify the environmental factors encountered during the period of immaturity that may influence survival and recruitment. 3. Using capture–mark–recapture multievent models allowing us to deal with uncertain and unobservable individual states, we analysed a long-term data set of wandering albatrosses to estimate both age- and sex-specific early-life survival and recruitment. We investigated environmental factors potentially driving these demographic traits using climatic and fisheries covariates and tested for density dependence. 4. Our study provides for the first time an estimate of annual survival during the first 2 years at sea for an albatross species (0·801 ± 0·014). Both age and sex affected early-life survival and recruitment processes of this long-lived seabird species. Early-life survival and recruitment were highly variable across years although the sensitivity of young birds to environmental variability decreased with age. Early-life survival was negatively associated with sea surface temperature, and recruitment rate was positively related to both Southern Annular Mode and sea surface temperature. We found strong evidence for density-dependent mortality of juveniles. Population size explained 41% of the variation of this parameter over the study period. 5. These results indicate that early-life survival and recruitment were strongly age and sex dependent in a dimorphic long-lived species. In addition, early-life demographic parameters were affected by natal environmental conditions and by environmental conditions faced during the period of immaturity. Finally, our results constitute one of the first demonstrations of density dependence on juvenile survival in seabirds, with major consequences for our understanding of population dynamics in seabirds.

This data is produced for white-tailed eagle population study at the University of Turku, Finland, to study density-dependence in natal dispersal. Data consists of 285 white-tailed eagle individuals in Finland. For each individual the natal dispersal distance and local breeder density (number of active territories) within 10 km and 30 km buffer is included.

South Africa is the sixth African country with the largest population, counting approximately 60.5 million individuals as of 2021. In 2023, the largest city in South Africa was Cape Town. The capital of Western Cape counted 3.4 million inhabitants, whereas South Africa's second largest city was Durban (eThekwini Municipality), with 3.1 million inhabitants. Note that when observing the number of inhabitants by municipality, Johannesburg is counted as largest city/municipality of South Africa.

From four provinces to nine provinces

Before Nelson Mandela became president in 1994, the country had four provinces, Cape of Good Hope, Natal, Orange Free State, and Transvaal and 10 “homelands” (also called Bantustans). The four larger regions were for the white population while the homelands for its black population. This system was dismantled following the new constitution of South Africa in 1996 and reorganized into nine provinces. Currently, Gauteng is the most populated province with around 15.9 million people residing there, followed by KwaZulu-Natal with 11.68 million inhabiting the province. As of 2022, Black African individuals were almost 81 percent of the total population in the country, while colored citizens followed amounting to around 5.34 million.

A diverse population

Although the majority of South Africans are identified as Black, the country’s population is far from homogenous, with different ethnic groups usually residing in the different “homelands”. This can be recognizable through the various languages used to communicate between the household members and externally. IsiZulu was the most common language of the nation with around a quarter of the population using it in- and outside of households. IsiXhosa and Afrikaans ranked second and third with roughly 15 percent and 12 percent, respectively.

There is a growing need to understand how species respond to habitat changes and the potential key role played by natal dispersal in population dynamics, structure and gene flow. However, few studies have explored differences in this process between conspecifics living in natural habitats and those inhabiting landscapes highly transformed by humans, such as cities. Here, we investigate how individual traits and social characteristics can influence the natal dispersal decisions of burrowing owls (Athene cunicularia) living in urban and rural areas, as well as the consequences in terms of reproductive success and apparent survival. We found short dispersal movements among individuals, with differences between urban and rural birds (i.e., the former covering shorter distances than the latter), maybe because of the higher conspecific density of urban compared to rural areas. Moreover, we found that urban and rural females as well as bold individuals (i.e., individuals with shorter flight initiation distance) exhibited longer dispersal distances than their counterparts. These dispersal decisions have effects on individual fitness. Individuals traveling longer distances increased their reproductive prospects (productivity during the first breeding attempt, and long term productivity). However, the apparent survival of females decreased when they dispersed farther from their natal territory. Although further research is needed to properly understand the ecological and evolutionary consequences of dispersal patterns in transformed habitats, our results provide information about the drivers and the consequences of the restricted natal movements of this species, which may explain its population structuring through restricted gene flow between and within urban and rural areas.

Movement of individuals, or their genes, can influence eco-evolutionary processes in structured populations. We have limited understanding of the extent to which spatial behaviour varies among groups and individuals within populations. Here we use genetic pedigree reconstruction in a long-term study of European badgers (Meles meles) to characterise the extent of extra-group paternity, occurring as a consequence of breeding excursions, and to test hypothesised drivers of variation at multiple levels. We jointly estimate parentage and paternity distance (PD; distance between a cub’s natal and its father’s social group), and test whether population density and sex ratio influence mean annual PD. We also model cub-level PD and extra-group paternity (EGP) to test for variation among social groups and parental individuals. Mean PD varied among years but was not explained by population density or sex ratio. However, cub-level analysis shows strong effects of social group, and parental identities, with some parental individuals being consistently more likely to produce cubs with extra-group partners. Group effects were partially explained by local sex ratio. There was also a strong negative correlation between maternal and paternal social group effects on cub paternity distance, indicating source-sink dynamics. Our analyses of paternity distance and EGP indicate variation in extra-group mating at multiple levels – among years, social groups and individuals. The latter in particular is a phenomenon seldom documented and suggests that gene flow among groups may be disproportionately mediated by a non-random subset of adults, emphasising the importance of the individual in driving eco-evolutionary dynamics.

Background: To optimally allocate limited health resources in responding to the HIV epidemic, South Africa has undertaken to generate local epidemiological profiles identifying high disease burden areas. Central to achieving this, is the need for readily available quality health data linked to both large and small geographic areas. South Africa has relied on national population-based surveys: the Household HIV Survey and the National Antenatal Sentinel HIV and Syphilis Prevalence Survey (ANC) amongst others for such data for informing policy decisions. However, these surveys are conducted approximately every 2 and 3 years creating a gap in data and evidence required for policy. At subnational levels, timely decisions are required with frequent course corrections in the interim. Routinely collected HIV testing data at public health facilities have the potential to provide this much needed information, as a proxy measure of HIV prevalence in the population, when survey data is not available. The South African District health information system (DHIS) contains aggregated routine health data from public health facilities which is used in this article.Methods: Using spatial interpolation methods we combine three “types” of data: (1) 2015 gridded high-resolution population data, (2) age-structure data as defined in South Africa mid-year population estimates, 2015; and (3) georeferenced health facilities HIV-testing data from DHIS for individuals (15–49 years old) who tested in health care facilities in the district in 2015 to delineate high HIV disease burden areas using density surface of either HIV positivity and/or number of people living with HIV (PLHIV). For validation, we extracted interpolated values at the facility locations and compared with the real observed values calculating the residuals. Lower residuals means the Inverse Weighted Distance (IDW) interpolator provided reliable prediction at unknown locations. Results were adjusted to provincial published HIV estimates and aggregated to municipalities. Uncertainty measures map at municipalities is provided. Data on major cities and roads networks was only included for orientation and better visualization of the high burden areas.Results: Results shows the HIV burden at local municipality level, with high disease burden in municipalities in eThekwini, iLembe and uMngundgudlovu; and around major cities and national routes.Conclusion: The methods provide accurate estimates of the local HIV burden at the municipality level. Areas with high population density have high numbers of PLHIV. The analysis puts into the hand of decision makers a tool that they can use to generate evidence for HIV programming. The method allows decision makers to routinely update and use facility level data in understanding the local epidemic.

Abstract

The health and demography of the South African population has been undergoing substantial changes as a result of the rapidly progressing HIV epidemic. Researchers at the University of KwaZulu-Natal and the South African Medical Research Council established The Africa Health Research Studies in 1997 funded by a core grant from The Wellcome Trust, UK. Given the urgent need for high quality longitudinal data with which to monitor these changes, and with which to evaluate interventions to mitigate impact, a demographic surveillance system (DSS) was established in a rural South African population facing a rapid and severe HIV epidemic. The DSS, referred to as the Africa Health Research Institute Demographic Information System (ACDIS), started in 2000.

ACDIS was established to ‘describe the demographic, social and health impact of the HIV epidemic in a population going through the health transition’ and to monitor the impact of intervention strategies on the epidemic. South Africa’s political and economic history has resulted in highly mobile urban and rural populations, coupled with complex, fluid households. In order to successfully monitor the epidemic, it was necessary to collect longitudinal demographic data (e.g. mortality, fertility, migration) on the population and to mirror this complex social reality within the design of the demographic information system. To this end, three primary subjects are observed longitudinally in ACDIS: physical structures (e.g. homesteads, clinics and schools), households and individuals. The information about these subjects, and all related information, is stored in a single MSSQL Server database, in a truly longitudinal way—i.e. not as a series of cross-sections.

The surveillance area is located near the market town of Mtubatuba in the Umkanyakude district of KwaZulu-Natal. The area is 438 square kilometers in size and includes a population of approximately 85 000 people who are members of approximately 11 000 households. The population is almost exclusively Zulu-speaking. The area is typical of many rural areas of South Africa in that while predominantly rural, it contains an urban township and informal peri-urban settlements. The area is characterized by large variations in population densities (20–3000 people/km2). In the rural areas, homesteads are scattered rather than grouped. Most households are multi-generational and range with an average size of 7.9 (SD:4.7) members. Despite being a predominantly rural area, the principle source of income for most households is waged employment and state pensions rather than agriculture. In 2006, approximately 77% of households in the surveillance area had access to piped water and toilet facilities.

To fulfil the eligibility criteria for the ACDIS cohort, individuals must be a member of a household within the surveillance area but not necessarily resident within it. Crucially, this means that ACDIS collects information on resident and non-resident members of households and makes a distinction between membership (self-defined on the basis of links to other household members) and residency (residing at a physical structure within the surveillance area at a particular point in time). Individuals can be members of more than one household at any point in time (e.g. polygamously married men whose wives maintain separate households). As of June 2006, there were 85 855 people under surveillance of whom 33% were not resident within the surveillance area. Obtaining information on non-resident members is vital for a number of reasons. Most importantly, understanding patterns of HIV transmission within rural areas requires knowledge about patterns of circulation and about sexual contacts between residents and their non-resident partners. To be consistent with similar datasets from other INDEPTH Member centres, this data set contains data from resident members only.

During data collection, households are visited by fieldworkers and information supplied by a single key informant. All births, deaths and migrations of household members are recorded. If household members have moved internally within the surveillance area, such moves are reconciled and the internal migrant retains the original identfier associated with him/her.

Geographic coverage

Demographic surveillance area situated in the south-east portion of the uMkhanyakude district of KwaZulu-Natal province near the town of Mtubatuba. It is bounded on the west by the Umfolozi-Hluhluwe nature reserve, on the South by the Umfolozi river, on the East by the N2 highway (except form portions where the Kwamsane township strandles the highway) and in the North by the Inyalazi river for portions of the boundary. The area is 438 square kilometers.

Analysis unit

Individual

Universe

Resident household members of households resident within the demographic surveillance area. Inmigrants are defined by intention to become resident, but actual residence episodes of less than 180 days are censored. Outmigrants are defined by intention to become resident elsewhere, but actual periods of non-residence less than 180 days are censored. Children born to resident women are considered resident by default, irrespective of actual place of birth. The dataset contains the events of all individuals ever resident during the study period (1 Jan 2000 to 31 Dec 2015).

Kind of data

Event history data

Frequency of data collection

This dataset contains rounds 1 to 37 of demographic surveillance data covering the period from 1 Jan 2000 to 31 December 2015. Two rounds of data collection took place annually except in 2002 when three surveillance rounds were conducted. From 1 Jan 2015 onwards there are three surveillance rounds per annum.

Sampling procedure

This dataset is not based on a sample but contains information from the complete demographic surveillance area.

Reponse units (households) by year: Year Households 2000 11856
2001 12321
2002 12981
2003 12165
2004 11841
2005 11312
2006 12065
2007 12165
2008 11790
2009 12145
2010 12485
2011 12455
2012 12087 2013 11988 2014 11778 2015 11938

In 2006 the number of response units increased due to the addition of a new village into the demographic surveillance area.

Sampling deviation

None

Mode of data collection

Proxy Respondent [proxy]

Research instrument

Bounded structure registration (BSR) or update (BSU) form: - Used to register characteristics of the BS - Updates characteristics of the BS - Information as at previous round is preprinted

Household registration (HHR) or update (HHU) form: - Used to register characteristics of the HH - Used to update information about the composition of the household - Information preprinted of composition and all registered households as at previous

Household Membership Registration (HMR) or update (HMU): - Used to link individuals to households - Used to update information about the household memberships and member status observations - Information preprinted of member status observations as at previous

Individual registration form (IDR): - Used to uniquely identify each individual - Mainly to ensure members with multiple household memberships are appropriately captured

Migration notification form (MGN): - Used to record change in the BS of residency of individuals or households _ Migrants are tracked and updated in the database

Pregnancy history form (PGH) & pregnancy outcome notification form (PON): - Records details of pregnancies and their outcomes - Only if woman is a new member - Only if woman has never completed WHL or WGH

Death notification form (DTN): - Records all deaths that have recently occurred - Iincludes information about time, place, circumstances and possible cause of death

Cleaning operations

On data entry data consistency and plausibility were checked by 455 data validation rules at database level. If data validaton failure was due to a data collection error, the questionnaire was referred back to the field for revisit and correction. If the error was due to data inconsistencies that could not be directly traced to a data collection error, the record was referred to the data quality team under the supervision of the senior database scientist. This could request further field level investigation by a team of trackers or could correct the inconsistency directly at database level.

No imputations were done on the resulting micro data set, except for:

a. If an out-migration (OMG) event is followed by a homestead entry event (ENT) and the gap between OMG event and ENT event is greater than 180 days, the ENT event was changed to an in-migration event (IMG). b. If an out-migration (OMG) event is followed by a homestead entry event (ENT) and the gap between OMG event and ENT event is less than 180 days, the OMG event was changed to an homestead exit event (EXT) and the ENT event date changed to the day following the original OMG event. c. If a homestead exit event (EXT) is followed by an in-migration event (IMG) and the gap between the EXT event and the IMG event is greater than 180 days, the EXT event was changed to an out-migration event (OMG). d. If a homestead exit event (EXT) is followed by an in-migration event (IMG) and the gap between the EXT event and the IMG event is less than 180 days, the IMG event was changed to an homestead entry event (ENT) with a date equal to the day following the EXT event. e. If the last recorded event for an individual is homestead exit (EXT) and this event is more than 180 days prior to the end of the surveillance period, then the EXT event is changed to an

Abstract

Animals select their habitat along environmental gradients, but the mechanisms that constrain the ecological requirements of an individual can differ between life stages. Dispersal is a key demographic process that determines gene flow and alters species distributions, yet few empirical studies have examined whether habitat selection in animals is changing during dispersal. In this study, we examined changes in habitat preferences during natal dispersal of red kites (Milvus milvus), a European raptor species. By deploying solar-powered GPS-GSM transmitters on nestlings, we continuously tracked individuals up to six years (2015-2020), from fledging to settlement. We applied habitat selection functions to the tracking data using hierarchical generalized additive models, a flexible method which combines individual- and population-level inference, while allowing for the contrast of the prospecting and settlement phases. During the prospecting phase (n = 204 birds), individuals were less responsive to their environment than during the settlement phase, resulting in a predicted wide distribution in Western Europe. During the settlement phase, individuals (n = 78 birds) selected a narrower range of environmental gradients, while avoiding areas of high elevation, steep topographic slopes, high human population density and highly heterogeneous landscapes. During this phase, individuals were also more philopatric, i.e., they were more inclined to choose an environment closer to their natal area, than during the prospecting phase. Suitable habitats predicted during settlement were much more spatially contrasted than during prospecting. Our study provides empirical evidence that habitat selection changes across natal dispersal phases in a long-lived species, indicating that species conservation strategies should account for different environmental constraints before and after settlement. Furthermore, our findings underscore the importance of long-term tracking data, with sufficient sample size, to study the link between habitat selection and natal dispersal.

Abstract

The ‘South African Population Research Infrastructure Network’ (SAPRIN) is a national research infrastructure funded through the Department of Science and Innovation and hosted by the South African Medical Research Council. One of SAPRIN’s initial goals has been to harmonise and share the longitudinal data from the three current Health and Demographic Surveillance System (HDSS) Nodes. These long-standing nodes are the MRC/Wits University Agincourt HDSS in Bushbuckridge District, Mpumalanga, established in 1993, with a population of 113 113 people; the University of Limpopo DIMAMO HDSS in the Capricorn District of Limpopo, established in 1996, with a current population of 38 479; and the Africa Health Research Institute (AHRI) HDSS in uMkhanyakude District, KwaZulu-Natal, established in 2000, with a current population of 139 250.

This dataset represents a snapshot of the continually evolving data in the underlying longitudinal databases maintained by the SAPRIN nodes. In these databases the rightmost extend of the individual's surveillance episode is indicated by the data collection date of the last time the individual's membership of a household under surveillance has been confirmed. Each dataset has a right censor date (31 December 2017 for the current version of the dataset) and individual surveillance episodes are terminated at that point if the individual is still under surveillance beyond the cut-off date.

Each individual surveillance episode is associated with a physical location, for internal residency episodes it is the actual place of residence of the individual, for external residence episodes (periods of temporary migration) it is the place of residence of the individual's household. If an individual change their place of residency from one location within the surveillance area to another location still within the surveillance area, the episode at the original location is terminated with a location exit event, and a new episode starts with a location entry event at the destination location. It is also possible for the household the individual is a member of, to change their place of residency in the surveillance area, whilst the individual is externally resident (is a temporary migrant), in which case the individual's external resident episode will also be split with a location exit-entry pair of events.

At every household visit written consent is obtained from the household respondent for continued participation in the surveillance and such consent can be withdrawn. When this happens all household members' surveillance episodes are terminated with a refusal event. It is possible for households to again provide consent to participate in the surveillance after some time, in such cases surveillance events are restarted with a permission event.

As mentioned previously, surveillance episodes are continually extended by the last data collection event if the individual remains under surveillance. In certain cases, individuals may be lost to follow-up and surveillance episodes where the date of last data collection is more than one year prior to the right censor data are terminated as lost to follow up at that last data collection date. Individuals with data collection dates within a year of the right censor date is considered still to be under surveillance up to this last data collection date.

Each surveillance episode contains the identifier of the household the individual is a member of during that episode. Under relatively rare circumstances it is possible for an individual to change household membership whilst still resident at the same location, or to change membership whilst externally resident, in these cases the surveillance episode will be split with a pair of membership end and membership start events. More commonly membership start and end events coincide with location exit and entry events or in- and out-migration events. Memberships also obviously start at birth or enumeration and end at death, refusal to participate or lost to follow-up.

In about half of the cases, individuals have a single episode from first enumeration, birth or in-migration, to their eventual death, out-migration or currently still under surveillance. In the remaining cases, individuals transition from internal residency to external residency via out-migration, or from one location to another via internal migration with a location exit and entry event, or some other rarer form of transition involving membership change, refusal or lost to follow-up. Usually these series of surveillance episodes are continuous in time, with no gaps between episodes, but gaps can form, e.g. when an individual out-migrates and end membership with the household and so is no longer under surveillance, only to return via in-migration at some future date and take up membership with same or different household.

The SAPRIN Individual Surveillance Episodes 2020 Datasets consists of three types of the Demographic surveillance datasets: 1.SAPRIN Individual Surveillance Episodes 2020: Basic Dataset. This dataset contains only the internal and external residency episodes for an individual. 2.SAPRIN Individual Surveillance Episodes 2020: Age-Year-Delivery Dataset. This dataset splits the basic surveillance episodes at calendar year end and at the date when the age in years (birth-day) of an individual changes. In the case of women who have given births, episodes are split at the time of delivery as well. 3.SAPRIN Individual Surveillance Episodes 2020: Detailed Dataset. This dataset adds to the dataset 2 time-varying attributes such as education, employment, marital status and socio-economic status.

Geographic coverage

The South African Population Research Infrastructure Network (SAPRIN) currently represents a network of three Health and Demographic Surveillance System (HDSS) nodes located in rural South Africa, namely: 1) MRC/Wits University Agincourt HDSS in Bushbuckridge District, Mpumalanga, which has collected data since 1993. The nodal website is: http://www.agincourt.co.za; 2) the University of Limpopo DIMAMO HDSS in the Capricorn District of Limpopo, which has collected data since 1996.The nodal website is: N/A; 3) and the Africa Health Research Institute (AHRI) HDSS in uMkhanyakude District, KwaZulu-Natal, which has collected data since 2000.The nodal website is: http://www.ahri.org.

The Agincourt HDSS covers a surveillance area of approximately 420 square kilometres and is located in the Bushbuckridge District, Mpumalanga in the rural northeast of South Africa close to the Mozambique border. At baseline in 1992, 57 600 people were recorded in 8900 households in 20 villages; by 2006, the population had increased to about 70 000 people in 11 700 households. As of December 2017, there were 113 113 people under surveillance of whom 28% were not resident within the surveillance area, with a total of about 2m person years of observation. 33% of the population is under 15 years old. The population is almost exclusively Shangaan-speaking.The Agincourt HDSS has population density of over 200 persons per square kilometre. The Agincourt HDSS extends between latitudes 24° 50´ and 24° 56´S and longitudes 31°08´ and 31°´ 25´ E. The altitude is about 400-600m above sea level.

DIMAMO is located in the Capricorn district, Limpopo Province approximately 40 kilometres from Polokwane, the capital city of Limpopo Province and 15-50 kilometres from the University of Limpopo. The site covers an area of approximately 400 square kilometres . The initial total population observed was about 8 000 but the field site was expanded in 2010. As of December 2017, there were 38 479 people under surveillance, of whom 22% were not resident within the surveillance area, with about 400,000 person years of observation. 30% of the population is under 15 years old. The population is predominantly Sotho speaking. Most households have electricity. Some households have piped water either inside the house or in their yards, but most fetch water from taps situated at strategic points in the villages. Most households have a pit latrine in their yards. The area lies between latitudes and 23°65´ and 23°90´S and longitudes 29°65´ and 29°85´E. The HDSS is located on a high plateau area (approximately 1250 m above sea level) where communities typically consist of households clustered in villages, with access to local land for small-scale food production.

Africa Health Research Institute (AHRI) is situated in the south-east portion of the Umkhanyakude district of KwaZulu-Natal province near the town of Mtubatuba. It is bounded on the west by the Umfolozi-Hluhluwe nature reserve, on the south by the Umfolozi river, on the east by the N2 highway (except form portions where the Kwamsane township stradles the highway) and in the north by the Inyalazi river for portions of the boundary. The surveillance area is approximately 850 square kilometres. As of December 2017, there were 139 250 people under surveillance of whom 28% were not resident within the surveillance area, with about 1.7m person years of observation. 32% of the population is under 15 years old. The population is almost exclusively Zulu-speaking. The surveillance area is typical of many rural areas of South Africa in that while predominantly rural, it contains an urban township and informal peri-urban settlements. The area is characterized by large variations in population densities (20-3000 people per square kilometre). The area lies between latitudes -28°24' and 28°20'N and longitudes 32°10' and 31°58'E.

Analysis unit

Households and individuals

Universe

Households resident in dwellings within the study area will be eligible for inclusion in the household component of SAPRIN. All individuals identified by the household proxy informant as a member of

The phenotype of adults can be strongly influenced by the environmental conditions experienced during development. Consequently, variation in habitat quality across space and through time also leads to differences in the phenotypes of adults. This could create carry-over effects where differences in the natal habitat quality of colonizers influence population dynamics in new habitats. We tested this hypothesis experimentally by simulating dispersal of Tribolium castaneum from low or high quality natal habitat into new patches of low or high quality habitat. Differences in natal habitat quality of colonizers altered population growth trajectories and led to carrying capacities that differed by up to 85% within a habitat type, indicating that patch dynamics are determined by the interaction of past and current habitat quality. Interestingly, even after multiple generations, natal habitat of colonizers determined differences in adult traits that were related to density-dependent population regulation. These changes in adult phenotype could at least partially explain why carry-over effects continued to alter population dynamics for multiple generations until the end of the experiment. These results highlight the importance of variable habitat quality and carry-over effects for population dynamics.

This dataset 'Giraffe Dispersal Modeling' contains data about social and spatial dispersal outcomes, and covariates, from 137 giraffe calves (67 male and 70 female) in the Tarangire Ecosystem, Tanzania, from 2012-2018. Includes ID, sex, natal social community membership, social and/or spatial dispersal outcome, net displacement distance in kilometers (end distance from origin), age (in months) of dispersal, age of first sortie, age first seen in bachelor herd, and mean distance of community members from boma or town, mean proportion of preferred vegetation in natal community's home range, and mean population density in the natal community. R code 'Spatial Dispersal Check' is to fit a smoothed line to the distance data for a calf whose final detection was beyond the distance threshold (see main text). Code calculates how many detections occur after the fitted line exceeds the threshold that are beyond the threshold distance.

Predicting population colonisations requires understanding how spatio-temporal changes in density affect dispersal. Density can inform on fitness prospects, acting as a cue for either habitat quality, or competition over resources. However, when escaping competition, high local density should only increase emigration if lower-density patches are available elsewhere. Few empirical studies on dispersal have considered the effects of density at the local and landscape scale simultaneously. To explore this, we analyze 5 years of individual-based data from an experimental introduction of wild guppies Poecilia reticulata. Natal dispersal showed a decrease in local density dependence as density at the landscape level increased. Landscape density did not affect dispersal among adults, but local density-dependent dispersal switched from negative (conspecific attraction) to positive (conspecific avoidance), as the colonisation progressed. This study demonstrates that densities at various scales interact to determine dispersal, and suggests that dispersal trade-offs differ across life stages.