Before the activity, students are divided into 3 groups that are assigned 3 different reading assignments (land use, atmosphere, or water quality). On the day of the activity, students work collaboratively with students from the same reading assignment group for 20 – 40 minutes to answer questions and address concepts from their particular assigned reading. Next, students are shuffled (jigsaw-style) into small teams of 3 students (one student from each reading group). Students educate each other with concepts from their respective reading groups and then work collaboratively on a shared project to select, define, and potentially solve an environmental challenge.

This statistic shows the 20 countries with the highest population growth rate in 2024. In SouthSudan, the population grew by about 4.65 percent compared to the previous year, making it the country with the highest population growth rate in 2024. The global population Today, the global population amounts to around 7 billion people, i.e. the total number of living humans on Earth. More than half of the global population is living in Asia, while one quarter of the global population resides in Africa. High fertility rates in Africa and Asia, a decline in the mortality rates and an increase in the median age of the world population all contribute to the global population growth. Statistics show that the global population is subject to increase by almost 4 billion people by 2100. The global population growth is a direct result of people living longer because of better living conditions and a healthier nutrition. Three out of five of the most populous countries in the world are located in Asia. Ultimately the highest population growth rate is also found there, the country with the highest population growth rate is Syria. This could be due to a low infant mortality rate in Syria or the ever -expanding tourism sector.

The world's population first reached one billion people in 1803, and reach eight billion in 2023, and will peak at almost 11 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 live 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 decade 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.

About this dataset

Context

The dataset tabulates the data for the Impact, TX population pyramid, which represents the Impact population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Impact, TX, is 0.0.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Impact, TX, is 41.7.
• Total dependency ratio for Impact, TX is 41.7.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Impact, TX is 2.4.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Impact population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Impact for the selected age group is shown in the following column.
• Population (Female): The female population in the Impact for the selected age group is shown in the following column.
• Total Population: The total population of the Impact for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Impact Population by Age. You can refer the same here

Population growth rates will respond to an environmental driver only if the driver impacts demographic rate(s) and the population is sensitive to impacted demographic rate(s). If populations vary in the sensitivity of population growth rate to demographic rates, the effect of an environmental driver on population growth rate could vary across populations, even if the effect of the driver on demographic rates does not vary across populations. Here, we use five years of demographic data of a common alpine plant, including data from a neighbor removal experiment and a climate warming experiment, to quantify the relative contribution of neighbor effects on demographic rates vs. sensitivity of population growth rate to demographic rates to across-population variation in neighbor effects on population growth rate. We find neighbor effects on population growth rate vary significantly across populations, and this effect is driven primarily by variation in sensitivity of population growth rate t...

In 2023, the natural growth rate of the population across China varied between 7.96 people per 1,000 inhabitants (per mille) in Tibet and -6.92 per mille in Heilongjiang province. The national total population growth rate turned negative in 2022 and ranged at -1.48 per mille in 2023. Regional disparities in population growth The natural growth rate is the difference between the birth rate and the death rate of a certain region. In China, natural population growth reached the highest values in the western regions of the country. These areas have a younger population and higher fertility rates. Although the natural growth rate does not include the direct effects of migration, migrants are often young people in their reproductive years, and their movement may therefore indirectly affect the birth rates of their home and host region. This is one of the reasons why Guangdong province, which received a lot of immigrants over the last decades, has a comparatively high population growth rate. At the same time, Jilin, Liaoning, and Heilongjiang province, all located in northeast China, suffer not only from low fertility, but also from emigration of young people searching for better jobs elsewhere. The impact of uneven population growth The current distribution of natural population growth rates across China is most likely to remain in the near future, while overall population decline is expected to accelerate. Regions with less favorable economic opportunities will lose their inhabitants faster. The western regions with their high fertility rates, however, have only small total populations, which limits their effect on China’s overall population size.

Understanding the influence of environmental variability on population dynamics is a fundamental goal of ecology. Theory suggests that, for populations in variable environments, temporal correlations between demographic vital rates (e.g., growth, survival, reproduction) can increase (if positive) or decrease (if negative) the variability of year-to-year population growth. Because this variability generally decreases long-term population viability, vital rate correlations may importantly affect population dynamics in stochastic environments. Despite long-standing theoretical interest, it is unclear whether vital rate correlations are common in nature, whether their directions are predominantly negative or positive, and whether they are of sufficient magnitude to warrant broad consideration in studies of stochastic population dynamics. We used long-term demographic data for three perennial plant species, hierarchical Bayesian parameterization of population projection models, and stochastic simulations to address the following questions: (1) What are the sign, magnitude, and uncertainty of temporal correlations between vital rates? (2) How do specific pairwise correlations affect the year-to-year variability of population growth? (3) Does the net effect of all vital rate correlations increase or decrease year-to-year variability? (4) What is the net effect of vital rate correlations on the long-term stochastic population growth rate (λS)? We found only four moderate to strong correlations, both positive and negative in sign, across all species and vital rate pairs; otherwise, correlations were generally weak in magnitude and variable in sign. The net effect of vital rate correlations ranged from a slight decrease to an increase in the year-to-year variability of population growth, with average changes in variance ranging from -1% to +22%. However, vital rate correlations caused virtually no change in the estimates of λS (mean effects ranging from -0.01% to +0.17%). Therefore, the proportional changes in the variance of population growth caused by demographic correlations were too small on an absolute scale to importantly affect population growth and viability. We conclude that in our three focal populations and perhaps more generally, vital rate correlations have little effect on stochastic population dynamics. This may be good news for population ecologists, because estimating vital rate correlations and incorporating them into population models can be data-intensive and technically challenging.

BackgroundExtensive studies in different fields have been performed to reconstruct the prehistory of populations in the Japanese archipelago. Estimates the ancestral population dynamics based on Japanese molecular sequences can extend our understanding about the colonization of Japan and the ethnogenesis of modern Japanese. Methodology/Principal FindingsWe applied Bayesian skyline plot (BSP) with a dataset based on 952 Japanese mitochondrial DNA (mtDNA) genomes to depict the female effective population size (Nef) through time for the total Japanese and each of the major mtDNA haplogroups in Japanese. Our results revealed a rapid Nef growth since ∼5 thousand years ago had left ∼72% Japanese mtDNA lineages with a salient signature. The BSP for the major mtDNA haplogroups indicated some different demographic history. Conclusions/SignificanceThe results suggested that the rapid population expansion acted as a major force in shaping current maternal pool of Japanese. It supported a model for population dynamics in Japan in which the prehistoric population growth initiated in the Middle Jomon Period experienced a smooth and swift transition from Jomon to Yayoi, and then continued through the Yayoi Period. The confounding demographic backgrounds of different mtDNA haplogroups could also have some implications for some related studies in future.

The English structural transformation from farming to manufacturing was accompanied by rapid technological change, expansion of trade, and massive population growth. While the roles of technology and trade in this process have been investigated, the literature has largely ignored the role of population growth. We examine population size effects on various aspects of structural development, characterizing their explicit dependence on preference-side and production-side characteristics of the economy, and trade. Our quantitative analysis of the English transformation assigns a major role to population growth, with especially notable contributions to post-1750 rise in the manufacturing employment share and the relative price dynamics.

In 2024, the population of Taiwan decreased by around **** people per thousand inhabitants (per mill). Taiwan's population declined in 2020 for the first time in decades and population decrease is expected to accelerate in the future. High fluctuations between 2020 and 2023 were mainly due to strong net migration during the COVID-19 pandemic. The natural growth rate, which denotes population growth not including the effects of net migration, was at around ***** per mill in 2024.

Population dynamics, its types. Population migration (external, internal), factors determining it, main trends. Impact of migration on population health.

Under the guidance of Moldoev M.I. Sir By Riya Patil and Rutuja Sonar

Abstract

Population dynamics influence development and vice versa, at various scale levels: global, continental/world-regional, national, regional, and local. Debates on how population growth affects development and how development affects population growth have already been subject of intensive debate and controversy since the late 18th century, and this debate is still ongoing. While these two debates initially focused mainly on natural population growth, the impact of migration on both population dynamics and development is also increasingly recognized. While world population will continue growing throughout the 21st century, there are substantial and growing contrasts between and within world-regions in the pace and nature of that growth, including some countries where population is stagnating or even shrinking. Because of these growing contrasts, population dynamics and their interrelationships with development have quite different governance implications in different parts of the world.

1. Population Dynamics

Population dynamics refers to the changes in population size, structure, and distribution over time. These changes are influenced by four main processes:

Birth rate (natality)

Death rate (mortality)

Immigration (inflow of people)

Emigration (outflow of people)

Types of Population Dynamics

Natural population change: Based on birth and death rates.

Migration-based change: Caused by people moving in or out of a region.

Demographic transition: A model that explains changes in population growth as societies industrialize.

Population distribution: Changes in where people live (urban vs rural).

2. Population Migration

Migration refers to the movement of people from one location to another, often across political or geographical boundaries.

Types of Migration

External migration (international):

Movement between countries.

Examples: Refugee relocation, labor migration, education.

Internal migration:

Movement within the same country or region.

Examples: Rural-to-urban migration, inter-state migration.

3. Factors Determining Migration

Migration is influenced by push and pull factors:

Push factors (reasons to leave a place):

Unemployment

Conflict or war

Natural disasters

Poverty

Lack of services or opportunities

Pull factors (reasons to move to a place):

Better job prospects

Safety and security

Higher standard of living

Education and healthcare access

Family reunification

4. Main Trends in Migration

Urbanization: Mass movement to cities for work and better services.

Global labor migration: Movement from developing to developed countries.

Refugee and asylum seeker flows: Due to conflict or persecution.

Circular migration: Repeated movement between two or more locations.

Brain drain/gain: Movement of skilled labor away from (or toward) a country.

5. Impact of Migration on Population Health

Positive Impacts:

Access to better healthcare (for migrants moving to better systems).

Skills and knowledge exchange among health professionals.

Remittances improving healthcare affordability in home countries.

Negative Impacts:

Migrants’ health risks: Increased exposure to stress, poor living conditions, and occupational hazards.

Spread of infectious diseases: Especially when health screening is lacking.

Strain on health services: In receiving areas, especially with sudden or large influxes.

Mental health challenges: Due to cultural dislocation, discrimination, or trauma.

Population dynamics is one of the fundamental areas of ecology, forming both the basis for the study of more complex communities and of many applied questions. Understanding population dynamics is the key to understanding the relative importance of competition for resources and predation in structuring ecological communities, which is a central question in ecology.

Population dynamics plays a central role in many approaches to preserving biodiversity, which until now have been primarily focused on a single species approach. The calculation of the intrinsic growth rate of a species from a life table is often the central piece of conservation plans. Similarly, management of natural resources, such as fisheries, depends on population dynamics as a way to determine appropriate management actions.

Population dynamics can be characterized by a nonlinear system of difference or differential equations between the birth sizes of consecutive periods. In such a nonlinear system, when the feedback elasticity of previous events on current birth size is larger, the more likely the dynamics will be volatile. Depending on the classification criteria of the population, the revealed cyclical behavior has various interpretations. Under different contextual scenarios, Malthusian cycles, Easterlin cycles, predator–prey cycles, dynastic cycles, and capitalist–laborer cycles have been introduced and analyzed

Generally, population dynamics is a nonlinear stochastic process. Nonlinearities tend to be complicated to deal with, both when we want to do analytic stochastic modelling and when analysing data. The way around the problem is to approximate the nonlinear model with a linear one, for which the mathematical and statistical theories are more developed and tractable. Let us assume that the population process is described as:

(1)Nt=f(Nt−1,εt)

where Nt is population density at time t and εt is a series of random variables with identical distributions (mean and variance). Function f specifies how the population density one time step back, plus the stochastic environment εt, is mapped into the current time step. Let us assume that the (deterministic) stationary (equilibrium) value of the population is N* and that ε has mean ε*. The linear approximation of Eq. (1) close to N* is then:

(2)xt=axt−1+bϕt

where xt=Nt−N*, a=f

f(N*,ε*)/f

N, b=ff(N*,ε*)/fε, and ϕt=εt−ε*

The term population refers to the members of a single species that can interact with each other. Thus, the fish in a lake, or the moose on an island, are clear examples of a population. In other cases, such as trees in a forest, it may not be nearly so clear what a population is, but the concept of population is still very useful.

Population dynamics is essentially the study of the changes in the numbers through time of a single species. This is clearly a case where a quantitative description is essential, since the numbers of individuals in the population will be counted. One could begin by looking at a series of measurements of the numbers of particular species through time. However, it would still be necessary to decide which changes in numbers through time are significant, and how to determine what causes the changes in numbers. Thus, it is more sensible to begin with models that relate changes in population numbers through time to underlying assumptions. The models will provide indications of what features of changes in numbers are important and what measurements are critical to make, and they will help determine what the cause of changes in population levels might be.

To understand the dynamics of biological populations, the study starts with the simplest possibility and determines what the dynamics of the population would be in that case. Then, deviations in observed populations from the predictions of that simplest case would provide information about the kinds of forces shaping the dynamics of populations. Therefore, in describing the dynamics in this simplest case it is essential to be explicit and clear about the assumptions made. It would not be argued that the idealized population described here would ever be found, but that focusing on the idealized population would provide insight into real populations, just as the study of Newtonian mechanics provides understanding of more realistic situations in physics.

Population migration

The vast majority of people continue to live in the countries where they were born —only one in 30 are migrants.

In most discussions on migration, the starting point is usually numbers. Understanding changes in scale, emerging trends, and shifting demographics related to global social and economic transformations, such as migration, help us make sense of the changing world we live in and plan for the future. The current global estimate is that there were around 281 million international migrants in the world in 2020, which equates to 3.6 percent of the global population.

Overall, the estimated number of international migrants has increased over the past five decades. The total estimated 281 million people living in a country other than their countries of birth in 2020 was 128 million more than in 1990 and over three times the estimated number in 1970.

There is currently a larger number of male than female international migrants worldwide and the growing gender gap has increased over the past 20 years. In 2000, the male to female split was 50.6 to 49.4 per cent (or 88 million male migrants and 86 million female migrants). In 2020 the split was 51.9 to 48.1 per cent, with 146 million male migrants and 135 million female migrants. The share of

The statistic shows the natural rate of population growth by continent in the middle of 2014. The natural rate of population growth in Africa was 2.5 percent in the middle of 2014.The natural rate of population growth arises from the birth rate minus the death rate and without including the effects of migration.Population growthAs shown in the statistic above, the natural rate of population growth continues to increase on almost every continent in 2013.Due to medical advances, better living conditions and the increase of agricultural productivity the world population is continuously rising. The development of the world population from 1950 to 2030 is estimated to be tripled according to United Nations’ data.The majority of the world population lives in Asia, but the population in Africa is forecasted to rise from 1,031 in year 2010 up to 4,185 in year 2100. This forecast is based on the rapid growth of the developing countries, such as Africa. Developing countries are well known for its urban residents living in slum conditions. A slum is defined as a thickly populated, metropolitan area with bad living conditions and people living below the poverty line.The urban population in developing countries, who lived in slums has increased steadily for the last decades. In 1990, around 656.7 million people were living in slums in developing countries, while this number rose to 827.7 million people living in slums of developing countries in 2010.The number of people living in slums worldwide is estimated to grow from 1,145,984 in year 2010 to 1,477,291 in year 2020 by the UN-HABITAT. In some countries the population living in slums grows faster than in others, naturally. The percentage of urban slum dwellers in Morocco for example nearly doubled from 13 percent to 24 percent between 2000 and 2010, while the same rate in Turkey only grew moderately from 13 to 18 percent.

1. Recent studies unravelled the effect of climate changes on populations through their impact on functional traits and demographic rates in terrestrial and freshwater ecosystems, but such understanding in marine ecosystems remains incomplete. 2. Here, we evaluate the impact of the combined effects of climate and functional traits on population dynamics of a long-lived migratory seabird breeding in the southern ocean: the black-browed albatross (Thalassarche melanophris, BBA). We address the following prospective question: ''Of all the changes in the climate and functional traits, which would produce the biggest impact on the BBA population growth rate?'' 3. We develop a structured matrix population model that includes the effect of climate and functional traits on the complete BBA life cycle. A detailed sensitivity analysis is conducted to understand the main pathway by which climate and functional trait changes affect the population growth rate. 4. The population growth rate of BBA is driven by the combined effects of climate over various seasons and multiple functional traits with carry-over effects across seasons on demographic processes. Changes in Sea Surface Temperature (SST) during late winter cause the biggest changes in the population growth rate, through their effect on juvenile survival. Adults appeared to respond to changes in winter climate conditions by adapting their migratory schedule rather than by modifying their at-sea foraging activity. However, the sensitivity of the population growth rate to SST affecting BBA migratory schedule is small. BBA foraging activity during the pre-breeding period has the biggest impact on population growth rate among functional traits. Finally, changes in SST during the breeding season have little effect on the population growth rate. 5. These results highlight the importance of early life histories and carry-over effects of climate and functional traits on demographic rates across multiple seasons in population response to climate change. Robust conclusions about the roles of various phases of the life cycle and functional traits in population response to climate change rely on an understanding of the relationships of traits to demographic rates across the complete life cycle.

Before 2025, the world's total population is expected to reach eight billion. Furthermore, it is predicted to reach over 10 billion in 2060, before slowing again as global birth rates are expected to decrease. Moreover, it is still unclear to what extent global warming will have an impact on population development. A high share of the population increase is expected to happen on the African continent.

Background: Rapid population growth and its impact on the environment is a major problem facing the world today, in addition to other issues that also require serious handling and attention. Rapid population growth, especially in cities and urban areas, puts pressure on the fulfillment of population needs that must be provided to ensure the survival of the population. Population growth has an impact on the increasing needs of the population for affordable housing, food needs, transportation. Method: This research uses a qualitative approach based on case studies and literature reviews. This approach involves a critical and in-depth evaluation of previous research, focusing on data collected from various sources related to the impact of population growth on affordable housing, food needs, and sustainable transportation. Findings: The rate of population growth has an impact on environmental sustainability, as a result of the exploitation of natural resources to fulfill various needs, including food needs. Population growth has a linear effect on the demand for food, such as rice and tubers, through the provision of agricultural land. This increase in consumption value occurs in an increasingly limited stock of natural resources, therefore a food fulfillment strategy is needed to achieve national food security and sovereignty, to meet the needs of the population and for food stocks to anticipate undesirable things, such as natural disasters and crop failures. Some of the efforts that can be made are food diversification, intensification, and extensification of agriculture accompanied by the active role of the government in providing infrastructure and supporting policies. Population growth also affects the level of population mobility. Each individual carries out daily activities such as school, work and other activities. This population mobility greatly affects the use of transportation modes to reach certain destinations. The mode of transportation consists of private vehicles and public vehicles. Conclusion: If the use of private vehicles is more than public vehicles, there is the potential for traffic congestion. In addition, the more vehicles used, the greater the carbon emissions produced so that it can cause greenhouse gas effects. One of the efforts that can be made is to implement sustainable transportation management through Transit Oriented Development (TOD) in the provision of transportation modes. TOD is expected to make private vehicle users switch to using public transportation. Novelty/Originality of this study: This research proposes a holistic approach to address the impacts of urban population growth, combining strategies for food diversification, transit-based development, and affordable housing. This framework is expected to be a practical innovation for sustainable urban development in countries with rapid population growth.

When comparing somatic growth thermal performance curves (TPCs), higher somatic growth across experimental temperatures is often observed for populations originating from colder environments. Such countergradient variation has been suggested to represent adaptation to seasonality, or shorter favorable seasons in colder climates. Alternatively, populations from cold climates may outgrow those from warmer climates at low temperature, and vice versa at high temperature, representing adaptation to temperature. Using modelling, we show that distinguishing between these two types of adaptation based on TPCs requires knowledge about (i) the relationship between somatic growth rate and population growth rate, which in turn depends on the scale of somatic growth (absolute or proportional), and (ii) the relationship between somatic growth rate and mortality rate in the wild. We illustrate this by quantifying somatic growth rate TPCs for three populations of Daphnia magna where population growth scales linearly with proportional somatic growth. For absolute somatic growth, the northern population outperformed the two more southern populations across temperatures, and more so at higher temperatures, consistent with adaptation to seasonality. In contrast, for the proportional somatic growth TPCs, and hence population growth rate, TPCs tended to converge towards the highest temperatures. Thus, if the northern population pays an ecological mortality cost of rapid growth in the wild, this may create crossing population growth TPCs consistent with adaptation to temperature. Future studies within this field should be more explicit in how they extrapolate from somatic growth in the lab to fitness in the wild. Methods D. magna ephippia were obtained from three populations: a pond in Værøy, Norway (67.687°N 12.672°E), a pond in Park Midden-Limburg, Zonhoven, Belgium (50.982°N 5.318°E), and a rice field which is flooded and dries out annually in the Delta del Ebro, Riet Vell, Spain (40.659°N 0.775°E). In the following, these three populations are referred to as the Norway, Belgium and Spain populations, respectively. We used 10 clones (originating from 10 different ephippia) from each population in the experiments, and these were reared at 17°C with a 16L:8D photoperiod for three to four parthenogenetic generations prior to the experiment. During this period, individuals were fed three times a week with Shellfish Diet 1800 (Reed Mariculture Inc, USA) at final concentration of algae 4 × 105 cells/ml, and the ADaM medium was changed once a week. For the experiment, second or later clutch neonates were collected and photographed less than 24 hours after birth. After photographing, neonates were placed individually in 50 ml tubes containing 17°C ADaM medium. Each tube was placed in a Memmert Peltier-cooled incubator IPP 260plus (Memmert, Germany). We used a 16L:8D photoperiod and the temperature in different cabinets was set to 12.0, 15.0, 17.0, 19.0, 22.0, 24.0 and 26.0 °C. Each temperature treatment received eight individuals from each of the 10 clones. Animals were fed every second day with concentrations that had previously been established to represent ad lib rations. Due to logistic constraints, the different temperature treatments were run simultaneously for one population at a time (Norway May-June 2015, Spain December-February 2018, Belgium July-September 2018). All individuals were checked daily for mortality and sexual maturity (presence of eggs in the brood chamber). Tubes were rotated daily within the climate cabinets during the maturity checks to avoid positional effects. Upon maturation individuals were photographed and terminated.

Genetic diversity and temperature increases associated with global climate change are known to independently influence population growth and extinction risk. Whether increasing temperature may influence the effect of genetic diversity on population growth, however, is not known. We address this issue in the model protist system Tetrahymena thermophila. We test the hypothesis that at temperatures closer to the species’ thermal optimum (i.e., the temperature at which population growth is maximal, or Topt), genetic diversity should have a weaker effect on population growth compared to temperatures away from the thermal optimum. To do so, we grew populations of T. thermophila with varying levels of genetic diversity at increasingly warmer temperatures and quantified their intrinsic population growth rate, r. We found that genetic diversity increases population growth at cooler temperatures, but that as temperature increases, this effect weakens. We also show that a combination of changes in...

This map shows the change in particulate matter 2.5 (PM 2.5) air quality data for the US between 2010 and 2016 based on NASA SEDAC gridded data. The color indicates better or worse air quality, and the size of the symbol indicates population growth.This map shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into state, county, congressional district (116th) and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality in the United States, including Puerto Rico. A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis. The county and state layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Each layer has been enriched with a set of 2019 US demographic attributes (excluding Puerto Rico) apportioned to the geography in order to map patterns alongside each other. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries:50km hex bins generated using the Generate Tessellation toolStates and counties come from 2018 TIGER boundaries with coastlines clipped116th Congressional Districts come from this ArcGIS Living Atlas layerData processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The Enrich tool was run to add 2019 Esri demographic and 2014-2018 ACS attributes to the geographies. Attributes such as population, poverty, minority population, and others were added to the layer.To create the population-weighted attributes on the state and county layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and summarized within the state and county boundaries.The summation of these values were then divided by the total population of each state/county.

R code for constructing and analyzing matrices with nonbreedersR code to construct and analyze matrices in systems with pre- or post-breeding census, different types of nonbreeders, and with or without negative frequency dependence in breeder survival or fecundity. Analyses include growth rate, stable stage structure, reproductive value, demographic variance, comparison to equivalent Leslie matrix model, sensitivity analysis, effect of only observing breeders, and more.Lee_Nonbreeder_matrix_analysis_code.RR code for running sensitivity analyses in systems with nonbreedersR code for running sensitivity analyses on systems with different types of nonbreeders, with or without negative frequency dependence in breeder survival or reproduction.Lee_Nonbreeder_sensitivity_analysis_code.R