According to latest figures, the Chinese population decreased by 1.39 million to around 1.408 billion people in 2024. After decades of rapid growth, China arrived at the turning point of its demographic development in 2022, which was earlier than expected. The annual population decrease is estimated to remain at moderate levels until around 2030 but to accelerate thereafter. Population development in China China had for a long time been the country with the largest population worldwide, but according to UN estimates, it has been overtaken by India in 2023. As the population in India is still growing, the country is very likely to remain being home of the largest population on earth in the near future. Due to several mechanisms put into place by the Chinese government as well as changing circumstances in the working and social environment of the Chinese people, population growth has subsided over the past decades, displaying an annual population growth rate of -0.1 percent in 2024. Nevertheless, compared to the world population in total, China held a share of about 17 percent of the overall global population in 2024. China's aging population In terms of demographic developments, the birth control efforts of the Chinese government had considerable effects on the demographic pyramid in China. Upon closer examination of the age distribution, a clear trend of an aging population becomes visible. In order to curb the negative effects of an aging population, the Chinese government abolished the one-child policy in 2015, which had been in effect since 1979, and introduced a three-child policy in May 2021. However, many Chinese parents nowadays are reluctant to have a second or third child, as is the case in most of the developed countries in the world. The number of births in China varied in the years following the abolishment of the one-child policy, but did not increase considerably. Among the reasons most prominent for parents not having more children are the rising living costs and costs for child care, growing work pressure, a growing trend towards self-realization and individualism, and changing social behaviors.

The region of present-day China has historically been the most populous region in the world; however, its population development has fluctuated throughout history. In 2022, China was overtaken as the most populous country in the world, and current projections suggest its population is heading for a rapid decline in the coming decades. Transitions of power lead to mortality The source suggests that conflict, and the diseases brought with it, were the major obstacles to population growth throughout most of the Common Era, particularly during transitions of power between various dynasties and rulers. It estimates that the total population fell by approximately 30 million people during the 14th century due to the impact of Mongol invasions, which inflicted heavy losses on the northern population through conflict, enslavement, food instability, and the introduction of bubonic plague. Between 1850 and 1870, the total population fell once more, by more than 50 million people, through further conflict, famine and disease; the most notable of these was the Taiping Rebellion, although the Miao an Panthay Rebellions, and the Dungan Revolt, also had large death tolls. The third plague pandemic also originated in Yunnan in 1855, which killed approximately two million people in China. 20th and 21st centuries There were additional conflicts at the turn of the 20th century, which had significant geopolitical consequences for China, but did not result in the same high levels of mortality seen previously. It was not until the overlapping Chinese Civil War (1927-1949) and Second World War (1937-1945) where the death tolls reached approximately 10 and 20 million respectively. Additionally, as China attempted to industrialize during the Great Leap Forward (1958-1962), economic and agricultural mismanagement resulted in the deaths of tens of millions (possibly as many as 55 million) in less than four years, during the Great Chinese Famine. This mortality is not observable on the given dataset, due to the rapidity of China's demographic transition over the entire period; this saw improvements in healthcare, sanitation, and infrastructure result in sweeping changes across the population. The early 2020s marked some significant milestones in China's demographics, where it was overtaken by India as the world's most populous country, and its population also went into decline. Current projections suggest that China is heading for a "demographic disaster", as its rapidly aging population is placing significant burdens on China's economy, government, and society. In stark contrast to the restrictive "one-child policy" of the past, the government has introduced a series of pro-fertility incentives for couples to have larger families, although the impact of these policies are yet to materialize. If these current projections come true, then China's population may be around half its current size by the end of the century.

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

Background

Large carnivores maintain the stability and functioning of ecosystems. Currently, many carnivore species face declining population sizes due to natural and anthropogenic pressures. The leopard, Panthera pardus, is probably the most widely distributed and highly adaptable large felid globally, still persisting in most of its historic range. However, we lack subspecies-level data on country or regional scale on population trends, as ecological monitoring approaches are difficult to apply on such wide-ranging species. We used genetic data from leopards sampled across the Indian subcontinent to investigate population structure and patterns of demographic decline.

Methods

We collected faecal samples from the Terai-Arc landscape of north India and identified 56 unique individuals using a panel of 13 microsatellite markers. We merged this data with already available 143 leopard individuals and assessed genetic structure at country scale. Subsequently, we investigated the demographic history of each identified subpopulations and compared genetic decline analyses with countrywide local extinction probabilities.

Results

Our genetic analyses revealed four distinct subpopulations corresponding to Western Ghats, Deccan Plateau-Semi Arid, Shivalik and Terai region of the north Indian landscape, each with high genetic variation. Coalescent simulations with microsatellite loci revealed a possibly human-induced 75-90% population decline between ∼120-200 years ago across India. Population-specific estimates of genetic decline are in concordance with ecological estimates of local extinction probabilities in these subpopulations obtained from occupancy modeling of the historic and current distribution of leopards in India.

Conclusions

Our results confirm the population decline of a widely distributed, adaptable large carnivore. We re-iterate the relevance of indirect genetic methods for such species in conjunction with occupancy assessment and recommend that detailed, landscape-level ecological studies on leopard populations are critical to future conservation efforts. Our approaches and inference are relevant to other widely distributed, seemingly unaffected carnivores such as the leopard.

Methods Research permissions and ethical considerations

All required permissions for our field surveys and biological sampling were provided by the Forest Departments of Uttarakhand (Permit no: 90/5-6), Uttar Pradesh (Permit no: 1127/23-2-12(G) and 1891/23-2-12) and Bihar (Permit no: Wildlife-589). Due to non-invasive nature of sampling, no ethical clearance was required for this study.

Sampling

To detect population structure and past population demography it is important to obtain genetic samples from different leopard habitats all across the study area. In this study, we used leopard genetic data generated from non-invasive samples collected across the Indian subcontinent. We conducted extensive field surveys across the Indian part of Terai-Arc landscape (TAL) covering the north-Indian states of Uttarakhand, Uttar Pradesh and Bihar between 2016-2018. This region has already been studied for large carnivore occupancy using traditional camera trapping as well as field surveys (Johnsingh et al., 2004; Harihar et al., 2009; Jhala et al., 2015; Chanchani et al., 2016). We foot surveyed all existing trails covering the entire region to collect faecal samples. Number of trails walked in a particular area was decided based on existing knowledge of leopard presence by the local people and frontline staff members of the sampling team. We collected a total of 778 fresh large carnivore faecal samples. These samples were collected from both inside (n=469) and outside (n=309) protected areas from different parts of this landscape. In the field, the samples were judged as large carnivores based on several physical characteristics such as scrape marks, tracks, faecal diameter etc. All faecal samples were collected in wax paper and stored individually in sterile zip-lock bags and stored inside dry, dark boxes in the field for a maximum of two weeks period (Biswas et al., 2019). All samples were collected with GPS locations and were transferred to the laboratory and stored in -20°C freezers until further processing.

In addition to the north Indian samples collected in this study, we used genetic data previously described in Mondol et al. (2015), representing mostly the Western Ghats and central Indian landscape. The data was earlier used in forensic analyses to assign seized leopard samples to their potential geographic origins in India (Mondol et al., 2015). Out of the 173 individual leopards described in the earlier study, we removed data from related individuals and samples with insufficient data (n=30) and used the remaining 143 samples for analyses in this study. These samples were collected from the states of Kerala (n=5), Tamil Nadu (n=4), Karnataka (n=53), Andhra Pradesh (n=3), Madhya Pradesh (n=12), Maharashtra (n=46), Gujarat (n=2), Rajasthan (n=5), Himachal Pradesh (n=8), Jharkhand (n=1), West Bengal (n=2) and Assam (n=2), respectively. The sample locations are presented in Figure 1.

DNA extraction, species and individual identification

For all field-collected faecal samples, DNA extraction was performed using protocols described in Biswas et al. (2019). In brief, each frozen faeces was thawed to room temperature and the upper layer was swabbed twice with Phosphate buffer saline (PBS) saturated sterile cotton applicators (HiMedia). The swabs were lysed with 30 µl of Proteinase K (20mg/ml) and 300 µl of ATL buffer (Qiagen Inc., Hilden, Germany) overnight at 56°C, followed by Qiagen DNeasy tissue DNA kit extraction protocol. DNA was eluted twice in 100 µl preheated 1X TE buffer. For every set of samples, extraction negatives were included to monitor possible contaminations.

Species identification was performed using leopard-specific multiplex PCR assay with NADH4 and NADH2 region primers described in Mondol et al., (2014) and cytochrome b primers used in Maroju et al., (2016). PCR reactions were done in 10 µl volumes containing 3.5 µl multiplex buffer mix (Qiagen Inc., Hilden, Germany), 4 µM BSA, 0.2 µM primer mix and 3 µl of scat DNA with conditions including initial denaturation (95°C for 15 min); 40 cycles of denaturation (94°C for 30 s), annealing (Ta for 30 s) and extension (72°C for 35 s); followed by a final extension (72°C for 10 min). Negative controls were included to monitor possible contamination. Leopard faeces were identified by viewing species-specific bands of 130 bp (NADH4) and 190 bp (NADH2) (Mondol et al., 2014) and 277 bp (cytochrome b) (Maroju et al., 2016) in 2% agarose gel.

For individual identification, we used the same panel of 13 microsatellite loci previously used in Mondol et al. (2014) (Table 1). To generate comparable data with the samples used from earlier study by Mondol et al. (2014) we employed stringent laboratory protocols. All PCR amplifications were performed in 10 µl volumes containing 5 µl Qiagen multiplex PCR buffer mix (QIAGEN Inc., Hilden, Germany), 0.2 µM labelled forward primer (Applied Biosystems, Foster City, CA, USA), 0.2 µM unlabelled reverse primer, 4 µM BSA and 3 µl of the faecal DNA extract. The reactions were performed in an ABI thermocycler with conditions including initial denaturation (94°C for 15 min); 45 cycles of denaturation (94°C for 30 sec), annealing (Ta for 30 sec) and extension (72°C for 30 sec); followed by final extension (72°C for 30 min). Multiple primers were multiplexed to reduce cost and save DNA (Table 1). PCR negatives were incorporated in all reaction setups to monitor possible contamination. The PCR products were analyzed using an automated ABI 3500XL Bioanalyzer with LIZ 500 size standard (Applied Biosystems, Foster City, CA, USA) and alleles were scored with GENEMAPPER version 4.0 (Softgenetics Inc., State Collage, PA, USA). During data generation from field-collected samples we used one reference sample (genotyped for all loci) from the earlier study for genotyping. As the entire new data is generated along with the reference sample and the alleles were scored along with the reference genotypes, the new data (allele scores) were comparable with earlier data for analyses.

To ensure high quality multi-locus genotypes from faecal samples, we followed a modified multiple-tube approach in combination with quality index analyses (Miquel et al., 2006) as described previously for leopards by Mondol et al. (2009a, 2014). All faecal samples were amplified and genotyped four independent times for all the loci. Samples producing identical genotypes for at least three independent amplifications (or a quality index of 0.75 or more) for each loci were considered reliable and used for all further analysis, while the rest were discarded.

Mounting observational records demonstrate human-caused faunal decline in recent decades, while accumulating archaeological evidence suggests an early biodiversity impact of human activities during the Holocene. A fundamental question arises concerning whether modern wildlife population declines began during early human disturbance. Here, we performed population genomic analysis of six common forest birds in East Asia to address this question. For five of them, demographic history inference based on 25-33 genomes of each species revealed dramatic population declines by 4-48-fold over millennia (two to five thousand years ago). Nevertheless, ecological niche models predicted extensive range persistence during the Holocene and imply limited demographic impact of historical climate change. Summary statistics further suggest high negative correlations between these population declines and human disturbance intensities and indicate a potential driver of human activities. ...

Genetic variation is fundamental to population fitness and adaptation to environmental change. Human activities are driving declines in many wild populations and could have similar effects on genetic variation. Despite the importance of estimating such declines, no global estimate of the magnitude of ongoing genetic variation loss has been conducted across species. By combining studies that quantified recent changes in genetic variation across a mean of 27 generations for 91 species, we conservatively estimate a 5.4-6.5% decline in within-population genetic diversity of wild organisms since the industrial revolution. This loss has been most severe for island species, which show a 30% average decline. We identified taxonomic and geographic gaps in temporal studies that must be urgently addressed. Our results are consistent with single time-point meta-analyses, which indicated that genetic variation is likely declining. However, our results represent the first confirmation of a global decline, and provide an estimate of the magnitude of the genetic variation lost from wild populations.

Until 2100, the world's population is expected to be ageing. Whereas people over 60 years made up less than 13 percent of the world's population in 2024, this share is estimated to reach 28.8 percent in 2100. On the other hand, the share of people between zero and 14 years was expected to decrease by almost ten percentage points over the same period.

A long-term study of a common bottlenose dolphin (Tursiops truncatus) population inhabiting the Gulf of Guayaquil, Ecuador (2°33′ S, 79°20′W), has been carried out for almost 30 years. Similarly, as in other parts of the world, this population is structured socially and spatially in well-defined subunits or communities. Two of these communities, referred to as Posorja and El Morro, have been studied with major intensity in the last 10 years in the western inner estuary, among others to calculate population parameters that allow assessing their viability in time. Calculated parameters include annual abundance, age and sex composition, annual crude birth rate, calf survival, calf production interval, and average annual mortality/emigration. With these parameters and others derived from other better-studied populations, the trend of both subunits was modeled using the software Vortex. Results show that even under an optimistic scenery both communities will be extinct in the short (Posorja) and mid-term (El Morro), if current stressors continue. Most population parameters calculated in both communities show similar values as in populations elsewhere, but a very low calf survival in Posorja and high mortality/emigration ratios in adults, and probably in juveniles in both communities, contribute to this trend. Population deterioration seems to be the result of different human-induced threats such as fisheries, maritime traffic and others still not well assessed, as well as stochastic demographic events. We recommend taking actions in the short term to halt population decline addressing the major causes of mortality affecting these dolphin communities.

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).

India population growth rate for 2023 was 0.88%, a 0.09% increase from 2022.

<ul style='margin-top:20px;'>

<li>India population growth rate for 2022 was <strong>0.79%</strong>, a <strong>0.03% decline</strong> from 2021.</li>
<li>India population growth rate for 2021 was <strong>0.82%</strong>, a <strong>0.15% decline</strong> from 2020.</li>
<li>India population growth rate for 2020 was <strong>0.97%</strong>, a <strong>0.07% decline</strong> from 2019.</li>
</ul>Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage . Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.

Context: The guanaco is one of the four species of South American camels, and is the largest native mammal inhabiting arid and semi-arid environments in South America. Although guanaco was abundant and widely distributed in the past, currently its density and distribution range are substantially reduced, inhabiting mainly in Argentine Patagonia in small isolated groups. The decline in guanaco populations is most likely related to the Anthropocene defaunation process that is affecting large mammals in developing countries worldwide, but the extent and causes of these changes are not well understood. Aims: Explore both the changes in the distribution of guanaco populations in Northwest Patagonia and the environmental and anthropic factors that shaped the distribution patterns, employing a long-term perspective spanning from the end of the Late Holocene to present times (i.e., last 2500 years). Methods: We combine archaeological information, ethnohistorical records and current observations and apply Species Distribution Models using bioclimatic and anthropic factors as explanatory variables. Key results: Guanaco spatial distribution in Northwest Patagonia changed significantly throughout time. This change consisted in the displacement of the species towards the east of the region and its disappearance from northwest Neuquén and southwest Mendoza in the last 30 years. In particular, the high-density urban settlements and roads, and secondly, competition with ovicaprine livestock (goats and sheep) for forage are the main factors explaining the change in guanaco distribution. Conclusions: Guanaco and human populations co-existed in the same areas during the Late Holocene and historic times, but during the 20th century the modern anthropic impact generated a spatial dissociation between both species, pushing guanaco populations to drier and unproductive areas that were previously peripheral in its distribution. Implications: As with many other large mammal species in developing countries, Northwest Patagonia guanaco populations are undergoing significant changes in their range due to modern anthropic activities. Considering that these events are directly related to population declines and extirpations, together with the striking low density recorded for Northwest Patagonia guanaco populations, urgent management actions are needed to mitigate current human impacts. Methods We collected guanaco occurrences from archaeological, ethnohistorical and current sighting data, and separate this data in periods (Lata Holocene, Historic and Current). Likewise, different bioclimatic and anthropic variables that could contribute to explain guanaco distribution in Northwest Patagonia were collected. Finally, the R script to model guanaco distribution in each period and estimate the explicatory power of each variable is also upload.

Many tropical species show declining populations. The pantropical order Trogoniformes has 76% of its species ranked as declining, reflecting a world-wide problem. Here we report on the reproductive ecology and life history traits of the declining and near-threatened old world Whitehead’s Trogon (Harpactes whiteheadi), the declining new world Collared Trogon (Trogon collaris) and the stable Masked Trogon (T. personatus). We also reviewed the literature on reproductive ecology and life history traits of trogons to assess possible commonalities that might help explain population declines. We found that the declining Whitehead’s and Masked Trogons had reasonable nest success (32% and 25%, respectively), while the stable Masked Trogon had poor reproductive success (9%), all contrary to population trends. However, the limited literature data suggested that poor reproductive success may be common among trogons, which may contribute to population declines. Parents fed young at a low rate and had long on-bouts for incubation and nestling warming that reduced activity at the nest, as favored by high nest predation risk over evolutionary time. We found that young fledged from the nest with poorly developed wings, as also favored by high nest predation risk. Evolved nestling periods among trogon species suggests that poor wing development is likely common. Wing development has been shown to affect juvenile survival after leaving the nest. The poor wing development may be an important contributor to population declines that deserves more attention. Evolved life history traits are important to recognize as creating population vulnerabilities in a changing world. Methods Whitehead’s Trogon was studied in Kinabalu Park, Sabah, Malaysian Borneo (6° 05'N, 116° 33'E), a 754 km2 protected area of primary forest. Research was conducted during the 2009–2020 breeding seasons from early February to mid-June. Seven study plots were established at elevations of 1,450–1,950 m. These plots were contiguously located and included ca. 560 ha, with each plot ca. 60–70 ha in size (Martin & Mouton, 2020). Collared and Masked Trogons were studied in the northern Andes in Yacambú National Park, a 269 km2 area in Lara State, western Venezuela (9°38′N 69°40′W). The fieldwork was restricted to primary cloud forest habitat between 1400 and 2000 m, encompassing a similar elevation range to our study in Borneo. Data was collected during seven breeding seasons from 2002 to 2008 and from late February to early July. Research was conducted on seven study plots similar in size (ca. 60-70 ha) to those on the Borneo site (Martin and Mouton 2020). These trogons were not focal study species, such that we did not collect as comprehensive data as for the Whitehead’s Trogon. In general, the same standardized data collection methods were used in both Borneo and Venezuela studies, described as following. We located nests by observational cues of breeding pairs and systematic search (Martin & Geupel, 1993; Şahin Arslan & Martin, 2019; Şahin Arslan, Muñoz, & Martin, 2023), and measured the nest and nest-substrate heights using clinometers. We obtained the elevation of the nest location with a GPS device (Garmin, Olathe, Kansas, USA) for Whitehead’s Trogon. A nest initiation date was specified as the day the first egg was laid in a nest, and the egg-laying season was characterized by the distribution of nest initiation dates. Nests were checked daily during egg-laying and the first two days of incubation to obtain the exact day the last egg was laid to ascertain the start day of incubation. If a nest was found during incubation and was of unknown age, we checked the nest daily until hatch. Nests were also checked daily or twice daily near hatching and fledging to obtain exact timing of transitions for measuring incubation and nestling period lengths (Martin, Oteyza, Boyce, Lloyd, & Ton, 2015; Martin, Oteyza, Mitchell, Potticary, & Lloyd, 2015; Şahin Arslan et al., 2023). Otherwise, nests were generally checked every other day in Borneo, but from 1-4 days in Venezuela, to determine status and predation (Martin & Geupel, 1993). Clutch size was only used from nests located during building or egg-laying. We did not include nests observed later to ensure no partial loss was included (Martin et al., 2006). The incubation period was defined as the number of days between the last egg laid and last egg hatched (Martin, Auer, Bassar, Niklison, & Lloyd, 2007; Nice, 1954). The nestling period was defined as the days between the last egg hatched and the last nestling fledged and only used for nests where the last egg laid and hatch days were observed within 24 h of precision (Martin, Lloyd, et al., 2011). Daily nest predation rates and daily survival rates were estimated using maximum likelihood estimation via the Mayfield method (Hensler & Nichols, 1981; Mayfield, 1961, 1975). This method is highly correlated with the logistic exposure method (Şahin Arslan & Martin, 2023; Shaffer, 2004) but allows more ready comparisons with the wider availability of Mayfield estimates in the literature. We considered a nest successful if parents were observed feeding young outside the nest or the young left within two days of normal fledging age. If nest contents disappeared earlier, we considered it to be due to predation. We used an electronic scale with 0.001 g accuracy (ACCULAB, Elk Grove, Illinois, USA) to weigh fresh eggs on the day the last egg was laid or within the first 2 d of incubation. Nestlings were weighed for the first 3 days and then every other day throughout the rest of the nestling period, while also measuring wing chord and tarsus length using calipers (Mitutoyo) with an accuracy of 0.01 mm. As a part of a banding program, some adults were captured using mist-nets, and their mass, wing chord and tarsus lengths were measured. Parental behavior at nests was recorded using video cameras for Whitehead’s Trogon during both incubation and nestling stages starting near sunrise. We put 30x zoom video-cameras 4–10 m from the nests and camouflaged the cameras to prevent possible disturbance. We generally sought 6 h video recordings of parental behavior at a nest, but they varied from 4–9 h each day of video recording (mean duration during incubation = 5.96 + 0.24 h, N = 27; during nestling period = 6.33 + 0.13 h, N = 97). Parental activity of the two trogon species in Venezuela were not video-recorded. Video recordings were used to quantify incubation nest attentiveness, as well as brooding attentiveness and feeding rates during the nestling period (Martin, Oteyza, Boyce, et al., 2015; Martin, Oteyza, Mitchell, et al., 2015; Şahin Arslan & Martin, 2019). Incubation nest attentiveness was measured as the percent of total video time that a parent sat on the eggs for each day of video recording (Martin, Oteyza, Boyce, et al., 2015). Brooding attentiveness for nestlings was calculated as the percent of video time that a parent sat on the nestlings for each day of video-recording, and feeding rates as the number of feeding trips of both parents to the nest-h for that recording. Statistics We conducted all analyses in R.4.2.2 (R Core Team 2022) and we present mean values with standard errors, ranges, and sample sizes. We estimated growth rate constants (K) for mass, tarsus length, and wing chord using the logistic growth model (Remeš & Martin, 2002). The model is based on the equation: W(t) = A/1 + e (−K∗(t−ti)), where W(t) is body mass, tarsus length, or wing chord length, A is the asymptotic size, t is age and ti is the age at the inflection point where growth rate changes from accelerating to decelerating, and K is the maximum rate of growth which is obtained at the inflection point (Martin, 2015). We tested for differences in the growth curves between Whitehead’s and Collared Trogons using the nls function in R, and using nest identity as a random effect, while specifying the above equation and running a model for each species and then testing for model differences between species using anova. We used generalized linear mixed-effects models through the glmer function in the lme4 package (Bates, Mächler, Bolker, & Walker, 2015) to investigate the fixed effect of nestling age and brood size on feeding rate, with nest identity as a random effect. Brooding behavior changed in a backwards logistic curve (Şahin Arslan et al., 2023) and is described by the same three parameters as for growth rate above, where in this case A = asymptote at hatching day, K = instantaneous rate of change at the inflection time point, t = the inflection time point where the curve changes from accelerating to decelerating. We used the SSlogis function in the nlme package (Pinheiro & Bates, 2023) to describe the relationship and test for differences between brood sizes in slope (K), intercept (A), and inflection time point (t) of brooding behavior by Whitehead’s Trogon while using nest identity as a random effect. P ≤ 0.05 was considered as statistically significant throughout.

Diversity was calculated on “Fished”(>30 cm SL) and “Non-Fished” (

In the past four centuries, the population of the United States has grown from a recorded 350 people around the Jamestown colony of Virginia in 1610, to an estimated 331 million people in 2020. The pre-colonization populations of the indigenous peoples of the Americas have proven difficult for historians to estimate, as their numbers decreased rapidly following the introduction of European diseases (namely smallpox, plague and influenza). Native Americans were also omitted from most censuses conducted before the twentieth century, therefore the actual population of what we now know as the United States would have been much higher than the official census data from before 1800, but it is unclear by how much. Population growth in the colonies throughout the eighteenth century has primarily been attributed to migration from the British Isles and the Transatlantic slave trade; however it is also difficult to assert the ethnic-makeup of the population in these years as accurate migration records were not kept until after the 1820s, at which point the importation of slaves had also been illegalized. Nineteenth century In the year 1800, it is estimated that the population across the present-day United States was around six million people, with the population in the 16 admitted states numbering at 5.3 million. Migration to the United States began to happen on a large scale in the mid-nineteenth century, with the first major waves coming from Ireland, Britain and Germany. In some aspects, this wave of mass migration balanced out the demographic impacts of the American Civil War, which was the deadliest war in U.S. history with approximately 620 thousand fatalities between 1861 and 1865. The civil war also resulted in the emancipation of around four million slaves across the south; many of whose ancestors would take part in the Great Northern Migration in the early 1900s, which saw around six million black Americans migrate away from the south in one of the largest demographic shifts in U.S. history. By the end of the nineteenth century, improvements in transport technology and increasing economic opportunities saw migration to the United States increase further, particularly from southern and Eastern Europe, and in the first decade of the 1900s the number of migrants to the U.S. exceeded one million people in some years. Twentieth and twenty-first century The U.S. population has grown steadily throughout the past 120 years, reaching one hundred million in the 1910s, two hundred million in the 1960s, and three hundred million in 2007. In the past century, the U.S. established itself as a global superpower, with the world's largest economy (by nominal GDP) and most powerful military. Involvement in foreign wars has resulted in over 620,000 further U.S. fatalities since the Civil War, and migration fell drastically during the World Wars and Great Depression; however the population continuously grew in these years as the total fertility rate remained above two births per woman, and life expectancy increased (except during the Spanish Flu pandemic of 1918).

Since the Second World War, Latin America has replaced Europe as the most common point of origin for migrants, with Hispanic populations growing rapidly across the south and border states. Because of this, the proportion of non-Hispanic whites, which has been the most dominant ethnicity in the U.S. since records began, has dropped more rapidly in recent decades. Ethnic minorities also have a much higher birth rate than non-Hispanic whites, further contributing to this decline, and the share of non-Hispanic whites is expected to fall below fifty percent of the U.S. population by the mid-2000s. In 2020, the United States has the third-largest population in the world (after China and India), and the population is expected to reach four hundred million in the 2050s.

Fished and Non-fished refer to the number of species within each of these categories. Fished species are >30 cm SL while non-fished are

In 1800, the population of Japan was just over 30 million, a figure which would grow by just two million in the first half of the 19th century. However, with the fall of the Tokugawa shogunate and the restoration of the emperor in the Meiji Restoration of 1868, Japan would begin transforming from an isolated feudal island, to a modernized empire built on Western models. The Meiji period would see a rapid rise in the population of Japan, as industrialization and advancements in healthcare lead to a significant reduction in child mortality rates, while the creation overseas colonies would lead to a strong economic boom. However, this growth would slow beginning in 1937, as Japan entered a prolonged war with the Republic of China, which later grew into a major theater of the Second World War. The war was eventually brought to Japan's home front, with the escalation of Allied air raids on Japanese urban centers from 1944 onwards (Tokyo was the most-bombed city of the Second World War). By the war's end in 1945 and the subsequent occupation of the island by the Allied military, Japan had suffered over two and a half million military fatalities, and over one million civilian deaths.

The population figures of Japan were quick to recover, as the post-war “economic miracle” would see an unprecedented expansion of the Japanese economy, and would lead to the country becoming one of the first fully industrialized nations in East Asia. As living standards rose, the population of Japan would increase from 77 million in 1945, to over 127 million by the end of the century. However, growth would begin to slow in the late 1980s, as birth rates and migration rates fell, and Japan eventually grew to have one of the oldest populations in the world. The population would peak in 2008 at just over 128 million, but has consistently fallen each year since then, as the fertility rate of the country remains below replacement level (despite government initiatives to counter this) and the country's immigrant population remains relatively stable. The population of Japan is expected to continue its decline in the coming years, and in 2020, it is estimated that approximately 126 million people inhabit the island country.

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.

Territorial species are often predicted to adhere to an ideal despotic distribution and under-match local food resources, meaning that individuals in high-quality habitat achieve higher fitness than those in low-quality habitat. However, conditions such as high density, territory compression, and frequent territorial disputes in high-quality habitat are expected to cause habitat quality to decline as population density increases and, instead, promote resource matching. We studied a highly human-subsidized and under-matched population of Steller’s jays (Cyanocitta stelleri) to determine how under-matching is maintained despite high densities, compressed territories, and frequent agonistic behaviors, which should promote resource matching. We examined the distribution of fitness among individuals in high-quality, subsidized habitat, by categorizing jays into dominance classes and characterizing individual consumption of human food, body condition, fecundity, and core area size and spatial distribution. Individuals of all dominance classes consumed similar amounts of human food and had similar body condition and fecundity. However, the most dominant individuals maintained smaller core areas that had greater overlap with subsidized habitat than those of subordinates. Thus, we found that 1) jays attain high densities in subsidized areas because dominant individuals do not exclude subordinates from human food subsidies and 2) jay densities do not reach the level necessary to facilitate resource matching because dominant individuals monopolize space in subsidized areas. Our results suggest that human-modified landscapes may decouple dominance from fitness and that incomplete exclusion of subordinates may be a common mechanism underpinning high densities and creating source populations of synanthropic species in subsidized environments. Methods Study system and sampling. We studied populations of Steller’s jays in two campgrounds within Big Basin Redwoods State Park, Santa Cruz County, California (hereafter Big Basin; Figure 1), to test our hypotheses about resource under-matching. Because of the availability of human food subsidies and previously established high fitness of jays utilizing campground areas, we considered campgrounds to be high-quality habitat and surrounding forest areas to be of lower quality (West and Peery 2017; West et al. 2019). We collected data during three breeding seasons, mid-May – mid-August, in 2017, 2018, and 2019. We captured and banded jays with unique color combinations for individual recognition and to assess individual fitness (see next sections). We used call playback and a combination of mist nets (Avinet Research Supply) and live traps (Havahart and homemade) to capture jays during all three years of the study. To characterize jay space use, we also deployed radio transmitters (Model A1070, Advanced Telemetry Systems) on jays using backpack-style harnesses made of 0.1” natural tubular spectra tape (Bally Ribbon Mills) in each year of the study. Handling time was kept to a minimum (usually <5 minutes, slightly longer for birds receiving radio tags), and samples taken are discussed in the subsequent sections. All appropriate guidelines for humane and ethical use of animals in research were followed, and research was conducted under IACUC protocol A005411-R01-A01 and scientific collection permit SC-13714. To our knowledge, no Steller’s jays were injured or died as a result of our activities. To examine jay space use, we tracked each radio tagged jay to determine their precise location (± 10 m) 25-35 times per season. We allowed at least two hours between relocations of the same individual to ensure independence between relocations (Swihart and Slade 1985), and we varied the time of day during which we tracked individuals. We also collected roost locations (between 2200 and 0300) to ensure that we fully characterized jay home ranges. We tracked birds by searching on foot with telemetry equipment and marked jay locations using a handheld GPS unit. Observations of jays and jay behavior throughout the breeding season allowed us to assess the breeding status of jays and determine the identities of socially monogamous jay pairs. We only used male jays for these analyses because female jays are generally subordinate to males (Brown 1963), and our sample size of uniquely identified females did not allow dominance assessment. Defining dominance classes. We classified jays into dominance classes by conducting controlled feeding trials (hereafter ‘behavior trials’) at picnic tables. Because jays have site-based dominance where territorial defense typically weakens as distance from the nest site increases (Brown 1963), we determined a dominance ranking of jays at individual picnic tables dispersed throughout the entire campground to ensure we fully captured spatial variation in dominance for each individual. During each trial, we placed approximately 10 peanuts at the center of a picnic table and then observed jays as they interacted with conspecifics to exploit the food source (Brown 1963; West and Peery 2017). We recorded every banded individual present at each trial, the winner and loser of each interaction, and the aggression level of each interaction on a 0-5 scale. An aggression level of zero indicated that individuals did not interact when feeding at the same time on a table, and so no winner was recorded. Aggression levels were defined as follows: 1: one jay wing-flapped and vocalized with an ‘aap’ or ‘wek’ call at another; 2: one jay displaced another; 3: one jay chased another; 4: jays aggressively sidled with one another but did not make contact; 5: jays physically fought with one another (West and Peery 2017). We evaluated the results of behavior trials to determine the most dominant bird at each table. To be considered dominant at a table, an individual had to win at least 3 interactions at that table. At each table, an individual was considered dominant if it won the most interactions at that table or if it always won interactions against the bird that won the most interactions. In cases where there was not a consistent winner between two individuals that consistently won against all other individuals, or where consistent winners did not interact with one another, we classified the bird with the higher average aggression score in contests that they won as dominant. In cases where individuals’ wins and aggression scores tied, both were considered dominant at a given table. An individual was also considered dominant at a given table if it was the only individual (with the exception of its mate) to appear for two or more 15-minute trials at a specific table on different days. There were occasionally tables at which not enough interactions occurred to determine a dominant bird. We conducted between one and six trials at each of 49 picnic tables in Bloom’s Creek Campground and 65 picnic tables in Huckleberry Campground each year. We determined dominance for each year separately because dominance and core areas could shift from year to year. Within each year, we overlaid core areas (see below for core area delineation methods) in ArcMap (version 10.7; ESRI 2019) with the results of the behavior trials at each picnic table, and then classified jays into three social classes. ‘High’ dominance included individuals that were dominant at tables within and outside their core area, ‘medium’ dominance included individuals that were dominant only within their core area, and ‘low’ dominance included individuals that were not dominant anywhere within the campground (Figure 2). We used this method, rather than traditional Elo-ratings or other established methods because jay dominance hierarchies shift spatially throughout a given area (Brown 1963) and because we were simply interested in identifying the most dominant individual rather than revealing the entire dominance hierarchy.
Human food subsidy consumption. We evaluated individual consumption of human food subsidies using stable isotope analysis of δ13C in primary feathers. δ13C is a useful indicator of human food consumption because human foods are often made up of corn (a C4 plant) and corn byproducts, making them enriched in the heavy isotope of carbon. This makes them isotopically distinguishable from natural prey items in western North America because primary production in this area is driven by native C3 plants (Newsome et al. 2010; West et al. 2016). We clipped approximately 50 mm of the most recently grown new primary flight feather from each captured jay at the end of the breeding season (early-mid August) at least 40 days after the conclusion of behavior trials. Because feathers incorporate the isotopic signature of the diet during periods of feather growth (Hobson and Clark 1992) and a primary feather takes approximately 30 days to grow, these feather samples represented breeding season diet but were not contaminated by any peanut consumption that occurred during the behavior trials. We rinsed feather samples thrice in 2:1 Chloroform:Methanol solution to remove surface contaminants and then homogenized them using scissors. Homogenized feathers were dried for approximately 72 hours at 55˚C. Analysis of δ13C was conducted at the University of New Mexico Center for Stable Isotopes using a Thermo Scientific Delta V mass spectrometer connected to a high-temperature conversion elemental analyzer and a Costech 4010 elemental analyzer. We report δ13C results as parts per mil (‰) ratio relative to the international standard, Vienna-Pee Dee Belemnite limestone. We examined the relationship between dominance class and human food subsidy consumption using a linear mixed model with individual as a random effect because we captured some of the same individuals in multiple years of the study. We used δ13C as the continuous response variable and categorical dominance class (i.e., low, medium, high)

This statistic shows the total population of Greece from 2020 to 2024, with projections up until 2030. In 2024, the total population of Greece was around 10.4 million people. Population of Greece After a subtle year-over-year increase from 2004 to 2011, the population of Greece has experienced a slight drop from 2011 until 2014. Population growth decreased marginally in 2011 compared to the previous year, and once again in 2012 in comparison to 2011. Greek women also bore fewer children per woman on average in 2011, a slight decrease from 2010. But a lower fertility rate is not necessarily the only reason for the country’s total population decline, Greece’s recent economic downturn also plays a role. Due to poor decisions in regards to spending made by the government, Greece has suffered through an economic crisis since 2010, diminishing the incentive to live in the country. The unemployment rate dramatically surged since the crisis, reaching a decade high in 2013. Additionally, the country’s GDP has significantly dropped in the same time frame from 2008 to 2013, with the largest slump in GDP growth occurring in 2011. Despite a severe economic slump, Greece still managed to maintain a relatively high HDI value in 2012, preserving a spot among the top 30 countries worldwide. The HDI, or Human Development Index, is based on parameters such as literacy rate, education levels, GNI and life expectancy, which was one of the highest in the world in 2011.

Russian estimates suggest that the total population of the Soviet Union in 1941 was 195.4 million people, before it fell to 170.5 million in 1946 due to the devastation of the Second World War. Not only did the USSR's population fall as a consequence of the war, but fertility and birth rates also dropped due to the disruption. Hypothetical estimates suggest that, had the war not happened and had fertility rates remained on their pre-war trajectory, then the USSR's population in 1946 would have been 39 million higher than in reality. Gender differences When it comes to gender differences, the Soviet male population fell from 94 million in 1941, to 74 million in 1946, and the female population fell from 102 to 96 million. While the male and female population fell by 19 and 5.5 million respectively, hypothetical estimates suggest that both populations would have grown by seven million each had there been no war. In actual figures, adult males saw the largest change in population due to the war, as a drop of 18 to 21 percent was observed across the three age groups. In contrast, the adult female population actually grew between 1941 and 1946, although the population under 16 years fell by a number similar to that observed in the male population due to the war's impact on fertility.