Geographic information systems (GIS) can be used to map mosquito larval and adult habitats and human populations at risk for mosquito exposure and possible arbovirus transmission. Along with traditional methods of surveillance-based targeted mosquito control, GIS can help simplify and target efforts during routine surveillance and post-disaster (e.g., hurricane-related flooding) to protect emergency workers and public health. A practical method for prioritizing areas for emergency mosquito control has been developed and is described here. North Carolina (NC) One Map was used to identify state-level data layers of interest based on human population distribution and mosquito habitat in Brunswick, Columbus, Onslow, and Robeson Counties in eastern NC. Relevant data layers were included to create mosquito control treatment areas for targeted control and an 18-step protocol for map development is discussed. This protocol is expected to help state, territorial, tribal, and/or local public health officials and associated mosquito control programs efficiently create treatment area maps to improve strategic planning in advance of a disaster. This protocol may be applied to any NC county and beyond, thereby increasing local disaster preparedness.

Explore the Saudi Arabia World Development Indicators dataset , including key indicators such as Access to clean fuels, Adjusted net enrollment rate, CO2 emissions, and more. Find valuable insights and trends for Saudi Arabia, Bahrain, Kuwait, Oman, Qatar, China, and India.

Indicator, Access to clean fuels and technologies for cooking, rural (% of rural population), Access to electricity (% of population), Adjusted net enrollment rate, primary, female (% of primary school age children), Adjusted net national income (annual % growth), Adjusted savings: education expenditure (% of GNI), Adjusted savings: mineral depletion (current US$), Adjusted savings: natural resources depletion (% of GNI), Adjusted savings: net national savings (current US$), Adolescents out of school (% of lower secondary school age), Adolescents out of school, female (% of female lower secondary school age), Age dependency ratio (% of working-age population), Agricultural methane emissions (% of total), Agriculture, forestry, and fishing, value added (current US$), Agriculture, forestry, and fishing, value added per worker (constant 2015 US$), Alternative and nuclear energy (% of total energy use), Annualized average growth rate in per capita real survey mean consumption or income, total population (%), Arms exports (SIPRI trend indicator values), Arms imports (SIPRI trend indicator values), Average working hours of children, working only, ages 7-14 (hours per week), Average working hours of children, working only, male, ages 7-14 (hours per week), Cause of death, by injury (% of total), Cereal yield (kg per hectare), Changes in inventories (current US$), Chemicals (% of value added in manufacturing), Child employment in agriculture (% of economically active children ages 7-14), Child employment in manufacturing, female (% of female economically active children ages 7-14), Child employment in manufacturing, male (% of male economically active children ages 7-14), Child employment in services (% of economically active children ages 7-14), Child employment in services, female (% of female economically active children ages 7-14), Children (ages 0-14) newly infected with HIV, Children in employment, study and work (% of children in employment, ages 7-14), Children in employment, unpaid family workers (% of children in employment, ages 7-14), Children in employment, wage workers (% of children in employment, ages 7-14), Children out of school, primary, Children out of school, primary, male, Claims on other sectors of the domestic economy (annual growth as % of broad money), CO2 emissions (kg per 2015 US$ of GDP), CO2 emissions (kt), CO2 emissions from other sectors, excluding residential buildings and commercial and public services (% of total fuel combustion), CO2 emissions from transport (% of total fuel combustion), Communications, computer, etc. (% of service exports, BoP), Condom use, population ages 15-24, female (% of females ages 15-24), Container port traffic (TEU: 20 foot equivalent units), Contraceptive prevalence, any method (% of married women ages 15-49), Control of Corruption: Estimate, Control of Corruption: Percentile Rank, Upper Bound of 90% Confidence Interval, Control of Corruption: Standard Error, Coverage of social insurance programs in 4th quintile (% of population), CPIA building human resources rating (1=low to 6=high), CPIA debt policy rating (1=low to 6=high), CPIA policies for social inclusion/equity cluster average (1=low to 6=high), CPIA public sector management and institutions cluster average (1=low to 6=high), CPIA quality of budgetary and financial management rating (1=low to 6=high), CPIA transparency, accountability, and corruption in the public sector rating (1=low to 6=high), Current education expenditure, secondary (% of total expenditure in secondary public institutions), DEC alternative conversion factor (LCU per US$), Deposit interest rate (%), Depth of credit information index (0=low to 8=high), Diarrhea treatment (% of children under 5 who received ORS packet), Discrepancy in expenditure estimate of GDP (current LCU), Domestic private health expenditure per capita, PPP (current international $), Droughts, floods, extreme temperatures (% of population, average 1990-2009), Educational attainment, at least Bachelor's or equivalent, population 25+, female (%) (cumulative), Educational attainment, at least Bachelor's or equivalent, population 25+, male (%) (cumulative), Educational attainment, at least completed lower secondary, population 25+, female (%) (cumulative), Educational attainment, at least completed primary, population 25+ years, total (%) (cumulative), Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative), Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative), Electricity production from coal sources (% of total), Electricity production from nuclear sources (% of total), Employers, total (% of total employment) (modeled ILO estimate), Employment in industry (% of total employment) (modeled ILO estimate), Employment in services, female (% of female employment) (modeled ILO estimate), Employment to population ratio, 15+, male (%) (modeled ILO estimate), Employment to population ratio, ages 15-24, total (%) (national estimate), Energy use (kg of oil equivalent per capita), Export unit value index (2015 = 100), Exports of goods and services (% of GDP), Exports of goods, services and primary income (BoP, current US$), External debt stocks (% of GNI), External health expenditure (% of current health expenditure), Female primary school age children out-of-school (%), Female share of employment in senior and middle management (%), Final consumption expenditure (constant 2015 US$), Firms expected to give gifts in meetings with tax officials (% of firms), Firms experiencing losses due to theft and vandalism (% of firms), Firms formally registered when operations started (% of firms), Fixed broadband subscriptions, Fixed telephone subscriptions (per 100 people), Foreign direct investment, net outflows (% of GDP), Forest area (% of land area), Forest area (sq. km), Forest rents (% of GDP), GDP growth (annual %), GDP per capita (constant LCU), GDP per unit of energy use (PPP $ per kg of oil equivalent), GDP, PPP (constant 2017 international $), General government final consumption expenditure (current LCU), GHG net emissions/removals by LUCF (Mt of CO2 equivalent), GNI growth (annual %), GNI per capita (constant LCU), GNI, PPP (current international $), Goods and services expense (current LCU), Government Effectiveness: Percentile Rank, Government Effectiveness: Percentile Rank, Lower Bound of 90% Confidence Interval, Government Effectiveness: Standard Error, Gross capital formation (annual % growth), Gross capital formation (constant 2015 US$), Gross capital formation (current LCU), Gross fixed capital formation, private sector (% of GDP), Gross intake ratio in first grade of primary education, male (% of relevant age group), Gross intake ratio in first grade of primary education, total (% of relevant age group), Gross national expenditure (current LCU), Gross national expenditure (current US$), Households and NPISHs Final consumption expenditure (constant LCU), Households and NPISHs Final consumption expenditure (current US$), Households and NPISHs Final consumption expenditure, PPP (constant 2017 international $), Households and NPISHs final consumption expenditure: linked series (current LCU), Human capital index (HCI) (scale 0-1), Human capital index (HCI), male (scale 0-1), Immunization, DPT (% of children ages 12-23 months), Import value index (2015 = 100), Imports of goods and services (% of GDP), Incidence of HIV, ages 15-24 (per 1,000 uninfected population ages 15-24), Incidence of HIV, all (per 1,000 uninfected population), Income share held by highest 20%, Income share held by lowest 20%, Income share held by third 20%, Individuals using the Internet (% of population), Industry (including construction), value added (constant LCU), Informal payments to public officials (% of firms), Intentional homicides, male (per 100,000 male), Interest payments (% of expense), Interest rate spread (lending rate minus deposit rate, %), Internally displaced persons, new displacement associated with conflict and violence (number of cases), International tourism, expenditures for passenger transport items (current US$), International tourism, expenditures for travel items (current US$), Investment in energy with private participation (current US$), Labor force participation rate for ages 15-24, female (%) (modeled ILO estimate), Development

Saudi Arabia, Bahrain, Kuwait, Oman, Qatar, China, India Follow data.kapsarc.org for timely data to advance energy economics research..

Original and derived data products referenced in the original manuscript are provided in the data package.

Description of the data and file structure

Original data:

Table_1_source_papers.csv: Papers that met review criteria and which are summarized in Table 1 of the manuscript.

1. ID: The paper identification number
2. Topic: The broad topic (i.e., each row of Table 1)
3. Authors: The names of the authors of the paper
4. Article Title: The title of the paper
5. Source Title: The name of the journal in which the paper was published
6. Abstract: The paper's abstract, retrieved from the Web of Science search
7. study_type: Classification of the study methodology/approach. "A" = a designed study that shows effect ,"B" = a pre/post study, "C" = a comparison of health outcomes or pathogen risk relative to a 'control/comparison' area, "D" = some quantitative effect but no control, "E" = qualitative comments but little supporting evidence, and/or a qualitative review.
8. pathogen_broad: Broad classification of the type of pathogen discussed in the paper.
9. transmission_type: Categorization of indirect, direct, sexual, vector, or other transmission modes.
10. pathogen_type: Categorization of bacteria, helminth, virus, protozoa, fungi, or other pathogen types.
11. country: Country in which the study was performed or results discussed. When countries were not available, regions were used. NA values indicate papers in which a geographic region was not relevant to the study (i.e., a methods-based study).

Derived data:

change_livestock_country.csv: A dataframe containing values used to generate Figure 4a in the manuscript.

1. County Name: The name of the county in Kenya
2. Sheep and goats 1980: The estimated number of sheep and goats in 1980
3. Sheep and goats 2016: The estimated number of sheep and goats in 2016
4. pct_change_shoat: The percent change in sheep and goat numbers from 1980 to 2016
5. Cattle 1980: The estimated number of cattle in 1980
6. Cattle 2016: The estimated number of cattle in 2016
7. pct_change_cattle: The percent change in cattle numbers from 1980 to 2016
8. Camel 1980: The estimated number of camels in 1980
9. Camel 2016: The estimated number of camels in 2016
10. pct_change_camel: The percent change in camel numbers from 1980 to 2016
11. human_pop 1980: The estimated human population in the county in 1980
12. human_pop 2016: The estimated human population in the county in 1980
13. pct_change_human: The percent change in the human population from 1980 to 2016
14. area_sq_km: The land area of the county
15. change_ind_per_sq_km_shoat: Absolute change in number of sheep and goats from 1980 to 2016
16. change_ind_per_sq_km_cattle: Absolute change in number of cattle from 1980 to 2016
17. change_ind_per_sq_km_camel: Absolute change in number of camels from 1980 to 2016

country_avg_schist_wormy_world.csv: A dataframe containing values used to generate Figure 3 in the manuscript.

• Country: The country in which the schistosome prevalence studies were performed.
• Latitude: The latitute in decimal degrees
• Longitude: The longitute in decimal degrees
• Maximum.prevalence: The mean maximum schistosomiasis prevalence of studies conducted within each country.

kenya_precip_change_1951_2020.csv: A dataframe containing values used to generate Figure 4b in the manuscript.

• Precipitation (mm): Binned annual precipitation values
• 1951-1980: The density of observations for each annual precipitation value for the 1951-1980 period
• 1971-2000: The density of observations for each annual precipitation value for the 1971-2000 period
• 1991-2020: The density of observations for each annual precipitation value for the 1991-2020 period

Sharing/Access information

Data were derived from the following sources:

• Ogutu, J. O., Piepho, H.-P., Said, M. Y., Ojwang, G. O., Njino, L. W., Kifugo, S. C., & Wargute, P. W. (2016). Extreme wildlife declines and concurrent increase in livestock numbers in Kenya: What are the causes? PloS ONE, 11(9), e0163249. https://doi.org/10.1371/journal.pone.0163249

• London Applied & Spatial Epidemiology Research Group (LASER). (2023). Global Atlas of Helminth Infections: STH and Schistosomiasis [dataset]. London School of Hygiene and Tropical Medicine. https://lshtm.maps.arcgis.com/apps/webappviewer/index.html?id=2e1bc70731114537a8504e3260b6fbc0

• World Bank Group. (2023). Climate Data & Projections—Kenya. Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org/country/kenya/climate-data-projections

The Global Demographic Data collection holds global gridded data products describing demographic information and water demand in relation to population data. Currently, water demand data are being distributed; population data will be added in the near future.

Country-level urban, rural and total population estimate data from World Resources Institute (WRI) for the years 1985, 1995, and 2025 were gridded by the University of New Hampshire's Water Systems Analysis Groupusing methods outlined in Vorosmarty et al. (2000) for use in estimating global water resources based on climate and population changes.

Currently available are five relative water demand (RWD) fraction data sets/ maps, produced by Vorosmarty et al. in their analysis of future water resources. The relative water demand is defined to be the total volume of water used either domestically, industrially or agriculturally (DIA) divided by the water discharge (Q). "Values of .2 to .4 indicate medium to high stress." (see Vorosmarty et al., 2000) This analysis deals only with sustainable water sources, and does not look at nonsustainable water sources, such a ground water mining. The RWD is computed on a .5 by .5 degree grid for two sentinel years: 1985 and 2025, which are two of the data sets. The ratio of the RWD for these two years provides a measure of change under scenarios of climate change only, population change only and the combination of climate change and population to produce the other three datasets. The ratio RWD values is relative to the RWD in the base year, 1985.

These data represent mosquito trap site results in the District of Columbia from 2016 to 2018. Trap locations are considered approximate address and/or the “nearest” street address or block to the stated coordinates in the data. Visit Fight the Bite: Protecting the District of Columbia from Mosquitoes- a collection of the 2016-2018 Arbovirus Surveillance Program conducted annually by DC Health, Health Regulation & Licensing Admin., Animal Services Div.Mosquitoes have the potential to spread harmful diseases. During the annual mosquito season in Washington DC, usually from April – October, DC Health deploys surveillance and mitigation methods to control the mosquito population in the District. DC Health (also known as the D.C. Department of Health or formerly DOH) has been trapping and testing mosquitoes for West Nile virus (WNV) for well over a decade. Starting in 2016, and in response to the Zika outbreak in Latin America and the Caribbean, DC Health substantially increased mosquito monitoring activities across the city. There were a total of 28 sites and 36 traps across the 8 wards. Data was submitted to the Centers for Disease Control MoquitoNet Portal.Note: the 2017 analysis does not include data for October. This is because October of 2017 would have skewed the results far too much based on a few variables that occurred. For example, the number of traps which had failed by the end of the season.Mosquito species in Washington, D.C.:Culex Pipiens, Salinarius and Culex Restuan: spread West Nile VirusAedes aegypti : according to the Centers for Disease Control (CDC), health experts have determined this species to be the most competent vector, capable of transmitting Zika to the human population. To date, none of the Aedes aegypti trapped in Washington, D.C. have been found to carry the Zika virus.Aedes albopictus: capable of spreading Zika to people. However, health experts are still learning whether it is likely to do so as it appears at this time, it is not as competent a vector for transmitting Zika as is the Aedes aegypti. Just because a mosquito can carry the virus does not mean that it will cause disease. So far, none of the Aedes albopictus trapped in Washington, D.C. have been found to carry the Zika virus.Aedes japonicus: normally found in South Florida, is present in D.C. in small numbers. Presently there is no indication that they are competent vectors for spreading Zika to the human population.

An outline of methods employed to develop spatial data sets of anthropogenic features and to evaluate human footprint model output.

Background: The Environmental Quality Index (EQI) reflects environmental performance and sustainability, with DKI Jakarta scoring 54.57—below its target. This study analyzes the influence of the Human Development Index (HDI), population growth, and the Information, Communication, and Technology Development Index (IDI) on DKI Jakarta’s EQI. Methods: A quantitative approach using time-series data (2008–2023) and multiple linear regression analysis was applied to evaluate the relationship between HDI, population growth, and IDI with environmental quality. Findings: HDI positively impacts environmental quality, contributing 5.776%. In contrast, a 1% increase in IDI and population growth correlates with a 2.183% and 173.456% decline in EQI, respectively, highlighting the environmental challenges of urbanization and technological expansion. Conclusion: Improving human resources, adopting green technologies, and fostering collaboration among stakeholders are critical to enhancing environmental quality. Novelty/Originality of this article: This study provides new insights into the interplay of HDI, IDI, and population growth in influencing environmental quality in a major urban area.

Pest Control Solutions Market Outlook

The global pest control solutions market size is projected to grow from $22.8 billion in 2023 to $36.3 billion by 2032, at a compound annual growth rate (CAGR) of 5.4%. Increasing urbanization and population growth, along with rising concerns about health and hygiene, are key factors driving this market's expansion. With the growing awareness about the diseases spread by pests and the damage they cause to property, the demand for effective pest control solutions is on the rise globally.

One of the primary growth factors is the increasing urbanization, which has led to higher population density in cities. This urban sprawl has resulted in a surge in pest infestations due to the closer proximity of human habitats. Additionally, climate change has played a significant role, as warmer temperatures generally promote the proliferation of pests. The increasing global temperature has facilitated the expansion of pest populations into regions previously unsuitable for their survival, thereby enlarging the demand for pest control solutions.

Another critical factor driving market growth is the heightened awareness of the health risks associated with pests. Diseases like malaria, Lyme disease, and dengue fever, which are transmitted by insects such as mosquitoes and ticks, have underscored the need for effective pest control. Governments and health organizations are increasingly emphasizing pest control measures to prevent outbreaks. Consequently, the regulatory framework supporting pest management practices is becoming more stringent, encouraging the adoption of advanced pest control solutions.

The growing focus on sustainable and eco-friendly pest control methods is also significantly contributing to market growth. Consumers and regulatory authorities are increasingly favoring biological and mechanical control methods over chemical solutions due to the adverse environmental and health impacts of pesticides. This shift towards sustainable practices has led to innovations in the pest control industry, such as the development of biopesticides and integrated pest management (IPM) systems. These methods not only reduce the reliance on chemical pesticides but also promote long-term pest management solutions.

Regionally, North America is a significant market for pest control solutions, driven by stringent regulations and high awareness levels. Europe follows closely, with increasing demand for sustainable and eco-friendly solutions. The Asia Pacific region is expected to witness the highest growth rate due to rapid urbanization and industrialization, coupled with increasing awareness about health and hygiene. Latin America and the Middle East & Africa are also experiencing steady growth, driven by the need to combat pest-related health issues and agricultural damage.

Product Type Analysis

The pest control solutions market can be segmented by product type into chemical control, biological control, mechanical control, and others. Chemical control dominates the market due to its immediate effectiveness and ease of application. Chemical pesticides are widely used in both commercial and residential settings to manage a variety of pests rapidly. However, the growing concerns over the environmental and health impacts of chemical pesticides are leading to increased regulatory scrutiny and a gradual shift towards more sustainable solutions.

Biological control methods are gaining significant traction as an eco-friendly alternative to chemical pesticides. This segment involves the use of natural predators, pathogens, or competitors to control pest populations. The rising consumer awareness about the adverse effects of chemicals on human health and the environment is driving the adoption of biological control methods. Innovations in biotechnology have also facilitated the development of more effective and targeted biopesticides, further boosting this segment's growth.

Mechanical control methods, which include traps, barriers, and physical removal, are also witnessing increased adoption. These methods are particularly favored in residential settings where chemical use may pose a risk to inhabitants. Mechanical control methods offer a non-toxic solution to pest management, making them suitable for use in sensitive environments such as schools and hospitals. The growing demand for integrated pest management (IPM) systems, whic

A description of the results for input models and methods for and results of corvid presence risk and exotic plant invasion risk model evaluation.

Despite major advances in quantitative approaches to natural resource management, there has been resistance to using these tools in the actual practice of managing ecological populations. Given a managed system and a set of assumptions, translated into a model, optimization methods can be used to solve for the most cost-effective management actions. However, when the underlying assumptions are not met, such methods can potentially lead to decisions that harm the environment and economy. Managers who develop decisions based on past experience and judgment, without the aid of mathematical models, can potentially learn about the system and develop flexible management strategies. However, these strategies are often based on subjective criteria and equally invalid and often unstated assumptions. Given the drawbacks of both methods, it is unclear whether simple quantitative models improve environmental decision making over expert opinion. In this study, we explore how well students, using their experience and judgment, manage simulated fishery populations in an online computer game and compare their management outcomes to the performance of model-based decisions. We consider harvest decisions generated using four different quantitative models: (1) the model used to produce the simulated population dynamics observed in the game, with the values of all parameters known (as a control), (2) the same model, but with unknown parameter values that must be estimated during the game from observed data, (3) models that are structurally different from those used to simulate the population dynamics, and (4) a model that ignores age structure. Humans on average performed much worse than the models in cases 1–3, but in a small minority of scenarios, models produced worse outcomes than those resulting from students making decisions based on experience and judgment. When the models ignored age structure, they generated poorly performing management decisions, but still outperformed students using experience and judgment 66% of the time.

The effective population size (Ne) is a fundamental parameter in population genetics that determines the relative strength of selection and random genetic drift, the effect of migration, levels of inbreeding, and linkage disequilibrium. In many cases where it has been estimated in animals, Ne is on the order of 10-20% of the census size. In this study, we use 12 microsatellite markers and 14,888 single nucleotide polymorphisms (SNPs) to empirically estimate Ne in Aedes aegypti, the major vector of yellow fever, dengue, chikungunya, and Zika viruses. We used the method of temporal sampling to estimate Ne on a global dataset made up of 46 samples of Ae. aegypti that included multiple time points from 17 widely distributed geographic localities. Our Ne estimates for Ae. aegypti fell within a broad range (~25-3,000), and averaged between 400 and 600 across all localities and time points sampled. Adult census size (Nc) estimates for this species range between one and five thousand, so the Ne/Nc ratio is about the same as for most animals. These Ne values are lower than estimates available for other insects and have important implications for the design of genetic control strategies to reduce the impact of this species of mosquito on human health.

Multiple, simultaneous environmental changes, in climatic/abiotic factors, in interacting species, and in direct human influences, are impacting natural populations and thus biodiversity, ecosystem services, and evolutionary trajectories. Determining whether the magnitudes of the population impacts of abiotic, biotic, and anthropogenic drivers differ, accounting for their direct effects and effects mediated through other drivers, would allow us to better predict population fates and design mitigation strategies. We compiled 644 paired values of the population growth rate (lambda) from high and low levels of an identified driver from demographic studies of terrestrial plants. Among abiotic drivers, natural disturbance (not climate), and among biotic drivers, interactions with neighboring plants had the strongest effects on lambda. However, when drivers were combined into the three main types, their average effects on lambda did not differ. For the subset of studies that measured both the average and variability of the driver, lambda was more sensitive to one standard deviation of change in abiotic drivers relative to biotic drivers, but sensitivity to biotic drivers was still substantial. Similar impact magnitudes for abiotic/biotic/anthropogenic drivers holds for plants of different growth forms, for different latitudinal zones, and for biomes characterized by harsher or milder abiotic conditions, suggesting that all three drivers have equivalent impacts across a variety of contexts. Thus the best available information about the integrated effects of drivers on all demographic rates provides no justification for ignoring drivers of any of these three types when projecting ecological and evolutionary responses of populations and of biodiversity to environmental changes.

Methods The main data consist of pairs of estimates of population growth rates of terrestrial plants, one from a relatively high and one from a relatively low level of an identified environmental driver (i.e., a factor such as climate, soil, interactions with competitors, herbivores, pathogens, or pollinators, or anthropogenic impacts). These estimates were taken from published studies. When available, levels of the environmental driver are also included, along with meta-data from each site (e.g., publication citation, species name, geographical location, etc.).

Dataset S1 includes the full data extracted from the data sources; see

Dataset S2 includes all data used in the statistical analyses, based on the full data from Dataset S1; see

The file gives the full citations for the papers in Datasets S1 and S2.

The statistic shows the total population of India from 2019 to 2029. In 2023, the estimated total population in India amounted to approximately 1.43 billion people.

Total population in India

India currently has the second-largest population in the world and is projected to overtake top-ranking China within forty years. Its residents comprise more than one-seventh of the entire world’s population, and despite a slowly decreasing fertility rate (which still exceeds the replacement rate and keeps the median age of the population relatively low), an increasing life expectancy adds to an expanding population. In comparison with other countries whose populations are decreasing, such as Japan, India has a relatively small share of aged population, which indicates the probability of lower death rates and higher retention of the existing population.

With a land mass of less than half that of the United States and a population almost four times greater, India has recognized potential problems of its growing population. Government attempts to implement family planning programs have achieved varying degrees of success. Initiatives such as sterilization programs in the 1970s have been blamed for creating general antipathy to family planning, but the combined efforts of various family planning and contraception programs have helped halve fertility rates since the 1960s. The population growth rate has correspondingly shrunk as well, but has not yet reached less than one percent growth per year.

As home to thousands of ethnic groups, hundreds of languages, and numerous religions, a cohesive and broadly-supported effort to reduce population growth is difficult to create. Despite that, India is one country to watch in coming years. It is also a growing economic power; among other measures, its GDP per capita was expected to triple between 2003 and 2013 and was listed as the third-ranked country for its share of the global gross domestic product.

Foot and Mouth Disease Vaccines Market Outlook

The global foot and mouth disease vaccines market size was valued at approximately USD 2.1 billion in 2023 and is projected to reach USD 3.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.1% during the forecast period. This growth is primarily driven by the increasing prevalence of foot and mouth disease, particularly in emerging economies, coupled with heightened awareness about the economic impact of livestock diseases. Additionally, government initiatives to control and prevent outbreaks are fostering demand for vaccines, as these initiatives often include subsidies or direct funding for vaccination programs, thus making it economically feasible for farmers to vaccinate their livestock.

An important growth factor in the foot and mouth disease vaccines market is the increasing global demand for animal products, which is driving livestock population growth. This trend is particularly strong in developing countries where rising incomes are leading to greater consumption of meat and dairy products. As a result, there is a heightened need to protect livestock from diseases that can significantly impact productivity and economic returns. The direct impact of foot and mouth disease on livestock includes reduced milk production, weight loss, and decreased fertility, all of which significantly affect the livelihood of farmers and the economy at large. Hence, the demand for effective vaccination solutions is becoming more pronounced as stakeholders in the livestock industry seek to maintain herd health and productivity.

Technological advancements in vaccine development are another key factor propelling growth in this market. The development of new vaccine formulations that offer broader protection, longer-lasting immunity, and require fewer doses is making vaccination programs more effective and appealing. For instance, the emergence of synthetic and recombinant vaccines offers hope for more precise and efficient disease control methods. These technological strides are helping to overcome some of the traditional challenges associated with foot and mouth disease vaccination, such as the requirement for frequent re-vaccination and cold chain logistics. As the vaccine technology landscape evolves, the market is likely to see enhanced adoption rates, particularly in regions with established veterinary infrastructures.

The integration of governmental and non-governmental organization efforts to control foot and mouth disease outbreaks is another significant growth driver. By aligning strategies, these organizations enhance vaccine distribution channels and awareness campaigns, leading to higher vaccination rates. In many countries, foot and mouth disease is a notifiable disease, necessitating coordinated responses at national and regional levels. This collaborative approach not only improves access to vaccines but also ensures quick response times during outbreaks, minimizing economic losses. Consequently, governments and international bodies are increasingly investing in research and development of vaccines, further stimulating the market.

In addition to foot and mouth disease, the development and deployment of vaccines for other zoonotic diseases like Brucella Melitensis are gaining attention. Brucella Melitensis is a significant pathogen affecting livestock, particularly goats and sheep, and poses a risk to human health through zoonotic transmission. The Brucella Melitensis Vaccine plays a crucial role in controlling this disease, thereby protecting both animal and human populations. The integration of such vaccines into livestock health programs not only enhances animal welfare but also contributes to public health by reducing the incidence of brucellosis in humans. As awareness of zoonotic diseases increases, the demand for comprehensive vaccination strategies, including the Brucella Melitensis Vaccine, is expected to rise, further driving growth in the animal health market.

Vaccine Type Analysis

The foot and mouth disease vaccines market is segmented into conventional vaccines and emergency vaccines. Conventional vaccines have been the longstanding choice in disease prevention strategies owing to their proven efficacy in controlling outbreaks over the years. These vaccines are designed for regular administration and are part of routine vaccination schedules in many countries. They work by activating the immune system to recognize and combat the foot and mouth disease virus, thereby reducing the incidence of outbreaks.

Wildfires in forests globally have become more frequent and intense due to changes in climate and human management. Shrub layer fuels allow fire to spread vertically to forest canopy, creating high-intensity fires. Our research provides a deep-time perspective on shrub fuel loads in fire-prone southeastern Australia. Comparing 2,833 records for vegetation cover, past climate, biomass burning, and human population size across different phases of human occupation, we demonstrate that Indigenous population expansion and cultural fire use resulted in a 50% reduction in shrub cover, from approximately 30% from the early-mid Holocene (12-6 ka) to 15% during the late-mid Holocene (6-1 ka). Following British colonization, shrub cover has increased to the highest ever recorded (mean of 35% land cover), increasing the risk of high-intensity fires. Methods Vegetation reconstruction We applied REVEALS (32) to quantify past vegetation using 31 pollen records in southeast Australia (Table S1), covering the Holocene (n=29) and Last Interglacial periods (n=2). The REVEALS model was run to convert raw pollen data (counts) into estimates of land cover (%) by correcting for biases in pollen production (i.e. different plant species produce different amounts of pollen) and pollen dispersal (i.e. dispersal patterns differ in response to pollen grain properties) (32). Pollen productivity estimates (PPEs) required for REVEALS for the 19 most abundant pollen taxa were derived from Mariani et al. These 19 taxa cover a large proportion of the vegetation and pollen counts, for example, across >275 vegetation quadrats for the state of Victoria (5), a median of 81% were target taxa (± 24%). Based on the modern (moss) pollen counts, a median of 96% of moss samples were made up by target taxa (±22%). For fossil samples across the region, these 19 target taxa constitute approx. 75% of pollen counts (median value, ±14%). The missing % are usually within the Proteaceae and Fabaceae families (shrub layer), which means our reconstruction of shrub cover is on the conservative side and reconstructed values might be slightly higher. REVEALS was executed using the R package disqover version 0.9.09 accessible through (https://github.com/MartinTheuerkauf/disqover.), using the Lagrangian stochastic model (LSM) for pollen dispersal parameterized as previous studies in Europe and Australia (5, 58–61). The median across all REVEALS results combining each site per 200 year time bins were compiled using MS Excel and the R code provided. The results of the Holocene estimates were compared with post-colonial vegetation estimates previously published (n= 51; Mariani et al.). Statistical analyses were undertaken to assess significance of shrub cover % change amongst the throughout the reconstruction period. A pairwise t-test was conducted on square-root transformed percentage data for shrub cover (Table S1). Square-root transformation was required prior to the t-test, as the dataset did not have a normal distribution and parametric tests assume normality. The square-root transformation provided a normal distribution (Figure S11a,b) and the autocorrelation of the shrubs time series is presented in Figure S11c. To further support the results from the t-test, we undertook a Kruskal-Wallis test (62) with Wilcoxon pairwise comparisons (63). This test is non-parametric, hence the raw data (non-normal) were supplied and results are presented in Figure S11d. Human population A total of 2,358 radiocarbon ages of archaeological evidence of past human occupation across southeast Australia were used in this study. Initially 6,522 radiocarbon ages were extracted from the SahulArch database, accessible through OCTOPUS v.2 (https://octopusdata.org/); Codilean et al. (936 and subject to screening for region of interest, Holocene age range, and appropriate associations with archaeological deposit (if context was indicated as ‘sterile’ or ‘non-occupation’ by original study, these were excluded). The resulting 2,368 radiocarbon ages were then calibrated using SHCal20, and the summed probability density (SPD) of calibrated ages was calculated using the thinning approach in the rcarbon package using version 1.5.1 to infer past human population changes. We acknowledge that the number of radiocarbon dates and associated uncertainties can influence summed probability estimates, especially for estimates aimed at detecting short-term variations and rates of change. Our study is focused on long-term multi-millennia-scale changes and does not consider rates of change. Bayesian bounded population growth models were further used to assess the fit of the SPD of radiocarbon ages using the nimbleCarbon package version 0.2.5 in R. The models were fitted through Markov Chain Monte-Carlo and ranked using the Watanabe–Akaike information criterion (WAIC). The fitted population growth models include exponential, double exponential, and exponential logistic models (Fig. S4; see Crema and Shoda, for method details). Among the fitted models for the SPD, the exponential logistic model ranked as the top model with the lowest WAIC, while the double exponential model ranked the lowest with the highest WAIC (Fig. S4). Biomass burning Sedimentary charcoal records from 108 lakes and wetlands across southeastern Australia were collated from the Global Charcoal Database and Neotoma (Table S2). Charcoal concentrations were converted to charcoal accumulation rates using the available chronological information. As elsewhere in the world, researchers in Australia have used various methods to quantify charcoal, necessitating data transformations to extract regional-scale palaeofire trends. Because these transformations tend to mask inter-site variability, we grouped records by analysis method prior to min-max rescaling of all records within each group and square-root transformation. As woody charcoal is preferentially preserved and accumulated in the sedimentary records, as opposed to grass charcoal (38), we can interpret our charcoal influx trends as woody biomass burned. The method is fully described in Mariani et al. and Rowe et al. Palaeoclimate Five Holocene terrestrial palaeomoisture records (see Table S3) were compiled in this investigation comprising three precipitation/evaporation (P/E) proxy reconstruction types. Lake level reconstructions where lower lake levels represent diminished lake recharge into closed lakes through precipitation. Palaeosalinity records where higher salinities indicate periods of increasing lake desiccation and reduced regional recharge (drier conditions). A mean annual rainfall reconstruction for Swallow Lagoon, Stradbroke Island, directly reconstructing precipitation. Whilst the palaeoclimate records span a large climatic gradient (Fig. S2), the individual trends were found to be coherent, justifying their compositing (Fig. S6). All data were individually z-scored and then merged using MS Excel, before GAM smoothing (k=100), to create a composite record for the southeastern Australian region. Lake level reconstructions from sediment grain size analysis at the maar crater Lakes Keilambete and Gnotuk were used as indicators of regional precipitation patterns from westerly wind circulation. Both lakes have relatively simple hydrological inputs and are considered good indicators of evaporation-precipitation oscillations. Palaeosalinity reconstructions as Total Dissolved Solids (TDS g/L) for Lake Keilambete were originally procured through ostracod Modern Analogue Technique (MAT) reconstructions using an analogue database of 491 samples. A depauperate fossil record (n=3 species) of ostracods at Lake Gnotuk meant only grain size lake levels were included in the index. Palaeosalinity reconstructions (Log g/L-1) for Lake Jacka and NW Jacka (72) were calculated from ostracod assemblages using a weighted-averaging transfer function with 119 modern analogue samples. The main control on modern assemblage composition was the total salinity of the lakes. At Blue Lake, palaeosalinity (Log10TDS g/L) were originally derived from the Weighted Averaging of Modern Analogues Technique (WMAT) utilizing 534 analogue samples. The rainfall reconstruction at Swallow Lagoon (56) was included to consider ENSO-derived palaeomoisture signals. The rainfall reconstruction of Swallow Lagoon used δ13C ratios from ancient Melaleuca quinquenervia leaf fragments as a proxy of historical rainfall (mm), calibrated against a 12-year monthly record of Melaleuca quinquenervia litter δ13C and recorded rainfall. The record reflects mean annual rainfall for the total ENSO system rather than El Niño/La Niña events, where the 1cm samples represent an average of 24.4 years of data. Generalized linear modelling Generalized linear model (GLM) was used to identify the main driver(s) of ladder fuels (shrub) cover with palaeoclimate synthesis, biomass burned, and SPD of archaeological ages set as predictors. Variables were randomly sampled 100 times without replacement at a lower resolution to remove the effect of autocorrelation. The lower resolution includes 30, 60 and 90% of datasets to check the consistency of the results (Table S3). A separate GLM was also fitted to predict mid-late Holocene shrub cover changes using early-mid Holocene GLM (predictors: human population –SPD, palaeoclimate index and charcoal influx) as a training set. The mid-late Holocene model was intended to reflect shrub cover changes under the scenario of no human influence and so, only palaeoclimate index and charcoal influx were included as predictors. GLMs were fitted using the MASS package version 7.3 in R (74).

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.

Additional file 2. Summary of studies comparing DEG analysis methods.

Recovering and maintaining large carnivore populations is a global conservation challenge that requires better knowledge of the factors affecting their populations, particularly in shared landscapes (i.e., non-protected areas where people occupy and or utilize the land). The Mexican wolf (Canis lupus baileyi) is an endangered wolf subspecies being recovered on shared landscapes in the Southwest United States and Mexico. We used data from the U.S. program to model population growth, evaluate the impact of management removal and illegal killing relative to other demographic factors, and test hypotheses about factors influencing rates of management removal and illegal killing. From 1998–2019, the population growth averaged 12% per year. Rates of natural reproduction, illegal killing, and other mortality remained consistent over the 22 years; while releases, translocations, and management removals varied markedly between two time periods, phase 1: 1998–2007 and phase 2: 2008–2019. The number of wolves removed for conflict management was higher during phase 1 (average ~13 per year, rate = 24.8%) than phase 2 (average of ~5 per year, rate = 5.2%). This decrease in management removal resulted in the wolf population resuming growth after a period of population stagnation. Two factors influenced this decrease, a change in policy regarding removal of wolves (stronger modeling support) and a decrease in the number of captive-reared adult wolves released into the wild (weaker modeling support). Illegal mortality was relatively constant across both phases, but after the decrease in management removal, illegal mortality became the most important factor (relative importance shifted from 28.2% to 50.1%). Illegal mortality was positively correlated with rates of reintroduction and translocation of wolves and negatively correlated with the rate of management removal.

Synthesis and applications. Using management removal to reduce human-carnivore conflict can have negative population impacts if not used judiciously. Recovering and maintaining carnivore populations in shared landscapes may require greater tolerance of conflict and more emphasis on effective conflict prevention strategies and compensation programs for affected stakeholders.

Methods Within the United States, Mexican wolves are being recovered in south-central Arizona and New Mexico; specifics of the area can be found in (U.S. Fish and Wildlife Service 2017). Mexican wolves have been monitored intensively since the beginning of the reintroduction effort in 1998. To aid monitoring, a high percentage of wolves are radio-collared each year (range 38% to 100%, weighted average based on end-of-year population count and collars was 52%). Utilizing radio collars and other methods the Interagency Field Team (i.e., employees from Arizona Game and Fish Department, New Mexico Department of Game and Fish, USDA APHIS-Wildlife Services, US Forest Services, US Fish and Wildlife Service, and White Mountain Apache Tribe) then conducts annual population counts and pup counts and monitors continually for mortality events. Initially (1998–2004), the Interagency Field Team determined population estimates and pup counts via howling surveys (Harrington and Mech 1982, Fuller and Sampson 1988), tracks, and visual observations during aerial (fixed wing) and ground radio-telemetry efforts (White and Garrott 1990). Ground observations were collected opportunistically through the least intrusive methods possible and the Interagency Field Team avoided any disturbance of den areas. In later years (2005–2019), they incorporated helicopter counts in January to verify and collect additional information from ground counts and incorporated the increased use of remote cameras, observations at den sites, and trapping for younger pups (2009–2019). Currently, the Interagency Field Team utilizes data collected from Nov 1 through mid-February to develop an end-of-the-year observed minimum population count. The only processing of the data that we have done was to combine different sources of non-management and non-illegal killing into “other mortality”. We combined natural mortality, mortality from vehicles, and other legal mortality into other mortality for our analysis.

1. Efforts to monitor and conserve populations and ecosystems in human-dominated landscapes can benefit from an empirical social-ecological systems approach.
2. Here we illustrate how latent variable structural equation modelling of regional time series data can effectively describe interconnected drivers of population fluctuations in dynamic landscapes and can help to reveal previously unknown system drivers.
3. Using a declining farmland-dependent bird species (Ammodramus savannarum) in the eastern United States (1994-2015) as a case study, our analysis reveals how farm management decisions drive population fluctuations (R2 = 20%), while management is in turn highly influenced by climate (R2 = 23-51%), but not by regional conservation spending.
4. Synthesis and applications. Structural equation modelling revealed potential social-ecological pathways for halting regional population declines in a grassland bird, the Grasshopper Sparrow. Lower population growth rates followed years of higher hay yields (~4 percentage points per metric ton increase in hay yield) and later harvests (~2 percentage points per 10-day delay in harvest). Thus, one pathway for stabilising regional populations could involve compensating farmers for reducing hay harvests, potentially requiring a six-fold increase in current annual agri-environmental conservation spending.

Methods This data set was gathered from various sources published by federal agencies and in the public domain (U.S. Department of Agriculture, U.S. National Oceanic and Atmospheric Administration, United States Geological Survey, United States Bureau of Labor Statistics). Two variables (SPEND and deltaSPEND) contain data on conservation spending from the EWG Conservation Database (Source: Environmental Working Group, www.ewg.org. Reproduced with permission).

Variables were then converted into other units or processed / derived in various ways (e.g., adjusted for inflation, relativized by area). The specific data sources and processing methods used are described in detail in the Methods and Supporting Information (Appendices S2 and S3) of the accompanying manuscript (Allen, Lockwood, & Burger, 2020).

Allen, M. C.; Lockwood, J.; Burger, J. (2020). Finding clarity in ecological outcomes using empirical integrated social-ecological systems: a case study of agriculture-dependent grassland birds. Journal of Applied Ecology. (Manuscript number: JAPPL-2020-00322)

NJ Division of Fish & Wildlife (DFW) manages deer herd in New Jersey through the use of deer management zones (DMZ). The Division, under authority of the Fish and Game Council designates these boundaries. Deer Management Zone boundaries are comprised of major and minor roads, waterways and geographic formations. Included for references are the county and township data. DMZs are updated on an as needed basis. New Jersey's deer herd is a major component of the landscape throughout all but the most urbanized areas of the state. Deer affect our forests, farms, gardens, backyards and roadways. From a population reduced to a handful of deer in the early 1900s they rebounded during the 20th Century to a thriving herd today. A healthy deer herd, managed at levels that are compatible with current land use practices and the human population, has great value to the people of the state. Deer are photographed, watched and hunted by many in New Jersey and visitors from elsewhere. Deer hunters spend more than 100 million dollars each year as they enjoy approximately 1.5 million recreation-days hunting deer. Money spent in the course of deer hunting benefits a wide variety of New Jersey businesses. Please visit http://www.njfishandwildlife.com/ for more information and detailed instructions pertaining to permit/license issues.