Demographic compensation – the opposing responses of vital rates along environmental gradients – potentially delays anticipated species’ range contraction under climate change, but no consensus exists on its actual contribution. We calculated population growth rate (λ) and demographic compensation across the distributional ranges of 81 North American tree species, and examined their responses to simulated warming and tree competition. We found that 43% of species showed stable population size at both northern and southern edges. Demographic compensation was detected in 25 species, yet fifteen of them still showed a potential retraction from southern edges, indicating that compensation alone cannot maintain range stability. Simulated climatic warming caused larger decreases in λ for most species, and weakened the effectiveness of demographic compensation in stabilizing ranges. These findings suggest that climate stress may surpass the limited capacity of demographic compensation and pose a threat to the viability of North American tree populations.

Theory suggests that the drivers of demographic variation and local adaptation are shared and may feedback on one other. Despite some evidence for these links in controlled settings, the relationship between local adaptation and demography remains largely unexplored in natural conditions. Using 10 years of demographic data and two reciprocal transplant experiments, we tested predictions about the relationship between the magnitude of local adaptation and demographic variation (population growth rates and their elasticities to vital rates) across 10 populations of a well-studied annual plant. In both years, we found a strong unimodal relationship between mean home-away local adaptation and stochastic population growth rates. Other predicted links were either weakly or not supported by our data. Our results suggest that declining and rapidly growing populations exhibit reduced local adaptation, potentially due to maladaptation and relaxed selection, respectively., This dataset includes long-term data collected using observations and environmetnal sensors, data on population dynamics derived from field census data, and data from 2 years of reciprocal transplants in field conditions. Data describing population dynamics have been processed from raw census data using matrix population models. All other data processing is performed using code that is archived along with the data., Annotated code necessary to reproduce the analyses and figures presented in the associated manuscript are included in this archive., # Data from: Local adaptation is highest in populations with stable long-term growth

Lauren N. Carley et al.

lauren.n.carley@gmail.com

STRUCTURE OF THIS ARCHIVE:

Details on the purpose of each file in these folders, and their subdirectories, is provided below, following the general outline:

• Clarkia-LTREB-transplant-archive/
  • README.txt
  • 1-data/
  • 2-analyses/
  • 2a-seed-prediction/
  • 2b-aster/
  • 2c-dist-calcs/
    • out/
  • 2d-dist-analyses/
  • 2e-permutation-tests/
    • Fdists/
  • 3-figures/
  • supplemental/

NOTE: Throughout the whole directory, variables in datasets are unitless unless otherwise defined, and "NA" values represent missing data unless otherwise defined.

Clarkia-LTREB-transplant-archive-R2/

This directory contains all of the other subdirectories, which take you through data processing, modeling, and analysis step by step.

It also contains one file:

README.txt

You are curren...

This statistic shows the total population of France from 2020 to 2024, with projections up until 2030. In 2024, the total population of France amounted to 68.44 million people. See the population of Italy for comparison. France's population Although the total French population has annually increased, population growth has been in a slump from 2006 to 2012. However, the decrease of population growth is seemingly irrelative to births in the country, primarily because France’s fertility rate has remained relatively steady over the past decade, based on information from 2011. Yearly population growth could potentially be attributed to a positive lifestyle in the country and a steady economic growth. France is ranked in the top 30 countries with the highest Human Development Index , also known as HDI, which is determined based on life expectancy at birth, literacy rate, education levels and gross national income per capita. France, in this case, was ranked 12th out of the top 20 countries with the highest life expectancy in 2011. From an economic standpoint, France has remained stable, despite several complications within the European Union. Since the 2008 financial crisis, France’s unemployment rate has increased and has experienced several swings year-to-year up until 2014. However, despite fluctuating unemployment rates, GDP growth has very slightly been on the rise on a yearly basis, ever since experiencing a dramatic drop in 2009. Additionally, the GDP itself has continuously been fluctuating since 2008), after enduring a continuous increase in the years prior.

Poland's natural population increase has undergone significant fluctuations since 1946, with a notable decline of nearly 137,000 in 2023. This recent downturn marks a stark contrast to the post-World War II era, when the country experienced substantial population growth. Declining fertility and population projections A key factor contributing to Poland's population decline is the falling fertility rate, which reached a low of 1.16 children per woman in 2023. This figure is well below the replacement level needed to maintain a stable population. The impact of low fertility is evident in long-term population projections, with forecasts suggesting Poland's population could decrease to approximately 29.5 million by 2100. Migration patterns Migration plays a complex role in Poland's population dynamics. While the country has historically been a source of emigrants, recent years have seen a shift towards positive net migration. In 2024, Poland recorded more inflows than outflows, resulting in a net migration of nearly 9,300 people. The three most common countries from which people emigrated to Poland were Ukraine (nearly 40,000 people), Belarus (14,000 people), and Germany (3,800 people).

The study associated with this dataset proposes a way of performing age-specific sensitivity analysis of stable, stochastic and transient growth for stage-classified populations. Here, you find simulation code in R to produce figures in the manuscript and matrices reporting demographic data upon which code computations are performed., ,

1- Temporal fluctuations in growth rates can arise from both variation in age-specific vital rates and temporal fluctuations in age structure (i.e., the relative abundance of individuals in each age-class). However, empirical assessments of temporal fluctuations in age structure and their effects on population growth rate are rare. Most research has focused on understanding the contribution of changing vital rates to population growth rates and these analyses routinely assume that: (i) populations have stable age distributions, (ii) environmental influences on vital rates and age structure are stationary (i.e., the mean and/or variance of these processes does not change over time), and (iii) dynamics are independent of density. 2- Here we quantified fluctuations in age structure and assessed whether they were stationary for four populations of free-ranging vertebrates: moose (observed for 48 years), elk (15 years), tawny owls (15 years) and gray wolves (17 years). We also assessed the extent that fluctuations in age structure were useful for predicting annual population growth rates using models which account for density-dependence. 3- Fluctuations in age structure were of a similar magnitude to fluctuations in abundance. For three populations (moose, elk, owls), the mean and the skew of the age distribution fluctuated without stabilizing over the observed time periods. More precisely, the sample variance (interannual variance) of age structure indices increased with the length of the study period which suggests that fluctuations in age structure were non-stationary for these populations – at least over the 15-48 year periods analysed. 4- Fluctuations in age structure were associated with population growth rate for two populations. In particular, population growth varied from positive to negative for moose and from near zero to negative for elk as the average age of adults increased over its observed range. 5- Non-stationarity in age structure may represent an important mechanism by which abundance becomes non-stationary – and therefore difficult to forecast – over time scales of concern to wildlife managers. Overall, our results emphasize the need for vertebrate populations to be modelled using approaches that consider transient dynamics and density-dependence, and that do not rely on the assumption that environmental processes are stationary.

How does the sustainable level of consumption depend on productivity growth and the size and growth rate of the population? What is the effect of uncertainty over these growth rates? I address these questions using a model in which productivity and population growth are stochastic, and social welfare allows for human lives to have (positive or negative) intrinsic value. I show how the maximum sustainable consumption-wealth ratio depends on expected rates of productivity and population growth, volatility of those growth rates, and the extent to which welfare depends directly on the size of the population. For plausible parameter values, the sustainable consumption-wealth ratio is well below the optimal ratio that maximizes welfare. This raises a question: Given its cost, should sustainability be a social objective?

Isolation caused by anthropogenic habitat fragmentation can destabilize populations. Populations relying on the inflow of immigrants can face reduced fitness due to inbreeding depression as fewer new individuals arrive. Empirical studies of the demographic consequences of isolation are critical to understanding how populations persist through changing conditions. We used a 34-year demographic and environmental dataset from a population of cooperatively-breeding Florida Scrub-Jays (Aphelocoma coerulescens) to create mechanistic models linking environmental and demographic factors to population growth rates. We found that the population has not declined despite both declining immigration and increasing inbreeding, owing to a coinciding response in breeder survival. We find evidence of density-dependent immigration, breeder survival, and fecundity, indicating that interactions between vital rates and local density play a role in buffering the population against change. Our study elucidates the impacts of isolation on demography and how long-term stability is maintained via demographic responses. Methods All work was approved by the Cornell University Institutional Animal Care and Use Committee (IACUC 2010-0015) and authorized by permits from the US Fish and Wildlife Service (TE824723-8), the US Geological Survey (banding permit 07732), and the Florida Fish and Wildlife Conservation Commission (LSSC-10-00205).

This statistic shows the population growth in Iceland from 2013 to 2023. In 2023, Iceland's population increased by approximately 2.93 percent compared to the previous year. Iceland's recovery Population growth in Iceland took a nose dive after the economic crisis of 2008; in 2007, the population growth rate was as high at 2.53 percent, but by 2010 it had dipped into the red figures. One reason for this may be that during the economic crisis unemployment went up, which may have caused some Icelanders to leave the country in search of work elsewhere, or reducing so-called economic migration into the country, as Iceland had been experiencing significant economic strength before the crisis. GDP growth did not begin to recover until 2011. Also, interestingly, the year after the crisis, the fertility rate went up slightly, but not for long - the fertility rate is now below the natural replacement rate. Iceland views childcare as a state responsibility, and most children attend daycare at a young age allowing both parents the option to work if they desire to do so. This is most likely possible because the total Icelandic population is actually quite small. As few as 330,000 people inhabit the island as of 2015, so maintaining the number of inhabitants while keeping the economy running and stable is of particular importance. Icelandic people also live a long time, due to a high standard of living, and life expectancy is on average 82 years of age - one of the highest life expectancies in the world.

Ratio of land consumption rate, to population growth rate according to the UN guidelines for Sustainable Development Goals indicator 11.3.1.

Population size is a main indicator of conservation potential, thought to predict both current and long-term population viability. However, few studies have directly examined the links between the size and the genetic and demographic properties of populations, using metrics that integrate effects across the whole life cycle. In this study, we combined six years of demographic data with SNP-based estimates of genetic diversity from 18 Swedish populations of the orchid Gymnadenia conopsea. We assessed whether stochastic growth rate increases with population size and genetic diversity, and used stochastic LTRE analysis to evaluate how underlying vital rates contribute to among-population variation in growth rate. For each population, we also estimated the probability of quasi-extinction (shrinking below a threshold size) and of a severe (90%) decline in population size, within the next 30 years. Estimates of stochastic growth rate indicated that ten populations are declining, seven increasing, and one population is approximately stable. SLTRE decomposition showed that low mean adult survival and growth characterized strongly declining populations, whereas high mean fecundity characterized strongly increasing populations. Stochastic growth rate increased with population size, mainly due to higher survival in larger populations, but was not related to genetic diversity. One third of the populations were predicted to go extinct and eight populations to undergo a 90% decrease in population size in the coming 30 years. Low survival in small populations most likely reflects a positive association between local environmental conditions and population size. Synthesis: The association between G. conopsea population size and viability was driven by variation in survival, and there was no sign that ongoing declines are due to genetic erosion. This suggests that large populations occur in favourable habitats that buffer effects of climatic variation. The results also illustrate that demographic metrics can be more informative than genetic metrics, regarding conservation priority. Methods The dataset contains six years of demographic data (2017-2022) from each of 18 populations of Gymnadenia conopsea on the island of Öland in Sweden, and the code to run integral projection models in R.

Text A, Representation theorem for a right eigenvector of an irreducible non-negative matrix. Text B, Theorem for infinite series expansion of characteristic equation. Text C, Original definition of type-reproduction number. Text D, Extension theorem of type-reproduction number. (ZIP)

This statistic shows the growth of Switzerland's population from 2013 to 2023, in comparison to the previous year. In 2023, Switzerland's population grew by approximately 1.26 percent compared to the previous year. See Switzerland's population figures for comparison. The Swiss population The Swiss population has been growing at a steady rate for the past few years; in general the country has experienced around a one percent population growth rate since the 1970s. Between 2004 and 2007, population growth was slightly below one percent, but has rebounded since then. This growth is supported by immigration, as the fertility rate is well below the replacement rate. The country’s strong and stable economy and the free movement of people within the European Union has helped attract foreigners. In 2015, the population of Switzerland was around 8.25 million and its foreign-born population amounted to 2.26 million people that same year, meaning that around 1 out of every four people in Switzerland are of foreign origin. But even if you are born in Switzerland, you are not automatically granted Swiss nationality, and many people who are of “foreign” origin were actually born in Switzerland but keep the nationality of their parents or do not go through what can be a lengthy process to obtain Swiss nationality. Another characteristic of the Swiss population is that Swiss people are getting older. Due to its high standard of living, Switzerland has one of the highest life expectancies in the world, and the median age of the population is now estimated at 42.3 years.

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

Financial overview and grant giving statistics of Rotarian Action Grp for Population Growth & Sustainable Development I

This statistic depicts the age distribution in the United States from 2013 to 2023. In 2023, about 17.59 percent of the U.S. population fell into the 0-14 year category, 64.97 percent into the 15-64 age group and 17.43 percent of the population were over 65 years of age. The increasing population of the United States The United States of America is one of the most populated countries in the world, trailing just behind China and India. A total population count of around 320 million inhabitants and a more-or-less steady population growth over the past decade indicate that the country has steadily improved its living conditions and standards for the population. Leading healthier lifestyles and improved living conditions have resulted in a steady increase of the life expectancy at birth in the United States. Life expectancies of men and women at birth in the United States were at a record high in 2012. Furthermore, a constant fertility rate in recent years and a decrease in the death rate and infant mortality, all due to the improved standard of living and health care conditions, have helped not only the American population to increase but as a result, the share of the population younger than 15 and older than 65 years has also increased in recent years, as can be seen above.

From now until 2100, India and China will remain the most populous countries in the world, however China's population decline has already started, and it is on course to fall by around 50 percent in the 2090s; while India's population decline is projected to begin in the 2060s. Of the 10 most populous countries in the world in 2100, five will be located in Asia, four in Africa, as well as the United States. Rapid growth in Africa Rapid population growth across Africa will see the continent's population grow from around 1.5 billion people in 2024 to 3.8 billion in 2100. Additionally, unlike China or India, population growth in many of these countries is not expected to go into decline, and instead is expected to continue well into the 2100s. Previous estimates had projected these countries' populations would be much higher by 2100 (the 2019 report estimated Nigeria's population would exceed 650 million), yet the increased threat of the climate crisis and persistent instability is delaying demographic development and extending population growth. The U.S. as an outlier Compared to the nine other largest populations in 2100, the United States stands out as it is more demographically advanced, politically stable, and economically stronger. However, while most other so-called "advanced countries" are projected to see their population decline drastically in the coming decades, the U.S. population is projected to continue growing into the 2100s. This will largely be driven by high rates of immigration into the U.S., which will drive growth despite fertility rates being around 1.6 births per woman (below the replacement level of 2.1 births per woman), and the slowing rate of life expectancy. Current projections estimate the U.S. will have a net migration rate over 1.2 million people per year for the remainder of the century.

These data were compiled to help understand how climate change may impact dryland pinyon-juniper ecosystems in coming decades, and how resource management might be able to minimize those impacts. Objective(s) of our study were to model the demographic rates of PJ woodlands to estimate the areas that may decline in the future vs. those that will be stable. We quantified populations growth rates across broad geographic areas, and identified the relative roles of recruitment and mortality in driving potential future changes in population viability in 5 tree species that are major components of these dry forests. We used this demographic model to project pinyon-juniper population stability under future climate conditions, assess how robust these projected changes are, and to identify where on the landscape management strategies that decrease tree competition would effectively resist population decline. These data represent estimated recruitment, mortality and population growth across ...

Many places around the world have experienced population growth in the past decade and even population decline due to the COVID pandemic. According to worldometer’s current statistics the global population continues to thrive reaching a little over 8 billion and still growing. Although, Kazakhstan only ranks 64 we can see that they have a decent 1.21 percent yearly change with the net change being about 225,000 to the total of 19 million. When we look at their 2021 stats from Our World in Data for birth rates and death rates per 1,000 people, we can see that they are still a growing population as the birth rate (21.54) is double the death rate (10.23). Birthrates measure the number of births in a population by using a percentage or a ratio per 1,000 people and Death rates measure using the same methods (Marston, Knox, Liverman, Del Casino, Robbins, 2019, p. 39). Not only does this contribute to the growing population, but groups of people who weren’t living there whose ethnicity is from Kazakhstan are moving back into their home country. Ethnicity is defined as a “state of belonging to a social group that has a common national or cultural tradition; socially created system of rules about who belongs to a particular group” (Marston, Knox, Liverman, Del Casino, Robbins, 2019, p. 36). Population growth isn’t necessarily a bad thing as long as it is sustainable, but for Kazakhstan population growth can be dangerous as generally they have been struggling with basic economic rights and are being directed to the northern region.

In 2023, the annual population growth in Mexico stood at 0.87 percent. Between 1961 and 2023, the figure dropped by 2.25 percentage points, though the decline followed an uneven course rather than a steady trajectory.