Refuting Creationism - How Biologists Are Refining The Theory of Evolution, Not Abadonning It In Favour Of Creationist Superstition

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New Findings Supply Corrective to Evolutionary Hypothesis | University of Arkansas

In a highly mathematical, technical paper in PLOS Computational Biology, some of which is above my paygrade, two evolutionary biologists have shown that the mysterious apparent accelerations in the rate of evolution are actually due to statistical 'noise' and not a true change in the rate of evolution.

I've written this post about it not because it contains some interesting information about biological organisms or that long history of life on Earth that occurred before creationists believe their little god created a small, flat planet with a dome over it, but so creationists can see how badly they've been deceived by their cult leaders who have been telling them for the best part of half a century that mainstream biologists are about to abandon the Theory of Evolution any day now in favour of their childish superstition with its magical creation of species without ancestors.

Of course, to a simplistic, black vs white thinking creationist, any discovery that scientists might have been wrong about some precise detail or others can be presented as 'proof' that the entire body of science is wrong, but that's not how grown-up science works. To think otherwise is the equivalent of thinking having to detour round a parked car in a road means the satnav is broken.

Two computational biologists have recently published evidence that the appearance of accelerated rates of evolutionary change is due to statistical 'noise' in the data* What impact would this finding have on the basic principles of evolution? If the recent findings by computational biologists suggesting that accelerated rates of evolutionary change are largely due to statistical noise are accurate, this would have several important implications for the study of evolutionary biology. However, it is essential to clarify that these findings would likely not overturn the basic principles of evolution (such as natural selection, genetic drift, mutation, and gene flow), but they could impact how we interpret the tempo and mode of evolutionary change. Here are a few possible impacts:

1. Reevaluation of Evolutionary Rates
   
   • One key implication would be that observed patterns of rapid evolutionary change (in specific lineages or over short time periods) might need to be reinterpreted. If statistical noise has exaggerated the perception of these rates, what may have seemed like "punctuated bursts" of rapid evolution could, in fact, represent more gradual processes.
   • Some models of evolution, such as punctuated equilibrium (which argues that species experience long periods of stasis interrupted by short, rapid bursts of change), might need reconsideration. While this does not disprove punctuated equilibrium, it could require revisiting the evidence used to support it.
   
   
2. Statistical Models in Evolutionary Biology
   
   • The study would emphasize the importance of statistical rigor in evolutionary studies. Researchers would need to account for and reduce noise in their data more carefully to avoid overestimating rates of evolution.
   • This might lead to more robust methods for analyzing phylogenies, genomic sequences, and fossil data to ensure that we distinguish genuine signals of evolutionary change from artifacts produced by incomplete data or noisy statistical models.
   
   
3. Impact on Molecular Evolution Studies
   
   • Much of the evidence for accelerated evolution, especially at the molecular level, comes from comparative genomics, which measures changes in DNA sequences over time. If noise in the data has been overestimating these rates, it would affect interpretations of molecular clocks, the timing of divergence events, and the rates of adaptive evolution.
   • This could also alter our understanding of when certain key evolutionary transitions took place, such as the divergence between major groups (e.g., between humans and chimpanzees) or the timing of the origin of new traits.
   
   
4. Evolutionary Innovation and Adaptation
   
   • If rapid changes are less common than previously thought, this could affect theories about the mechanisms underlying evolutionary innovation. For example, the appearance of new traits or adaptations may occur more slowly than we assumed, and might more often involve cumulative changes rather than rapid shifts.
   • This might challenge the idea that certain environmental pressures (e.g., mass extinctions or rapid climate change) consistently trigger bursts of rapid adaptation.
   
   
5. Reevaluation of Case Studies
   
   • Many well-known examples of rapid evolution (e.g., the finches of the Galápagos, the peppered moth) might need closer scrutiny to ensure that the rates of change observed in these cases are genuine and not artifacts of noisy data.
   • Fossil and genomic data used to reconstruct evolutionary timelines might also need reevaluation in light of these findings, with the possibility that some inferred rates of change may need adjustment.
   
   
6. Broader Implications for Evolutionary Theory
   
   • The fundamental concepts of evolution—such as descent with modification, natural selection, and common ancestry—would remain intact, as these are supported by a vast range of evidence beyond the statistical models of evolutionary rates. However, the findings could influence the details of how we model evolutionary dynamics, especially over short timescales or within particular lineages.
   • The study could lead to a greater emphasis on distinguishing between "real" evolutionary signals and those that result from data imperfections, potentially improving the accuracy of evolutionary research going forward.

In summary, the basic principles of evolution are robust and unlikely to be overturned by this finding. However, it could lead to more refined methods in evolutionary biology, impacting how we interpret rates of change and the timing of evolutionary events, potentially leading to more accurate evolutionary histories.

The truth is that biologists have no doubt about the basic principle of evolutionary diversification from a common ancestor, driven by environmental selectors; their debate is only over the details of the processes and how they relate to environmental changes.

The scientists' work is explained in a University of Arkansas news release:

New Findings Supply Corrective to Evolutionary Hypothesis

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For decades, researchers have observed that rates of evolution seem to accelerate over short time periods – say five million years versus fifty million years. This broad pattern has suggested that “younger” groups of organisms, in evolutionary terms, have higher rates of speciation, extinction and body size evolution, among other differences from older ones.

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Evolutionary processes appear to operate at different time scales, perhaps necessitating the need for a new theory linking microevolution and macroevolution. The larger question has tantalized scientists: why?

There are plausible explanations. A new species may inhabit a new island chain, allowing for more variation as it spreads into new niches. An asteroid may hit the earth, increasing extinction rates. Perhaps species evolve to an “optimal” trait value and then plateau.

A paper published in PLOS Computational Biology now proposes an entirely new explanation for understanding this evolutionary pattern: statistical “noise.” The paper, “Noise leads to the perceived increase in evolutionary rates over short times scales,” was written by Brian C. O’Meara, a professor in the Department of Ecology and Evolutionary Biology at the University of Tennessee, and Jeremy M. Beaulieu, an associate professor of in the Department of Biological Sciences at the University of Arkansas.

The authors note that “by employing a novel statistical approach, we found that this time-independent noise, often overlooked as inconsequential, creates a misleading hyperbolic pattern, making it seem like evolutionary rates increase over shorter time frames when, in fact, they do not. In other words, our findings suggest that smaller, younger clades [groups with common ancestors] appear to evolve faster not due to intrinsic properties but because of statistical noise.”

The study blends math, statistics and biology to show that this long-held hyperbolic pattern is an anomaly because it doesn’t account for the fact that all species on earth are defined as much by their unique traits as the variation that exists in those traits.

It’s a common principle in science that the simplest possible explanation to fit the data is usually the right one. Evolution taking place on completely different time scales is far less likely than noise in the numbers.

Ultimately, the study underscores the critical importance of accounting for inherent biases and errors in interpreting biodiversity patterns across both shallow and deep time scales.

In an unpublished summary of their work, the authors note that “[o]ur results might be seen as upsetting: a pattern that could have launched a thousand papers with really interesting biological hypotheses can be explained as an artifact.

“However, this is actually progress – we have explained a common pattern we see in the world. Biology is rich in mysteries: actually answering one lets us move on to the next. There are still many questions about biological rates, but the current paradigm of plotting rates against time should probably end.”

Abstract
Across a variety of biological datasets, from genomes to conservation to the fossil record, evolutionary rates appear to increase toward the present or over short time scales. This has long been seen as an indication of processes operating differently at different time scales, even potentially as an indicator of a need for new theory connecting macroevolution and microevolution. Here we introduce a set of models that assess the relationship between rate and time and demonstrate that these patterns are statistical artifacts of time-independent errors present across ecological and evolutionary datasets, which produce hyperbolic patterns of rates through time. We show that plotting a noisy numerator divided by time versus time leads to the observed hyperbolic pattern; in fact, randomizing the amount of change over time generates patterns functionally identical to observed patterns. Ignoring errors can not only obscure true patterns but create novel patterns that have long misled scientists.

Author summary
For decades, evolutionary biologists have observed that rates of evolution seem to accelerate over short time periods, a pattern seen across diverse data sources, from genomes to the fossil record. This observation has sparked debates about its implications for understanding the link between microevolution and macroevolution. Our research challenges this widely accepted notion, revealing that these apparent patterns are actually statistical artifacts resulting from time-independent "noise". By employing a novel statistical approach, we found that this time-independent noise, often overlooked as inconsequential, create a misleading hyperbolic pattern, making it seem like evolutionary rates increase over shorter time frames when, in fact, they do not. In other words, our findings suggest that smaller, younger clades appear to evolve faster not due to intrinsic properties but because of statistical noise. Ultimately, our study underscores the critical importance of accounting for inherent biases and errors in interpreting biodiversity patterns across both shallow and deep time scales.

Introduction
Biology is characterized by diversity: how a modern moss survives and evolves is very different from how Cambrian trilobites did the same. Biologists thus take immediate notice when broad patterns manifest across a diverse set of lineages. One such recurring pattern is that evolutionary rates exhibit an exponential increase towards the present or over shorter time scales, highlighting a potentially new fundamental principle in how life evolves. The consistency of this pattern across dimensions of diversity, from genomes to the fossil record [1–6], highlight its universality and arguably point to the need for new conceptual bridges connecting disparate timescales of evolutionary change [7,8], despite past work showing potential artifactual causes [9]. It also challenges the long-held view that the processes playing out in the past generally behave similarly in the present—that is, life seems to evolve “faster” now. Understanding this pattern offers the potential for new insights into the underlying mechanisms that shape biodiversity.

Here we introduce a set of novel models that assess the relationship between rate and time that includes hyperbolic and linear functions of time. We show that one potentially crucial but largely overlooked factor (but see [10,11]) affecting rate patterns is the impact of empirical errors inherent in rate estimates, and that this apparently has driven the pattern observed across extinction risk, trait evolution, and diversification rate estimates over time, based on model fitting and new randomization tests. We suspect that questions about rates changing over time scales can only be properly examined, across a variety of fields, once the biasing effect of uncertainty is taken directly into consideration.

Impish problems
The concern over spurious correlations between ratios and shared factors has a longstanding history in the statistical literature, dating back to Pearson’s pioneering work more than a century ago [12]. Pearson illustrated this problem by recounting a biologist’s study of skeletal measurements, specifically femur and tibia length normalized as fractions of the humerus length. Expecting a high correlation to validate correct groupings, the biologist was unaware that an “imp” had randomly shuffled bones between specimens. Surprisingly, the high correlation would persist even after this randomization, revealing that such spurious relationships arise from inherent properties of the variables rather than indicating meaningful biological connections [12–15].

Similarly, when plotting an evolutionary rate against its corresponding denominator, time, the representation becomes a plot of time against its reciprocal (i.e., k/time vs time), resulting in a relationship that is negatively biased [5,6]. In fact, if the numerator is held constant, the slope on a log-log scale must be -1.0 [1,16]. However, the numerator in an evolutionary rate, which quantifies the absolute amount of evolutionary change between two time points, is unlikely to remain constant across all timescales, as seen empirically [17]. Our hypothesis is that the observed hyperbolic pattern comes about due to this reciprocal relationship, especially given uncertainty in rates. We show mathematically why this might be the case, develop an approach for estimating the magnitude of the effect, and do randomizations and simulations to suggest that this is driving nearly all the observed empirical patterns.

Components of evolutionary rate patterns through time
In the context of evolutionary biology, a rate is a measure of evolutionary change per unit of time,
\[\large r\left(t \right) = \frac{x \left(t \right)}{t} \tag{1}\] Evolutionary change, \( x(t)\) encompasses a variety of measures, including number of nucleotide substitutions in DNA [18], the number of transitions between discrete phenotypes [19], speciation and extinction events [20], or the absolute change in a continuous trait after some interval of time [21]. The specifics of the different types of evolutionary rates and how they are estimated are varied. For instance, one model may assume changes follow a Poisson distribution on a nested tree structure (e.g., substitution rates), while others may assume trait changes are drawn from a normal distribution with the rate being the measure of the variance (e.g., Brownian motion). Nevertheless, at its core, an evolutionary rate reduces to some measure of change over time.

While it is common to perceive uncertainty and error as obscuring patterns rather than contributing to them, these uncertainties and errors may be key drivers of the repeated pattern of rates increasing towards the present. For illustrative purposes, we focus on the simplest approach for calculating rates of morphological evolution to demonstrate the impact of biases and errors on the relationship between rate and time. Measurements are assembled as a set of paired comparisons that differ in some trait value (e.g., body size) measured at two separate time points, with ratio of the differences in trait and time being an estimate of the rate,
\[\large \hat{r}(t) = \frac{|x_2 - x_1|}{t_2-t_1},{t_2} \gt {t_1} \tag{2}\] Here \( x_1\) and \( x_2\) are the initial and final values of the trait measurement between two time points, \( t_1\) and \( t_2\), respectively. Expressing a rate in such a way is similar to the “darwin,” a widely used unit of evolutionary change first defined by Haldane [22]: with the darwin, the \( x_i\) are the traits measured in log space. Various phenomenological patterns can describe how rates change through time. The simplest is that \( \hat{r}(t)\) is a constant rate of change, maintaining a constant value regardless of the measurement interval. For example, under a molecular clock, a mutation rate resulting from DNA copying errors is expected to be constant across clades of different ages. In other words, even though a pair of taxa sharing a common ancestor 5 million years ago are expected to have far fewer mutations than a pair sharing an ancestor 50 million years ago, on a per-time basis the rate will be identical. The biological rationale for such consistency in rate is rooted in the assumption that processes governing rates operate similarly in the past as they do in the present–a foundational premise in much of evolutionary biology. On the other hand, if mutation rates were increasing towards the present (perhaps due to a decrease in the protective ozone layer, or loss of function of mutation repair enzymes), then we would expect the rates to increase near the present. Measurements of \( x_i\) often reflect the mean for a species or even a measurement of a single individual and, therefore, represent an estimate of the true value. If each measurement estimate, \( x_i\), carries some level of noise attributed to factors like finite sample size (e.g., how representative is this particular flower of the species as a whole) or measurement error (e.g., how long is this rather stretchy squid, does this messy DNA band represent an A or a T), then \( \hat{x}_i = x_i + \varepsilon_i \), with \( \varepsilon_i\) denoting an error component that can lead to overestimation or underestimation of the measurement. Thus, we can express the rate estimate as including error: \[\large \hat{r}(t) = \frac{|(x_2 + {\varepsilon_2}) - (x_1 + {\varepsilon_1})|}{t_1 - t_1},t_2 \gt t_1. \tag{3}\]
We can reorder the terms to get: \[\large \hat{r}(t) = |\left(\frac{x_2 - x_1}{t_2 - t_1}\right)+\left(\frac{\varepsilon_2 - \varepsilon_1}{t_2 - t_1}\right)|,t_2 \gt t_1 \tag{4}\] The first term on the right hand-side reflects the underlying model. For example, with a constant nonzero rate the first term should be constant: larger trait differences (numerator) are balanced out by larger time intervals (denominator). However, the effect of the second fraction, the error, is quite different. Since these errors are not inherently time-dependent (e.g., sequencing errors in a DNA analysis or measurement of body lengths in dinosaurs), there are no a priori reasons for the magnitude of the error in a 5-million-year-old clade would necessarily be any greater or lesser than the magnitude in a 50-million-year-old clade–the numerator will come from a consistent, time independent, distribution. But this is divided by different amounts of time in the denominator. A hyperbola will invariably result from the second term when the ratio of these differences in error are scaled by time, and then plotted against time (even if the time estimate itself also has error, as it inevitably does). Thus, the overall pattern of empirical rates versus time is whatever the true pattern is plus a hyperbola coming from measurement error. At short times, under most models we expect the true difference in trait values to be small, while the uncertainty in measurements may remain high, leading to the hyperbola term dominating.

To assess the relative contribution of constant, hyperbolic, and linear functions towards a rate estimate over time, we derived a novel least-squares approach [23]. Our method allowed us to predict changes in observed evolutionary rates sampled through time by minimizing the logarithm of the residual sum of squares between the predicted and observed values. We derived a model that, at its most complex, is given as, \[\large \hat{r}(t) = \frac{h}{t} + mt + b. \tag{5}\]
Here h denotes the hyperbolic component [in units of x(t)], m is scalar modulating the effect of time up and down linearly [in units of \( x(t)^{-2}\)], and b is a constant base rate [in units of \( x(t)^{-1}\)]. Essentially, the \( \frac{h}{t}\) represents the \( \left(\frac{x_2 - x_1}{t_2 - t_1}\right)\) term in the equation above, while the mt+b terms represent the \( \left(\frac{f_2 - f_1}{t_2 - t_1}\right)\) term, if we are willing to assume a simple model where the underlying rate varies linearly with time (including the possibility of it being constant). The full model assumes all three components have impacted the fitted value for \( \hat{r}(t)\) in some way. We also fit a range of restrictions to this model where one or more of the parameters are set to zero. For example, restricting h = m = 0 is a model in which the rates would be inferred to be constant through time, as one would expect from a process like a molecular clock. The maximum likelihood fit of a model was assessed using the logarithm of the residual sum of squares and first converting this into a measure of the model variance and then into a log-likelihood. To facilitate comparisons across a set of models, we converted the log-likelihood into an Akaike Information Criterion score (AIC); we also assessed confidence regions (e.g., S1–S6 Figs) around each of the parameters.

O’Meara BC, Beaulieu JM (2024)
Noise leads to the perceived increase in evolutionary rates over short time scales.
PLoS Comput Biol 20(9): e1012458. https://doi.org/10.1371/journal.pcbi.1012458

Copyright: © 2024 The authors.
Published by PLoS. Open access.
Reprinted under a Creative Commons Attribution 4.0 International license (CC BY 4.0)

Although there are perfectly sound ways to explain apparent changes in the rate of evolution, such as changes in the rate of environmental change due to, for example, climate change, it makes no difference to the basic principles of evolution by natural selection whether these changes are more apparent then real, and caused by statistical 'noise' in the data. The fact of the underlying mechanisms of change in allele frequency over time in a given population remain unchanged.

Palaeontology is essentially a sampling process and sampling processes produce statistical anomalies or 'noise' which any biologist will be well aware of.

There is no comfort for creationists to know that the rate of evolutionary change might have been smoother than we thought, since nowhere in the historical record is there every any sign of species being magically created without ancestors or without evidence of descent with modification.

But if any creationists would like to show that this paper supports the notion of special creation by magic or in some way shows the scientists have given up on the Theory of Evolution as the best explanation for the data in favour of childish creationist superstations, then feel free to show where.

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