Throughout most of human history, global population growth was very low; between 10,000BCE and 1700CE, the average annual increase was just 0.04 percent. Therefore, it took several thousand years for the global population to reach one billion people, doing so in 1803. However, this period marked the beginning of a global phenomenon known as the demographic transition, from which point population growth skyrocketed. With the introduction of modern medicines (especially vaccination), as well as improvements in water sanitation, food supply, and infrastructure, child mortality fell drastically and life expectancy increased, causing the population to grow. This process is linked to economic and technological development, and did not take place concurrently across the globe; it mostly began in Europe and other industrialized regions in the 19thcentury, before spreading across Asia and Latin America in the 20th century. As the most populous societies in the world are found in Asia, the demographic transition in this region coincided with the fastest period of global population growth. Today, Sub-Saharan Africa is the region at the earliest stage of this transition. As population growth slows across the other continents, with the populations of the Americas, Asia, and Europe expected to be in decline by the 2070s, Africa's population is expected to grow by three billion people by the end of the 21st century.

Globally, about 25 percent of the population is under 15 years of age and 10 percent is over 65 years of age. Africa has the youngest population worldwide. In Sub-Saharan Africa, more than 40 percent of the population is below 15 years, and only three percent are above 65, indicating the low life expectancy in several of the countries. In Europe, on the other hand, a higher share of the population is above 65 years than the population under 15 years. Fertility rates The high share of children and youth in Africa is connected to the high fertility rates on the continent. For instance, South Sudan and Niger have the highest population growth rates globally. However, about 50 percent of the world’s population live in countries with low fertility, where women have less than 2.1 children. Some countries in Europe, like Latvia and Lithuania, have experienced a population decline of one percent, and in the Cook Islands, it is even above two percent. In Europe, the majority of the population was previously working-aged adults with few dependents, but this trend is expected to reverse soon, and it is predicted that by 2050, the older population will outnumber the young in many developed countries. Growing global population As of 2025, there are 8.1 billion people living on the planet, and this is expected to reach more than nine billion before 2040. Moreover, the global population is expected to reach 10 billions around 2060, before slowing and then even falling slightly by 2100. As the population growth rates indicate, a significant share of the population increase will happen in Africa.

In the Cook Islands in 2024, the population decreased by about 2.24 percent compared to the previous year, making it the country with the highest population decline rate in 2024. Of the 20 countries with the highest rate of population decline, the majority are island nations, where emigration rates are high (especially to Australia, New Zealand, and the United States), or they are located in Eastern Europe, which suffers from a combination of high emigration rates and low birth rates.

The world's population first reached one billion people in 1805, and reached eight billion in 2022, and will peak at almost 10.2 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two-thirds of the world's population lives in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a few years later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

Highest educational attainment information by neighborhoods in Johns Creek, GA.Neighborhood boundaries are created and maintained by Johns Creek, GA.Demographics data is from Esri GeoEnrichment Services.

Habitat Strategy Applications: Ecosystem Diagnosis and Treatment (EDT) stream reach priorities indicate the potential benefits to ESA-listed salmon and steelhead species if stream corridor conditions are restored or protected. EDT modeling supports estimation of salmon and steelhead population performance based on modeled degradation and restoration of physical habitat in individual stream reaches. EDT modeling incorporates 46 different reach level habitat attributes, integrates all potential life history trajectories of salmon and steelhead populations, and calculates four population performance parameters that align with Viable Salmonid Population (VSP) parameters (abundance, productivity, diversity and spatial structure). EDT stream reaches provide details on the importance of a reach to the overall performance of each population (Species Reach Potential (SRP) ratings), and priorities relating to preservation and restoration of habitat. Reach Tier ratings of 1 through 4 are assigned based on these attributes in accordance with the following table.

Tier Rating

Definition

1

Reaches modeled to have high population performance benefits (High Species Reach Potential Rating) for one or more Primary populations.

2

Reaches modeled to have medium population performance benefits (Medium SRP) for one or more Primary populations, and/or high population performance benefits (High SRP) for one or more Contributing populations.

3

Reaches modeled to have medium population performance benefits (Medium SRP) for one or more Contributing populations, and/or high population performance benefits (High SRP) for one or more Stabilizing populations.

4

Reaches modeled to have medium population performance benefits (Medium SRP) for one or more Stabilizing populations, and/or low population performance benefits (Low SRP) for all populations.

Non-Tiered

Reaches either not modeled in EDT, or modeled to have negligible population performance benefits for all populations are considered non-tiered.

More information on how EDT is developed and its applications can be found in the Washington Lower Columbia Salmon Recovery and Fish & Wildlife Subbasin Plan. Watershed and population specific details are in the Volume II subbasin chapters and EDT technical details are in Volume III, Appendix E.

Attributes

EDT Reach Name: the name of the EDT stream reach.

Description: a brief description of the location of the stream reach.

Reach Length Mi: the estimated length in miles of the main channel for each stream reachConfinement: a confinement rating for each stream reach based on the extent that the valley floodplain of the reach is confined by natural features.Downstream Reach: the name of the directly downstream EDT stream reach.Upstream Reach: the name of the directly upstream EDT stream reach.

Upstream Trib Reach: where it is located, the name of the directly upstream tributary EDT stream reach.

Tier: a rating of 1 – 4 for each EDT stream reach in a subbasin.

Gradient: an average estimated percent slope for each stream reach.

SRP Rating - W Steelhead: Species Reach Potential rating Low – High for winter steelhead if potentially present in a stream reach.

SRP Rating - S Steelhead: Species Reach Potential rating Low – High for summer steelhead if potentially present in a stream reach.

SRP Rating - Fall Chinook: Species Reach Potential rating Low – High for fall tule Chinook if potentially present in a stream reach.

SRP Rating - Spring Chinook: Species Reach Potential rating Low – High for spring Chinook if potentially present in a stream reach.

SRP Rating - Coho: Species Reach Potential rating Low – High for coho salmon if potentially present in a stream reach.

SRP Rating - Chum: Species Reach Potential rating Low – High for fall chum if potentially present in a stream reach.

Restoration Potential: percentage representing the restoration potential of an EDT stream reach based on the relative effect of degradation and restoration scenarios on modeled population performance. If greater population performance benefits are modeled in the restoration scenario than the degradation scenario, the restoration priority will be greater than the preservation priority. This metric informs the relative value of active restoration versus habitat acquisition/protection.

Protection Potential: percentage representing the preservation potential of an EDT stream reach based on the relative effect of degradation and restoration scenarios on modeled population performance. If greater population performance benefits are modeled in the degradation scenario than the restoration scenario, the preservation priority will be greater than the restoration priority. This metric informs the relative value of habitat acquisition/protection versus active restoration.

Downstream RM: the estimated river mile of the downstream end of the EDT stream reach.

Upstream RM: the estimated river mile of the upstream end of the EDT stream reach.

Multi-Species - Stream Ch/Bank Stabilization: multi-species habitat priority rating for stream channel habitat structure and bank stability habitat based on EDT limiting factors and population designations for salmon and steelhead modeled as present in the EDT stream reach. Multi-species habitat priorities represent the general restoration or protection project foci to best address high priority limiting factors as modeled in EDT, for the highest priority populations from a regional recovery perspective.

Multi-Species - Off-Channel/Side-Channel: multi-species habitat priority rating for off-channel and side-channel habitat based on EDT limiting factors and population designations for salmon and steelhead modeled as present in the EDT stream reach. Multi-species habitat priorities represent the general restoration or protection project foci to best address high priority limiting factors as modeled in EDT, for the highest priority populations from a regional recovery perspective.

Multi-Species - Floodplain/Channel Migration: multi-species habitat priority rating for floodplain function and channel migration processes based on EDT limiting factors and population designations for salmon and steelhead modeled as present in the EDT stream reach. Multi-species habitat priorities represent the general restoration or protection project foci to best address high priority limiting factors as modeled in EDT, for the highest priority populations from a regional recovery perspective.

Multi-Species - Riparian Condition: multi-species habitat priority rating riparian conditions and functions based on EDT limiting factors and population designations for salmon and steelhead modeled as present in the EDT stream reach. Multi-species habitat priorities represent the general restoration or protection project foci to best address high priority limiting factors as modeled in EDT, for the highest priority populations from a regional recovery perspective.

Multi-Species - Water Quality: multi-species habitat priority rating for water quality based on EDT limiting factors and population designations for salmon and steelhead modeled as present in the EDT stream reach. Multi-species habitat priorities represent the general restoration or protection project foci to best address high priority limiting factors as modeled in EDT, for the highest priority populations from a regional recovery perspective.

Multi-Species - Instream Flow: multi-species habitat priority rating for instream flows based on EDT limiting factors and population designations for salmon and steelhead modeled as present in the EDT stream reach. Multi-species habitat priorities represent the general restoration or protection project foci to best address high priority limiting factors as modeled in EDT, for the highest priority populations from a regional recovery perspective.

Multi-Species - Stream Management: multi-species habitat priority rating for regulated stream management for habitat functions based on EDT limiting factors and population designations for salmon and steelhead modeled as present in the EDT stream reach. Multi-species habitat priorities represent the general restoration or protection project foci to best address high priority limiting factors as modeled in EDT, for the highest priority populations from a regional recovery perspective.

Multi-Species - Watershed/Hillslope Processes: multi-species habitat priority rating for watershed conditions and hillslope processes based on EDT limiting factors and population designations for salmon and steelhead modeled as present in the EDT stream reach. Multi-species habitat priorities represent the general restoration or protection project foci to best address high priority limiting factors as modeled in EDT, for the highest priority populations from a regional recovery perspective.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

It is estimated that more than 8 billion people live on Earth and the population is likely to hit more than 9 billion by 2050. Approximately 55 percent of Earth’s human population currently live in areas classified as urban. That number is expected to grow by 2050 to 68 percent, according to the United Nations (UN).The largest cities in the world include Tōkyō, Japan; New Delhi, India; Shanghai, China; México City, Mexico; and São Paulo, Brazil. Each of these cities classifies as a megacity, a city with more than 10 million people. The UN estimates the world will have 43 megacities by 2030.Most cities' populations are growing as people move in for greater economic, educational, and healthcare opportunities. But not all cities are expanding. Those cities whose populations are declining may be experiencing declining fertility rates (the number of births is lower than the number of deaths), shrinking economies, emigration, or have experienced a natural disaster that resulted in fatalities or forced people to leave the region.This Global Cities map layer contains data published in 2018 by the Population Division of the United Nations Department of Economic and Social Affairs (UN DESA). It shows urban agglomerations. The UN DESA defines an urban agglomeration as a continuous area where population is classified at urban levels (by the country in which the city resides) regardless of what local government systems manage the area. Since not all places record data the same way, some populations may be calculated using the city population as defined by its boundary and the metropolitan area. If a reliable estimate for the urban agglomeration was unable to be determined, the population of the city or metropolitan area is used.Data Citation: United Nations Department of Economic and Social Affairs. World Urbanization Prospects: The 2018 Revision. Statistical Papers - United Nations (ser. A), Population and Vital Statistics Report, 2019, https://doi.org/10.18356/b9e995fe-en.

In a time of global change, having an understanding of the nature of biotic and abiotic factors that drive a species’ range may be the sharpest tool in the arsenal of conservation and management of threatened species. However, such information is lacking for most tropical and epiphytic species due to the complexity of life history, the roles of stochastic events, and the diversity of habitat across the span of a distribution. In this study, we conducted repeated censuses across the core and peripheral range of Trichocentrum undulatum, a threatened orchid that is found throughout the island of Cuba (species core range) and southern Florida (the northern peripheral range). We used demographic matrix modeling as well as stochastic simulations to investigate the impacts of herbivory, hurricanes, and logging (in Cuba) on projected population growth rates (? and ?s) among sites. Methods Field methods Censuses took place between 2013 and 2021. The longest census period was that of the Peripheral population with a total of nine years (2013–2021). All four populations in Cuba used in demographic modeling that were censused more than once: Core 1 site (2016–2019, four years), Core 2 site (2018–2019, two years), Core 3 (2016 and 2018 two years), and Core 4 (2018–2019, two years) (Appendix S1: Table S1). In November 2017, Hurricane Irma hit parts of Cuba and southern Florida, impacting the Peripheral population. The Core 5 population (censused on 2016 and 2018) was small (N=17) with low survival on the second census due to logging. Three additional populations in Cuba were visited only once, Core 6, Core 7, and Core 8 (Table 1). Sites with one census or with a small sample size (Core 5) were not included in the life history and matrix model analyses of this paper due to the lack of population transition information, but they were included in the analysis on the correlation between herbivory and fruit rate, as well as the use of mortality observations from logging for modeling. All Cuban sites were located between Western and Central Cuba, spanning four provinces: Mayabeque (Core 1), Pinar del Rio (Core 2 and Core 6), Matanzas (Core 3 and Core 5), and Sancti Spiritus (Core 4, Core 7, Core 8). At each population of T. undulatum presented in this study, individuals were studied within ~1-km strips where T. undulatum occurrence was deemed representative of the site, mostly occurring along informal forest trails. Once an individual of T. undulatum was located, all trees within a 5-m radius were searched for additional individuals. Since tagging was not permitted, we used a combination of information to track individual plants for the repeated censuses. These include the host species, height of the orchid, DBH of the host tree, and hand-drawn maps. Individual plants were also marked by GPS at the Everglades Peripheral site. If a host tree was found bearing more than one T. undulatum, then we systematically recorded the orchids in order from the lowest to highest as well as used the previous years’ observations in future censuses for individualized notes and size records. We recorded plant size and reproductive variables during each census including: the number of leaves, length of the longest leaf (cm), number of inflorescence stalks, number of flowers, and the number of mature fruits. We also noted any presence of herbivory, such as signs of being bored by M. miamensis, and whether an inflorescence was partially or completely affected by the fly, and whether there was other herbivory, such as D. boisduvalii on leaves. We used logistic regression analysis to examine the effects of year (at the Peripheral site) and sites (all sites) on the presence or absence of inflorescence herbivory at all the sites. Cross tabulation and chi-square analysis were done to examine the associations between whether a plant was able to fruit and the presence of floral herbivory by M. miamensis. The herbivory was scored as either complete or partial. During the orchid population scouting expeditions, we came across a small population in the Matanzas province (Core 5, within 10 km of the Core 3 site) and recorded the demographic information. Although the sampled population was small (N = 17), we were able to observe logging impacts at the site and recorded logging-associated mortality on the subsequent return to the site. Matrix modeling Definition of size-stage classes To assess the life stage transitions and population structures for each plant for each population’s census period we first defined the stage classes for the species. The categorization for each plant’s stage class depended on both its size and reproductive capabilities, a method deemed appropriate for plants (Lefkovitch 1965, Cochran and Ellner 1992). A size index score was calculated for each plant by taking the total number of observed leaves and adding the length of the longest leaf, an indication of accumulated biomass (Borrero et al. 2016). The smallest plant size that attempted to produce an inflorescence is considered the minimum size for an adult plant. Plants were classified by stage based on their size index and flowering capacity as the following: (1) seedlings (or new recruits), i.e., new and small plants with a size index score of less than 6, (2) juveniles, i.e., plants with a size index score of less than 15 with no observed history of flowering, (3) adults, plants with size index scores of 15 or greater. Adult plants of this size or larger are capable of flowering but may not produce an inflorescence in a given year. The orchid’s population matrix models were constructed based on these stages. In general, orchid seedlings are notoriously difficult to observe and easily overlooked in the field due to the small size of protocorms. A newly found juvenile on a subsequent site visit (not the first year) may therefore be considered having previously been a seedling in the preceding year. In this study, we use the discovered “seedlings” as indicatory of recruitment for the populations. Adult plants are able to shrink or transition into the smaller juvenile stage class, but a juvenile cannot shrink to the seedling stage. Matrix elements and population vital rates calculations Annual transition probabilities for every stage class were calculated. A total of 16 site- and year-specific matrices were constructed. When seedling or juvenile sample sizes were < 9, the transitions were estimated using the nearest year or site matrix elements as a proxy. Due to the length of the study and variety of vegetation types with a generally large population size at each site, transition substitutions were made with the average stage transition from all years at the site as priors. If the sample size of the averaged stage was still too small, the averaged transition from a different population located at the same vegetation type was used. We avoided using transition values from populations found in different vegetation types to conserve potential environmental differences. A total of 20% (27/135) of the matrix elements were estimated in this fashion, the majority being seedling stage transitions (19/27) and noted in the Appendices alongside population size (Appendix S1: Table S1). The fertility element transitions from reproductive adults to seedlings were calculated as the number of seedlings produced (and that survived to the census) per adult plant. Deterministic modeling analysis We used integral projection models (IPM) to project the long-term population growth rates for each time period and population. The finite population growth rate (?), stochastic long-term growth rate (?s), and the elasticity were projected for each matrices using R Popbio Package 2.4.4 (Stubben and Milligan 2007, Caswell 2001). The elasticity matrices were summarized by placing each element into one of three categories: fecundity (transition from reproductive adults to seedling stage), growth (all transitions to new and more advanced stage, excluding the fecundity), and stasis (plants that transitioned into the same or a less advanced stage on subsequent census) (Liu et al. 2005). Life table response experiments (LTREs) were conducted to identify the stage transitions that had the greatest effects on observed differences in population growth between select sites and years (i.e., pre-post hurricane impact and site comparisons of same vegetation type). Due to the frequent disturbances that epiphytes in general experience as well as our species’ distribution in hurricane-prone areas, we ran transient dynamic models that assume that the populations censused were not at stable stage distributions (Stott et al. 2011). We calculated three indices for short-term transient dynamics to capture the variation during a 15-year transition period: reactivity, maximum amplification, and amplified inertia. Reactivity measures a population’s growth in a single measured timestep relative to the stable-stage growth, during the simulated transition period. Maximum amplification and amplified inertia are the maximum of future population density and the maximum long-term population density, respectively, relative to a stable-stage population that began at the same initial density (Stott et al. 2011). For these analyses, we used a mean matrix for Core 1, Core 2 Core 3, and Core 4 sites and the population structure of their last census. For the Peripheral site, we averaged the last three matrices post-hurricane disturbance and used the most-recent population structure. We standardized the indices across sites with the assumption of initial population density equal to 1 (Stott et al. 2011). Analysis was done using R Popdemo version 1.3-0 (Stott et al. 2012b). Stochastic simulation We created matrices to simulate the effects of episodic recruitment, hurricane impacts, herbivory, and logging (Appendix S1: Table S2). The Peripheral population is the longest-running site with nine years of censuses (eight

Demographic processes that ensure the recovery and resilience of marine populations are critical as climate change sends an increasing proportion on a trajectory of decline. Yet for some populations, recovery potential remains high. We conducted annual monitoring over 9-years (2012–2020) to assess the recovery of coral populations belonging to genus Pocillopora. These populations experienced a catastrophic collapse following a severe typhoon in 2009. From the start of the monitoring period, high initial recruitment led to the establishment of a juvenile population that rapidly transitioned to sexually mature adults, which dominated the population within six years after the disturbance. As a result, coral cover increased from 1.1% to 20.2% during this time. To identify key demographic drivers of recovery and population growth rates (λ), we applied kernel resampled Integral Projection Models (IPMs), constructing eight successive models to examine annual change. IPMs were able to capture r..., Data collection Orchid Island (22°03′N, 121°32′E) is a 45 km2 volcanic, tropical island 64 km off the coast of Taiwan, encircled by a narrow fringing reef (5–10 m depth), leading to a dramatic drop-off. Such reef topography is sensitive to typhoons that are both frequent and intense in the region (Ribas-Deulofeu et al., 2021). In 2009, the island was severely affected by Typhoon Morakot (Hall et al., 2013), the deadliest typhoon to hit Taiwan in recorded history, which caused a ~66% decline in mean live coral cover (~60% to ~20%) along reefs in southern Taiwan (Kuo et al., 2011).     Three years after this major disturbance in 2012, three parallel 20 m transects were established at ~8 m depth spaced ~2.5 m apart at a site to the southwest of the island (named Green Grassland; 22°00'N 121°34'E). Usually, this reef site is relatively sheltered from both the waves generated by the winter north-easterly monsoon and summer south-westerly winds. However, on this occasion was proven susc..., , # Data from: Natural coral recovery despite negative population growth

This Mullaetal_2024_dataset_README.txt file was generated on 2023-11-09 by AJM (zeezyuk@gmail.com).

DOI: 10.5061/dryad.msbcc2g5n

Abstract

Demographic processes that ensure the recovery and resilience of marine populations are critical as climate change sends an increasing proportion on a trajectory of decline. Yet for some populations, recovery potential remains high. We conducted annual monitoring over 9-years (2012–2020) to assess the recovery of coral populations belonging to genus Pocillopora. These populations experienced a catastrophic collapse following a severe typhoon in 2009. From the start of the monitoring period, high initial recruitment led to the establishment of a juvenile population that rapidly transitioned to sexually mature adults, which dominated the population within six years after the disturbance. As a result, coral cover increased from 1.1% to 20.2% during...

DOCTORATE DISSERTATION:Studies were carried out in Tilden Park, Contra Costa County, and Sagehen Creek, Nevada County, California., from 1951 to 1955. Population trends of Microtus montanus at Sagehen Creek and Microtus californicus at Tilden Park showed no interspecific synchrony. A local population of sooty grouse (Dendragapus fuliginosus) at Sagehen Creek also showed little indication of synchrony between the long grouse-hare cycle and the short microtine cycle. Both M. montanus and M. californicus exhibit an inverse relationship between population density and natality, as measured by ovulation rate and litter size. The age at which reproductive maturity is reached is apparently quite constant and independent of the stage of the population cycle. In M. montanus the length of the breeding season and seasonal litter production changed only slightly, declining as population density increased up to the cyclic peak. In contrast, length of the breeding season and litter production varied considerably in M. californicus. The conclusion reached is that variable reproduction is not a cause of the cyclic changes observed in population density. Thus, mortality must be the variable that produces the differences in populations from year to year. The population of adult voles immediately prior to the beginning of the breeding season is about the same each year. The level that the population reaches at the end of each breeding season depends upon the survival of individuals born during that breeding season. Changes in the survival rates of young voles are considered to be the main cause of the population differences found in the summer and autumn of the different years. Incidental to the work on population cycles, certain basic aspects of comparative reproduction have been uncovered. In M. californicus, a vole with a long breeding season, litter size is small and precocious breeding is rare. M. montanus, with a short breeding season, has large litters, and precocious breeding and polyovuly are common. Another difference between the species is that litter size is highest at the start of the breeding season in M. montanus, and then progressively declines. In M. californicus, litter size is initially low, rises to a peak in the middle of the breeding season, and then declines again. This pattern correlates with changes in the quality of forage available to the voles. A possible nutritional basis for the population fluctuations was investigated by applying commercial nitrogen and phosphorus fertilizer to several of the meadows at Sagehen Creek in 1954. The M. montanus population apparently responded to the fertilization in that the decline in numbers during the summer of 1954 was not as severe in the fertilized meadows as in the unfertilized meadows, and breeding was prolonged. The response was, however, temporary. A side study on Sooty Grouse was undertaken in Sagehen Creek Basin. The major objectives were to devise some was of determining the relative abundance of the grouse from year to year, and to get some measure of the annual reproduction.

The project includes 9 workpackages and 23 deliverables. Their implementation contributed in the achievement of the three major goals that have been initially addressed, namely: - to form a NEETs composite indicator at a national level, - to record and map the new category of social vulnerability, which are the NEETs - to develop empirically established policy proposals to combat the social exclusion of NEETs as well as to contribute to their multilevel and sustainable social integration. The methodological strategy was based on a back 'n forth process among all collaborating partners in combination with the research processes, while in the meantime, the international experience concerning the NEETs phenomenon has been widely exploited. Two phases of quantitative and two phases of qualitative research in -an unusual for a social research project large number of subjects- have been implemented (800 and 3500 subjects respectively for the two phases of the quantitative and a total of 144 semi-structured and narrative interviews for the qualitative phase). The findings of the research set the base for a) the NEETs composite indicator, b) the establishment of an integrated policy proposal (in multiple levels of public policies and within different scenarios), c) the formation of the NEETs GIS, and d) the creation of a road map for an integrated intervention to prevent the social exclusion of NEETs (with actions deriving from the strategic environment of the intervention and act in combination so as to form the parametric environment), leading to an applied public policy complex. At the same time a) the main characteristics of NEETs problems have been recorded, b) their demographic and social characteristics have been analyzed, c) the main factors contributing to the characterization of a young person as a NEET (gender, age, urbanity, educational level, family income, nationality) have been examined, d) their views and attitudes in respect to education and training, employment, social welfare and the political system as well as their strategies regarding a way out of the situation they are suffering have been analyzed, e) the impact deriving from the expansion of the NEETs phenomenon at an economic and social level have been investigated, (impact assessment) f) research developing categories to address the profile of the NEET in Greece (in relation to the profiles of the NEETs in Europe) have been formed, g) integrated interpretations on this multi-perspective and complex phenomenon have been attempted. Essentially, NEETs have been 'mapped' in quantity (how many they are on the basis of their gender, age, urbanity, education, family income, nationality, characteristics, biography, attitudes, behavioral patterns, views and distribution at a regional and municipal level) as well as in quality. In a country where the percentage of the NEETs population reaches 16,9 % (one of the highest in Europe), and in which their basic characteristics are different than those of other national cases, the problem of the NEETs can't afford to be ignored, as is the case until today. For those reasons, the Barometer of the Absents is an innovative project for Greece, as it is considered the first research at a national level to address the major problem of NEETs (of young people "not existing anywhere"), and one of the few relating projects at a European level. This research has been implemented at a period when the management of the NEETs problem is considered one of the major policy priorities of the EU2020. Young people not in education, training or employment, that is, people absent from all basic activities of the Social State and job market, are worth of our attention. Not only because there are large in number, but mostly because they are at the ultimate level of social vulnerability in their most productive and dynamic age. The "Barometer of the Absent" is not only attempting to measure, analyze and interpret the problem, but it is mostly attempting to provide with an grounded proposal towards its effective management.

The ancestral proportions and the accuracy of the inferred local ancestry for the SAC.

Breakdown of the effect sizes that make up the phenotypes simulated for the Nama.

The feasibility to sequence entire genomes of virtually any organism provides unprecedented insights into the evolutionary history of populations and species. Nevertheless, many population genomic inferences – including the quantification and dating of admixture, introgression and demographic events, and inference of selective sweeps – are still limited by the lack of high-quality haplotype information. The newest generation of sequencing technology now promises significant progress. To establish the feasibility of haplotype-resolved genome resequencing at population scale, we investigated properties of linked-read sequencing data of songbirds of the genus Oenanthe across a range of sequencing depths. Our results based on the comparison of downsampled (25x, 20x, 15x, 10x, 7x, and 5x) with high-coverage data (46-68x) of seven bird genomes mapped to a reference suggest that phasing contiguities and accuracies adequate for most population genomic analyses can be reached already with moderate sequencing effort. At 15x coverage, phased haplotypes span about 90% of the genome assembly, with 50 and 90 percent of phased sequences located in phase blocks longer than 1.25-4.6 Mb (N50) and 0.27-0.72 Mb (N90). Phasing accuracy reaches beyond 99% starting from 15x coverage. Higher coverages yielded higher contiguities (up to about 7 Mb/1Mb (N50/N90) at 25x coverage), but only marginally improved phasing accuracy. Phase block contiguity improved with input DNA molecule length; thus, higher-quality DNA may help keeping sequencing costs at bay. In conclusion, even for organisms with gigabase-sized genomes like birds, linked-read sequencing at moderate depth opens an affordable avenue towards haplotype-resolved genome resequencing at population scale. 10X Genomics linked-reads (60x coverage) were assembled using the Supernova 2.1 assembler. To remove duplicate scaffolds of at least 99% identity from the pseudohaploid assembly, we ran the dedupe procedure in BBTools (https://sourceforge.net/projects/bbmap/) allowing up to 7,000 edits. This reduced the assembly to 11,030 scaffolds. We then aimed to ensure that all duplicate scaffolds were removed and retain only scaffolds whose integrity can be confirmed by the presence of syntenic regions in another songbird genome. To this end, we performed a lastz alignment against the collared flycatcher assembly version 1.5, which is the highest-quality assembly available from the Muscicapidae family. For this we used lastz 1.04 with settings M=254, K=4500, L=3000, Y=15000, C=2, T=2, and --matchcount=10000. This resulted in 295 scaffolds with unique hits in the flycatcher assembly.

This service-related database for a WFS download service provides the occurrence and the number of individuals of eider ducks from counting flights in the area of the Schleswig-Holstein Wadden Sea National Park from 2009. The data are sightings (individual numbers) along a specific flight route (point data). Since the marine ducks are in the remote areas of the Wadden Sea (North Friesland and Dithmarschen) throughout the year, four censuses are commissioned for this monitoring to record the stocks around the year. This spatial data set of the Eider duck monitoring contains the data of all four flights from 2009 onwards, so that the variability can be mapped over the course of the year, but also between the years (UIG relevant). This data serves as a basis for further data compilations: Winter stock, max. Mauser stock and autumn stock. 1. Winter stock: a survey, preferably in January, as there are hardly any migration events at that time, coordinated with the survey of eider ducks in Lower Saxony, in order to prevent double counting if individuals switch between resting places. 2. Mauser stock: two records at the moulting time (July-Sept). For evaluations or technical statements, the value of the maximum mouse population is required, so only the data of the flight with the highest number should be used. 3. Autumn stock as maximum stock of the resting population of eider ducks: a survey in October, since at this time the autumn migration reaches its maximum. This value is used as the stock size of the resting population.

Effective wildlife population management requires an understanding of the abundance of the target species. In the UK, the increase in numbers and range of the non-native invasive grey squirrel Sciurus carolinensis poses a substantial threat to the existence of the native red squirrel S. vulgaris, to tree health, and to the forestry industry. Reducing the number of grey squirrels is crucial to mitigate their impacts.

Camera traps are increasingly used to estimate animal abundance, and methods have been developed that do not require the identification of individual animals. Most of these methods have been focussed on medium to large mammal species with large range sizes and may be unsuitable for measuring local abundances of smaller mammals that have variable detection rates and hard-to-measure movement behaviour.

The aim of this study was to develop a practical and cost-effective method, based on a camera trap index, that could be used by practitioners to estimate target densities of grey squirrels in woodlands to provide guidance on the numbers of traps or contraceptive feeders required for local grey squirrel control.

Camera traps were deployed in ten independent woods of between 6 and 28 ha in size. An index, calculated from the number of grey squirrel photographs recorded per camera per day had a strong linear relationship (R² = 0.90) with the densities of squirrels removed in trap and dispatch operations. From different time filters tested, a 5 minute filter was applied, where photographs of squirrels recorded on the same camera within 5 minutes of a previous photograph were not counted. There were no significant differences between the number of squirrel photographs per camera recorded by three different models of camera, increasing the method's practical application.

This study demonstrated that a camera index could be used to inform the number of feeders or traps required for grey squirrel management through culling or contraception. Results could be obtained within six days without requiring expensive equipment or a high level of technical input. This method can easily be adapted to other rodent or small mammal species, making it widely applicable to other wildlife management interventions.

Among animal species, the songs of male humpback whales (Megaptera novaeangliae) are a rare example of social learning between entire populations. Understanding fine-scale similarity in song patterns and structural features will better clarify how accurately songs are learned during inter-population transmission. Here, six distinct song types (2009–2015) transmitted from the east Australian to New Caledonian populations were quantitatively analysed using fine-scale song features. Results found that New Caledonian whales learned each song type with high accuracy regardless of the pattern's complexity. However, there were rare instances of themes (stereotyped patterns of sound units) only sung by a single population. These occurred more often in progressively changing 'evolutionary' songs compared to rapidly changing 'revolutionary' songs. Our results suggest that populations do not need to reduce complexity to accurately learn song patterns. Populations may also incorporate changes and embellishments into songs in the form of themes which are suggested to be learnt as distinct segments. Maintaining complex song patterns with such accuracy suggests significant acoustic contact, supporting the hypothesis that song learning may occur on shared feeding grounds or migration routes. This study improves the understanding of inter-population mechanisms for large-scale cultural transmission in animals.

The world population data sourced from Meta Data for Good is some of the most accurate population density data in the world. The data is accumulated using highly accurate technology to identify buildings from satellite imagery and can be viewed at up to 30-meter resolution. This building data is combined with publicly available census data to create the most accurate population estimates. This data is used by a wide range of nonprofit and humanitarian organizations, for example, to examine trends in urbanization and climate migration or discover the impact of a natural disaster on a region. This can help to inform aid distribution to reach communities most in need. There is both country and region-specific data available. The data also includes demographic estimates in addition to the population density information. This population data can be accessed via the Humanitarian Data Exchange website.

The spatial distribution of animals has consequences for nutrition, predator-prey dynamics, spread of diseases, and population dynamics in general. Animals must establish a home range to secure adequate resources to fuel their energetic needs. Home ranges, therefore, are temporally and spatially dynamic given the changing requirements of an animal and the availability of resources on the landscape. We used data from two populations of bighorn sheep with contrasting population dynamics following pneumonia epizootics and different habitat quality on their summer range to test the hypothesis that the distribution and size of home ranges are influenced by environmental conditions and reproductive status. We used a combination of data from 768 vegetation transects and remotely sensed metrics to index forage quality of consecutive biweekly home ranges for 27 bighorn sheep, June–August 2019–2021. There were population differences in space use that were consistent with resource limitations in t..., We used data from two populations of Rocky Mountain bighorn sheep within the Greater Yellowstone Ecosystem in northwest Wyoming, USA, 2019–2021 (Figure 1). The Whiskey Mountain population experienced a pneumonia epizootic in 1991 (Ryder et al. 1992) and has since exhibited population decline via low juvenile recruitment (22 juveniles per 100 adult females in winter on average 2019–2021; Wyoming Game and Fish Department 2021a), leaving the population at ~20% of its former population size (upwards of 1,500 animals; Wyoming Game and Fish Department, unpublished data). The Jackson population experienced pneumonia epizootics in 2001 and 2012 but has been able to recover to previous population size (~ 400 animals) and maintain higher juvenile recruitment (38.6 juveniles per 100 adult females; Wyoming Game and Fish Department 2021b). Study animals were seasonal elevational migrants. Summer ranges were high-elevation (~3,000m) alpine habitats with alpine meadows, talus fields, and rocky outcrop..., , # Disparate home range dynamics reflect nutritional inadequacies on summer range for a large herbivore

https://doi.org/10.5061/dryad.44j0zpcmc

Description of the data and file structure

PCA_data This file contains all the meterics that went into the PCA with varimax reduction. For more detail on exactly how these metrics were quantified see Wagler et al. 2023. Implications of forage quality for population recovery of bighorn sheep following a pneumonia epizootic. Journal of Wildlife Management 87:e22452. Each metric reflects the mean of that variable for the line point intercept transect. DMD_rx = mean dry matter digestibility (%) for each plant along the transect. this metric accounts for for inorganic hits (inorganic hits accounted for in the mean with a 0) CP_rx = mean crude protien (%) for each plant along the transect. this metric accounts for inorganic hits (inorganic hits accounted for in the mean with a 0) Biomass_kgha_tr...