OpenStax Biology 2e
Population dynamics can be applied to human population growth. Earth’s human population is growing rapidly, to the extent that some worry about the ability of the earth’s environment to sustain this population. Long-term exponential growth carries the potential risks of famine, disease, and large-scale death.
Although humans have increased the carrying capacity of their environment, the technologies used to achieve this transformation have caused unprecedented changes to Earth’s environment, altering ecosystems to the point where some may be in danger of collapse. The depletion of the ozone layer, erosion due to acid rain, and damage from global climate change are caused by human activities. The ultimate effect of these changes on our carrying capacity is unknown. As some point out, it is likely that the negative effects of increasing carrying capacity will outweigh the positive ones—the world’s carrying capacity for human beings might actually decrease.
The human population is currently experiencing exponential growth even though human reproduction is far below its biotic potential. To reach its biotic potential, all females would have to become pregnant every nine months or so during their reproductive years. Also, resources would have to be such that the environment would support such growth. Neither of these two conditions exists. In spite of this fact, human population is still growing exponentially.
A consequence of exponential human population growth is a reduction in time that it takes to add a particular number of humans to the Earth. The image below shows that 123 years were necessary to add 1 billion humans in 1930, but it only took 24 years to add two billion people between 1975 and 1999. As already discussed, our ability to increase our carrying capacity indefinitely my be limited. Without new technological advances, the human growth rate has been predicted to slow in the coming decades. However, the population will still be increasing and the threat of overpopulation remains.
Clark, M., Douglas, M., Choi, J. Biology 2e. Houston, Texas: OpenStax. Access for free at: https://openstax.org/details/books/biology-2e
One needs only to peruse the daily news to be aware that humanity is on a dangerous and challenging trajectory. This essay explores the prospect of adopting a science-based framework for confronting these potentially adverse prospects. It explores a perspective based on relevant ecological and behavioral science. The objective is to involve concerned citizens of the world in this enterprise. The overall objective is to maintain Planet Earth as a favorable home for the future of humanity. Nine ecological principles explain one major aspect of what is happening and provide critical guidelines for appropriate action. Nine social behaviors explore how we might integrate social science insights with those from ecology. Twenty predictions are proposed based on these ecological and social science principles plus existing trends. If these trends are not vigorously and courageously confronted, we will likely be on track for the demise of our civilization. As we examine these challenges, our job will be especially complicated because a major segment of humanity is not prepared to accept evidence based on science, and this generates much resistance to any efforts directed toward effective control of current and future challenges. In these complex circumstances, we must remain as cooperative and optimistic as possible so that we can promote the needed willpower and ingenuity. This essay has broad support as it is a contribution to the Scientists’ Warning to Humanity Program of the Alliance of World Scientists (Ripple et al., 2017).
Keywords: Authoritarianism; Cooperation; Democracy; Morality; Population dynamics; Social behavior.
The rapidly growing human population in sub-Saharan Africa generates increasing demand for agricultural land and forest products, which presumably leads to deforestation. Conversely, a greening of African drylands has been reported, but this has been difficult to associate with changes in woody vegetation. There is thus an incomplete understanding of how woody vegetation responds to socio-economic and environmental change. Here we used a passive microwave Earth observation data set to document two different trends in land area with woody cover for 1992-2011: 36% of the land area (6,870,000 km2) had an increase in woody cover largely in drylands, and 11% had a decrease (2,150,000 km2), mostly in humid zones. Increases in woody cover were associated with low population growth, and were driven by increases in CO2 in the humid zones and by increases in precipitation in drylands, whereas decreases in woody cover were associated with high population growth. The spatially distinct pattern of these opposing trends reflects, first, the natural response of vegetation to precipitation and atmospheric CO2, and second, deforestation in humid areas, minor in size but important for ecosystem services, such as biodiversity and carbon stocks. This nuanced picture of changes in woody cover challenges widely held views of a general and ongoing reduction of the woody vegetation in Africa.
The advent of next-generation sequencing technology has allowed the collection of vast amounts of genetic variation data. A recurring discovery from studying larger and larger samples of individuals had been the extreme, previously unexpected, excess of very rare genetic variants, which has been shown to be mostly due to the recent explosive growth of human populations. Here, we review recent literature that inferred recent changes in population size in different human populations and with different methodologies, with many pointing to recent explosive growth, especially in European populations for which more data has been available. We also review the state-of-the-art methods and software for the inference of historical population size changes that lead to these discoveries. Finally, we discuss the implications of recent population growth on personalized genomics, on purifying selection in the non-equilibrium state it entails and, as a consequence, on the genetic architecture underlying complex disease and the performance of mapping methods in discovering rare variants that contribute to complex disease risk.
Inference of Super-exponential Human Population Growth via Efficient Computation of the Site Frequency Spectrum for Generalized Models
The site frequency spectrum (SFS) and other genetic summary statistics are at the heart of many population genetic studies. Previous studies have shown that human populations have undergone a recent epoch of fast growth in effective population size. These studies assumed that growth is exponential, and the ensuing models leave an excess amount of extremely rare variants. This suggests that human populations might have experienced a recent growth with speed faster than exponential. Recent studies have introduced a generalized growth model where the growth speed can be faster or slower than exponential. However, only simulation approaches were available for obtaining summary statistics under such generalized models. In this study, we provide expressions to accurately and efficiently evaluate the SFS and other summary statistics under generalized models, which we further implement in a publicly available software. Investigating the power to infer deviation of growth from being exponential, we observed that adequate sample sizes facilitate accurate inference; e.g., a sample of 3000 individuals with the amount of data expected from exome sequencing allows observing and accurately estimating growth with speed deviating by ≥10% from that of exponential. Applying our inference framework to data from the NHLBI Exome Sequencing Project, we found that a model with a generalized growth epoch fits the observed SFS significantly better than the equivalent model with exponential growth (P-value [Formula: see text]). The estimated growth speed significantly deviates from exponential (P-value [Formula: see text]), with the best-fit estimate being of growth speed 12% faster than exponential.
Keywords: coalescent; generalized models; human demographic history; population growth; software.
Background: Recent studies have shown that human populations have experienced a complex demographic history, including a recent epoch of rapid population growth that led to an excess in the proportion of rare genetic variants in humans today. This excess can impact the burden of private mutations for each individual, defined here as the proportion of heterozygous variants in each newly sequenced individual that are novel compared to another large sample of sequenced individuals.
Results: We calculated the burden of private mutations predicted by different demographic models, and compared with empirical estimates based on data from the NHLBI Exome Sequencing Project and data from the Neutral Regions (NR) dataset. We observed a significant excess in the proportion of private mutations in the empirical data compared with models of demographic history without a recent epoch of population growth. Incorporating recent growth into the model provides a much improved fit to empirical observations. This phenomenon becomes more marked for larger sample sizes, e.g. extrapolating to a scenario in which 10,000 individuals from the same population have been sequenced with perfect accuracy, still about 1 in 400 heterozygous sites (or about 6,000 variants) at the 10,001 st individual are predicted to be novel, 18-times as predicted in the absence of recent population growth. The proportion of private mutations is additionally increased by purifying selection, which differentially affect mutations of different functional annotations.
Conclusions: The burden of private mutations for each individual, which are singletons (i.e. appearing in a single copy) in a larger sample that includes this individual, is predicted to be greatly increased by recent population growth, as well as by purifying selection. Comparison with empirical data supports that European populations have experienced recent rapid population growth, consistent with previous studies. These results have important implications to the design and analysis of sequencing-based association studies of complex human disease as they pertain to private and very rare variants. They also imply that personalized genomics will indeed have to be very personal in accounting for the large number of private mutations.
Human populations have experienced dramatic growth since the Neolithic revolution. Recent studies that sequenced a very large number of individuals observed an extreme excess of rare variants and provided clear evidence of recent rapid growth in effective population size, although estimates have varied greatly among studies. All these studies were based on protein-coding genes, in which variants are also impacted by natural selection. In this study, we introduce targeted sequencing data for studying recent human history with minimal confounding by natural selection. We sequenced loci far from genes that meet a wide array of additional criteria such that mutations in these loci are putatively neutral. As population structure also skews allele frequencies, we sequenced 500 individuals of relatively homogeneous ancestry by first analyzing the population structure of 9,716 European Americans. We used very high coverage sequencing to reliably call rare variants and fit an extensive array of models of recent European demographic history to the site frequency spectrum. The best-fit model estimates ∼ 3.4% growth per generation during the last ∼ 140 generations, resulting in a population size increase of two orders of magnitude. This model fits the data very well, largely due to our observation that assumptions of more ancient demography can impact estimates of recent growth. This observation and results also shed light on the discrepancy in demographic estimates among recent studies.
Human populations have experienced recent explosive growth, expanding by at least three orders of magnitude over the past 400 generations. This departure from equilibrium skews patterns of genetic variation and distorts basic principles of population genetics. We characterized the empirical signatures of explosive growth on the site frequency spectrum and found that the discrepancy in rare variant abundance across demographic modeling studies is mostly due to differences in sample size. Rapid recent growth increases the load of rare variants and is likely to play a role in the individual genetic burden of complex disease risk. Hence, the extreme recent human population growth needs to be taken into consideration in studying the genetics of complex diseases and traits.
Current demographics suggest future energy supplies will be inadequate to slow human population growth
Influential demographic projections suggest that the global human population will stabilize at about 9-10 billion people by mid-century. These projections rest on two fundamental assumptions. The first is that the energy needed to fuel development and the associated decline in fertility will keep pace with energy demand far into the future. The second is that the demographic transition is irreversible such that once countries start down the path to lower fertility they cannot reverse to higher fertility. Both of these assumptions are problematic and may have an effect on population projections. Here we examine these assumptions explicitly. Specifically, given the theoretical and empirical relation between energy-use and population growth rates, we ask how the availability of energy is likely to affect population growth through 2050. Using a cross-country data set, we show that human population growth rates are negatively related to per-capita energy consumption, with zero growth occurring at ∼13 kW, suggesting that the global human population will stop growing only if individuals have access to this amount of power. Further, we find that current projected future energy supply rates are far below the supply needed to fuel a global demographic transition to zero growth, suggesting that the predicted leveling-off of the global population by mid-century is unlikely to occur, in the absence of a transition to an alternative energy source. Direct consideration of the energetic constraints underlying the demographic transition results in a qualitatively different population projection than produced when the energetic constraints are ignored. We suggest that energetic constraints be incorporated into future population projections.