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.

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.

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.

Abstract The future population trajectory, as well as climate change, are aspects that generate uncertainties as to their probable effects on the economy, especially on agricultural production and food industry. This paper simulates the effects of population scenarios and one of climate change using the GTAP computable general equilibrium model. A version of GTAP 10 was created to identify Agriculture, Forestry and Food Industry activities, and eight regions, called Agricultural Economic Blocks, using multivariate analysis techniques. The dynamic simulations of the accumulated deviation between thebaseline and the policy scenarios up to 2050 in isolation indicated widespread negative effects of climate change on the GDP and economic activities of the blocs. The results of the population scenarios indicated that the blocks made up of richer countries and with more diversified economies would tend to win at the expense of the others in terms of GDP. On the other hand, they would generally encourage the blocks’ Agriculture, Forestry and Food Industry productions. Taken together, the negative effects of climate change would tend to outweigh the positive effects of population scenarios and more intensively on those which project less population growth.

In 2023, the annual population growth in India was 0.88 percent. Between 1961 and 2023, the figure dropped by 1.52 percentage points, though the decline followed an uneven course rather than a steady trajectory.

The U.S. Census Bureau releases annual estimates of population by counties and municipalities as part of the Population Estimates Program (PEP). This is an estimate of population on July 1 of each year. Adjustments to previous estimate years are made with each release, dating back to the year of the last decennial census. Decennial figures for April 1 of the most recent decennial year will not get updated, but the July 1 estimate for that same year can adjust with each PEP release. The U.S. Census Bureau produces these estimates based on administrative records. At the municipal level, the PEP reports only population totals. At the county level, PEP data gives estimates for age, sex, race, and ethnicity. PEP releases come out in the spring following the latest estimate year. The demographic estimates of the PEP are used as control totals for the American Community Survey results released later that year.

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...

Heat waves are transient environmental events but can have lasting impacts on populations through lethal and sub-lethal effects on demographic vital rates. Sub-lethal temperature stress affects individual energy balance, potentially affecting individual fitness and population growth. Environmental temperature can, however, have distinct effects on different life-history traits, and the net effect of short-term temperature stress on population growth may lead to different population responses over different time frames. Furthermore, sublethal temperature responses may be density dependent, leading to potentially complicated feedbacks between heat stress and demographic responses over time. Here, we test the hypotheses that: (i) populations subjected to higher heat wave temperatures and longer heat wave durations are more negatively affected than those subjected to less intense and shorter heat waves, (ii) heat wave effects are more pronounced during density-dependent population growth ph...

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.

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.

The files included contain the development data used in the modeling of the demographic transition. The WDI indicators dataset is publicly available at the World Bank data catalog. We use the 2010 dataset in our analysis. The Barro-Lee dataset provides information on educational attainment. We use average years of schooling for the female population as our education indicator in the paper.

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.

Existing studies show how population growth and rising incomes will cause a massive increase in the future global demand for food. We add to the literature by estimating the potential effect of increases in human weight, caused by rising BMI and height, on future calorie requirements. Instead of using a market based approach, the estimations are solely based on human energy requirements for maintenance of weight. We develop four different scenarios to show the effect of increases in human height and BMI. In a world where the weight per age-sex group would stay stable, we project calorie requirements to increases by 61.05 percent between 2010 and 2100. Increases in BMI and height could add another 18.73 percentage points to this. This additional increase amounts to more than the combined calorie requirements of India and Nigeria in 2010. These increases would particularly affect Sub-Saharan African countries, which will already face massively rising calorie requirements due to the high population growth. The stark regional differences call for policies that increase food access in currently economically weak regions. Such policies should shift consumption away from energy dense foods that promote overweight and obesity, to avoid the direct burden associated with these conditions and reduce the increases in required calories. Supplying insufficient calories would not solve the problem but cause malnutrition in populations with weak access to food. As malnutrition is not reducing but promoting rises in BMI levels, this might even aggravate the situation.

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

Understanding the impact of herbivory on plant populations is a fundamental goal of ecology. Damage to individual plants can be visually striking and affect the fates of individuals, but these impacts do not necessarily translate into population-level differences in vital rates (survival, growth, or fecundity) or population growth rates. In biological control of weeds, quantitative assessments of population-level impacts of released agents on both target invasive plants and native, nontarget plants are needed to inform evaluations of the benefits and risks of releasing agents into new regions. Here we present a 3-yr experimental demographic field study using the European root-feeding biocontrol weevil, Mogulones crucifer, first released in Canada in 1997 to control the invasive weed Cynoglossum officinale (Boraginaceae). Mogulones crucifer is an effective “search and destroy” agent in Canada, but sporadically feeds, oviposits, and develops on native nontarget Boraginaceae. We investigated the population-level impacts of this biocontrol insect on its target weed and a native nontarget plant, Hackelia micrantha (Boraginaceae), by releasing large numbers of weevils into naturally occurring patches of H. micrantha growing isolated from or interspersed with C. officinale. We followed the fates of individual plants on release and nonrelease (control) sites for two transition years, developed matrix models to project population growth rates (λ) for each plant species, and examined the contributions from differences in vital rates to changes in λ using life table response experiments (LTRE). In contrast to studies of the insect–plant interaction in its native range, as a biocontrol agent, M. crucifer increased mortality of C. officinale rosettes in the year immediately following release, depressing the weed's λ to below the population replacement level. However, λ for H. micrantha was never depressed below the replacement level, and any differences between release and nonrelease sites in the nontarget could not be explained by significant contributions from vital rates in the LTRE. This study is the first to simultaneously and experimentally examine target and nontarget population-level impacts of a weed biocontrol insect in the field, and supports the theoretical prediction that plant life history characteristics and uneven herbivore host preferences can interact to produce differences in population-level impacts between target and nontarget plant species.

How climate change influences the dynamics of plant populations is not well understood, as few plant studies have measured responses of vital rates to climatic variables and modeled the impact on population growth. I used 25 years of demographic data to analyze how survival, growth, and fecundity respond to date of spring snow melt for a subalpine plant. Fecundity was estimated by seed production (over 15 years) and also divided into flower number, fruit set, seeds per fruit, and escape from seed predation. Despite no apparent effects on flower number, plants produced more seeds in years with later snowmelt. Survival and probability of flowering were reduced by early snow melt in the previous year. Based on demographic models, earlier snowmelt with warming is expected to lead to negative population growth, driven especially by changes in seedling establishment and seed production. These results provide a rare example of how climate change is expected to influence the dynamics of a plant population. They furthermore illustrate the potential for strong population impacts even in the absence of more commonly reported visual signs, such as earlier blooming or reduced floral display in early melting years.

Data for "Climate change impacts on population growth across a species’ range differ due to nonlinear responses of populations to climate and variation in rates of climate change "

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.

From the AfriPop website..."High resolution, contemporary data on human population distributions are a prerequisite for the accurate measurement of the impacts of population growth, for monitoring changes and for planning interventions. The AfriPop project was initiated in July 2009 with an aim of producing detailed and freely-available population distribution maps for the whole of Africa. Based on the approaches outlined in detail here and here, and summarized on the methods page, fine resolution satellite imagery-derived settlement maps are combined with land cover maps to reallocate contemporary census-based spatial population count data. Assessments have shown that the resultant maps are more accurate than existing population map products, as well as the simple gridding of census data. Moreover, the 100m spatial resolution represents a finer mapping detail than has ever before been produced at national extents. The approaches used in AfriPop dataset production are designed with operational application in mind, using simple and semi-automated methods to produce easily updatable maps. Given the speed with which population growth and urbanisation are occurring across much of Africa, and the impacts these are having on the economies, environments and health of nations, such features are a necessity for both research and operational applications."Data Source: AfriPop.org

Natural and anthropogenic disturbances co-occur in most systems, but how they interact to shape demographic outcomes remains poorly understood. Such interactions may alter dynamics of populations in non-additive ways, making demographic predictions challenging when focusing on only one disturbance. Thus, understanding the interactive effects of such disturbances is critically important to determine the population viability of most species under a diversity of stressors. We used a hierarchical integral projection model (IPM), parameterized with 13 years of field data across 20 populations, encompassing 2435 individuals of an endangered herb, Liatris ohlingerae. We examined interactive effects of vertebrate herbivory, fire and anthropogenic activities (sand roads) on vital rates (e.g. survival, growth, reproduction, recruitment) and ultimately on population growth rates (λ), to test the hypothesis that interactions amplify or dampen differences in λ depending on environmental contexts. We constructed megamatrices to determine coupled dynamics in individuals damaged vs. not damaged by herbivores in roadsides and in Florida scrub with different times since fire. We identified strong interactive effects of fire with herbivory and habitat with herbivory on vital rates and on population growth rates in the IPM model. We also found different patterns of variation in λ between habitat and time-since-fire scenarios; population growth rates were higher in roadside populations compared to scrub populations and declined with increasing time since fire. Herbivory had interactive effects with both fire and human disturbances on λ. Herbivory resulted in decreased differences in λ due to anthropogenic disturbance and slightly increased differences in λ due to time since fire. Synthesis. The co-occurrence of various disturbances may both amplify and dampen the effects of other disturbances on population growth rate, thus shaping complex population dynamics that are neither linear nor additive. These realistic nonlinearities represent challenges in understanding and projecting of population dynamics. Here, we examined the effects of various sources of disturbance on the population dynamics of an endangered plant species, finding complex interactions affecting population growth rates. We argue that integration of multiple, interacting stressors in IPMs will allow more accurate estimation of the overall effects of ecological processes on species viability.