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The human population is at the centre of research on global environmental change. On the one hand, population dynamics influence the environment and the global climate system through consumption-based carbon emissions. On the other hand, the health and well-being of the population are already being affected by climate change. A knowledge of population dynamics and population heterogeneity is thus fundamental to improving our understanding of how population size, composition, and distribution influence global environmental change and how these changes affect population subgroups differentially by demographic characteristics and spatial distribution. The increasing relevance of demographic research on the topic, coupled with availability of theoretical concepts and advancement in data and computing facilities, has contributed to growing engagement of demographers in this field. In the past 25 years, demographic research has enriched climate change research-with the key contribution being in moving beyond the narrow view that population matters only in terms of population size-by putting a greater emphasis on population composition and distribution, through presenting both empirical evidence and advanced population forecasting to account for demographic and spatial heterogeneity. What remains missing in the literature is research that investigates how global environmental change affects current and future demographic processes and, consequently, population trends. If global environmental change does influence fertility, mortality, and migration, then population estimates and forecasts need to adjust for climate feedback in population projections. Indisputably, this is the area of new research that directly requires expertise in population science and contribution from demographers.

Local population dynamics are driven by local processes such as temporal variation of productivity, survival, emigration and population stage structure, and by processes originating from outside the local population, such as immigration. Populations may operate as sources that contribute more individuals than have died or as sinks that depend on neighbouring populations. Knowing demographic processes driving the dynamics of a local population and the significance of a local population in a system of multiple populations is crucial for understanding population dynamics and requires detailed demographic analyses. We studied demographic drivers in a red kite Milvus milvus population located in Germany that was monitored for 34 years using integrated population modelling. We specified the model in such a way that the numbers of experienced breeders, local recruits, locally born non‐breeders and immigrants are estimated explicitly, applied a retrospective perturbation analysis to identify the demographic drivers and assessed the source‐sink status of the population. The study population increased on average by 1% per year. The number of breeders was about double than the number of locally born non‐breeders, and the number of experienced breeders exceeded the number of local recruits and immigrants by a factor of six to nine. The retrospective analysis identified productivity, i.e. the number of fledglings per breeding pair, as the main demographic driver, followed by adult survival and immigration. As other studies show close links between food supply and productivity, it is likely that food supply plays a critical role in red kite population dynamics. The study population contributed more individuals than it lost through mortality, but due to emigration of locally born individuals it was not self‐sustainable and depended on immigration. This quantifies the population as a dependent source and shows that red kite populations are linked across large spatial scales.

In this article, we use newly developed statistical methods to examine the generative processes that give rise to widespread patterns in friendship networks. The methods incorporate both traditional demographic measures on individuals (age, sex, and race) and network measures for structural processes operating on individual, dyadic, and triadic levels. We apply the methods to adolescent friendship networks in 59 U.S. schools from the National Longitudinal Survey of Adolescent Health (Add Health). We model friendship formation as a selection process constrained by individuals' sociality (propensity to make friends), selective mixing in dyads (friendships within race, grade, or sex categories are differentially likely relative to cross-category friendships), and closure in triads (a friend's friends are more likely to become friends), given local population composition. Blacks are generally the most cohesive racial category, although when whites are in the minority, they display stronger selective mixing than do blacks when blacks are in the minority. Hispanics exhibit disassortative selective mixing under certain circumstances; in other cases, they exhibit assortative mixing but lack the higher-order cohesion common in other groups. Grade levels are always highly cohesive, while females form triangles more than males. We conclude with a discussion of how network analysis may contribute to our understanding of sociodemographic structure and the processes that create it.

With the growing utilization and exploration of the ocean, anthropogenic noise increases significantly and gives rise to a new kind of pollution: noise pollution. In this review, the source and the characteristics of noise in the sea, the significance of sound to marine organisms, and the impacts of noise on marine organisms are summarized. In general, the studies about the impact of noise on marine organisms are mainly on adult fish and mammals, which account for more than 50% and 20% of all the cases reported. Studies showed that anthropogenic noise can cause auditory masking, leading to cochlear damage, changes in individual and social behavior, altered metabolisms, hampered population recruitment, and can subsequently affect the health and service functions of marine ecosystems. However, since different sampling methodologies and unstandarized measurements were used and the effects of noise on marine organisms are dependent on the characteristics of the species and noise investigated, it is difficult to compare the reported results. Moreover, the scarcity of studies carried out with other species and with larval or juvenile individuals severely constrains the present understanding of noise pollution. In addition, further studies are needed to reveal in detail the causes for the detected impacts.

According to official census results, the Puerto Rican population became significantly whiter in the first half of the twentieth century. Social scientists have long speculated about the source of this trend, but until now, available data did not permit competing hypotheses of Puerto Rico's whitening to be evaluated empirically. This article revisits the question of how Puerto Rico whitened using newly available Public Use Micro-Samples from the 1910 and 1920 U.S. Censuses of Puerto Rico. Demographic analysis reveals that racial reclassification between censuses generated a \"surplus\" of nearly 100,000 whites in the 1920 enumerated population. Previous studies of intercensus change in the racial composition of populations have demonstrated that racial reclassification occurs. Going beyond previous studies, we investigate empirically the underlying social mechanisms that fueled change in categorical membership. Reclassification between censuses may reflect the movement of individuals across racial boundaries (boundary crossing), the movement of racial boundaries across individuals (boundary shifting), or both of these boundary dynamics simultaneously. Operationalization of these conceptually distinct boundary dynamics shows that Puerto Rico whitened in the second decade of the twentieth century primarily through boundary shifting-an expansion of the social definition of whiteness itself. Our analysis helps advance general sociological understanding of how symbolic boundaries change.

The combined effects of declining fertility and increased longevity have accelerated population aging in different parts of the world. Unlike other countries, Puerto Rico is also experiencing unprecedented levels of working-age out-migration. The full impact of high out-migration on Puerto Rican demography is not fully understood. Placing Puerto Rico’s aging process in an international context is useful in identifying the role out-migration is having on the accelerated aging of the Puerto Rican society. Using the World Population Prospects 2019 estimates, we compared the pattern of rapid aging found for Puerto Rico with the trajectories of six other countries with the highest population of 65+ in the World, Europe, and the Caribbean from 1960 to 2020. Prior to 2010, the aging process in Puerto Rico was comparable to the other countries. After 2010, the percent of the population over 65 years in Puerto Rico nearly doubled from 13.1% to 21%. The nearly doubling of the percent of older adults is not observed in any of the comparison countries. We find that the rapid aging of Puerto Rico, changing from a linear trend to an exponential one, is a result of accelerating levels of out-migration, which is concentrated in the working-age population.

In recent years, a range of novel smartphone-derived data streams about human mobility have become available on a near-real-time basis. These data have been used, for example, to perform traffic forecasting and epidemic modeling. During the COVID-19 pandemic in particular, human travel behavior has been considered a key component of epidemiological modeling to provide more reliable estimates about the volumes of the pandemic's importation and transmission routes, or to identify hot spots. However, nearly universally in the literature, the representativeness of these data, how they relate to the underlying real-world human mobility, has been overlooked. This disconnect between data and reality is especially relevant in the case of socially disadvantaged minorities. The objective of this study is to illustrate the nonrepresentativeness of data on human mobility and the impact of this nonrepresentativeness on modeling dynamics of the epidemic. This study systematically evaluates how real-world travel flows differ from census-based estimations, especially in the case of socially disadvantaged minorities, such as older adults and women, and further measures biases introduced by this difference in epidemiological studies. To understand the demographic composition of population movements, a nationwide mobility data set from 318 million mobile phone users in China from January 1 to February 29, 2020, was curated. Specifically, we quantified the disparity in the population composition between actual migrations and resident composition according to census data, and shows how this nonrepresentativeness impacts epidemiological modeling by constructing an age-structured SEIR (Susceptible-Exposed-Infected- Recovered) model of COVID-19 transmission. We found a significant difference in the demographic composition between those who travel and the overall population. In the population flows, 59% (n=20,067,526) of travelers are young and 36% (n=12,210,565) of them are middle-aged (P<.001), which is completely different from the overall adult population composition of China (where 36% of individuals are young and 40% of them are middle-aged). This difference would introduce a striking bias in epidemiological studies: the estimation of maximum daily infections differs nearly 3 times, and the peak time has a large gap of 46 days. The difference between actual migrations and resident composition strongly impacts outcomes of epidemiological forecasts, which typically assume that flows represent underlying demographics. Our findings imply that it is necessary to measure and quantify the inherent biases related to nonrepresentativeness for accurate epidemiological surveillance and forecasting.

The artificial bee colony (ABC) algorithm is an effective swarm-based meta-heuristic algorithm for optimization problems. Nevertheless, slow convergence speed has affected its competitiveness. In order to improve its performance, an improved ABC with dynamic composition (ABCDC) is proposed in this paper. Since the original ABC and its most variants use constant ratio between employed bees and onlooker bees, which causes that the number of onlooker bees is insufficient to exploit the searching space in limited time. Therefore, we propose a mechanism to adjust the number of employed bees and onlooker bees in order to find the global optimum more effectively. Moreover, Symmetric Latin Hypercube Design is utilized to enhance the diversity of initial population. Besides, two differential search equations with self-adaptive parameters are used in the employed bee phase and onlooker bee phase. Finally, to evaluate the performance of ABCDC, comparisons with four state-of-the-art ABC variations and the original one have been done on 22 benchmark problems with different dimensions. And four meta-heuristic algorithms were also involved to fully evaluate the effectiveness of ABCDC. The experimental results demonstrate that ABCDC is better than the competitors in terms of its solution quality and convergence speed.

Copepods are one of the most abundant and diverse live food sources for mesopelagic and bathypelagic fishes and crustaceans. They could contribute to the overlap of the transition period from live feed to an artificial weaning diet in marine larvae production. However, the culture conditions still need optimization to provide sufficient production to cover the increasing demand for marine hatcheries. Therefore, the present study investigated the effects of different salinity levels (5, 10, 15, 20, 25, and 30 ppt) on the population growth, growth rate, and population composition (males, females, copepodite, and nauplii ratio) of the marine copepod, Oithona nana. The experiment continued for 15 days, under laboratory-controlled conditions of temperature (27 ± 1 °C), pH (7.7 ± 0.15), and continuous gentle aeration in 30 L glass aquaria. The copepod culture aquaria were supplemented with a mixture of soybean and yeast (0.5 g 10−6 individual−1 24-h−1) as a feed source. The highest significant population growth and population growth rate of O. nana were achieved with a salinity level of 20 ppt. Regarding population composition, O. nana cultured at the salinity level of 20 ppt recorded the highest significant percentages of copepodite and nauplii. The results concluded that copepod, O. nana, is capable of withstanding abrupt changes in the salinity, but there are limits to their tolerance, with an optimal salinity level of 20 ppt. This salinity level achieved the highest population growth and the highest percentages of copepodite and nauplii of marine Copepoda, O. nana.

Identifying ancestry along each chromosome in admixed individuals provides a wealth of information for understanding the population genetic history of admixture events and is valuable for admixture mapping and identifying recent targets of selection. We present PCAdmix (available at https://sites.google.com/site/pcadmix/home), a Principal Components-based algorithm for determining ancestry along each chromosome from a high-density, genome-wide set of phased single-nucleotide polymorphism (SNP) genotypes of admixed individuals. We compare our method to HAPMIX on simulated data from two ancestral populations, and we find high concordance between the methods. Our method also has better accuracy than LAMP when applied to three-population admixture, a situation as yet unaddressed by HAPMIX. Finally, we apply our method to a data set of four Latino populations with European, African, and Native American ancestry. We find evidence of assortative mating in each of the four populations, and we identify regions of shared ancestry that may be recent targets of selection and could serve as candidate regions for admixture-based association mapping.