Proxy For Population Research Articles

Fingerprints of El Niño Southern Oscillation on global and regional oceanic chlorophyll-a timeseries (1997–2022)

With the advancement of present-day ocean color satellites, the spatiotemporal variations of oceanic Chlorophyll-a (Chl-a) concentration, a proxy for phytoplankton population, can be monitored at regional and global scales. Estimating long-term changes in Chl-a concentration, however, is mainly constrained by the limited availability of ocean color data and the significant influence of climate oscillations like the El Niño Southern Oscillation (ENSO). In this study, we investigate the influence of ENSO on regional and global Chl-a timeseries using two ocean color datasets spanning from September 1997 to December 2022. Our analysis found that global Chl-a concentration exhibits a significant increasing trend over the study period. Most of the increasing trends are detected in the Southern Hemisphere (SH) and high-latitudinal regions, including the Southern Ocean. In light-limited regions like the Southern Ocean, enhanced Chl-a concentration shows a strong positive correlation with warming sea surface temperature (SST). Conversely, most oceans in the Northern Hemisphere (NH) exhibit decreasing trends, which are highly correlated with warming SST and ENSO phases. Additionally, we identify distinctive fingerprints of four extreme ENSO events: two extreme El Niño events (1997–1998 and 2015–2016) and two triple La Niña events (1998–2001 and 2020–2022) in these timeseries. Furthermore, empirical orthogonal function (EOF) analysis reveals that the dominant mode of interannual Chl-a variability is associated with an ENSO-like pattern, accounting for about 13 % of the total variability. Intriguingly, this principal mode is highly correlated with the phases of ENSO (R = -0.87) and Pacific Decadal Oscillation (PDO) (R = −0.90) on an interannual scale. This study provides insights into assessing the impacts of ENSO on long-term Chl-a trend estimation.

The development of early farming diets and population change in the Maya region and their climate context

We explore the impact of a millennium-long period of decreased rainfall during the transition from the Middle to Late Holocene on human diet and population dynamics in the northern neotropical Maya Lowlands. To do so, we use a nearby paleo-precipitation reconstruction to evaluate the timing of this period of reduced rainfall and compare those data to population proxies and stable isotopes of carbon and nitrogen from directly dated human bone spanning the interval of interest. Our results show that decreased precipitation coincided with archaeological and isotopic evidence for increasing reliance on maize as a dietary staple. Two rockshelter sites located in Southern Belize have produced a mortuary assemblage that spans the Holocene. These assemblages provide a unique opportunity to examine how early farmers during the transition to agriculture may have been affected by Middle to Late Holocene climate change. Between 5600 and 4200 cal BP lowland populations were already consuming significant amounts of maize reflected in proxies for the protein and the whole diet portions, suggesting the establishment of farming communities at least 800–2200 years before the emergence of public architecture. We demonstrate that increasing reliance on maize shows the resilience of farming populations to climate variability during this crucial period just prior to massive demographic change and the emergence of complex economic institutions.

European Wikipedia platforms, sharing economy and national differences in participation: a case study

The following exploratory study considers which macro-level factors can lead to the sharing economy being more popular in certain countries and less so in others. An example of commons-based peer production in the form of the level of contributing to Wikipedia in 24 European countries is used as a proxy for participation in the sharing economy. Demographic variables (number of native speakers, a proxy for population) and development ones (Human Development Index-level and internet penetration) are found to be less significant than cultural values (particularly self-expression and secular-rational axes of the Inglehart-Welzel model). Three clusters of countries are identified, with the Scandinavian/Baltic/Protestant countries being roughly five times as productive as the Eastern Europe/Balkans/Orthodox ones.

An alternative approach to estimating agglomeration and productivity using geography, demography and evidence from satellite imagery

ABSTRACT In this article we combine novel geographical, demographic and satellite data with an instrumental variables (IVs) estimation approach to tackle a thorny set of data and econometric issues. Studies of agglomeration effects on economic productivity at the city scale have been growing in popularity in both the literature and the interests of urban policymakers in recent years. Despite growing sophistication in econometric approaches, underlying weaknesses in the quality and availability of data at the city scale have hindered efforts to unravel the endogeneity issues, or circularity between city scale, amenity and productivity. We set out both to extend the agglomeration literature to the case of Australia – which hitherto has been lightly studied in this field – and to take advantage of Australia’s unique geography and reliance on migration to drive population change. We also draw on satellite data measuring night-time luminosity as a proxy for economic productivity. We demonstrate the construction of convincing and robust IVs that proxy for population at the city scale. Our econometric estimates indicate that a 1% increase in city populations causes productivity to be enhanced by 0.24–1.70% on average.

Long-term demographic trends and spatio-temporal distribution of past human activity in Central Europe: Comparison of archaeological and palaeoecological proxies

Estimating past population dynamics has become a major research topic for archaeology, which uses several proxies for studying past demography. The Czech Republic represents a unique region with abundant digital archaeological and palynological datasets comprising tens of thousands of records covering the whole Holocene. We used these datasets to quantify long-term demographic trends and to delimit past human activity in space and time. Our results, based on large data from both disciplines, are one of the first quantitative results covering the whole Czech Republic. Through summed probability distributions of radiocarbon dates and spatio-temporal modelling of human activity based on archaeological sites, we identified four major demographic events and processes between 10,000 BCE and 1000 CE – the beginning of the Neolithic at 5400 BCE, the Eneolithic/Bronze Age transition in the 3rd millennium BCE, Bronze Age expansion after 1500 BCE, and mediaeval expansion after 500 CE. We identified two settlement cores in the lowlands, with additional inner and outer peripheries with different demographic histories. Our study clearly shows that prehistoric and early historic population dynamics were not a homogenised process and were regionalized according to local environmental and social conditions. The comparison of archaeological results with pollen-based vegetation proxies also showed regional aspects in human-vegetation interactions. Agro-pastoral communities dwelling in the lowlands mostly influenced the openness of the landscape, used for fields and pastures, whereas contemporary communities with a similar economic mode residing in a different region are more visible in pollen records through species and structural changes in woodlands. The agro-pastoral subsistence strategy did not start everywhere with the onset of the Neolithic: in some regions it failed after several centuries, in others, the foraging lifestyle persisted significantly longer, and farming became a major economic strategy much later. Our study shows that archaeological site-based and 14C-based demographic proxies cannot be utilized for all periods and regions due to several limitations. Only the combination of different quantitative and qualitative archaeological proxies for population does reveal important details.

National scale habitat suitability analysis to evaluate and improve conservation areas for a mature forest specialist species

Species distribution modelling is an important tool to inform conservation, particularly if combined with site prioritisation approaches. Results can then be used in both suggestion of new protected areas as well as evaluation of the existing ones. We applied MaxEnt analysis to a species of mature forests, the Eurasian Pygmy Owl ( Glaucidium passerinum ), which is recognised as a biodiversity indicator species. Furthermore, we used habitat suitability and uncertainty maps in site prioritisation for conservation and evaluation of the existing nature conservation area (NCA) network. We found species to be strongly positively associated with time since forestry activities at a local scale (25ha) and abundance of mature forests from local through home range (450ha) to landscape (1960ha) scales. The sum of habitat suitability as a proxy for apparent population allowed us to estimate that the existing NCAs hold only 23% of the population. We found 68% of priority sites (PS) for species conservation to be outside NCAs. Strict forestry restrictions form the most suitable conservation regimes, with more than 51% of PS in NCA network having insufficient regimes. Inclusion of PS in NCAs would increase the network to 26% of the national territory with 41% of the species apparent population. Currently, the most suitable conservation regimes cover less than 2% of the country area. • Eurasian pygmy owl reveals unprotected mature forest concentrations. • Site prioritisation highlight gaps within and between currently protected areas. • 68% of priority sites (PS) for a species are outside any conservation area. • 51% of PS in protected areas have insufficient conservation regime for species. • PS inclusion in protected areas would increase the network to only 26% of land.

Migration Responses to the COVID-19 Pandemic: A Case Study of New England Showing Movements down the Urban Hierarchy and Ensuing Impacts on Real Estate Markets

As the COVID-19 pandemic swept across the United States, the media began reporting stories of people leaving cities for rural destinations, setting off frenzied real estate activity in receiving communities. This article builds a case study of New England using nonconventional data collected from mobile devices as a proxy for population to explore the temporal and spatial patterns of movement down the urban hierarchy since the onset of the COVID-19 pandemic. Two research questions guide the analysis: (1) How have urban–rural migration systems in New England shifted since the onset of the COVID-19 pandemic? (2) In what ways have real estate markets been affected by these apparent migration shifts? The analysis reveals shifts of population away from metropolitan core areas of the region and into micropolitan and noncore counties. These population shifts were most pronounced in late summer and fall 2020 with loosening travel restrictions. By the end of 2020, migration systems in New England once more resembled prepandemic patterns. Further, these places down the urban hierarchy consistently showed more substantial increases in real estate activity as reflected in rising prices, reduced inventories, and increased sales volume. These real estate dynamics suggest urban to rural migration during the COVID-19 pandemic might be initiating new waves of rural gentrification.

Machine learning predictions of chlorophyll-a in the Han river basin, Korea.

This study developed a model to predict concentrations of chlorophyll-a ([Chl-a]) as a proxy for algal population with data from multiple monitoring stations in the Han river basin, by using machine-learning predictive models, then analyzed the relationship between [Chl-a] and the input variables of the optimized model. Daily water quality and meteorological data from 2012 to 2020 were collected from the real-time water quality information system and the meteorological administration of Korea. To quantify model accuracy, the coefficient of determination, root mean square error, and mean absolute error were applied. Among random forest (RF), support vector machine, and artificial neural network, the RF with random dataset showed the highest accuracy. The RF was optimized when 78 trees were applied to the model. Input variables for the best RF model were total organic carbon (feature importance: 27%), total nitrogen (19%), pH (13%), water temperature (8%), total phosphorus (8%), electrical conductivity (7%), dissolved oxygen (6%), minimum air temperature (AT) (4%), mean AT (3%), and maximum AT (3%). The feature-importance analysis showed that total organic carbon was the most important variable to predict [Chl-a] in the Han river basin. Total nitrogen was a more important variable than total phosphorus.

Identification of Tobacco-Related Cancer Diagnoses among Individuals with Psychiatric Disorders: A Population-Based Matched Cohort Study Using a Competing Risks Approach from British Columbia

Background: Individuals with psychiatric disorders (PD) have a high prevalence of tobacco use. Therefore, we assessed the hazard of receiving a tobacco-related (TR) cancer diagnosis among individuals with PD. Methods: Several population-based provincial databases were used to identify individuals in BC diagnosed with depression, schizophrenia, bipolar disorder, anxiety disorders, or multiple PD between 1990 and 2013. A primary population proxy comparison group (appendicitis) was also identified and matched to the psychiatric cohort based on age at cohort entry, gender, year of cohort entry, and postal code. We linked individuals in the cohort and comparison groups with the BC Cancer Registry. Using a competing risks approach, we estimated the effect of having a PD on the risk of receiving a TR cancer diagnosis, in light of the competing risk of mortality. Results: In total, 165,289 patients were included. Individuals with depression (HR = 0.81; p < 0.01; 95% CI: 0.73–0.91), anxiety disorders (HR = 0.84; p = 0.02; 95% CI: 0.73–0.97), or multiple PD (HR = 0.74; p < 0.01; 95% CI: 0.66–0.83) had a statistically significant lower risk of a TR cancer diagnosis compared to the comparison group. Individuals with schizophrenia (HR = 0.86; p = 0.40; 95% CI: 0.62–1.21) or bipolar disorder (HR = 0.58; p = 0.12; 95% CI: 0.29–1.14), however, showed no evidence of a statistically significant difference from the comparison group. Interpretation: We found that individuals with depression, anxiety disorders, or multiple PD diagnoses had a significantly reduced risk of receiving a tobacco-related cancer diagnosis. These results were unexpected and could be explained by individuals with a PD having barriers to a cancer diagnosis rather than a true decreased incidence.

Fecal steroids as a potential tool for conservation paleobiology in East Africa

Conservation paleobiology seeks to leverage proxy reconstructions of ecological communities and environmental conditions to predict future changes and inform management decisions. Populations of East African megafauna likely changed during the Holocene in response to trends and events in the regional hydroclimate, but reconstructing these populations requires development of new proxies. We examine if fecal steroids are a viable proxy for megafauna populations since they are well preserved in sedimentary archives. We measured eleven fecal steroids in 87 fresh dung samples representing 22 species of megafauna in the Maasai Mara National Reserve (Kenya) and a further seven samples from captive animals. Using this reference library, four distinctive groups are identified, which reflect diet and biochemical modification of these inputs during digestion by the gut microbiome. Carnivore dung is characterized by more than ~ 75% cholesterol and primate dung includes uniquely high proportions of coprostanol. Two groups of herbivore are distinguished by their differing proportions of phytosterols that are consumed by eating plants and 5β-stanols produced during digestion. Under cross validation a random forests statistical model accurately classified 72% of dung samples to the species level using fecal steroids. Variability among individuals and between wild and captive animals suggests that fecal steroids in herbivore dung may reflect diversity and variability in diet, while a lack of variability in carnivore dung indicates that they cannot be identified to the species level in most instances. Our results suggest that fecal steroids may have utility in reconstructing the time-evolving composition megafauna populations in East Africa.

Occurrence of Two-Year Cyclicity, “Saw-Blade Fluctuation”, in Vendace Populations in Finland

The tendency towards two-year cyclicity is considered typical of many Fennoscandian vendace populations, especially in fluctuation of recruitment, based on time series of individual lakes. We used two robust indicators to identify and quantify two-year cycles in vendace population proxy time series at different life-stages — spawning stock biomass (SB), density of newly hatched larvae (LD) and recruitment (REC) — from 22 Finnish lakes. Then we applied Fisher's meta-analytical test to assess the adequacy of the evidence to support the hypothesis that vendace population dynamics include two-year cyclicity. The results supported this hypothesis for REC but not for SB or LD. Yet, the indicators and test are conservative and time-series of SB and LD are shorter than those for REC. The appearance of cycles in REC is associated with high post-recruitment mortality, consequently practically only one spawning per cohort. Cycles may be typical for the recovery period from low abundance period also. Still, some populations with moderate post-REC mortality and non-cyclic SB abundance exhibited cycles in REC. Such dynamics presuppose the existence of more complex regulation based on the interaction of different life stages.

Relationships between pest density and associated leaf necrosis for an invasive leaf-mining weevil, Orchestes fagi, on American beech (Fagus grandifolia)

Pest density – plant damage relationships are essential guides for decision-making in integrated pest management. In this article, we established pest density – leaf damage relationships for the beech leaf-mining weevil, Orchestes fagi L. (formerly Rhynchaenus fagi, Coleoptera: Curculionidae), in its invasive range of Nova Scotia, Canada. Outbreaks of O. fagi cause tree-wide leaf necrosis in American beech (Fagus grandifolia Ehrh.), which can eventually result in tree mortality. In 2014 and 2016, we collected weekly samples in stands with American beech and assessed leaves for densities during different life stages (eggs, larvae, and pupae), population proxy measures (adult feeding damage, egg slits, and larval galleries), and percent necrosis. In general, feeding damage and leaf necrosis plateaued soon after the end of budburst, but before the larval mine expanded. This strongly suggested that leaf necrosis may be linked to damage caused by adults or by mine initiation rather than that caused by larval mine expansion and gallery development. The density of O. fagi per leaf for life stages and population proxies all significantly explained ∼42%–81% of the variation in end-of-season percent leaf necrosis. Results from this study provide a variety of relationships that could be used in both short- and long-term monitoring efforts for O. fagi.

Population level models for testing hunter-gatherer resilience and settlement response to the combined impact of abrupt climatic events and sea level change: A case study from the Holocene of northern Britain

Isolating the impacts of abrupt climatic events on Holocene hunter-gatherers from those of gradual environmental change is methodologically challenging and conflicts with the lived experience of Mesolithic communities for whom the world was in continuous flux. We explore the combined impacts of abrupt climate events (ACEs) and gradual sea level change on the Mesolithic communities of northern Britain by using a summed calibrated probability distribution (SCPD) of radiocarbon dates as a population proxy, addressing sources of potential bias, including the history of research, differential site destruction, calibration effects and changes in settlement pattern. Our study is placed into a European context by reviewing studies that have reached contrasting conclusions about the impacts of ACEs on Holocene hunter-gatherer communities. We suggest such differences arise from variation in their specific ecological settings, cultural repertoires, and social environments, concluding that Holocene hunter-gatherers in northern Britain were especially vulnerable to environmental change.

Development Research at High Geographic Resolution: An Analysis of Night-Lights, Firms, and Poverty in India Using the SHRUG Open Data Platform

Abstract The SHRUG is an open data platform describing multidimensional socioeconomic development across 600,000 villages and towns in India. This paper presents three illustrative analyses only possible with high-resolution data. First, it confirms that nighttime lights are highly significant proxies for population, employment, per capita consumption, and electrification at very local levels. However, elasticities between night-lights and these variables are far lower in time series than in cross section, and vary widely across context and level of aggregation. Next, this study shows that the distribution of manufacturing employment across villages follows a power law: the majority of rural Indians have considerably less access to manufacturing employment than is suggested by aggregate data. Third, a poverty mapping exercise explores local heterogeneity in living standards and estimates the potential targeting improvement from allocating programs at the village—rather than at the district—level. The SHRUG can serve as a model for open high-resolution data in developing countries.

Survey, Shovel Probes, and Population Estimates: Studying Regional Demography in the Intermediate Area Using Subsurface Sherd Deposits

Shovel probes are a common form of archaeological data collection in densely vegetated landscapes. They were once the subject of critical analyses that evaluated their utility for archaeological survey, specifically the discovery of archaeological sites. In the decades that have passed since these classic studies were published, the objectives of regional survey have continued to evolve. Many archaeologists now recognize regional survey as a fundamentally demographic endeavor, one whose aim is to understand how many people lived where in a landscape during different periods of time. This recognition has placed greater demands on methods of regional data collection than those envisioned in the classic shovel probe literature. In addition to discovering prehistoric settlements, surveys must also reliably collect the full range of data that is needed for making the population estimates (be they relative or absolute) that lie at the heart of settlement demography. This paper evaluates the utility of shovel probes for studying regional settlement demography using the area and density of ceramic sherd scatters, a commonly used population proxy in numerous parts of the world. This evaluation is empirically grounded in analyses of data from the Intermediate Area (southern Central America and northern South America), the results of which are used to assess, and in some instances modify, regional survey results from the Middle Terraba Basin in southern Costa Rica.

Modeling Community-Scale Natural Resource Use in a Transboundary Southern African Landscape: Integrating Remote Sensing and Participatory Mapping

Remote sensing analyses focused on non-timber forest product (NTFP) collection and grazing are current research priorities of land systems science. However, mapping these particular land use patterns in rural heterogeneous landscapes is challenging because their potential signatures on the landscape cannot be positively identified without fine-scale land use data for validation. Using field-mapped resource areas and household survey data from participatory mapping research, we combined various Landsat-derived indices with ancillary data associated with human habitation to model the intensity of grazing and NTFP collection activities at 100-m spatial resolution. The study area is situated centrally within a transboundary southern African landscape that encompasses community-based organization (CBO) areas across three countries. We conducted four iterations of pixel-based random forest models, modifying the variable set to determine which of the covariates are most informative, using the best fit predictions to summarize and compare resource use intensity by resource type and across communities. Pixels within georeferenced, field-mapped resource areas were used as training data. All models had overall accuracies above 60% but those using proxies for human habitation were more robust, with overall accuracies above 90%. The contribution of Landsat data as utilized in our modeling framework was negligible, and further research must be conducted to extract greater value from Landsat or other optical remote sensing platforms to map these land use patterns at moderate resolution. We conclude that similar population proxy covariates should be included in future studies attempting to characterize communal resource use when traditional spectral signatures do not adequately capture resource use intensity alone. This study provides insights into modeling resource use activity when leveraging both remotely sensed data and proxies for human habitation in heterogeneous, spectrally mixed rural land areas.

Collapse and continuity: A multi-proxy reconstruction of settlement organization and population trajectories in the Northern Fertile Crescent during the 4.2kya Rapid Climate Change event.

The rise and fall of ancient societies have been attributed to rapid climate change events. One of the most discussed of these is the 4.2kya event, a period of increased aridity and cooling posited as the cause of societal changes across the globe, including the collapse of the Akkadian Empire in Mesopotamia. Studies seeking to correlate social and climatic changes around the 4.2kya event have tended to focus either on highly localized analyses of specific sites or surveys or more synthetic overviews at pan-continental scales, and temporally on the event and its aftermath. Here we take an empirical approach at a large spatial scale to investigate trends in population and settlement organization across the entirety of Northern Fertile Crescent (Northern Mesopotamia and the Northern Levant) from 6,000 to 3,000 cal BP. We use Summed Probability Distributions of radiocarbon dates and data from eighteen archaeological surveys as proxies for population, and a dataset of all settlements over ten hectares in size as a proxy for the degree of urbanization. The goal is to examine the spatial and temporal impact of the 4.2kya event and to contextualize it within longer term patterns of settlement. We find that negative trends are visible during the event horizon in all three proxies. However, these occur against a long-term trend of increased population and urbanization supported through unsustainable overshoot and the exploitation of a drier zone with increased risk of crop failure. We argue that the 4.2kya event occurred during a period of unprecedented urban and rural growth which may have been unsustainable even without an exogenous climate forcing.

Why are population growth rate estimates of past and present hunter-gatherers so different?

Hunter–gatherer population growth rate estimates extracted from archaeological proxies and ethnographic data show remarkable differences, as archaeological estimates are orders of magnitude smaller than ethnographic and historical estimates. This could imply that prehistoric hunter–gatherers were demographically different from recent hunter–gatherers. However, we show that the resolution of archaeological human population proxies is not sufficiently high to detect actual population dynamics and growth rates that can be observed in the historical and ethnographic data. We argue that archaeological and ethnographic population growth rates measure different things; therefore, they are not directly comparable. While ethnographic growth rate estimates of hunter–gatherer populations are directly linked to underlying demographic parameters, archaeological estimates track changes in the long-term mean population size, which reflects changes in the environmental productivity that provide the ultimate constraint for forager population growth. We further argue that because of this constraining effect, hunter–gatherer populations cannot exhibit long-term growth independently of increasing environmental productivity.This article is part of the theme issue ‘Cross-disciplinary approaches to prehistoric demography’.

Two centuries of settlement and urban development in the United States.

Over the past 200 years, the population of the United States grew more than 40-fold. The resulting development of the built environment has had a profound impact on the regional economic, demographic, and environmental structure of North America. Unfortunately, constraints on data availability limit opportunities to study long-term development patterns and how population growth relates to land-use change. Using hundreds of millions of property records, we undertake the finest-resolution analysis to date, in space and time, of urbanization patterns from 1810 to 2015. Temporally consistent metrics reveal distinct long-term urban development patterns characterizing processes such as settlement expansion and densification at fine granularity. Furthermore, we demonstrate that these settlement measures are robust proxies for population throughout the record and thus potential surrogates for estimating population changes at fine scales. These new insights and data vastly expand opportunities to study land use, population change, and urbanization over the past two centuries.

How Data-Poor Countries Remain Data Poor: Underestimation of Human Settlements in Burkina Faso as Observed from Nighttime Light Data

The traditional ways of measuring global sustainable development and economic development schemes and their progress suffer from a number of serious shortcomings. Remote sensing and specifically nighttime light has become a popular supplement to official statistics by providing an objective measure of human settlement that can be used as a proxy for population and economic development measures. With the increased availability and use of the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and data in social science, it has played an important role in data collection, including measuring human development and economic growth. Numerous studies are using nighttime light data to analyze dynamic regions such as expansions of urban areas and rapid industrialization often highlight the problem of saturation in urban centers with high light intensity. However, the quality of nighttime light data and its appropriateness for analyzing areas and regions with low and fluctuating levels of light have rarely been questioned or studied. This study examines the accuracy of DMSP-OLS and VIIRS-DNB by analyzing 147 communities in Burkina Faso to provide insights about problems related to the study of areas with a low intensity of nighttime light during the studied period from 1992 to 2012. It found that up to 57% of the communities studied were undetectable throughout the period, and only 9% of communities studied had a 100% detection rate. Unsurprisingly, the result provides evidence that detection rates in both datasets are particularly low (3%) for settlements with 0–9999 inhabitants, as well as for larger settlements with population of 10,000–24,999 (28%). Cross-checking with VIIRS-DNB for the year 2012 shows similar results. These findings suggest that careful consideration must be given to the use of nighttime light data in research and global comparisons to monitor the progress of the United Nation’s Sustainable Development Goals, especially when including developing countries with areas containing low electrification rates and low population density.