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Friday, January 9, 2015

Upper-tropospheric moistening in response to anthropogenic warming

  1. Lei Shic
  1. Edited by John H. Seinfeld, California Institute of Technology, Pasadena, CA, and approved June 27, 2014 (received for review May 23, 2014)

Significance

The fact that water vapor is the most dominant greenhouse gas underscores the need for an accurate understanding of the changes in its distribution over space and time. Although satellite observations have revealed a moistening trend in the upper troposphere, it has been unclear whether the observed moistening is a facet of natural variability or a direct result of human activities. Here, we use a set of coordinated model experiments to confirm that the satellite-observed increase in upper-tropospheric water vapor over the last three decades is primarily attributable to human activities. This attribution has significant implications for climate sciences because it corroborates the presence of the largest positive feedback in the climate system.

Abstract

Water vapor in the upper troposphere strongly regulates the strength of water-vapor feedback, which is the primary process for amplifying the response of the climate system to external radiative forcings. Monitoring changes in upper-tropospheric water vapor and scrutinizing the causes of such changes are therefore of great importance for establishing the credibility of model projections of past and future climates. Here, we use coupled ocean–atmosphere model simulations under different climate-forcing scenarios to investigate satellite-observed changes in global-mean upper-tropospheric water vapor. Our analysis demonstrates that the upper-tropospheric moistening observed over the period 1979–2005 cannot be explained by natural causes and results principally from an anthropogenic warming of the climate. By attributing the observed increase directly to human activities, this study verifies the presence of the largest known feedback mechanism for amplifying anthropogenic climate change.
Because water vapor is the principal greenhouse gas, variations in its concentration strongly influence the climate’s response to both anthropogenic and natural forcings (1). Changes in the amount of water vapor in the upper troposphere play a particularly important role because the trapping of outgoing terrestrial radiation is proportional to the logarithm of water-vapor concentration (1, 2), and climate models predict enhanced moistening in the upper troposphere compared with the boundary layer (3). Although short-term fluctuations of upper-tropospheric water vapor are consistent among reanalysis datasets, decadal variations show substantial discrepancies even in sign (4, 5). Hence, long-term monitoring of upper-tropospheric water-vapor changes, and understanding causes responsible for such changes are essential for enhancing confidence in the prediction of future climate change (4, 6).
Changes in upper-tropospheric water vapor have been examined based on satellite-observed radiances of 6.7-μm water-vapor channels (3, 7, 8), which are closely related to the layer–mean relative humidity in the upper troposphere (9). Decadal trends in upper-tropospheric relative humidity exhibits distinct regional patterns associated with changes in the atmospheric circulation, but the decadal trends over larger domains are small due to opposing changes at regional scales (8). Analyzing the global-scale changes in 6.7-μm water-vapor radiances reveals little change over the past three decades. However, when the 6.7-μm radiances are examined relative to microwave radiance emissions from oxygen, a distinct radiative signature of upper-tropospheric moistening can be revealed (3).

Although the presence of a moistening trend has been detected in the satellite record, the cause of this moistening has not been determined. Thus, it remains unclear whether the observed moistening could result from natural fluctuations in the climate system, or whether human activities have significantly contributed to the trend. Because climate feedbacks can behave differently in response to natural climate variations compared with anthropogenic warming (10), fully validating the presence and strength of this feedback ultimately requires the detection of a change in upper-tropospheric water vapor that is directly attributable to human activities. Given the importance of upper-tropospheric water vapor, a direct verification of its feedback is critical to establishing the credibility of model projections of anthropogenic climate change.

A new set of coordinated climate change experiments have been conducted for the fifth phase of the Coupled Model Intercomparison Project (CMIP5; ref. 11). One of the climate change scenarios included in the CMIP5 is a historical experiment in which coupled ocean–atmosphere models are integrated with historical changes in forcing agents over the period 1850–2005. Climate variability produced from the historical experiment can then be analyzed in more detail in combination with two related experiments: one integrated with only anthropogenic forcings from well-mixed greenhouse gases, and the other integrated with only natural forcings from volcanoes and changes in solar activity. These two experiments can help identify the causes for recent changes in climate, provided the historical experiment with all forcings is capable of reproducing the observed trends. In this study, we use the historical climate change experiments from CMIP5 to demonstrate that the satellite-observed changes in upper-tropospheric water vapor are inconsistent with naturally forced variability and can only be explained by anthropogenic forcing.

Temporal Variations and Trends of Upper-Tropospheric Water Vapor

The National Oceanic and Atmospheric Administration (NOAA) operational polar-orbiting satellites have been taking measurements of the 6.7-μm water-vapor channel (channel 12) radiances from High-Resolution Infrared Radiation Sounder (HIRS) version 2 (HIRS/2) since November 1978. Because climate monitoring was not the primary purpose of the HIRS mission, various attempts have been made to correct for biases, and to minimize intersatellite discrepancies, to make the HIRS record more suitable for climate study (8, 12). The bias-corrected, intercalibrated HIRS water-vapor channel radiance dataset (13) is used to examine the decadal timescale variability of upper-tropospheric water vapor. Unfortunately, the continuity of the 6.7-μm water-vapor record ends in 2005 due to the shift of central wavelength from 6.7 μm (HIRS/2) to 6.5 μm (HIRS/3), which also coincides with the end of the CMIP5 historical experiment. We therefore limit our observational analysis to the 27-y period 1979–2005.
A time series of global, monthly mean brightness temperature anomalies of HIRS channel 12 (T12) is given in Fig. 1A (red line). Brightness temperature anomalies are computed relative to the mean seasonal cycle over the period 1980–2004. For the period 1979–2005, the brightness temperature anomalies vary within ±0.4 K, with only a very small positive trend over this period.

The time series of HIRS channel 12 brightness temperature anomalies simulated from the CMIP5 historical experiment of 20 coupled ocean–atmosphere models (Materials and Methods) is also presented in Fig. 1A. The multimodel ensemble mean is shown by the blue line, with vertical bars denoting the intermodel spread. Note that multimodel averaging dampens the amplitude of the monthly variability compared with that of the satellite observations. Nevertheless, the CMIP5 models capture the observed decadal variability despite substantial biases in climatological mean distribution (14). The observed linear trend in T12 (for more details about uncertainties in estimated linear trends, see SI Materials and Methods) is similar to that computed from the multimodel mean and lies near the center of the distribution of trends from the individual models (Fig. 1, Right). The small magnitude of the trend shown in both the satellite observations and the model ensemble confirms that global-mean upper-tropospheric relative humidity remained nearly constant on decadal timescales (3).

In addition to HIRS instruments, the NOAA operational polar-orbiting satellites are equipped with a microwave sounding unit (MSU) that provides weighted-average temperature information for deep atmospheric layers between the surface and the stratosphere, by way of four channels located in the 60-GHz oxygen absorption band. The remote sensing systems (RSS) reprocessed the brightness temperatures from the MSU and its follow-on, advanced MSU (AMSU), to construct a bias-corrected, intercalibrated MSU/AMSU dataset (15).

We use the MSU/AMSU channel 2 brightness temperatures (T2) in which the stratospheric contribution is removed using a combination of different viewing angles (16, 17). The time series of the observed T2 anomalies indicates sporadic warming and cooling associated with El Niño–La Niña events with a distinct warming trend over this period. Although the amplitude of this interannual variability is not captured in the multimodel mean (blue line in Fig. 1B) because El Niño–La Niña events do not occur simultaneously among the models, the multimodel ensemble mean of the historical experiment does show decadal-scale warming that is consistent with the MSU/AMSU observations. The observed trend in T2 (Fig. 1, Right) is slightly smaller than that predicted by the multimodel ensemble mean although it lies well within the distribution of individual model trends and is consistent with previous studies (18, 19).

The water-vapor channel radiances are influenced not only by variations in water-vapor concentration, which alter the atmospheric opacity, but also by atmospheric temperature variations, which alter the Plank emission. Although spatiotemporal variations in water vapor are significant, changes in atmospheric oxygen concentrations, which determine T2 emissions, are negligible (3). Thus, the difference T2 – T12 measures the divergence in emission levels between upper-tropospheric water vapor and oxygen. This divergence provides a direct measure of the extent of upper-tropospheric moistening; i.e., the increased concentration of water vapor elevates the emission level for T12 and offsets the warming evident in T2, which experiences no change in emission level (3).

Based on these properties, a time series of the brightness temperature difference, T2 –T12, is constructed to quantify the global-scale changes in upper-tropospheric water vapor for both satellite observations and CMIP5 historical simulations (Fig. 1C). El Niño–La Niña events dominate subdecadal-scale variations in the satellite observations but are not evident in the CMIP5 ensemble mean due to multimodel averaging. However, on decadal timescales both the satellite observations and the coupled ocean–atmosphere model simulations exhibit a distinct increase in global-mean upper-tropospheric water vapor. Moreover, the observed linear trend in T2 – T12 is very similar to that predicted by the multimodel mean and lies near the center of the distribution of individual model trends.

To demonstrate that the difference T2 – T12 is a measure of the concentration of upper-tropospheric water vapor, the model-simulated changes in T2 – T12 are computed by holding the concentration of water vapor constant over time. The resulting trend (green line in Fig. 1C) is near zero and lies well outside both the model simulations with changing water vapor and the observed trend. Both calculations use the same sets of temperature profiles, indicating that the increase in T2 –T12 is due to the increased concentration of water vapor in the upper troposphere and not due to changes in temperature.

Detection and Attribution of the Moistening Trend

To examine whether internally generated variability could produce the moistening trend, we analyze output of the corresponding CMIP5 preindustrial control run (11), which contains only unforced, internal climate variability. In contrast to the historical experiment, none of the simulated brightness temperature records (T12, T2, or T2 – T12) show a significant trend over a 27-y period (Fig. 2). The histograms presented on the right further demonstrate that decadal trends with a magnitude equal to that observed do not occur in any of the unforced experiments. A constant water-vapor scenario results in near-zero trends in T2 – T12. These results suggest that the upper-tropospheric moistening observed during the satellite era does not result from internal variability but from a combination of historical changes in anthropogenic and natural forcings.
Fig. 2.

Time series of global-mean brightness temperature anomaly of (A) T12, (B) T2, and (C) T2 – T12, simulated from CMIP5 preindustrial control experiment for a 27-y period. Each line denotes an individual coupled ocean–atmosphere model. The corresponding histograms of decadal trend are given on Right with the bin size of 0.02 K decade−1. The decadal trend of multimodel ensemble mean for which model-simulated changes in T2 – T12 were computed under a constant water-vapor scenario is represented by a green dashed line (0.00 ± 0.01 K decade−1) with a horizontal error bar denoting ±2 SE of the linear trend.
Vertical lines in red represent decadal trends from satellite observations over the period 1979–2005.
To examine different forcing contributions, we assess the relative contribution of anthropogenic greenhouse gases to historical changes in the upper-tropospheric water vapor by analyzing two additional CMIP5 experiments linked to the historical experiment. In these experiments, the coupled ocean–atmosphere models are integrated with anthropogenic greenhouse gases (i.e., historicalGHG), and with natural forcing sources (i.e., historicalNat), respectively. For 12 out of 20 models in which output is available for all three experiments, the decadal trends are computed for the five 30-y periods. Fig. 3 compares decadal trends for the multimodel ensemble mean with horizontal error bars denoting ±2 SE of the linear trend (±2 SE of the linear trend are computed using the method in ref. 20).
Fig. 3.

Decadal trends of multimodel ensemble mean brightness temperature simulated from CMIP5 historical experiment (red circles), HistoricalNat (blue triangles), and HistoricalGHG (green triangles) for five 30-y periods: (A) T12, (B) T2, and (C) T2 – T12. Error bars denote ±2 SE of the linear trend.

Decadal trends of the model-simulated T12 show both positive and negative values for the historicalNat experiment, but signs are predominantly positive for the historicalGHG experiment. Although the influences of changes in aerosols and land use cannot be ruled out, an increase in anthropogenic greenhouse gases seems to be responsible for the decadal trend over the satellite era, because trends from the historical and historicalNat experiments lie clearly outside each other’s range.

For decadal trends of T2, the range of estimated decadal trends is generally wider for historicalNat than historicalGHG (Fig. 3B), indicating that subdecadal variability could be more significant in the former. The increase of anthropogenic greenhouse gases consistently leads to a warming trend for all periods. Although the impact of natural forcing sources can negate greenhouse-gas-induced warming signals (e.g., for the period 1946–1975; refs. 2123), it is mostly weaker and more variable. Given these characteristics, the warming trend over the satellite era is primarily attributable to the increase of anthropogenic greenhouse gases.

For the T2 – T12 (Fig. 3C), decadal trends for historicalGHG and historicalNat fall within each other’s range for the first two periods, but become significantly different from each other in later periods. The magnitude of decadal trend due to natural forcings is generally small, whereas the contribution of anthropogenic greenhouse gases is always positive, and is amplified throughout the whole period. Comparisons with the historical experiment indicate that decadal trends for the historical experiment are affected by changes in natural forcing sources, as well as anthropogenic greenhouse gases. For example, a negative (thus drying) trend for the historical experiment over the period 1946–1975 is mainly induced by natural forcing sources, because increases in anthropogenic greenhouse gases induce a significantly positive (moistening) trend. Concerning the satellite-derived moistening trend in recent decades, the relations of trend and associated range among three experiments lead to the conclusion that an increase in anthropogenic greenhouse gases is the main cause of increased moistening in the upper troposphere.

Discussion and Conclusions

To illustrate the importance of the observed upper-tropospheric moistening in amplifying the climate sensitivity, radiative kernels (2426) are used to quantify the strength of the water-vapor feedback from all levels with that obtained for the upper troposphere alone (SI Materials and Methods). The histogram in Fig. 4 compares the distribution of model-simulated water-vapor feedback during the historical scenario with and without historical forcings. Simulations with anthropogenically induced warming simulate large positive feedbacks from water vapor and are distinctly different from generated from natural forcing alone (blue dashed line). To highlight the importance of the upper troposphere, the feedback calculations are repeated using only water-vapor changes in the troposphere above 600 hPa from the historical simulation (green dashed line in Fig. 4). Approximately 80% of the total water-vapor feedback results from water vapor in the upper troposphere. Although the absolute increase in water vapor is small at these levels, the absorptivity scales with the fractional changes in water vapor, which are typically 2–3 times larger in the upper troposphere compared with the surface (SI Materials and Methods). Note that the observational estimate for the period 2000–2010 (27) lies within the distribution of model simulations only when anthropogenic forcing is included, further indicating that the observed changes in upper-tropospheric water vapor are a direct result of anthropogenic warming.
Fig. 4.

The histogram shows a distribution of the water-vapor feedback strength computed using a radiative kernel for two 10-y periods (i.e., 1979–1988 and 1989–1998) of the historical scenario with a red line denoting a multimodel mean (1.92 ± 0.99 W m−2 K−1). The bin size of the histogram is 0.2 W m−2 K−1. A blue dashed line indicates a multimodel mean of the water-vapor feedback strength for which water vapor in the troposphere would change under natural forcing alone (i.e., HistNat), and the case that the evolution of upper-tropospheric water vapor was not modified by HistNat is represented by a green dashed line (i.e., Hist UTWV-only). The multimodel mean values for the HistNat and Hist UTWV-only are 0.08 ± 0.99 W m−2 K−1 and 1.53 ± 0.87 W m−2 K−1, respectively. Horizontal error bars represent ±2 intermodel SD. A violet dashed line denotes the observational estimate of the water vapor feedback for the period 2000–2010 (∼1.2 W m−2 K−1) (27).

Bias-corrected, intercalibrated satellite observations produce a radiative signature, suggesting that moisture in the upper troposphere has increased over the past ∼30 y (3). When integrated with historical changes in forcing agents, coupled ocean–atmosphere models are found to produce decadal trends consistent with satellite observations. In contrast, coupled ocean–atmosphere models fail to capture observed trends in the preindustrial control experiment, suggesting that upper-tropospheric moistening over the satellite era is not an internally generated variability. Two additional model experiments, integrated with anthropogenic greenhouse gases and natural forcing sources separately, further indicate that the observed moistening trend is mainly induced by an increase in anthropogenic greenhouse gases. As a result, it is expected that the influence of a projected increase in anthropogenic greenhouse gases will amplify upper-tropospheric moistening, and is thus likely to amplify global warming via enhanced water-vapor feedback.

Materials and Methods

Decadal trends of upper-tropospheric water vapor determined from the satellite observations are compared with those simulated from CMIP5 coupled ocean–atmosphere climate models, to ascertain whether the satellite-determined decadal-scale variations are due to anthropogenic forcing agents. In doing so, the historical experiment output from 20 climate models (ACCESS1-0, BNU-ESM, CCSM4, CNRM-CM5, GFDL-CM3, GDFL-ESM2G, GFDL-ESM2M, GISS-E2-R, HadGEM2-ES, INMCM4, IPSL-CM5A-LR, IPSL-CM5A-MR, MIROC-ESM-CHEM, MIROC-ESM, MIROC5, MPI-ESM-LR, MPI-ESM-P, MRI-CGCM3, NorESM1-M, and NorESM1-ME; see Table 1 for information about climate models) is contrasted with the corresponding preindustrial control run results (i.e., piControl) that represent an unforced climate variability. The output of ocean–atmosphere coupling experiments is analyzed, as suppressing the ocean–atmosphere interactions could inhibit the internally generated variability that might not be in phase with externally forced variability (28, 29). Forcing agents included in the historical experiment are: well-mixed greenhouse gases, tropospheric and stratospheric ozone, land use, volcanoes, solar forcing, sulfate, black carbon, organic carbon, dust, and sea salt, and their detailed prescriptions may vary depending on models. The CMIP5 includes two additional experiments designed to investigate the response of the climate system to changes in anthropogenic sources (i.e., historicalGHG), and natural sources (historicalNat). Ref. 11 provides detailed information on the CMIP5 experiments. To avoid uncertainties inherent to the inversion processes of satellite-observed radiances, atmospheric profiles of temperature and specific humidity produced from the CMIP5 experiments are inserted into a fast radiative transfer model (30) to compute synthetic brightness temperatures that would be observed by satellites for given atmospheric conditions.
Table 1.

A list of CMIP5 climate models used in this study

Acknowledgments

We thank two anonymous reviewers and the editor for their constructive and valuable comments, which led to an improved version of the manuscript. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Materials and Methods) for producing and making available their model output. For CMIP, the US Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. This research was supported by grants from the National Aeronautics and Space Administration and the National Oceanic and Atmospheric Administration Climate Program Office. B.J.S. was supported by the Korea Meteorological Administration Research and Development Program under Grant CATER 2012–2061.

12.8 Key uncertainties and research priorities

Uncertainties in future climate projections are discussed in great detail in Working Group I Section 10.5 (Meehl et al., 2007). For Europe, a major uncertainty is the future behaviour of the NAO and North Atlantic THC. Also important, but not specific to Europe, are the uncertainties associated with the still insufficient resolution of GCMs (e.g., Etchevers et al., 2002; Bronstert, 2003), and with downscaling techniques and regional climate models (Mearns et al., 2003; Haylock et al., 2006; Déqué et al., 2007).
Uncertainties in climate impact assessment also stem from the uncertainties of land-use change and socio-economic development (Rounsevell et al., 2005, 2006) following European policies (e.g., CAP), and European Directives (Water Framework Directive, European Maritime Strategy Directive). Although most impact studies use the SRES scenarios, the procedures for scenario development are the subject of debate (Castle and Henderson, 2003a, b; Grübler et al., 2004; Holtsmark and Alfsen, 2005; van Vuuren and Alfsen, 2006). While current scenarios appear to reflect well the course of events in the recent past (van Vuuren and O’Neill, 2006), further research is needed to better account for the range of possible scenarios (Tol, 2006). This might be important for Europe given the many economies in transition.

Uncertainties in assessing future climate impacts also arise from the limitations of climate impact models including (i) structural uncertainty due to the inability of models to capture all influential factors, e.g., the models used to assess health impacts of climate change usually neglect social factors in the spread of disease (Kuhn et al., 2004; Reiter et al., 2004; Sutherst, 2004), and climate-runoff models often neglect the direct effect of increasing CO2 concentration on plant transpiration (Gedney et al., 2006), (ii) lack of long-term representative data for model evaluation, e.g., current vector-monitoring systems are often unable to provide the reliable identification of changes (Kovats et al., 2001). Hence, more attention should be given to structural improvement of models and intensifying efforts of long-term monitoring of the environment, and systematic testing of models against observed data in field trials or catchment monitoring programmes (Hildén et al., 2005). Another way to address the uncertainty of deterministic models is to use probabilistic modelling which can produce an ensemble of scenarios, (e.g., Wilby and Harris, 2006; Araújo and New, 2007; ENSEMBLES project, http://ensembles-eu.metoffice.com/).

Until now, most impact studies have been conducted for separate sectors even if, in some cases, several sectors have been included in the same study (e.g., Schröter et al., 2005). Few studies have addressed impacts on various sectors and systems including their possible interactions by integrated modelling approaches (Holman et al., 2005; Berry et al., 2006). Even in these cases, there are various levels (supra-national, national, regional and sub-regional) that need to be jointly considered, since, if adaptation measures are to be implemented, knowledge down to the lowest decision level will be required. The varied geography, climate and human values of Europe pose a great challenge for evaluation of the ultimate impacts of climate change.

Although there are some good examples, such as the ESPACE-project (Nadarajah and Rankin, 2005), national-scale programmes, such as the FINADAPT project, studies of adaptation to climate change and of adaptation costs are at an early stage and need to be carried out urgently. These studies need to match adaptation measures to specific climate change impacts (e.g., targeted to alleviating impacts on particular types of agriculture, water management or on tourism at specific locations). They need to take into account regional differences in adaptive capacity (e.g., wide regional differences exist in Europe in the style and application of coastal management). Adaptation studies need to consider that in some cases both positive and negative impacts may occur as a result of climate change (e.g., the productivity of some crops may increase, while others decrease at the same location, e.g., Alexandrov et al., 2002). Key research priorities for impacts of climate change, adaptation and implications are included in Table 12.5.
Table 12.5. Key uncertainties and research needs. 

Impact of climate change 
  • Improved long-term monitoring of climate-sensitive physical (e.g., cryosphere), biological (e.g., ecosystem) and social sectors (e.g., tourism, human health).
  • Improvement of climate impact models, including better understanding of mechanism of climate impacts, e.g., of heat/cold morbidity, differences between impacts due to short-term climate variability and long-term climate change, and the effects of extreme events, e.g., heatwaves, droughts, on longer-term dynamics of both managed and natural ecosystems.
  • Simultaneous consideration of climatic and non-climatic factors, e.g., the synergistic effect of climate change and air pollution on buildings, or of climate change and other environmental factors on the epidemiology of vector-borne diseases; the validation and testing of climate impact models through the enhancement of experimental research; increased spatial scales; long-term field studies and the development of integrated impact models.
  • Enhancement of climate change impact assessment in areas with little or no previous investigation, e.g., groundwater, shallow lakes, flow regimes of mountain rivers, renewable energy sources, travel behaviour, transport infrastructure, tourist demand, major biogeochemical cycles, stability, composition and functioning of forests, natural grasslands and shrublands), nutrient cycling and crop protection in agriculture.
  • More integrated impact studies, e.g., of sensitive ecosystems including human dimensions.
  • Better understanding of the socio-economic consequences of climate change for different European regions with different adaptive capacity.
 
Adaptation measures 
  • The comprehensive evaluation (i.e., of effectiveness, economy and constraints) of adaptation measures used in past in different regions of Europe to reduce the adverse impacts of climate variability and extreme meteorological events.
  • Better understanding, identification and prioritisation of adaptation options for coping with the adverse effects of climate change on crop productivity, on the quality of aquatic ecosystems, on coastal management and the capacity of health services.
  • Evaluation of the feasibility, costs and benefits of potential adaptation options, measures and technologies.
  • Quantification of bio-climatic limitations of prevalent plant species.
  • Continuation of studies on the regional differences in adaptive capacity.
 
Implementation 
  • Identification of populations at risk and the lag time of climate change impacts.
  • Approaches for including climate change in management policy and institutions.
  • Consideration of non-stationary climate in the design of engineering structures.
  • Identification of the implications of climate change for water, air, health and environmental standards.
  • Identification of the pragmatic information needs of managers responsible for adaptation.

Neuroscience and Destiny

brain
A newly-published review of neuroscience research looking at the predictive value of functional and anatomical imaging raises interesting questions about the role of such studies in learning, psychiatric treatment, and even the treatment of criminals. “Prediction as a Humanitarian and Pragmatic Contribution from Human Cognitive Neuroscience” by Gabrieli, Ghosh, and Whitfield-Gabrieli and published in Neuron, does a thorough job of explaining the current state of the research and pointing to where future research is needed.

The basic idea is to use noninvasive imaging to look at the structure or function of the brain as a way of predicting future behavior, and then using those predictions to help guide treatment and education interventions, and perhaps decisions regarding parole or further treatment of criminal behavior. This concept raises many issues, including the technology being used, the state of the research, the ultimate potential for this line of research, and ethical considerations.

The major question underlying this entire endeavor is, to what extent is brain anatomy and function destiny?

The technology

There are two basic ways to look at the brain to determine its functional potential with regard to specific tasks (such as reading, impulse control, tendency toward depression, etc.). The first is to look at anatomy independent of any current task being performed. Essentially this approach uses MRI scanning to measure the thickness or overall size of gray matter regions or of white matter tracks. If, for example, the “cable” that connects the two main language processing areas (called the arcuate fasciculus) is thicker, this may correlate with a greater ability to learn a new language or improve reading skills.
The advantage of this approach is that the technology is highly reliable and precise. This is simply a physical measurement.

The second approach is to look at brain activity during a specific task. There are several options for this approach: fMRI scanning (which measures blood flow) has a good spatial resolution but a poor temporal resolution, while looking at electrical or magnetic activity through EEG or MEG respectively has better temporal resolution, but worse spatial resolution.

These approaches have the advantage of looking at actual brain activity – seeing which parts of the brain are active during a specific task. They are currently less precise than purely anatomical techniques. They also have the added difficulty of being dependent on the subject performing the task that they are being instructed to perform (such as attempting to match words that rhyme). The researchers cannot know for sure how focused a subject is on the target task, or what mental method they are using to achieve the task.

Taken together, these techniques are relative new and powerful tools for looking at brain anatomy and function. They are detailed enough to be useful, but still have significant limitations.

The research

The authors do an excellent job of reviewing the logic and methods behind research looking into using imaging technology to predict future outcomes (although their paper can get very technical at times). I will give a simplified summary here. Essentially, the first phase of such research is to look for correlations between some brain attribute and some outcome measure (such as reading skill or level of depression). Researchers can look for correlations within a group, between groups, or with change from baseline.
They correctly caution that correlations do not necessary equal causation. They further point out that such correlations are always overestimates, and that the more variability there is in the brain regions and markers being measured, the greater the ability to tease out some correlation (to find some apparent signal in the noise). Correlations therefore need to be confirmed by making predictions with a fresh data set.

They outline various statistical methods for creating a model based on correlations and then testing the model by using it to make predictions about a different data set.

The bulk of the paper then reviews existing research in various areas. Overall existing research is mostly preliminary. Most studies are small in size and look mainly at correlations. Few have done the follow up of testing their models with predictions using fresh data. Therefore, while this may be an exciting area of research, the published studies have not yet matured to the point where we can make practical use of the results.

What the current research does show is that there is a modest but real correlation between brain imaging and various cognitive outcomes. The data is most robust and convincing with respect to language. This makes sense, since there are brain structures dedicated to language processing, and the robustness of this processing is likely to correlate to language ability.

Another area where the authors point out the research is fairly solid is depression, for example looking at brain responses to depressed faces and outcomes of treatment for depression. I also assess the research looking at impulse control and the frontal lobe structures that correlate with such control as being fairly solid.

However, even in these areas where there is a clear correlation, the amount of predictive value tends to be fairly modest. In many of the studies reviewed, positive imaging predicts only 20% or so of later variability. In a study of reading outcomes, traditional ability measures predicted 65% of later outcome, while brain imaging predicted 57%. However, when the two types of information were combined, predictive value increased to 81%.

Future research and applications

The authors correctly point out that if this line of research is to be useful clinically then the research needs to progress to much larger studies which look at the predictive value of models based on correlations. In addition, we need to get more and more detailed in terms of correlating specific brain structures or activity and specific clinical outcomes.

I do wonder, however, what the ultimate limit will be for this line of research. The brain is a complex system, with many different tasks and abilities interacting in a complex way to yield an end result. There may be some low-hanging fruit to pick, such as with reading and language skill. With such tasks there may be a tight correlation between the organization of the brain and ultimate ability. For other more complex outcomes, such as criminality, there may be significant limits on how predictive such approaches can be.

It may be necessary to take the research farther still – looking at many different parts of the brain and then computer modeling their interaction. We may still get to the limits predicted by chaos theory, however. We can’t predict weather accurately beyond 5 days or so, and we may not be able to predict human behavior and outcomes beyond a certain limit also.

In addition there is the issue of the relative contribution of brain anatomy compared to plasticity (the ability of the brain to change its wiring), environment and learning. We are likely only looking at potential by looking at the brain. This of course will have a statistical predictive value, but this does not mean that brain anatomy is destiny for an individual. If we can’t apply this data to an individual (only statistically to groups) then perhaps the vision of the authors will never be fully realized.

This issue blends into the ethics of looking at the brain in order to determine future outcomes. Should a criminal be denied parole because his brain imaging shows relatively-low impulse control, which predicts a higher probability of recidivism?

Where this type of information can be useful (and the authors do point this out) is adding predictive information to help guide treatment decision in certain psychiatric disorder and education interventions. If we can identify at an early age those students who will have difficulty with language and reading, then they can be given greater resources in that area to help them keep up with their peers. This is already being done, using standard testing, but the evidence suggests brain imaging might add to the predictive power of such testing.

In the clinical setting brain imaging may help predict who will respond better to one type of drug over another, or to cognitive behavior therapy. Medical interventions are largely based on statistical data with large groups, and this approach would be no different.

Conclusion

I largely agree with the authors that the new wave of brain anatomical and function imaging has brought with it a new age of understanding the brain. I also agree that there is tremendous potential to use this type of imaging to guide interventions for education and psychiatry. Applications to criminal justice are more complex and fraught with ethical considerations.

We are, however, still years away from practical applications. We need, as the authors suggest, to do large studies of predictive value. But then we also need, in my opinion, to do clinical outcomes research – looking at the net outcomes from employing this type of data in real world situations.

One fear that I have (and the authors also point this out) is that the allure of such data will give it a mystique that goes beyond its real world applicability. If people think we can peer into someone’s brain and predict their future, there would be the strong temptation to treat brain scans as if they were destiny, and short circuit more thorough and nuanced methods for evaluating individuals and optimizing interventions.

In the end I think that this approach will be one more tool in our toolbox, and it can be a powerful tool. Such data will still need to be used thoughtfully, with a full appreciation of its limitations.

Thursday, January 8, 2015

Evidence of common descent

From Wikipedia, the free encyclopedia
 
Evidence of common descent of living organisms has been found by scientists working in a variety of fields over many decades and has demonstrated common descent and that life on earth developed from a last universal ancestor, that evolution does occur, and is able to show the natural processes by which the biodiversity of life on Earth developed. This evidence supports the modern evolutionary synthesis, the current scientific theory that explains how and why life changes over time. Evolutionary biologists document evidence of common descent through making testable predictions, testing hypotheses, and developing theories that illustrate and describe its causes.

Comparison of the DNA genetic sequences of organisms has revealed that organisms that are phylogenetically close have a higher degree of DNA sequence similarity than organisms that are phylogenetically distant. Further evidence for common descent comes from genetic detritus such as pseudogenes, regions of DNA that are orthologous to a gene in a related organism, but are no longer active and appear to be undergoing a steady process of degeneration from cumulative mutations.
Fossils are important for estimating when various lineages developed in geologic time. As fossilization is an uncommon occurrence, usually requiring hard body parts and death near a site where sediments are being deposited, the fossil record only provides sparse and intermittent information about the evolution of life. Scientific evidence of organisms prior to the development of hard body parts such as shells, bones and teeth is especially scarce, but exists in the form of ancient microfossils, as well as impressions of various soft-bodied organisms. The comparative study of the anatomy of groups of animals shows structural features that are fundamentally similar or homologous, demonstrating phylogenetic and ancestral relationships with other organisms, most especially when compared with fossils of ancient extinct organisms. Vestigial structures and comparisons in embryonic development are largely a contributing factor in anatomical resemblance in concordance with common descent. Since metabolic processes do not leave fossils, research into the evolution of the basic cellular processes is done largely by comparison of existing organisms’ physiology and biochemistry. Many lineages diverged at different stages of development, so it is possible to determine when certain metabolic processes appeared by comparing the traits of the descendants of a common ancestor. Universal biochemical organization and molecular variance patterns in all organisms also show a direct correlation with common descent.

Further evidence comes from the field of biogeography because evolution with common descent provides the best and most thorough explanation for a variety of facts concerning the geographical distribution of plants and animals across the world. This is especially obvious in the field of insular biogeography. Combined with the theory of plate tectonics common descent provides a way to combine facts about the current distribution of species with evidence from the fossil record to provide a logically consistent explanation of how the distribution of living organisms has changed over time.

The development and spread of antibiotic resistant bacteria, like the spread of pesticide resistant forms of plants and insects provides evidence that evolution due to natural selection is an ongoing process in the natural world. Alongside this, are observed instances of the separation of populations of species into sets of new species (speciation). Speciation has been observed directly and indirectly in the lab and in nature. Multiple forms of such have been described and documented as examples for individual modes of speciation. Furthermore, evidence of common descent extends from direct laboratory experimentation with the selective breeding of organisms—historically and currently—and other controlled experiments involving many of the topics in the article. This article explains the different types of evidence for evolution with common descent along with many specialized examples of each.

Evidence from comparative physiology and biochemistry

Genetics

While on board HMS Beagle, Charles Darwin collected numerous specimens, many new to science, which supported his later theory of evolution by natural selection.

One of the strongest evidences for common descent comes from the study of gene sequences. Comparative sequence analysis examines the relationship between the DNA sequences of different species,[1] producing several lines of evidence that confirm Darwin's original hypothesis of common descent. If the hypothesis of common descent is true, then species that share a common ancestor inherited that ancestor's DNA sequence, as well as mutations unique to that ancestor. More closely related species have a greater fraction of identical sequence and shared substitutions compared to more distantly related species.

The simplest and most powerful evidence is provided by phylogenetic reconstruction. Such reconstructions, especially when done using slowly evolving protein sequences, are often quite robust and can be used to reconstruct a great deal of the evolutionary history of modern organisms (and even in some instances of the evolutionary history of extinct organisms, such as the recovered gene sequences of mammoths or Neanderthals). These reconstructed phylogenies recapitulate the relationships established through morphological and biochemical studies. The most detailed reconstructions have been performed on the basis of the mitochondrial genomes shared by all eukaryotic organisms, which are short and easy to sequence; the broadest reconstructions have been performed either using the sequences of a few very ancient proteins or by using ribosomal RNA sequence[citation needed].

Phylogenetic relationships also extend to a wide variety of nonfunctional sequence elements, including repeats, transposons, pseudogenes, and mutations in protein-coding sequences that do not result in changes in amino-acid sequence. While a minority of these elements might later be found to harbor function, in aggregate they demonstrate that identity must be the product of common descent rather than common function[citation needed].

Universal biochemical organisation and molecular variance patterns

All known extant (surviving) organisms are based on the same biochemical processes: genetic information encoded as nucleic acid (DNA, or RNA for many viruses), transcribed into RNA, then translated into proteins (that is, polymers of amino acids) by highly conserved ribosomes. Perhaps most tellingly, the Genetic Code (the "translation table" between DNA and amino acids) is the same for almost every organism, meaning that a piece of DNA in a bacterium codes for the same amino acid as in a human cell. ATP is used as energy currency by all extant life. A deeper understanding of developmental biology shows that common morphology is, in fact, the product of shared genetic elements.[2] For example, although camera-like eyes are believed to have evolved independently on many separate occasions,[3] they share a common set of light-sensing proteins (opsins), suggesting a common point of origin for all sighted creatures.[4][5] Another noteworthy example is the familiar vertebrate body plan, whose structure is controlled by the homeobox (Hox) family of genes.

DNA sequencing

Comparison of the DNA sequences allows organisms to be grouped by sequence similarity, and the resulting phylogenetic trees are typically congruent with traditional taxonomy, and are often used to strengthen or correct taxonomic classifications. Sequence comparison is considered a measure robust enough to correct erroneous assumptions in the phylogenetic tree in instances where other evidence is scarce. For example, neutral human DNA sequences are approximately 1.2% divergent (based on substitutions) from those of their nearest genetic relative, the chimpanzee, 1.6% from gorillas, and 6.6% from baboons.[6][7] Genetic sequence evidence thus allows inference and quantification of genetic relatedness between humans and other apes.[8][9] The sequence of the 16S ribosomal RNA gene, a vital gene encoding a part of the ribosome, was used to find the broad phylogenetic relationships between all extant life. The analysis, originally done by Carl Woese, resulted in the three-domain system, arguing for two major splits in the early evolution of life. The first split led to modern Bacteria and the subsequent split led to modern Archaea and Eukaryotes.

Some DNA sequences are shared by very different organisms. It has been predicted by the theory of evolution that the differences in such DNA sequences between two organisms should roughly resemble both the biological difference between them according to their anatomy and the time that had passed since these two organisms have separated in the course of evolution, as seen in fossil evidence. The rate of accumulating such changes should be low for some sequences, namely those that code for critical RNA or proteins, and high for others that code for less critical RNA or proteins; but for every specific sequence, the rate of change should be roughly constant over time. These results have been experimentally confirmed. Two examples are DNA sequences coding for rRNA, which is highly conserved, and DNA sequences coding for fibrinopeptides (amino acid chains that are discarded during the formation of fibrin), which are highly non-conserved.[10]

Endogenous retroviruses

Endogenous retroviruses (or ERVs) are remnant sequences in the genome left from ancient viral infections in an organism. The retroviruses (or virogenes) are always passed on to the next generation of that organism that received the infection. This leaves the virogene left in the genome. Because this event is rare and random, finding identical chromosomal positions of a virogene in two different species suggests common ancestry.[11] Cats (Felidae) present an notable instance of virogene sequences demonstrating common descent. The standard phylogenetic tree for Felidae have smaller cats (Felis chaus, Felis silvestris, Felis nigripes, and Felis catus) diverging from larger cats such as the subfamily Pantherinae and other carnivores. The fact that small cats have an ERV where the larger cats do not suggests that the gene was inserted into the ancestor of the small cats after the larger cats had diverged.[12] Another example of this is with humans and chimps. Humans contain numerous ERVs that comprise a considerable percentage of the genome. Sources vary, however, 1%[13] to 8%[14] has been proposed.
Humans and chimps share seven different occurrences of virogenes while all primates share similar retroviruses congruent with phylogeny.[15]

Proteins

The proteomic evidence also supports the universal ancestry of life. Vital proteins, such as the ribosome, DNA polymerase, and RNA polymerase, are found in everything from the most primitive bacteria to the most complex mammals. The core part of the protein is conserved across all lineages of life, serving similar functions. Higher organisms have evolved additional protein subunits, largely affecting the regulation and protein-protein interaction of the core. Other overarching similarities between all lineages of extant organisms, such as DNA, RNA, amino acids, and the lipid bilayer, give support to the theory of common descent. Phylogenetic analyses of protein sequences from various organisms produce similar trees of relationship between all organisms.[16] The chirality of DNA, RNA, and amino acids is conserved across all known life. As there is no functional advantage to right- or left-handed molecular chirality, the simplest hypothesis is that the choice was made randomly by early organisms and passed on to all extant life through common descent. Further evidence for reconstructing ancestral lineages comes from junk DNA such as pseudogenes, "dead" genes that steadily accumulate mutations.[17]

Pseudogenes

Pseudogenes, also known as noncoding DNA, are extra DNA in a genome that do not get transcribed into RNA to synthesize proteins. Some of this noncoding DNA has known functions, but much of it has no known function and is called "Junk DNA". This is an example of a vestige since replicating these genes uses energy, making it a waste in many cases. A pseudogene can be produced when a coding gene accumulates mutations that prevent it from being transcribed, making it non-functional. But since it is not transcribed, it may disappear without affecting fitness, unless it has provided some beneficial function as non-coding DNA. Non-functional pseudogenes may be passed on to later species, thereby labeling the later species as descended from the earlier species.

Other mechanisms

There is also a large body of molecular evidence for a number of different mechanisms for large evolutionary changes, among them: genome and gene duplication, which facilitates rapid evolution by providing substantial quantities of genetic material under weak or no selective constraints; horizontal gene transfer, the process of transferring genetic material to another cell that is not an organism's offspring, allowing for species to acquire beneficial genes from each other; and recombination, capable of reassorting large numbers of different alleles and of establishing reproductive isolation. The Endosymbiotic theory explains the origin of mitochondria and plastids (e.g. chloroplasts), which are organelles of eukaryotic cells, as the incorporation of an ancient prokaryotic cell into ancient eukaryotic cell. Rather than evolving eukaryotic organelles slowly, this theory offers a mechanism for a sudden evolutionary leap by incorporating the genetic material and biochemical composition of a separate species. Evidence supporting this mechanism has been found in the protist Hatena: as a predator it engulfs a green algae cell, which subsequently behaves as an endosymbiont, nourishing Hatena, which in turn loses its feeding apparatus and behaves as an autotroph.[18][19]

Since metabolic processes do not leave fossils, research into the evolution of the basic cellular processes is done largely by comparison of existing organisms. Many lineages diverged when new metabolic processes appeared, and it is theoretically possible to determine when certain metabolic processes appeared by comparing the traits of the descendants of a common ancestor or by detecting their physical manifestations. As an example, the appearance of oxygen in the earth's atmosphere is linked to the evolution of photosynthesis.

Specific examples

Chromosome 2 in humans

Fusion of ancestral chromosomes left distinctive remnants of telomeres, and a vestigial centromere

Evidence for the evolution of Homo sapiens from a common ancestor with chimpanzees is found in the number of chromosomes in humans as compared to all other members of Hominidae. All hominidae have 24 pairs of chromosomes, except humans, who have only 23 pairs. Human chromosome 2 is a result of an end-to-end fusion of two ancestral chromosomes.[20][21]

The evidence for this includes:
  • The correspondence of chromosome 2 to two ape chromosomes. The closest human relative, the common chimpanzee, has near-identical DNA sequences to human chromosome 2, but they are found in two separate chromosomes. The same is true of the more distant gorilla and orangutan.[22][23]
  • The presence of a vestigial centromere. Normally a chromosome has just one centromere, but in chromosome 2 there are remnants of a second centromere.[24]
  • The presence of vestigial telomeres. These are normally found only at the ends of a chromosome, but in chromosome 2 there are additional telomere sequences in the middle.[25]
Chromosome 2 thus presents very strong evidence in favour of the common descent of humans and other apes. According to J. W. IJdo, "We conclude that the locus cloned in cosmids c8.1 and c29B is the relic of an ancient telomere-telomere fusion and marks the point at which two ancestral ape chromosomes fused to give rise to human chromosome 2."[25]

Cytochrome c and b

A classic example of biochemical evidence for evolution is the variance of the ubiquitous (i.e. all living organisms have it, because it performs very basic life functions) protein Cytochrome c in living cells. The variance of cytochrome c of different organisms is measured in the number of differing amino acids, each differing amino acid being a result of a base pair substitution, a mutation. If each differing amino acid is assumed the result of one base pair substitution, it can be calculated how long ago the two species diverged by multiplying the number of base pair substitutions by the estimated time it takes for a substituted base pair of the cytochrome c gene to be successfully passed on. For example, if the average time it takes for a base pair of the cytochrome c gene to mutate is N years, the number of amino acids making up the cytochrome c protein in monkeys differ by one from that of humans, this leads to the conclusion that the two species diverged N years ago.
The primary structure of cytochrome c consists of a chain of about 100 amino acids. Many higher order organisms possess a chain of 104 amino acids.[26]

The cytochrome c molecule has been extensively studied for the glimpse it gives into evolutionary biology. Both chicken and turkeys have identical sequence homology (amino acid for amino acid), as do pigs, cows and sheep. Both humans and chimpanzees share the identical molecule, while rhesus monkeys share all but one of the amino acids:[27] the 66th amino acid is isoleucine in the former and threonine in the latter.[26]

What makes these homologous similarities particularly suggestive of common ancestry in the case of cytochrome c, in addition to the fact that the phylogenies derived from them match other phylogenies very well, is the high degree of functional redundancy of the cytochrome c molecule. The different existing configurations of amino acids do not significantly affect the functionality of the protein, which indicates that the base pair substitutions are not part of a directed design, but the result of random mutations that aren't subject to selection.[28]

In addition, Cytochrome b is commonly used as a region of mitochondrial DNA to determine phylogenetic relationships between organisms due to its sequence variability. It is considered most useful in determining relationships within families and genera. Comparative studies involving cytochrome b have resulted in new classification schemes and have been used to assign newly described species to a genus, as well as deepen the understanding of evolutionary relationships.[29]

Recent African origin of modern humans

Mathematical models of evolution, pioneered by the likes of Sewall Wright, Ronald Fisher and J. B. S. Haldane and extended via diffusion theory by Motoo Kimura, allow predictions about the genetic structure of evolving populations. Direct examination of the genetic structure of modern populations via DNA sequencing has allowed verification of many of these predictions. For example, the Out of Africa theory of human origins, which states that modern humans developed in Africa and a small sub-population migrated out (undergoing a population bottleneck), implies that modern populations should show the signatures of this migration pattern. Specifically, post-bottleneck populations (Europeans and Asians) should show lower overall genetic diversity and a more uniform distribution of allele frequencies compared to the African population. Both of these predictions are borne out by actual data from a number of studies.[30]

Evidence from comparative anatomy

Comparative study of the anatomy of groups of animals or plants reveals that certain structural features are basically similar. For example, the basic structure of all flowers consists of sepals, petals, stigma, style and ovary; yet the size, colour, number of parts and specific structure are different for each individual species. The neural anatomy of fossilized remains may also be compared using advanced imaging techniques.[31]

Atavisms

Hindlegs of a humpback whale reported in 1921 by the American Museum

An atavism is an evolutionary throwback, such as traits reappearing that had disappeared generations ago.[32] Atavisms occur because genes for previously existing phenotypical features are often preserved in DNA, even though the genes are not expressed in some or most of the organisms possessing them.[33] Some examples of this are hind-legged snakes[34] or whales[35] (In July 1919 a humpback whale was caught by a ship operating out of Vancouver that had legs 4 ft 2 in (1.27 m) long.[36]); the extra toes of ungulates that do not even reach the ground,[37] chicken's teeth,[38] reemergence of sexual reproduction in Hieracium pilosella and Crotoniidae;[39] and humans with tails,[32] extra nipples[citation needed], and large canine teeth[citation needed].

Evolutionary developmental biology and embryonic development

Evolutionary developmental biology is the biological field that compares the developmental process of different organisms to determine ancestral relationships between species. A large variety of organism’s genomes contain a small fraction of genes that control the organisms development. Hox genes are an example of these types of nearly universal genes in organisms pointing to an origin of common ancestry.
Embryological evidence comes from the development of organisms at the embryological level with the comparison of different organisms embryos similarity. Remains of ancestral traits often appear and disappear in different stages of the embryological development process. Examples include such as hair growth and loss (lanugo) during human development;[40] development and degeneration of a yolk sac; terrestrial frogs and salamanders passing through the larval stage within the egg—with features of typically aquatic larvae—but hatch ready for life on land;[41] and the appearance of gill-like structures (pharyngeal arch) in vertebrate embryo development. Note that in fish, the arches continue to develop as branchial arches while in humans, for example, they give rise to a variety of structures within the head and neck.

Homologous structures and divergent (adaptive) evolution

If widely separated groups of organisms are originated from a common ancestry, they are expected to have certain basic features in common. The degree of resemblance between two organisms should indicate how closely related they are in evolution:
  • Groups with little in common are assumed to have diverged from a common ancestor much earlier in geological history than groups with a lot in common;
  • In deciding how closely related two animals are, a comparative anatomist looks for structures that are fundamentally similar, even though they may serve different functions in the adult. Such structures are described as homologous and suggest a common origin.
  • In cases where the similar structures serve different functions in adults, it may be necessary to trace their origin and embryonic development. A similar developmental origin suggests they are the same structure, and thus likely derived from a common ancestor.
When a group of organisms share a homologous structure that is specialized to perform a variety of functions to adapt different environmental conditions and modes of life, it is called adaptive radiation. The gradual spreading of organisms with adaptive radiation is known as divergent evolution.

Nested hierarchies and classification

Taxonomy is based on the fact that all organisms are related to each other in nested hierarchies based on shared characteristics. Most existing species can be organized rather easily in a nested hierarchical classification. This is evident from the Linnaean classification scheme. Based on shared derived characters, closely related organisms can be placed in one group (such as a genus), several genera can be grouped together into one family, several families can be grouped together into an order, etc.[42] The existence of these nested hierarchies was recognized by many biologists before Darwin, but he showed that his theory of evolution with its branching pattern of common descent could explain them.[42][43] Darwin described how common descent could provide a logical basis for classification:[44]

Evolutionary trees

An evolutionary tree (of Amniota, for example, the last common ancestor of mammals and reptiles, and all its descendants) illustrates the initial conditions causing evolutionary patterns of similarity (e.g., all Amniotes produce an egg that possesses the amnios) and the patterns of divergence amongst lineages (e.g., mammals and reptiles branching from the common ancestry in Amniota). Evolutionary trees provide conceptual models of evolving systems once thought limited in the domain of making predictions out of the theory.[45] However, the method of phylogenetic bracketing is used to infer predictions with far greater probability than raw speculation. For example, paleontologists use this technique to make predictions about nonpreservable traits in fossil organisms, such as feathered dinosaurs, and molecular biologists use the technique to posit predictions about RNA metabolism and protein functions.[46][47]
Thus evolutionary trees are evolutionary hypotheses that refer to specific facts, such as the characteristics of organisms (e.g., scales, feathers, fur), providing evidence for the patterns of descent, and a causal explanation for modification (i.e., natural selection or neutral drift) in any given lineage (e.g., Amniota). Evolutionary biologists test evolutionary theory using phylogenetic systematic methods that measure how much the hypothesis (a particular branching pattern in an evolutionary tree) increases the likelihood of the evidence (the distribution of characters among lineages).[48][49][50] The severity of tests for a theory increases if the predictions "are the least probable of being observed if the causal event did not occur."[51] "Testability is a measure of how much the hypothesis increases the likelihood of the evidence."[52]

Vestigial structures

A strong and direct evidence for common descent comes from vestigial structures.[53] Rudimentary body parts, those that are smaller and simpler in structure than corresponding parts in the ancestral species, are called vestigial organs. They are usually degenerated or underdeveloped. The existence of vestigial organs can be explained in terms of changes in the environment or modes of life of the species.
Those organs are typically functional in the ancestral species but are now either nonfunctional or re-purposed. Examples are the pelvic girdles of whales, haltere (hind wings) of flies and mosquitos, wings of flightless birds such as ostriches, and the leaves of some xerophytes (e.g. cactus) and parasitic plants (e.g. dodder). However, vestigial structures may have their original function replaced with another. For example, the halteres in dipterists help balance the insect while in flight and the wings of ostriches are used in mating rituals.

Specific examples

Figure 5d
Figure 5a: Skeleton of a Baleen whale with the hind limb and pelvic bone structure circled in red. This bone structure stays internal during the entire life of the species.
Figure 5c
Figure 5b: Adaptation of insect mouthparts: a, antennae; c, compound eye; lb, labrium; lr, labrum; md, mandibles; mx, maxillae.
(A) Primitive state — biting and chewing: e.g. grasshopper. Strong mandibles and maxillae for manipulating food.
(B) Ticking and biting: e.g. honey bee. Labium long to lap up nectar; mandibles chew pollen and mould wax.
(C) Sucking: e.g. butterfly. Labrum reduced; mandibles lost; maxillae long forming sucking tube.
(D) Piercing and sucking, e.g.. female mosquito. Labrum and maxillae form tube; mandibles form piercing stylets; labrum grooved to hold other parts.
Figure 5e
Figure 5c: Illustration of the Eoraptor lunensis pelvis of the saurischian order and the Lesothosaurus diagnosticus pelvis of the ornithischian order in the Dinosauria superorder. The parts of the pelvis show modification over time. The cladogram is shown to illustrate the distance of divergence between the two species.
Figure 5d: The principle of homology illustrated by the adaptive radiation of the forelimb of mammals. All conform to the basic pentadactyl pattern but are modified for different usages. The third metacarpal is shaded throughout; the shoulder is crossed-hatched.
Figure 5e: The path of the recurrent laryngeal nerve in giraffes. The laryngeal nerve is compensated for by subsequent tinkering from natural selection.

Hind structures in whales

Whales possess internally reduced hind parts such as the pelvis and hind legs (Fig. 5a).[54][55] Occasionally, the genes that code for longer extremities cause a modern whale to develop legs. On October 28, 2006, a four-finned bottlenose dolphin was caught and studied due to its extra set of hind limbs.[56] These legged Cetacea display an example of an atavism predicted from their common ancestry.

Insect mouthparts

Many different species of insects have mouthparts derived from the same embryonic structures, indicating that the mouthparts are modifications of a common ancestor's original features. These include a labrum (upper lip), a pair of mandibles, a hypopharynx (floor of mouth), a pair of maxillae, and a labium. (Fig. 5b) Evolution has caused enlargement and modification of these structures in some species, while it has caused the reduction and loss of them in other species. The modifications enable the insects to exploit a variety of food materials.

Other arthropod appendages

Insect mouthparts and antennae are considered homologues of insect legs. Parallel developments are seen in some arachnids: The anterior pair of legs may be modified as analogues of antennae, particularly in whip scorpions, which walk on six legs. These developments provide support for the theory that complex modifications often arise by duplication of components, with the duplicates modified in different directions.

Pelvic structure of dinosaurs

Similar to the pentadactyl limb in mammals, the earliest dinosaurs split into two distinct orders—the saurischia and ornithischia. They are classified as one or the other in accordance with what the fossils demonstrate. Figure 5c, shows that early saurischians resembled early ornithischians. The pattern of the pelvis in all species of dinosaurs is an example of homologous structures. Each order of dinosaur has slightly differing pelvis bones providing evidence of common descent. Additionally, modern birds show a similarity to ancient saurischian pelvic structures indicating the evolution of birds from dinosaurs. This can also be seen in Figure 5c as the Aves branch off the Theropoda suborder.

Pentadactyl limb

The pattern of limb bones called pentadactyl limb is an example of homologous structures (Fig. 5d). It is found in all classes of tetrapods (i.e. from amphibians to mammals). It can even be traced back to the fins of certain fossil fishes from which the first amphibians evolved such as tiktaalik. The limb has a single proximal bone (humerus), two distal bones (radius and ulna), a series of carpals (wrist bones), followed by five series of metacarpals (palm bones) and phalanges (digits). Throughout the tetrapods, the fundamental structures of pentadactyl limbs are the same, indicating that they originated from a common ancestor. But in the course of evolution, these fundamental structures have been modified. They have become superficially different and unrelated structures to serve different functions in adaptation to different environments and modes of life. This phenomenon is shown in the forelimbs of mammals. For example:
  • In the monkey, the forelimbs are much elongated to form a grasping hand for climbing and swinging among trees.
  • In the pig, the first digit is lost, and the second and fifth digits are reduced. The remaining two digits are longer and stouter than the rest and bear a hoof for supporting the body.
  • In the horse, the forelimbs are adapted for support and running by great elongation of the third digit bearing a hoof.
  • The mole has a pair of short, spade-like forelimbs for burrowing.
  • The anteater uses its enlarged third digit for tearing down ant hills and termite nests.
  • In the whale, the forelimbs become flippers for steering and maintaining equilibrium during swimming.
  • In the bat, the forelimbs have turned into wings for flying by great elongation of four digits, while the hook-like first digit remains free for hanging from trees.

Recurrent laryngeal nerve in giraffes

The recurrent laryngeal nerve is a fourth branch of the vagus nerve, which is a cranial nerve. In mammals, its path is unusually long. As a part of the vagus nerve, it comes from the brain, passes through the neck down to heart, rounds the dorsal aorta and returns up to the larynx, again through the neck. (Fig. 5e)
This path is suboptimal even for humans, but for giraffes it becomes even more suboptimal. Due to the lengths of their necks, the recurrent laryngeal nerve may be up to 4m long (13 ft), despite its optimal route being a distance of just several inches.

The indirect route of this nerve is the result of evolution of mammals from fish, which had no neck and had a relatively short nerve that innervated one gill slit and passed near the gill arch. Since then, the gill it innervated has become the larynx and the gill arch has become the dorsal aorta in mammals.[57][58]

Route of the vas deferens

Route of the vas deferens from the testis to the penis.

Similar to the laryngeal nerve in giraffes, the vas deferens is part of the male anatomy of many vertebrates; it transports sperm from the epididymis in anticipation of ejaculation. In humans, the vas deferens routes up from the testicle, looping over the ureter, and back down to the urethra and penis. It has been suggested that this is due to the descent of the testicles during the course of human evolution—likely associated with temperature. As the testicles descended, the vas deferens lengthened to accommodate the accidental "hook" over the ureter.[58][59]

Evidence from paleontology

An insect trapped in amber.

When organisms die, they often decompose rapidly or are consumed by scavengers, leaving no permanent evidences of their existence. However, occasionally, some organisms are preserved. The remains or traces of organisms from a past geologic age embedded in rocks by natural processes are called fossils. They are extremely important for understanding the evolutionary history of life on Earth, as they provide direct evidence of evolution and detailed information on the ancestry of organisms. Paleontology is the study of past life based on fossil records and their relations to different geologic time periods.

For fossilization to take place, the traces and remains of organisms must be quickly buried so that weathering and decomposition do not occur. Skeletal structures or other hard parts of the organisms are the most commonly occurring form of fossilized remains (Paul, 1998), (Behrensmeyer, 1980) and (Martin, 1999). There are also some trace "fossils" showing moulds, cast or imprints of some previous organisms.

As an animal dies, the organic materials gradually decay, such that the bones become porous. If the animal is subsequently buried in mud, mineral salts infiltrate into the bones and gradually fill up the pores. The bones harden into stones and are preserved as fossils. This process is known as petrification. If dead animals are covered by wind-blown sand, and if the sand is subsequently turned into mud by heavy rain or floods, the same process of mineral infiltration may occur. Apart from petrification, the dead bodies of organisms may be well preserved in ice, in hardened resin of coniferous trees (amber), in tar, or in anaerobic, acidic peat. Fossilization can sometimes be a trace, an impression of a form. Examples include leaves and footprints, the fossils of which are made in layers that then harden.

Fossil record

Fossil trilobite. Trilobites were hard-shelled arthropods, related to living horseshoe crabs and spiders, that first appeared in significant numbers around 540 mya, dying out 250 mya.

It is possible to find out how a particular group of organisms evolved by arranging its fossil records in a chronological sequence. Such a sequence can be determined because fossils are mainly found in sedimentary rock. Sedimentary rock is formed by layers of silt or mud on top of each other; thus, the resulting rock contains a series of horizontal layers, or strata. Each layer contains fossils typical for a specific time period when they formed. The lowest strata contain the oldest rock and the earliest fossils, while the highest strata contain the youngest rock and more recent fossils.

A succession of animals and plants can also be seen from fossil discoveries. By studying the number and complexity of different fossils at different stratigraphic levels, it has been shown that older fossil-bearing rocks contain fewer types of fossilized organisms, and they all have a simpler structure, whereas younger rocks contain a greater variety of fossils, often with increasingly complex structures.[60]

For many years, geologists could only roughly estimate the ages of various strata and the fossils found. They did so, for instance, by estimating the time for the formation of sedimentary rock layer by layer. Today, by measuring the proportions of radioactive and stable elements in a given rock, the ages of fossils can be more precisely dated by scientists. This technique is known as radiometric dating.

Throughout the fossil record, many species that appear at an early stratigraphic level disappear at a later level. This is interpreted in evolutionary terms as indicating the times when species originated and became extinct. Geographical regions and climatic conditions have varied throughout the Earth's history. Since organisms are adapted to particular environments, the constantly changing conditions favoured species that adapted to new environments through the mechanism of natural selection.

Extent of the fossil record

Charles Darwin collected fossils in South America, and found fragments of armor he thought were like giant versions of the scales on the modern armadillos living nearby. The anatomist Richard Owen showed him that the fragments were from gigantic extinct glyptodons, related to the armadillos. This was one of the patterns of distribution that helped Darwin to develop his theory.[61]
Cynognathus, a Eucynodont, one of a grouping of Therapsids ("mammal-like reptiles") that is ancestral to all modern mammals.

Despite the relative rarity of suitable conditions for fossilization, approximately 250,000 fossil species are known.[62] The number of individual fossils this represents varies greatly from species to species, but many millions of fossils have been recovered: for instance, more than three million fossils from the last Ice Age have been recovered from the La Brea Tar Pits in Los Angeles.[63] Many more fossils are still in the ground, in various geological formations known to contain a high fossil density, allowing estimates of the total fossil content of the formation to be made. An example of this occurs in South Africa's Beaufort Formation (part of the Karoo Supergroup, which covers most of South Africa), which is rich in vertebrate fossils, including therapsids (reptile/mammal transitional forms).[64] It has been estimated that this formation contains 800 billion vertebrate fossils.[65]

Limitations

The fossil record is an important source for scientists when tracing the evolutionary history of organisms. However, because of limitations inherent in the record, there are not fine scales of intermediate forms between related groups of species. This lack of continuous fossils in the record is a major limitation in tracing the descent of biological groups. When transitional fossils are found that show intermediate forms in what had previously been a gap in knowledge, they are often popularly referred to as "missing links".
There is a gap of about 100 million years between the beginning of the Cambrian period and the end of the Ordovician period. The early Cambrian period was the period from which numerous fossils of sponges, cnidarians (e.g., jellyfish), echinoderms (e.g., eocrinoids), molluscs (e.g., snails) and arthropods (e.g., trilobites) are found. The first animal that possessed the typical features of vertebrates, the Arandaspis, was dated to have existed in the later Ordovician period. Thus few, if any, fossils of an intermediate type between invertebrates and vertebrates have been found, although likely candidates
include the Burgess Shale animal, Pikaia gracilens,[66] and its Maotianshan shales relatives, Myllokunmingia, Yunnanozoon, Haikouella lanceolata,[67] and Haikouichthys.[68]

Some of the reasons for the incompleteness of fossil records are:
  • In general, the probability that an organism becomes fossilized is very low;
  • Some species or groups are less likely to become fossils because they are soft-bodied;
  • Some species or groups are less likely to become fossils because they live (and die) in conditions that are not favourable for fossilization;
  • Many fossils have been destroyed through erosion and tectonic movements;
  • Most fossils are fragmentary;
  • Some evolutionary change occurs in populations at the limits of a species' ecological range, and as these populations are likely small, the probability of fossilization is lower (see punctuated equilibrium);
  • Similarly, when environmental conditions change, the population of a species is likely to be greatly reduced, such that any evolutionary change induced by these new conditions is less likely to be fossilized;
  • Most fossils convey information about external form, but little about how the organism functioned;
  • Using present-day biodiversity as a guide, this suggests that the fossils unearthed represent only a small fraction of the large number of species of organisms that lived in the past.

Specific examples

Evolution of the horse

Evolution of the horse showing reconstruction of the fossil species obtained from successive rock strata. The foot diagrams are all front views of the left forefoot. The third metacarpal is shaded throughout. The teeth are shown in longitudinal section.

Due to an almost-complete fossil record found in North American sedimentary deposits from the early Eocene to the present, the horse provides one of the best examples of evolutionary history (phylogeny).
This evolutionary sequence starts with a small animal called Hyracotherium (commonly referred to as Eohippus), which lived in North America about 54 million years ago then spread across to Europe and Asia. Fossil remains of Hyracotherium show it to have differed from the modern horse in three important respects: it was a small animal (the size of a fox), lightly built and adapted for running; the limbs were short and slender, and the feet elongated so that the digits were almost vertical, with four digits in the forelimbs and three digits in the hindlimbs; and the incisors were small, the molars having low crowns with rounded cusps covered in enamel.[69]

The probable course of development of horses from Hyracotherium to Equus (the modern horse) involved at least 12 genera and several hundred species. The major trends seen in the development of the horse to changing environmental conditions may be summarized as follows:
  • Increase in size (from 0.4 m to 1.5 m — from 15in to 60in);
  • Lengthening of limbs and feet;
  • Reduction of lateral digits;
  • Increase in length and thickness of the third digit;
  • Increase in width of incisors;
  • Replacement of premolars by molars; and
  • Increases in tooth length, crown height of molars.
Fossilized plants found in different strata show that the marshy, wooded country in which Hyracotherium lived became gradually drier. Survival now depended on the head being in an elevated position for gaining a good view of the surrounding countryside, and on a high turn of speed for escape from predators, hence the increase in size and the replacement of the splayed-out foot by the hoofed foot. The drier, harder ground would make the original splayed-out foot unnecessary for support. The changes in the teeth can be explained by assuming that the diet changed from soft vegetation to grass. A dominant genus from each geological period has been selected to show the slow alteration of the horse lineage from its ancestral to its modern form.[70]

Transition from fish to amphibians

Prior to 2004, paleontologists had found fossils of amphibians with necks, ears, and four legs, in rock no older than 365 million years old. In rocks more than 385 million years old they could only find fish, without these amphibian characteristics. Evolutionary theory predicted that since amphibians evolved from fish, an intermediate form should be found in rock dated between 365 and 385 million years ago. Such an intermediate form should have many fish-like characteristics, conserved from 385 million years ago or more, but also have many amphibian characteristics as well. In 2004, an expedition to islands in the Canadian arctic searching specifically for this fossil form in rocks that were 375 million years old discovered fossils of Tiktaalik.[71] Some years later, however, scientists in Poland found evidence of fossilised tetrapod tracks predating Tiktaalik.[72]

Evidence from geographical distribution

Data about the presence or absence of species on various continents and islands (biogeography) can provide evidence of common descent and shed light on patterns of speciation.

Continental distribution

All organisms are adapted to their environment to a greater or lesser extent. If the abiotic and biotic factors within a habitat are capable of supporting a particular species in one geographic area, then one might assume that the same species would be found in a similar habitat in a similar geographic area, e.g. in Africa and South America. This is not the case. Plant and animal species are discontinuously distributed throughout the world:
Even greater differences can be found if Australia is taken into consideration, though it occupies the same latitude as much of South America and Africa. Marsupials like kangaroos, bandicoots, and quolls make up about half of Australia's indigenous mammal species.[74] By contrast, marsupials are today totally absent from Africa and form a smaller portion of the mammalian fauna of South America, where opossums, shrew opossums, and the monito del monte occur. The only living representatives of primitive egg-laying mammals (monotremes) are the echidnas and the platypus. The short-beaked echidna (Tachyglossus aculeatus) and its subspecies populate Australia, Tasmania, New Guinea, and Kangaroo Island while the long-beaked echidna (Zaglossus bruijni) lives only in New Guinea. The platypus lives in the waters of eastern Australia. They have been introduced to Tasmania, King Island, and Kangaroo Island. These Monotremes are totally absent in the rest of the world.[75] On the other hand, Australia is missing many groups of placental mammals that are common on other continents (carnivorans, artiodactyls, shrews, squirrels, lagomorphs), although it does have indigenous bats and murine rodents; many other placentals, such as rabbits and foxes, have been introduced there by humans.

Other animal distribution examples include bears, located on all continents excluding Africa, Australia and Antarctica, and the polar bear only located solely in the Arctic Circle and adjacent land masses.[76] Penguins are located only around the South Pole despite similar weather conditions at the North Pole. Families of sirenians are distributed exclusively around the earth’s waters, where manatees are located in western Africa waters, northern South American waters, and West Indian waters only while the related family, the Dugongs, are located only in Oceanic waters north of Australia, and the coasts surrounding the Indian Ocean Additionally, the now extinct Steller's Sea Cow resided in the Bering Sea.[77]

The same kinds of fossils are found from areas known to be adjacent to one another in the past but that, through the process of continental drift, are now in widely divergent geographic locations. For example, fossils of the same types of ancient amphibians, arthropods and ferns are found in South America, Africa, India, Australia and Antarctica, which can be dated to the Paleozoic Era, when these regions were united as a single landmass called Gondwana.[78] Sometimes the descendants of these organisms can be identified and show unmistakable similarity to each other, even though they now inhabit very different regions and climates.

Island biogeography

Four of the 13 finch species found on the Galápagos Archipelago, have evolved by an adaptive radiation that diversified their beak shapes to adapt them to different food sources.

Types of species found on islands

Evidence from island biogeography has played an important and historic role in the development of evolutionary biology. For purposes of biogeography, islands are divided into two classes. Continental islands are islands like Great Britain, and Japan that have at one time or another been part of a continent. Oceanic islands, like the Hawaiian islands, the Galapagos islands and St. Helena, on the other hand are islands that have formed in the ocean and never been part of any continent. Oceanic islands have distributions of native plants and animals that are unbalanced in ways that make them distinct from the biotas found on continents or continental islands. Oceanic islands do not have native terrestrial mammals (they do sometimes have bats and seals), amphibians, or fresh water fish. In some cases they have terrestrial reptiles (such as the iguanas and giant tortoises of the Galapagos islands) but often (for example Hawaii) they do not. This despite the fact that when species such as rats, goats, pigs, cats, mice, and cane toads, are introduced to such islands by humans they often thrive. Starting with Charles Darwin, many scientists have conducted experiments and made observations that have shown that the types of animals and plants found, and not found, on such islands are consistent with the theory that these islands were colonized accidentally by plants and animals that were able to reach them. Such accidental colonization could occur by air, such as plant seeds carried by migratory birds, or bats and insects being blown out over the sea by the wind, or by floating from a continent or other island by sea, as for example by some kinds of plant seeds like coconuts that can survive immersion in salt water, and reptiles that can survive for extended periods on rafts of vegetation carried to sea by storms.[79]

Endemism

Many of the species found on remote islands are endemic to a particular island or group of islands, meaning they are found nowhere else on earth. Examples of species endemic to islands include many flightless birds of New Zealand, lemurs of Madagascar, the Komodo dragon of Komodo,[80] the Dragon’s blood tree of Socotra,[81] Tuatara of New Zealand,[82][83] and others. However many such endemic species are related to species found on other nearby islands or continents; the relationship of the animals found on the Galapagos Islands to those found in South America is a well-known example.[79]
All of these facts, the types of plants and animals found on oceanic islands, the large number of endemic species found on oceanic islands, and the relationship of such species to those living on the nearest continents, are most easily explained if the islands were colonized by species from nearby continents that evolved into the endemic species now found there.[79]

Other types of endemism do not have to include, in the strict sense, islands. Islands can mean isolated lakes or remote and isolated areas. Examples of these would include the highlands of Ethiopia, Lake Baikal, Fynbos of South Africa, forests of New Caledonia, and others. Examples of endemic organisms living in isolated areas include the Kagu of New Caledonia,[84] cloud rats of the Luzon tropical pine forests of the Philippines,[85][86] the boojum tree (Fouquieria columnaris) of the Baja California peninsula,[87] the Baikal Seal[88] and the omul of Lake Baikal.

Adaptive radiations

Oceanic islands are frequently inhabited by clusters of closely related species that fill a variety of ecological niches, often niches that are filled by very different species on continents. Such clusters, like the Finches of the Galapagos, Hawaiian honeycreepers, members of the sunflower family on the Juan Fernandez Archipelago and wood weevils on St. Helena are called adaptive radiations because they are best explained by a single species colonizing an island (or group of islands) and then diversifying to fill available ecological niches. Such radiations can be spectacular; 800 species of the fruit fly family Drosophila, nearly half the world's total, are endemic to the Hawaiian islands. Another illustrative example from Hawaii is the Silversword alliance, which is a group of thirty species found only on those islands. Members range from the Silverswords that flower spectacularly on high volcanic slopes to trees, shrubs, vines and mats that occur at various elevations from mountain top to sea level, and in Hawaiian habitats that vary from deserts to rainforests. Their closest relatives outside Hawaii, based on molecular studies, are tarweeds found on the west coast of North America. These tarweeds have sticky seeds that facilitate distribution by migrant birds.[89] Additionally, nearly all of the species on the island can be crossed and the hybrids are often fertile,[41] and they have been hybridized experimentally with two of the west coast tarweed species as well.[90] Continental islands have less distinct biota, but those that have been long separated from any continent also have endemic species and adaptive radiations, such as the 75 lemur species of Madagascar, and the eleven extinct moa species of New Zealand.[79][91]

Ring species

Ensatina salamander which forms a ring around the Californian Central Valley

In biology, a ring species is a connected series of neighboring populations that can interbreed with relatively closely related populations, but for which there exist at least two "end" populations in the series that are too distantly related to interbreed. Often such non-breeding-though-genetically-connected populations co-exist in the same region thus creating a "ring". Ring species provide important evidence of evolution in that they illustrate what happens over time as populations genetically diverge, and are special because they represent in living populations what normally happens over time between long deceased ancestor populations and living populations. If any of the populations intermediate between the two ends of the ring were gone they would not be a continuous line of reproduction and each side would be a different species.[92][93]

Specific examples

Figure 6a: Current distribution of Glossopteris placed on a Permian map showing the connection of the continents. (1, South America; 2, Africa; 3, Madagascar; 4, India; 5, Antarctica; and 6, Australia)
Figure 6b: Present day distribution of marsupials. (Distribution shown in blue. Introduced areas shown in green.)
Figure 6c: A dymaxion map of the world showing the distribution of present species of camelid. The solid black lines indicate migration routes and the blue represents current camel locations.

Distribution of Glossopteris

The combination of continental drift and evolution can sometimes be used to predict what will be found in the fossil record. Glossopteris is an extinct species of seed fern plants from the Permian. Glossopteris appears in the fossil record around the beginning of the Permian on the ancient continent of Gondwana.[94] Continental drift explains the current biogeography of the tree. Present day Glossopteris fossils are found in Permian strata in southeast South America, southeast Africa, all of Madagascar, northern India, all of Australia, all of New Zealand, and scattered on the southern and northern edges of Antarctica. During the Permian, these continents were connected as Gondwana (see figure 6a) in agreement with magnetic striping, other fossil distributions, and glacial scratches pointing away from the temperate climate of the South Pole during the Permian.[79][95]

Distribution of marsupials

The history of marsupials also provides an example of how the theories of evolution and continental drift can be combined to make predictions about what will be found in the fossil record. The oldest metatherian fossils (Metatheria being a larger clade that groups marsupials with some of their extinct relatives) are found in present-day China.[96] Metatherians spread westward into modern North America (still attached to Eurasia) and then to South America, which was connected to North America until around 65 mya. Marsupials reached Australia via Antarctica about 50 mya, shortly after Australia had split off suggesting a single dispersion event of just one species.[97]

The theory of evolution suggests that the Australian marsupials descended from the older ones found in the Americas. The theory of continental drift says that between 30 and 40 million years ago South America and Australia were still part of the Southern hemisphere super continent of Gondwana and that they were connected by land that is now part of Antarctica. Therefore combining the two theories scientists predicted that marsupials migrated from what is now South America across what is now Antarctica to what is now Australia between 40 and 30 million years ago. A first marsupial fossil of the extinct family Polydolopidae was found on Seymour Island on the Antarctic Peninsula in 1982.[98] Further fossils have subsequently been found, including members of the marsupial orders Didelphimorphia (opossum) and Microbiotheria,[99] as well as ungulates and a member of the enigmatic extinct order Gondwanatheria, possibly Sudamerica ameghinoi.[100][101][102]

Migration, isolation, and distribution of the Camel

The history of the camel provides an example of how fossil evidence can be used to reconstruct migration and subsequent evolution. The fossil record indicates that the evolution of camelids started in North America (see figure 6c), from which, six million years ago, they migrated across the Bering Strait into Asia and then to Africa, and 3.5 million years ago through the Isthmus of Panama into South America. Once isolated, they evolved along their own lines, giving rise to the Bactrian camel and Dromedary in Asia and Africa and the llama and its relatives in South America. Camelids then went extinct in North America at the end of the last ice age.[103]

Evidence from observed natural selection

Examples for the evidence for evolution often stem from direct observation of natural selection in the field and the laboratory. Scientists have observed and documented a multitude of events where natural selection is in action. The most well known examples are antibiotic resistance in the medical field along with better-known laboratory experiments documenting evolution's occurrence. Natural selection is tantamount to common descent in that long-term occurrence and selection pressures can lead to the diversity of life on earth as found today. All adaptations—documented and undocumented changes concerned—are caused by natural selection (and a few other minor processes). The examples below are only a small fraction of the actual experiments and observations.

Specific examples of natural selection in the lab and in the field

Antibiotic and pesticide resistance

The development and spread of antibiotic-resistant bacteria, like the spread of pesticide-resistant forms of plants and insects, is evidence for evolution of species, and of change within species. Thus the appearance of vancomycin-resistant Staphylococcus aureus, and the danger it poses to hospital patients, is a direct result of evolution through natural selection. The rise of Shigella strains resistant to the synthetic antibiotic class of sulfonamides also demonstrates the generation of new information as an evolutionary process.[104] Similarly, the appearance of DDT resistance in various forms of Anopheles mosquitoes, and the appearance of myxomatosis resistance in breeding rabbit populations in Australia, are both evidence of the existence of evolution in situations of evolutionary selection pressure in species in which generations occur rapidly.

E. coli long-term evolution experiment

Experimental evolution uses controlled experiments to test hypotheses and theories of evolution. In one early example, William Dallinger set up an experiment shortly before 1880, subjecting microbes to heat with the aim of forcing adaptive changes. His experiment ran for around seven years, and his published results were acclaimed, but he did not resume the experiment after the apparatus failed.[105]
Richard Lenski observed that some strains of E. coli evolved a complex new ability, the ability to metabolize citrate, after tens of thousands of generations.[106][107] The evolutionary biologist Jerry Coyne commented, saying, "the thing I like most is it says you can get these complex traits evolving by a combination of unlikely events. That's just what creationists say can't happen."[106][108] The E. coli long-term evolution experiment that began in 1988 is still in progress, and has shown adaptations including the evolution of a strain of E. coli that was able to grow on citric acid in the growth media—a trait absent in all other known forms of E. coli, including the initial strain.

Humans

Natural selection is observed in contemporary human populations, with recent findings demonstrating that the population at risk of the severe debilitating disease kuru has significant over-representation of an immune variant of the prion protein gene G127V versus non-immune alleles. Scientists postulate one of the reasons for the rapid selection of this genetic variant is the lethality of the disease in non-immune persons.[109][110] Other reported evolutionary trends in other populations include a lengthening of the reproductive period, reduction in cholesterol levels, blood glucose and blood pressure.[111]

Lactose tolerance in humans

Lactose intolerance is the inability to metabolize lactose, because of a lack of the required enzyme lactase in the digestive system. The normal mammalian condition is for the young of a species to experience reduced lactase production at the end of the weaning period (a species-specific length of time). In humans, in non-dairy consuming societies, lactase production usually drops about 90% during the first four years of life, although the exact drop over time varies widely.[112] However, certain human populations have a mutation on chromosome 2 that eliminates the shutdown in lactase production, making it possible for members of these populations to continue consumption of raw milk and other fresh and fermented dairy products throughout their lives without difficulty. This appears to be an evolutionarily recent (around 7,000 years) adaptation to dairy consumption, and has occurred independently in both northern Europe and east Africa in populations with a historically pastoral lifestyle.[113][114]

Nylon-eating bacteria

Nylon-eating bacteria are a strain of Flavobacterium that are capable of digesting certain byproducts of nylon 6 manufacture. There is scientific consensus that the capacity to synthesize nylonase most probably developed as a single-step mutation that survived because it improved the fitness of the bacteria possessing the mutation. This is seen as a good example of evolution through mutation and natural selection that has been observed as it occurs.[115][116][117][118]

PCB tolerance

After General Electric dumped polychlorinated biphenyls (PCBs) in the Hudson River from 1947 through 1976, tomcods living in the river were found to have evolved an increased resistance to the compound's toxic effects.[119] At first, the tomcod population was devastated, but it recovered. Scientists identified the genetic mutation that conferred the resistance. The mutated form was present in 99 per cent of the surviving tomcods in the river, compared to fewer than 10 percent of the tomcods from other waters.[119]

Peppered moth

One classic example of adaptation in response to selection pressure is the case of the peppered moth. The color of the moth has gone from light to dark to light again over the course of a few hundred years due to the appearance and later disappearance of pollution from the Industrial Revolution in England.

Radiotrophic fungus

Radiotrophic fungi are fungi that appear to use the pigment melanin to convert gamma radiation into chemical energy for growth[120][121] and were first discovered in 2007 as black molds growing inside and around the Chernobyl Nuclear Power Plant.[120] Research at the Albert Einstein College of Medicine showed that three melanin-containing fungi, Cladosporium sphaerospermum, Wangiella dermatitidis, and Cryptococcus neoformans, increased in biomass and accumulated acetate faster in an environment in which the radiation level was 500 times higher than in the normal environment.

Urban wildlife

Urban wildlife is wildlife that is able to live or thrive in urban environments. These types of environments can exert selection pressures on organisms, often leading to new adaptations. For example, the weed Crepis sancta, found in France, has two types of seed, heavy and fluffy. The heavy ones land nearby to the parent plant, whereas fluffy seeds float further away on the wind. In urban environments, seeds that float far often land on infertile concrete. Within about 5–12 generations, the weed evolves to produce significantly heavier seeds than its rural relatives.[122][123] Other examples of urban wildlife are rock pigeons and species of crows adapting to city environments around the world; African penguins in Simon's Town; baboons in South Africa; and a variety of insects living in human habitations.

Evidence from speciation

Speciation is the evolutionary process by which new biological species arise. Speciation can occur from a variety of different causes and are classified in various forms (e.g. allopatric, sympatric, polyploidization, etc.). Scientists have observed numerous examples of speciation in the laboratory and in nature, however, evolution has produced far more species than an observer would consider necessary.
For example, there are well over 350,000 described species of beetles.[124] Great examples of observed speciation come from the observations of island biogeography and the process of adaptive radiation, both explained in an earlier section. The examples shown below provide strong evidence for common descent and are only a small fraction of the instances observed.

Specific examples

Blackcap

The bird species, Sylvia atricapilla, commonly referred to as Blackcaps, lives in Germany and flies southwest to Spain while a smaller group flies northwest to Great Britain during the winter. Gregor Rolshausen from the University of Freiburg found that the genetic separation of the two populations is already in progress. The differences found have arisen in about 30 generations. With DNA sequencing, the individuals can be assigned to a correct group with an 85% accuracy. Stuart Bearhop from the University of Exeter reported that birds wintering in England tend to mate only among themselves, and not usually with those wintering in the Mediterranean.[125] It is still inference to say that the populations will become two different species, but researchers expect it due to the continued genetic and geographic separation.[126]

Drosophila melanogaster

A common fruit fly (Drosophila melanogaster).

William R. Rice and George W. Salt found experimental evidence of sympatric speciation in the common fruit fly. They collected a population of Drosophila melanogaster from Davis, California and placed the pupae into a habitat maze. Newborn flies had to investigate the maze to find food. The flies had three choices to take in finding food. Light and dark (phototaxis), up and down (geotaxis), and the scent of acetaldehyde and the scent of ethanol (chemotaxis) were the three options. This eventually divided the flies into 42 spatio-temporal habitats.

They then cultured two strains that chose opposite habitats. One of the strains emerged early, immediately flying upward in the dark attracted to the acetaldehyde. The other strain emerged late and immediately flew downward, attracted to light and ethanol. Pupae from the two strains were then placed together in the maze and allowed to mate at the food site. They then were collected. A selective penalty was imposed on the female flies that switched habitats. This entailed that none of their gametes would pass on to the next generation. After 25 generations of this mating test, it showed reproductive isolation between the two strains. They repeated the experiment again without creating the penalty against habitat switching and the result was the same; reproductive isolation was produced.[127][128][129]

Hawthorn fly

One example of evolution at work is the case of the hawthorn fly, Rhagoletis pomonella, also known as the apple maggot fly, which appears to be undergoing sympatric speciation.[130] Different populations of hawthorn fly feed on different fruits. A distinct population emerged in North America in the 19th century some time after apples, a non-native species, were introduced. This apple-feeding population normally feeds only on apples and not on the historically preferred fruit of hawthorns. The current hawthorn feeding population does not normally feed on apples. Some evidence, such as the fact that six out of thirteen allozyme loci are different, that hawthorn flies mature later in the season and take longer to mature than apple flies; and that there is little evidence of interbreeding (researchers have documented a 4–6% hybridization rate) suggests that speciation is occurring.[131][132][133][134][135]

London Underground mosquito

The London Underground mosquito is a species of mosquito in the genus Culex found in the London Underground. It evolved from the overground species Culex pipiens.

This mosquito, although first discovered in the London Underground system, has been found in underground systems around the world. It is suggested that it may have adapted to human-made underground systems since the last century from local above-ground Culex pipiens,[136] although more recent evidence suggests that it is a southern mosquito variety related to Culex pipiens that has adapted to the warm underground spaces of northern cities.[137]

The species have very different behaviours,[138] are extremely difficult to mate,[136] and with different allele frequency, consistent with genetic drift during a founder event.[139] More specifically, this mosquito, Culex pipiens molestus, breeds all-year round, is cold intolerant, and bites rats, mice, and humans, in contrast to the above ground species Culex pipiens that is cold tolerant, hibernates in the winter, and bites only birds. When the two varieties were cross-bred the eggs were infertile suggesting reproductive isolation.[136][138]

The genetic data indicates that the molestus form in the London Underground mosquito appears to have a common ancestry, rather than the population at each station being related to the nearest aboveground population (i.e. the pipiens form). Byrne and Nichols' working hypothesis was that adaptation to the underground environment had occurred locally in London only once.

These widely separated populations are distinguished by very minor genetic differences, which suggest that the molestus form developed: a single mtDNA difference shared among the underground populations of ten Russian cities;[140] a single fixed microsatellite difference in populations spanning Europe, Japan, Australia, the middle East and Atlantic islands.[137]

Mollies

The Shortfin Molly (Poecilia mexicana) is a small fish that lives in the Sulfur Caves of Mexico. Years of study on the species have found that two distinct populations of mollies—the dark interior fish and the bright surface water fish—are becoming more genetically divergent.[141] The populations have no obvious barrier separating the two; however, it was found that the mollies are hunted by a large water bug (Belostoma spp). Tobler collected the bug and both types of mollies, placed them in large plastic bottles, and put them back in the cave. After a day, it was found that, in the light, the cave-adapted fish endured the most damage, with four out of every five stab-wounds from the water bugs sharp mouthparts. In the dark, the situation was the opposite. The mollies’ senses can detect a predator’s threat in their own habitats, but not in the other ones. Moving from one habitat to the other significantly increases the risk of dying. Tobler plans on further experiments, but believes that it is a good example of the rise of a new species.[142]\

Polar bear

A remarkable example of natural selection, geographic isolation, and speciation in progress is the relationship of the polar bear (Ursus maritimus) and the brown bear (Ursus arctos). Once thought to be two entirely different species, recent evidence suggests that both bears can interbreed and produce fertile offspring. Molecular data gives estimates of a divergence time ranging from 70,000 to 1.5 million years ago. The oldest known fossil evidence of polar bears dates around 100,000 years ago. Scientists hypothesize that around 200,000 years ago (when the Arctic Ocean was entirely covered with ice and the earth was at its near-glacial maximum), glaciers isolated a population of brown bears (approximately 125,000 years ago) of which evolved over time adapting to their environment.[143] This process is known as allopatric speciation. The bears acquired significant physiological differences from the brown bear allowing the polar bear to comfortably survive in conditions that the brown bear could not. The ability to swim sixty miles or more at a time in freezing waters, to blend in with the snow, and to stay warm in the arctic environment are some of the adaptations of the polar bear. Additionally, the elongation of the neck makes it easier to keep their heads above water while swimming alongside the oversized webbed feet that act as paddles when swimming. The polar bear has also evolved small papillae and vacuole-like suction cups on the soles to make them less likely to slip on the ice alongside the fact that their feet have become covered with heavy matting to protect the bottoms from intense cold and to provide traction. They also have smaller ears for a reduction of heat loss, eyelids that act like sunglasses, accommodations for their all-meat diet, a large stomach capacity to enable opportunistic feeding, and the ability to fast for up to nine months while recycling their urea.[144][145] Despite all these differing traits, the two bear species have now been reunited due to the warming of the Arctic and the receding glaciers. Surprisingly, the bears can interbreed but Ursus maritimus is considered a subspecies of Ursus arctos. This example presents a macro-evolutionary change involving an amalgamation of several fields of evolutionary biology, e.g. adaptation through natural selection, geographic isolation, speciation, and hybridization.

Thale cress

Arabidopsis thaliana (colloquially known as thale cress, mouse-ear cress or Arabidopsis).

Kirsten Bomblies et al. from the Max Planck Institute for Developmental Biology discovered that two genes passed down by each parent of the thale cress plant, Arabidopsis thaliana. When the genes are passed down, it ignites a reaction in the hybrid plant that turns its own immune system against it. In the parents, the genes were not detrimental, but they evolved separately to react defectively when combined.[146]

To test this, Bomblies crossed 280 genetically different strains of Arabidopsis in 861 distinct ways and found that 2 per cent of the resulting hybrids were necrotic. Along with allocating the same indicators, the 20 plants also shared a comparable collection of genetic activity in a group of 1,080 genes. In almost all of the cases, Bomblies discovered that only two genes were required to cause the autoimmune response. Bomblies looked at one hybrid in detail and found that one of the two genes belonged to the NB-LRR class, a common group of disease resistance genes involved in recognizing new infections. When Bomblies removed the problematic gene, the hybrids developed normally.[146]

Over successive generations, these incompatibilities could create divisions between different plant strains, reducing their chances of successful mating and turning distinct strains into separate species.[147]

Interspecies fertility or hybridization

Understood from laboratory studies and observed instances of speciation in nature, finding species that are able reproduce successfully or create hybrids between two different species infers that their relationship is close. In conjunction with this, hybridization has been found to be a precursor to the creation of new species by being a source of new genes for a species. The examples provided are only a small fraction of the observed instances of speciation through hybridization. Plants are often subject to the creation of a new species though hybridization.

Mimulus peregrinus

The creation of a new allopolyploid species (Mimulus peregrinus) was observed on the banks of the Shortcleuch Water—a river in Leadhills, South Lanarkshire, Scotland. Parented from the cross of the two species Mimulus guttatus (containing 14 pairs of chromosomes) and Mimulus luteus (containing 30-31 pairs from a chromosome duplication), M. peregrinus has six copies of its chromosomes (caused by the duplication of the sterile hybrid triploid). Due to the nature of these species, they have the ability to self-fertilize. Because of its number of chromosomes it is not able to pair with M. guttatus, M. luteus, or their sterile triploid offspring. M. peregrinus will either die producing no offspring or reproduce with itself making a new species.[148]

Raphanobrassica

Raphanobrassica includes all intergeneric hybrids between the genera Raphanus (radish) and Brassica (cabbages, etc.).[149][150]

The Raphanobrassica is an allopolyploid cross between the radish (Raphanus sativus) and cabbage (Brassica oleracea). Plants of this parentage are now known as radicole. Two other fertile forms of Raphanobrassica are known. Raparadish, an allopolyploid hybrid between Raphanus sativus and Brassica rapa is grown as a fodder crop. "Raphanofortii" is the allopolyploid hybrid between Brassica tournefortii and Raphanus caudatus.

The Raphanobrassica is a fascinating plant, because (in spite of its hybrid nature), it is not sterile. This has led some botanists to propose that the accidental hybridization of a flower by pollen of another species in nature could be a mechanism of speciation common in higher plants.

Salsify

Purple Salsify, Tragopogon porrifolius

Tragopogon is one example where hybrid speciation has been observed. In the early 20th century, humans introduced three species of salsify into North America. These species, the western salsify (Tragopogon dubius), the meadow salsify (Tragopogon pratensis), and the oyster plant (Tragopogon porrifolius), are now common weeds in urban wastelands. In the 1950s, botanists found two new species in the regions of Idaho and Washington, where the three already known species overlapped. One new species, Tragopogon miscellus, is a tetraploid hybrid of T. dubius and T. pratensis. The other new species, Tragopogon mirus, is also an allopolyploid, but its ancestors were T. dubius and T. porrifolius. These new species are usually referred to as "the Ownbey hybrids" after the botanist who first described them. The T. mirus population grows mainly by reproduction of its own members, but additional episodes of hybridization continue to add to the T. mirus population.[151]

T. dubius and T. pratensis mated in Europe but were never able to hybridize. A study published in March 2011 found that when these two plants were introduced to North America in the 1920s, they mated and doubled the number of chromosomes in there hybrid Tragopogon miscellus allowing for a "reset" of its genes, which in turn, allows for greater genetic variation. Professor Doug Soltis of the University of Florida said, "We caught evolution in the act…New and diverse patterns of gene expression may allow the new species to rapidly adapt in new environments".[152][153] This observable event of speciation through hybridization further advances the evidence for the common descent of organisms and the time frame in which the new species arose in its new environment. The hybridizations have been reproduced artificially in laboratories from 2004 to present day.

Welsh groundsel

Welsh groundsel is an allopolyploid, a plant that contains sets of chromosomes originating from two different species. Its ancestor was Senecio × baxteri, an infertile hybrid that can arise spontaneously when the closely related groundsel (Senecio vulgaris) and Oxford ragwort (Senecio squalidus) grow alongside each other. Sometime in the early 20th century, an accidental doubling of the number of chromosomes in an S. × baxteri plant led to the formation of a new fertile species.[154][155]

York groundsel

The York groundsel (Senecio eboracensis) is a hybrid species of the self-incompatible Senecio squalidus (also known as Oxford ragwort) and the self-compatible Senecio vulgaris (also known as Common groundsel). Like S. vulgaris, S. eboracensis is self-compatible, however, it shows little or no natural crossing with its parent species, and is therefore reproductively isolated, indicating that strong breed barriers exist between this new hybrid and its parents.

It resulted from a backcrossing of the F1 hybrid of its parents to S. vulgaris. S. vulgaris is native to Britain, while S. squalidus was introduced from Sicily in the early 18th century; therefore, S. eboracensis has speciated from those two species within the last 300 years.

Other hybrids descended from the same two parents are known. Some are infertile, such as S. x baxteri. Other fertile hybrids are also known, including S. vulgaris var. hibernicus, now common in Britain, and the allohexaploid S. cambrensis, which according to molecular evidence probably originated independently at least three times in different locations. Morphological and genetic evidence support the status of S. eboracensis as separate from other known hybrids.[156]

Evidence from artificial selection

The Chihuahua mix and Great Dane illustrate the range of sizes among dog breeds.

Artificial selection demonstrates the diversity that can exist among organisms that share a relatively recent common ancestor. In artificial selection, one species is bred selectively at each generation, allowing only those organisms that exhibit desired characteristics to reproduce. These characteristics become increasingly well developed in successive generations. Artificial selection was successful long before science discovered the genetic basis. Examples of artificial selection would be dog breeding, genetically modified food, flower breeding, cultivation of foods such as wild cabbage,[157] and others.

Evidence from computation and mathematical iteration

Computer science allows the iteration of self-changing complex systems to be studied, allowing a mathematical understanding of the nature of the processes behind evolution; providing evidence for the hidden causes of known evolutionary events. The evolution of specific cellular mechanisms like spliceosomes that can turn the cell's genome into a vast workshop of billions of interchangeable parts that can create tools that create tools that create tools that create us can be studied for the first time in an exact way.

"It has taken more than five decades, but the electronic computer is now powerful enough to simulate evolution,"[158] assisting bioinformatics in its attempt to solve biological problems.

Computational evolutionary biology has enabled researchers to trace the evolution of a large number of organisms by measuring changes in their DNA, rather than through physical taxonomy or physiological observations alone. It has compared entire genomes permitting the study of more complex evolutionary events, such as gene duplication, horizontal gene transfer, and the prediction of factors important in speciation. It has also helped build complex computational models of populations to predict the outcome of the system over time and track and share information on an increasingly large number of species and organisms.

Future endeavors are to reconstruct a now more complex tree of life.

Christoph Adami, a professor at the Keck Graduate Institute made this point in Evolution of biological complexity:
To make a case for or against a trend in the evolution of complexity in biological evolution, complexity must be both rigorously defined and measurable. A recent information-theoretic (but intuitively evident) definition identifies genomic complexity with the amount of information a sequence stores about its environment. We investigate the evolution of genomic complexity in populations of digital organisms and monitor in detail the evolutionary transitions that increase complexity. We show that, because natural selection forces genomes to behave as a natural "Maxwell Demon", within a fixed environment, genomic complexity is forced to increase.[159]
David J. Earl and Michael W. Deem—professors at Rice University made this point in Evolvability is a selectable trait:
Not only has life evolved, but life has evolved to evolve. That is, correlations within protein structure have evolved, and mechanisms to manipulate these correlations have evolved in tandem. The rates at which the various events within the hierarchy of evolutionary moves occur are not random or arbitrary but are selected by Darwinian evolution. Sensibly, rapid or extreme environmental change leads to selection for greater evolvability. This selection is not forbidden by causality and is strongest on the largest-scale moves within the mutational hierarchy. Many observations within evolutionary biology, heretofore considered evolutionary happenstance or accidents, are explained by selection for evolvability. For example, the vertebrate immune system shows that the variable environment of antigens has provided selective pressure for the use of adaptable codons and low-fidelity polymerases during somatic hypermutation. A similar driving force for biased codon usage as a result of productively high mutation rates is observed in the hemagglutinin protein of influenza A.[160]
"Computer simulations of the evolution of linear sequences have demonstrated the importance of recombination of blocks of sequence rather than point mutagenesis alone. Repeated cycles of point mutagenesis, recombination, and selection should allow in vitro molecular evolution of complex sequences, such as proteins."[161] Evolutionary molecular engineering, also called directed evolution or in vitro molecular evolution involves the iterated cycle of mutation, multiplication with recombination, and selection of the fittest of individual molecules (proteins, DNA, and RNA). Natural evolution can be relived showing us possible paths from catalytic cycles based on proteins to based on RNA to based on DNA.[161][162][163][164]

Specific examples

Avida simulation

Richard Lenski, Charles Ofria, et al. at Michigan State University developed an artificial life computer program with the ability to detail the evolution of complex systems. The system uses values set to determine random mutations and allows for the effect of natural selection to conserve beneficial traits. The program was dubbed Avida and starts with an artificial petri dish where organisms reproduce and perform mathematical calculations to acquire rewards of more computer time for replication. The program randomly adds mutations to copies of the artificial organisms to allow for natural selection. As the artificial life reproduced, different lines adapted and evolved depending on their set environments. The beneficial side to the program is that it parallels that of real life at rapid speeds.[165][166][167]

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