Historical chart and dataset showing World population growth rate by year from 1961 to 2023.

With a continued focus on the Sonoran Desert, students are introduced to the concepts of biomes, limiting factors (resources), carrying capacity and growth curves through a PowerPoint presentation. Abiotic factors (temperature, annual precipitation, seasons, etc.) determine the biome landscape. The vegetative component, as producers, determines the types of consumers that form its various communities. Students learn how the type and quantity of available resources defines how many organisms can be supported within the community, as well as its particular resident species. Students use mathematical models of natural relationships (in this case, sigmoid and exponential growth curves) to analyze population information and build upon it. With this understanding, students are able to explain how carrying capacity is determined by the limiting factors within the community and feeding relationships. By studying these ecological relationships, students see the connection between ecological relationships of organisms and the fundamentals of engineering design, adding to their base of knowledge towards solving the grand challenge posed in this unit.

This dataset contains growth curve data of a series of E. coli cells carrying reduced genomes, in the media of LB (rich medium), MAA (M63 supplied with 20 amino acids) and M63 (minimal medium).The dataset consists of three .xlsx files accessible via MS Excel and open office formats. Each file contains experimental growth curve data by time in a series of tabs, each representing one strain with the tab name bearing n. Individual columns in a tab represent individual wells per strain. Each separate file corresponds to one of the growth media above: LB, MAA, and M63, which represent the rich, supplementary, and poor growth conditions, respectively.Measurement data are provided at 30 minute or one hour intervals for all growth media and groupings.KHK growth curves_LB.xlsx - E. coli growth curve data by strain for LB (rich medium)KHK growth curves_M63.xlsx - E. coli growth curve data by strain for M63 (minimal medium)KHK growth curves_MAA.xlsx - E. coli growth curve data by strain for MAA (M63 supplied with 20 amino acids)Methodology (see related publication for full details)E. coli culture cell growth was detected at an absorbance of 600 nm, with readings obtained at 30-min or 1-h intervals for 24 to 48 h. The growth curves were obtained for each well. Repeated tests were performed, which resulted in 11 to 30 growth curves used for further calculations of growth rate and population density for each strain at each growth condition (medium). Growth curves were acquired in three different media: LB, M63 and MAA.BackgroundGenome reduction by removing dispensable genomic sequences in bacteria is commonly used in both fundamental and applied studies to determine the minimal genetic requirements for a living system or to develop highly efficient bioreactors. Nevertheless, whether and how the accumulative loss of dispensable genomic sequences disturbs bacterial growth remains unclear. To investigate the relationship between genome reduction and growth, a series of Escherichia coli strains carrying genomes reduced in a stepwise manner were used. Intensive growth analyses revealed that the accumulation of multiple genomic deletions caused decreases in the exponential growth rate and the saturated cell density in a deletion-length-dependent manner as well as gradual changes in the patterns of growth dynamics, regardless of the growth media. Accordingly, a perspective growth model linking genome evolution to genome engineering was proposed. This study provides the first demonstration of a quantitative connection between genomic sequence and bacterial growth, indicating that growth rate is potentially associated with dispensable genomic sequences.

China population growth rate for 2023 was -0.10%, a 0.09% decline from 2022.

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

<li>China population growth rate for 2022 was <strong>-0.01%</strong>, a <strong>0.1% decline</strong> from 2021.</li>
<li>China population growth rate for 2021 was <strong>0.09%</strong>, a <strong>0.15% decline</strong> from 2020.</li>
<li>China population growth rate for 2020 was <strong>0.24%</strong>, a <strong>0.12% 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.

Graph and download economic data for Population Growth for the United States (SPPOPGROWUSA) from 1961 to 2024 about population, rate, and USA.

Population growth (annual %) in World was reported at 0.9512 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Population growth (annual %) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Population growth (annual %) in United States was reported at 0.97642 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Population growth (annual %) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Dataset includes data and code related to this manuscript.

Graph and download economic data for Population Growth for Brazil (SPPOPGROWBRA) from 1961 to 2024 about Brazil, population, and rate.

Population growth (annual %) in China was reported at --0.10379 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. China - Population growth (annual %) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Until the 1800s, population growth was incredibly slow on a global level. The global population was estimated to have been around 188 million people in the year 1CE, and did not reach one billion until around 1803. However, since the 1800s, a phenomenon known as the demographic transition has seen population growth skyrocket, reaching eight billion people in 2023, and this is expected to peak at over 10 billion in the 2080s.

Historical chart and dataset showing Japan population growth rate by year from 1961 to 2023.

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.

Graph and download economic data for Population Growth for Greece (SPPOPGROWGRC) from 1961 to 2024 about Greece, population, and rate.

Population growth (annual %) in Germany was reported at --0.46708 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Germany - Population growth (annual %) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Brazil: Population growth, percent: The latest value from 2023 is 0.51 percent, an increase from 0.46 percent in 2022. In comparison, the world average is 1.15 percent, based on data from 196 countries. Historically, the average for Brazil from 1961 to 2023 is 1.72 percent. The minimum value, 0.46 percent, was reached in 2022 while the maximum of 3.02 percent was recorded in 1961.

The USA: Population growth, percent: The latest value from 2023 is 0.49 percent, an increase from 0.37 percent in 2022. In comparison, the world average is 1.15 percent, based on data from 196 countries. Historically, the average for the USA from 1961 to 2023 is 0.98 percent. The minimum value, 0.16 percent, was reached in 2021 while the maximum of 1.66 percent was recorded in 1961.

Graph and download economic data for Population Growth for Argentina (SPPOPGROWARG) from 1961 to 2024 about Argentina, population, and rate.

Sudan: Population growth, percent: The latest value from 2023 is 2.6 percent, a decline from 2.63 percent in 2022. In comparison, the world average is 1.15 percent, based on data from 196 countries. Historically, the average for Sudan from 1961 to 2023 is 2.78 percent. The minimum value, 1.1 percent, was reached in 1988 while the maximum of 4.29 percent was recorded in 1980.

IntroductionAfter more than 100 years of generating monoculture batch culture growth curves, microbial ecologists and evolutionary biologists still lack a reference method for inferring growth rates. Our work highlights the challenges of estimating the growth rate from growth curve data. It shows that inaccurate estimates of growth rates significantly impact the estimated relative fitness, a principal quantity in evolution and ecology. Methods and resultsFirst, we conducted a literature review and found which methods are currently used to estimate growth rates. These methods differ in the meaning of the estimated growth rate parameter. Mechanistic models estimate the intrinsic growth rate µ, whereas phenomenological methods – both model-based and model-free – estimate the maximum per capita growth rate µmax. Using math and simulations, we show the conditions in which µmax is not a good estimator of µ. Then, we demonstrate that inaccurate absolute estimates of µ are not overcome by calculating relative values. Importantly, we find that poor approximations for µ sometimes lead to wrongly classifying a beneficial mutant as deleterious. Finally, we re-analyzed four published data sets, using most of the methods found in our literature review. We detected no single best-fitting model across all experiments within a data set and found that the Gompertz models, which were among the most commonly used, were often among the worst-fitting. DiscussionOur study suggests how experimenters can improve their growth rate and associated relative fitness estimates and highlights a neglected but fundamental problem for nearly everyone who studies microbial populations in the lab.