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.

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.

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

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

1Expanded model descriptions:(i) General additive model (GAM) that allowed regional differences, fit in the ‘mgcv’ package of R with cross-validation used to determine the optimum amount of smoothing. GAM models allow curvilinear functions if the data support curvature.(ii) General linear model (normal errors with log link) with no regional effect on slope and intercept.(iii) General linear model (normal errors with log link) that allowed regional differences in the slope and intercept.(iv) Constant total mortality (no effect of human offtake on total mortality), with regional differences.(v) Constant total mortality (no effect of human offtake on total mortality).2Number of parameters in the model (non-integer values are expected for general additive models).3The coefficient of determination (R2) adjusted for degrees of freedom.4Akaike model weight.

This data set contains the information necessary to reproduce our article "Depenbusch L, Klasen S. The effect of bigger human bodies on the future global calorie requirements. PLoS ONE. 2019. Forthcoming" Abstract: 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. An earlier version appeared as GlobalFood Discussion Papers, No. 109. The data is stored as Stata Version 13 .dta file, and in Excel .xlsx format. In the Excel file the first row contains variable names, the second row contains variable labels. Age specifications in the label of the type "<=x" describe that the variable aggregates from the next smaller age group over all ages up to age "x".

The data set records one belt, one road, 1960-2017 countries' population data along 65 countries. The total population is the sum of population groups living in a certain time and a certain area. Population density is the number of people per unit land area. Population growth rate is the rate of population growth caused by natural and migration changes in a certain period of time. Total population, population density and population growth rate are the most basic indicators in population statistics. They are of great significance for understanding national conditions and national strength, formulating population plans and economic and social development plans, and carrying out population scientific research. Data sources: (1) United Nations Population Division, world population prospects: 2017, 2018 revision; (2) census reports and other statistical publications of the National Bureau of statistics; (3) Eurostat: population statistics; (4) United Nations Statistics Division, population and vital statistics reports (different years); (5) United States Census Bureau: international database; (6) Pacific Community Secretariat: statistical and demographic programme. The data set contains three data tables: total population, population density, population growth rate,

The relative effect of top-down versus bottom-up forces in regulating and limiting wildlife populations is an important theme in ecology. Untangling these effects is critical for a basic understanding of trophic dynamics and effective management. We examined the drivers of moose (Alces alces) population growth by integrating two independent sources of observations within a hierarchical Bayesian population model. This analysis used one of the largest existing spatiotemporal datasets on ungulate population dynamics globally. We documented a 20% population decline over the period examined. Moose population growth was negatively density-dependent. Although the mechanisms producing density-dependent suppression of population growth could not be determined, the relatively low densities at which moose populations were documented suggests it could be due primarily to density-dependent predation. Predation primarily limited population growth, except at low density, where it was regulating. Harvest appeared to be largely additive and contributed to population declines. Our results, highlight how population dynamics are context dependent and vary strongly across gradients in climate, forest type, and predator abundance. These results help clarify long-standing questions in population ecology and highlight the complex relationships between natural and human-caused mortality in driving ungulate population dynamics. Data is found here on Dryad (moose_data_dryad.RData), but the R scripts (run_jags_model.R and gompertz_jags.R) are found on Zenodo (https://doi.org/10.5281/zenodo.6030027). See the README.txt for a description of all 3 files. See the manuscript for details on how the dataset was collected and processed.

ContainsFinal Analysis Output.xlsx: Current and reference concentrations of DRP, TP, NO3-N and TN along with pivot table analysisCode: Python code used to implement the model in ArcGIS Pro.Models: R code to explore different models for implementation via Python in ArcGIS!geotiffs: GeoTIFF raster files at level 6 of HydroBasins for current, zero human effect and the difference between current and zer human effect.All_contams_DF_L6_Preds_v3: geopackage containing all GIS data.

Climate change is largely caused by human activity, primarily the burning of fossil fuels like oil, coal, and natural gas. Heatwaves, droughts, wildfires and floods caused by climate change are harming the planet and affecting billions of lives worldwide. Natural events can be more devastating due to a number of factors, including population growth, urbanization, poverty, and economic and social inequalities. Explore the impacts of climate change on flooding, wildfire, rainfall, and drought throughout Hawai'i.The history of billion-dollar disasters in the United States each year from 1980 to 2021, showing event type (colors), frequency (left-hand vertical axis), and cost (right-hand vertical axis.) The number and cost of weather and climate disasters is rising due to a combination of population growth and development along with the influence of human-caused climate change on some type of extreme events that lead to billion-dollar disasters.

Fish populations globally are susceptible to endangerment through exploitation and habitat loss. We present theoretical simulations to explore how reduced adult survival (age truncation) might affect short-lived freshwater fish species in human-altered contemporary environments. Our simulations evaluate two hypothetical "average fish" and five example fish species of age 1 or age 2 maturity. From a population equilibrium baseline representing a natural, unaltered environment we impose systematic reductions in adult survival and quantify how age truncation affects the causes of variation in population growth rate. We estimate the relative contributions to population growth rate arising from simulated temporal variation in age-specific vital rates and population structure. At equilibrium and irrespective of example species, population structure (first adult age class) and survival probability of the first two adult age classes are the most important determinants of population growth. As adult survival decreases, the first reproductive age class becomes increasingly important to variation in population growth. All simulated examples show the same general pattern of change with age truncation as known for exploited, longer-lived fish species in marine and freshwater environments. This implies age truncation is a general potential concern for fish biodiversity across life history strategies and ecosystems. Managers of short-lived, freshwater fishes in contemporary environments often focus on supporting reproduction to ensure population persistence. However, a strong focus on water management to support reproduction may reduce adult survival. Sustainability management needs a focus on mitigating adult mortality in human-altered ecosystems. A watershed spatial extent embracing land and water uses may be necessary to identify and mitigate causes of age truncation in freshwater species. Achieving higher adult survival will require paradigm transformations in society and government about water management priorities.

1. A major challenge in biodiversity conservation is to facilitate viable populations of large apex predators in ecosystems where they were recently driven to ecological extinction due to resource conflict with humans. 2. Monetary compensation for losses of livestock due to predation is currently a key instrument to encourage human–carnivore coexistence. However, a lack of quantitative estimates of livestock losses due to predation leads to disagreement over the practise of compensation payments. This disagreement sustains the human–carnivore conflict. 3. The level of depredation on year-round, free-ranging, semi-domestic reindeer by large carnivores in Fennoscandia has been widely debated over several decades. In Norway, the reindeer herders claim that lynx and wolverine cause losses of tens of thousands of animals annually and cause negative population growth in herds. Conversely, previous research has suggested that monetary predator compensation can result in positive population growth in the husbandry, with cascading negative effects of high grazer densities on the biodiversity in tundra ecosystems. 4. We utilized a long-term, large-scale dataset to estimate the relative importance of lynx and wolverine predation and density-dependent and climatic food limitation on claims for losses, recruitment and population growth rates in Norwegian reindeer husbandry. 5. Claims of losses increased with increasing predator densities, but with no detectable effect on population growth rates. Density-dependent and climatic effects on claims of losses, recruitment and population growth rates, were much stronger than the effects of variation in lynx and wolverine densities. 6. Synthesis and applications. Our analysis provides a quantitative basis for predator compensation and estimation of the costs of reintroducing lynx and wolverine in areas with free-ranging semi-domestic reindeer. We outline a potential path for conflict management which involves adaptive monitoring programs, open access to data, herder involvement, and development of management strategy evaluation (MSE) models to disentangle complex responses including multiple stakeholders and individual harvester decisions.

High mortality poses a serious threat to sustainable conservation of the African elephant (Loxodonta africana). Using detected carcass data collected by the Kenya Wildlife Service (KWS) during 1992-2017, we analyze temporal and spatial variation in elephant mortality in Kenya. We investigate the major mortality causes and means used to kill elephants, carcass category, tusk recovery status, variation in mortality with elephant age and sex classes, differences between inside and outside protected areas (PAs), the Proportion of Illegally Killed Elephants (PIKE) and the overall mortality rate (MR — the number of dead/number of live elephants in a given year). In total 9,182 elephant deaths were recorded during the 26 years. Elephant mortality increased over time and was attributed primarily to natural (33.1%) and human-related causes, particularly ivory poaching (31.5%) and human-elephant conflicts (19.9%). Elephant mortality varied across Kenya’s 47 counties in correspondence with variation in elephant population size and was the highest in the leading elephant range counties of Taita Taveta, Laikipia, Samburu and Meru. Mortality was higher for males and adults and outside the protected areas. Most elephant carcasses had tusks (75.1%) but a few did not (12.5%). Yearly PIKE values peaked in 2012, the year with the highest poaching levels in Kenya during 1992-2017. MR increased throughout 1992-2017. Temporal variation in elephant mortality probability was significantly influenced by human and livestock population densities, average annual maximum temperature and total annual rainfall and the strength of these influences varied across the seven leading elephant range counties of Kenya. Natural processes are increasingly contributing to elephant mortality likely due to climate change and the associated food and water stress, exacerbated by contracting range. Enhancing anti-poaching and strategies for mitigating climate change impacts and human-elephant conflict and reducing range contraction while sustaining habitat connectivity can help reduce mortality and promote elephant conservation. Strengthening enforcement of international wildlife laws can further reduce illegal trade in tusks and killing of elephants.

The conservation of many wildlife species requires understanding the demographic effects of climate change, including interactions between climate change and harvest, which can provide cultural, nutritional or economic value to humans. We present a demographic model that is based on the polar bear Ursus maritimus life cycle and includes density-dependent relationships linking vital rates to environmental carrying capacity (K). Using this model, we develop a state-dependent management framework to calculate a harvest level that (i) maintains a population above its maximum net productivity level (MNPL; the population size that produces the greatest net increment in abundance) relative to a changing K, and (ii) has a limited negative effect on population persistence. Our density-dependent relationships suggest that MNPL for polar bears occurs at approximately 0·69 (95% CI = 0·63–0·74) of K. Population growth rate at MNPL was approximately 0·82 (95% CI = 0·79–0·84) of the maximum intrinsic growth rate, suggesting relatively strong compensation for human-caused mortality. Our findings indicate that it is possible to minimize the demographic risks of harvest under climate change, including the risk that harvest will accelerate population declines driven by loss of the polar bear's sea-ice habitat. This requires that (i) the harvest rate – which could be 0 in some situations – accounts for a population's intrinsic growth rate, (ii) the harvest rate accounts for the quality of population data (e.g. lower harvest when uncertainty is large), and (iii) the harvest level is obtained by multiplying the harvest rate by an updated estimate of population size. Environmental variability, the sex and age of removed animals and risk tolerance can also affect the harvest rate. Synthesis and applications. We present a coupled modelling and management approach for wildlife that accounts for climate change and can be used to balance trade-offs among multiple conservation goals. In our example application to polar bears experiencing sea-ice loss, the goals are to maintain population viability while providing continued opportunities for subsistence harvest. Our approach may be relevant to other species for which near-term management is focused on human factors that directly influence population dynamics within the broader context of climate-induced habitat degradation.

DataVariables used during the analyses of the importance of partial seed consumption as a rescue for the Araucaria population subject to human seed collection. More information in the data sheet

Demographic data for introduced crab from multiple bays along the Central California coast, shallow subtidal (<3 m depth), in 2015. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=We conducted monthly trappings of invasive European green crabs to gather demographic data from several bays in northern California: Bodega Harbor, Tomales Bay, Bolinas Lagoon, San Francisco Bay, and Elkhorn Slough. All sites were accessed by foot via shore entry. At each of four sites within each bay, we placed 5 baited traps (folding Fukui fish traps) and 5 baited minnow traps in shallow intertidal areas. Traps arrays were set with fish and minnow traps alternating and with each 20 m apart. Traps were retrieved 24 hours later and traps were rebaited and collected again the following day.\u00a0Trapping was continued for three consecutive days with traps removed on the final day.\u00a0Each day, data for crab species, size, sex, reproductive condition, and injuries were collected for all crabs in the field. Following data collection, all crabs were returned to the lab, and frozen overnight prior to disposal.\u00a0

See Turner et al. (2016)\u00a0Biological Invasions\u00a018: 533-548 for additional methodological details:
Turner, B.C., de Rivera, C.E., Grosholz, E.D., & Ruiz, G.M. 2016. Assessing population increase as a possible outcome to management of invasive species. Biological Invasions, 18(2), pp 533\u2013548. doi:10.1007/s10530-015-1026-9 awards_0_award_nid=699764 awards_0_award_number=OCE-1514893 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1514893 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 cdm_data_type=Other comment=Demographic data for introduced crab from multiple bays in 2015 PI: Edwin Grosholz (UC Davis) Co-PI: Catherine de Rivera & Gregory Ruiz (Portland State University)
Version: 15 June 2017 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.701751.1 Easternmost_Easting=-121.738422 geospatial_lat_max=38.316968 geospatial_lat_min=36.823953 geospatial_lat_units=degrees_north geospatial_lon_max=-121.738422 geospatial_lon_min=-123.058725 geospatial_lon_units=degrees_east infoUrl=https://www.bco-dmo.org/dataset/701751 institution=BCO-DMO instruments_0_dataset_instrument_description=At each of four sites within each bay, we placed 5 baited traps (folding Fukui fish traps) and 5 baited minnow traps in shallow intertidal areas. instruments_0_dataset_instrument_nid=701774 instruments_0_description=Fukui produces multi-species, multi-purpose collapsible or stackable fish traps, available in different sizes. instruments_0_instrument_name=Fukui fish trap instruments_0_instrument_nid=701772 instruments_0_supplied_name=folding Fukui fish traps metadata_source=https://www.bco-dmo.org/api/dataset/701751 Northernmost_Northing=38.316968 param_mapping={'701751': {'lat': 'master - latitude', 'lon': 'master - longitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/701751/parameters people_0_affiliation=University of California-Davis people_0_affiliation_acronym=UC Davis people_0_person_name=Edwin Grosholz people_0_person_nid=699768 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Portland State University people_1_affiliation_acronym=PSU people_1_person_name=Catherine de Rivera people_1_person_nid=699771 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=Portland State University people_2_affiliation_acronym=PSU people_2_person_name=Gregory Ruiz people_2_person_nid=471603 people_2_role=Co-Principal Investigator people_2_role_type=originator people_3_affiliation=Woods Hole Oceanographic Institution people_3_affiliation_acronym=WHOI BCO-DMO people_3_person_name=Shannon Rauch people_3_person_nid=51498 people_3_role=BCO-DMO Data Manager people_3_role_type=related project=Invasive_predator_harvest projects_0_acronym=Invasive_predator_harvest projects_0_description=The usual expectation is that when populations of plants and animals experience repeated losses to predators or human harvest, they would decline over time. If instead these populations rebound to numbers exceeding their initial levels, this would seem counter-intuitive or even paradoxical. However, for several decades mathematical models of population processes have shown that this unexpected response, formally known as overcompensation, is not only possible, but even expected under some circumstances. In what may be the first example of overcompensation in a marine system, a dramatic increase in a population of the non-native European green crab was recently observed following an intensive removal program. This RAPID project will use field surveys and laboratory experiments to verify that this population explosion results from overcompensation. Data will be fed into population models to understand to what degree populations processes such as cannibalism by adult crabs on juvenile crabs and changes in maturity rate of reproductive females are contributing to or modifying overcompensation. The work will provide important insights into the fundamental population dynamics that can produce overcompensation in both natural and managed populations. Broader Impacts include mentoring graduate trainees and undergraduate interns in the design and execution of field experiments as well as in laboratory culture and feeding experiments. The project will also involve a network of citizen scientists who are involved with restoration activities in this region and results will be posted on the European Green Crab Project website. This project aims to establish the first example of overcompensation in marine systems. Overcompensation refers to the paradoxical process where reduction of a population due to natural or human causes results in a greater equilibrium population than before the reduction. A population explosion of green crabs has been recently documented in a coastal lagoon and there are strong indications that this may be the result of overcompensation. Accelerated maturation of females, which can accompany and modify the expression of overcompensation has been observed. This RAPID project will collect field data from this unusual recruitment class and conduct targeted mesocosm experiments. These will include population surveys and mark-recapture studies to measure demographic rates across study sites. Laboratory mesocosm studies using this recruitment class will determine size specific mortality. Outcomes will be used in population dynamics models to determine to what degree overcompensation has created this dramatic population increase. The project will seek answers to the following questions: 1) what are the rates of cannibalism by adult green crabs and large juveniles on different sizes of juvenile green crabs, 2) what are the consequences of smaller size at first reproduction for population dynamics and for overcompensation and 3) how quickly will the green crab population return to the levels observed prior to the eradication program five years earlier? projects_0_end_date=2016-11 projects_0_geolocation=Europe projects_0_name=RAPID: A rare opportunity to examine overcompensation resulting from intensive harvest of an introduced predator projects_0_project_nid=699765 projects_0_start_date=2014-12 sourceUrl=(local files) Southernmost_Northing=36.823953 standard_name_vocabulary=CF Standard Name Table v55 version=1 Westernmost_Easting=-123.058725 xml_source=osprey2erddap.update_xml() v1.3

Demographic data from introduced crab in Seadrift Lagoon (Central California coast, shallow subtidal (<3 m depth)) in 2015. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=We conducted monthly trapping of invasive European green crabs to gather demographic data in Seadrift Lagoon, Stinson Beach, CA (lat 37.907440 long -122.666169).\u00a0All sites were accessed by either kayak or by foot via shore entry.\u00a0At each of six sites, we placed 10 baited traps (folding Fukui fish traps) in shallow (<2 m) subtidal areas. Traps were retrieved 24 hours later and were rebaited and collected again the following day.\u00a0Trapping was continued for three consecutive days with traps removed on the final day.\u00a0Each day, data for crab species, size, sex, reproductive condition, injuries, and presence of marks were collected for all crabs in the field. Following data collection, all crabs were returned to the lab, frozen overnight disposed of in commercial agricultural compost. \u00a0

For each date and site, crabs from all traps (e.g. 10 traps per site) are pooled for counting and measuring.
Traps Used for each date (some with macroalgae "Ulva"):
02/19/2015\u00a0\u00a0 \u00a010 baited traps + 5 traps with ulva
02/20/2015\u00a0\u00a0 \u00a010 baited traps + 5 with ulva
03/05/2015\u00a0\u00a0 \u00a010 baited traps + 5 traps with ulva per site
03/06/2015\u00a0\u00a0 \u00a010 baited traps + 5 traps with ulva
03/24/2015\u00a0\u00a0 \u00a010 traps/site
04/08/2015\u00a0\u00a0 \u00a010 traps/site
04/15/2015\u00a0\u00a0 \u00a010 baited traps + 4 traps with ulva
04/24/2015\u00a0\u00a0 \u00a010 traps/site
05/27/2015\u00a0\u00a0 \u00a0site 1 & 5 had 10 traps, site 3 had 9 traps
06/23/2015\u00a0\u00a0 \u00a0site 1 & 3 had 15 traps, site 5 had 14 traps
06/24/2015\u00a0\u00a0 \u00a0site 1 & 3 had 15 traps, site 5 had 14 traps
07/21/2015\u00a0\u00a0 \u00a0traps per site: site 1=20, site 2=20, site 3=17, site 4=15, site 5=10, site 6=10, site 7=20
08/25/2017\u00a0\u00a0 \u00a010 traps/site
08/26/2015\u00a0\u00a0 \u00a010 traps/site
08/27/2015\u00a0\u00a0 \u00a010 traps/site
09/01/2015\u00a0\u00a0 \u00a010 traps/site
09/02/2015\u00a0\u00a0 \u00a010 traps/site
09/30/2015\u00a0\u00a0 \u00a010 traps/site
10/01/2015\u00a0\u00a0 \u00a010 traps/site
10/02/2015\u00a0\u00a0 \u00a010 traps/site
12/01/2015\u00a0\u00a0 \u00a010 traps/site
12/02/2015\u00a0\u00a0 \u00a010 traps/site
12/03/2015\u00a0\u00a0 \u00a010 traps/site

See Turner et al. (2016)\u00a0Biological Invasions\u00a018: 533-548 for additional methodological details:
Turner, B.C., de Rivera, C.E., Grosholz, E.D., & Ruiz, G.M. 2016. Assessing population increase as a possible outcome to management of invasive species. Biological Invasions, 18(2), pp 533\u2013548. doi:10.1007/s10530-015-1026-9 awards_0_award_nid=699764 awards_0_award_number=OCE-1514893 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1514893 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 cdm_data_type=Other comment=Monthly trapping in Seadrift Lagoon in 2015 PI: Edwin Grosholz (UC Davis) Co-PI: Catherine de Rivera & Gregory Ruiz (Portland State University)
Version: 02 June 2017 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.701863.1 Easternmost_Easting=-122.6661694 geospatial_lat_max=37.90744 geospatial_lat_min=37.90744 geospatial_lat_units=degrees_north geospatial_lon_max=-122.6661694 geospatial_lon_min=-122.6661694 geospatial_lon_units=degrees_east infoUrl=https://www.bco-dmo.org/dataset/701863 institution=BCO-DMO instruments_0_dataset_instrument_description=At each of the six sites used for monthly trapping plus three additional sites, we placed 15 baited traps (folding Fukui fish traps) in shallow ( instruments_0_dataset_instrument_nid=701870 instruments_0_description=Fukui produces multi-species, multi-purpose collapsible or stackable fish traps, available in different sizes. instruments_0_instrument_name=Fukui fish trap instruments_0_instrument_nid=701772 instruments_0_supplied_name=Fukui fish traps metadata_source=https://www.bco-dmo.org/api/dataset/701863 Northernmost_Northing=37.90744 param_mapping={'701863': {'lat': 'master - latitude', 'lon': 'master - longitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/701863/parameters people_0_affiliation=University of California-Davis people_0_affiliation_acronym=UC Davis people_0_person_name=Edwin Grosholz people_0_person_nid=699768 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Portland State University people_1_affiliation_acronym=PSU people_1_person_name=Catherine de Rivera people_1_person_nid=699771 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=Portland State University people_2_affiliation_acronym=PSU people_2_person_name=Gregory Ruiz people_2_person_nid=471603 people_2_role=Co-Principal Investigator people_2_role_type=originator people_3_affiliation=Woods Hole Oceanographic Institution people_3_affiliation_acronym=WHOI BCO-DMO people_3_person_name=Shannon Rauch people_3_person_nid=51498 people_3_role=BCO-DMO Data Manager people_3_role_type=related project=Invasive_predator_harvest projects_0_acronym=Invasive_predator_harvest projects_0_description=The usual expectation is that when populations of plants and animals experience repeated losses to predators or human harvest, they would decline over time. If instead these populations rebound to numbers exceeding their initial levels, this would seem counter-intuitive or even paradoxical. However, for several decades mathematical models of population processes have shown that this unexpected response, formally known as overcompensation, is not only possible, but even expected under some circumstances. In what may be the first example of overcompensation in a marine system, a dramatic increase in a population of the non-native European green crab was recently observed following an intensive removal program. This RAPID project will use field surveys and laboratory experiments to verify that this population explosion results from overcompensation. Data will be fed into population models to understand to what degree populations processes such as cannibalism by adult crabs on juvenile crabs and changes in maturity rate of reproductive females are contributing to or modifying overcompensation. The work will provide important insights into the fundamental population dynamics that can produce overcompensation in both natural and managed populations. Broader Impacts include mentoring graduate trainees and undergraduate interns in the design and execution of field experiments as well as in laboratory culture and feeding experiments. The project will also involve a network of citizen scientists who are involved with restoration activities in this region and results will be posted on the European Green Crab Project website. This project aims to establish the first example of overcompensation in marine systems. Overcompensation refers to the paradoxical process where reduction of a population due to natural or human causes results in a greater equilibrium population than before the reduction. A population explosion of green crabs has been recently documented in a coastal lagoon and there are strong indications that this may be the result of overcompensation. Accelerated maturation of females, which can accompany and modify the expression of overcompensation has been observed. This RAPID project will collect field data from this unusual recruitment class and conduct targeted mesocosm experiments. These will include population surveys and mark-recapture studies to measure demographic rates across study sites. Laboratory mesocosm studies using this recruitment class will determine size specific mortality. Outcomes will be used in population dynamics models to determine to what degree overcompensation has created this dramatic population increase. The project will seek answers to the following questions: 1) what are the rates of cannibalism by adult green crabs and large juveniles on different sizes of juvenile green crabs, 2) what are the consequences of smaller size at first reproduction for population dynamics and for overcompensation and 3) how quickly will the green crab population return to the levels observed prior to the eradication program five years earlier? projects_0_end_date=2016-11 projects_0_geolocation=Europe projects_0_name=RAPID: A rare opportunity to examine overcompensation resulting from intensive harvest of an introduced predator projects_0_project_nid=699765 projects_0_start_date=2014-12 sourceUrl=(local files) Southernmost_Northing=37.90744 standard_name_vocabulary=CF Standard Name Table v55 subsetVariables=lagoon,latitude,longitude version=1 Westernmost_Easting=-122.6661694 xml_source=osprey2erddap.update_xml() v1.3

Feral horses form relatively stable harems over time that are characterized by long-lasting bonds among their members, a characteristic that makes them an exceptional case of a social system among terrestrial ungulates. Their social system has been described as uniform despite the wide differences in their environment and demography. Horse populations subjected to human interference often show higher levels of population instability that can ultimately compromise their reproductive success. In this article, we describe demographic and dynamic changes of a Portuguese population of Garranos in Serra d’Arga (SA), which is impacted by human and predation pressures, over six breeding seasons. Furthermore, we tested several hypotheses related to the impact of anthropogenic disturbance on the structure and dynamics of this population. Our results revealed that the SA population had relatively little human interference at the start of the project in 2016. This was supported by the natural composition of the herd (total number of individuals, 206), which consisted of several single- and multi-male harems (n = 17 and 7, respectively) and bachelor males (n = 9). However, from 2017 to 2021, SA’s Garrano population suffered a drastic decline. Approximately two-thirds of the individuals and all bachelor males disappeared, and 76% of adult female transfers occurred after the death or disappearance of the harem male. Predatory pressures and poor management of the population, which allowed illegal human interference, contributed to this population crisis. A low population growth rate, reduced birth and foal survival rates, in addition to a delayed primiparous age were observed in this population and exacerbated after its drastic decline; suggesting the viability and survival of this Garrano population were compromised. Investigating the population demographic changes and their causes and consequences can provide guidelines for managing populations and help fight the extinction of horse breeds.

100 phylogeniesThe 100 trees used as phylogenetic hypotheses in this study were subsets of those developed by Jetz et al. (2012) and available through birdtree.org. Several species are represented by more than one record in this dataset, thus phylogeny tips for those taxa were transformed into multichotomies (i.e., polytomies) in each phylogeny. Here, 101 phylogenies are included. Models using the 74th phylogeny failed to converge so an additional phylogeny (i.e., phylogeny 101) was added to complete a set of 100 for the analyses. For all phylogenies the tip labels are coded to match the "record_id" field code in the corresponding dataset.FrancisGCB2015_FinalTree.treData: responses to noise, species traits, study-specific informationFrancisGCB2015Data.csv