In a post yesterday I described how rapid, repeatable, and identical changes in populations of guppies that were transplanted from downstream pools (with higher predation) to upstream pools (with lower predation) are not derived from random mutation. I highlighted that the implication of this is that the guppy experiments are not examples of Darwinian macroevolution.

A Simple Example

An engineering model would predict that guppies are designed with operational parameters for different traits (i.e., time to sexual maturation, size, color, etc.), which are set by overarching design logic. An operational parameter is a variable that is designed to change within predefined limits in a system. The parameter space corresponds to the extent to which the values of the variables can vary without system failure, and is likely designed based on complex requirement considerations for the organism and its greater ecosystem. A simple example would be the angle maximum you can turn the front wheels on a car. A typical car’s front wheels can only turn about 45 degrees to the right or the left, and no further. This varies slightly for different vehicles depending on their design requirements.

Mechanisms for changing an operational parameter over time in a population of organisms could be accomplished in several ways. One hypothesis involves preservation of genetic diversity amongst the population, where diversity means that individuals within a population represent different optimizations for unique environments. Think of this scenario as a normal distribution where certain individuals in the population, who are not well suited for one environment (i.e., downstream), could thrive if placed in a different location (i.e., upstream) and gradually their alleles would become dominant in that environment. In this setting the population’s architecture and built-in diversity reflect various optimizations for unique environmental situations. This model currently has the most support, and is the one seemingly favored by Reznick and other groups’ data.

A second hypothesis is that there is a sensing mechanism, which in response to detected environmental inputs would trigger germline reprogramming. This relies on organisms sensing changes in the environment and triggering internal programming of allele frequency in the next generation, rather than natural selection (NS) based on standing genetic variation being the source of change. While a fully described mechanism of this nature has yet to be identified in living systems, there is some evidence supporting this hypothesis.

First, many organisms — stickleback fish, Drosophila, and Darwin’s finches, to name a few — have been observed to have a similar pattern to the guppy of repetitive, parallel alterations for specific traits in numerous distinct populations. (Whiting et al. 2022) These observations of coordinated allele frequency changes across different loci suggest the involvement of factors beyond simple selection.

Additionally, a recent study found substantial differences in germline mutation rates among three separate guppy families. (Lin et al. 2023) These de novo mutations were shared among many siblings, indicating they occurred early in embryonic development. These findings are consistent with certain individuals within a population being programmed for adaptation, while others are not.

However, it’s important to note that much more research is needed to fully develop and test this hypothesis. To this end, a study utilizing genetic and pedigreed analysis found that changes in allele frequency were well predicted by accounting for founders alone. (Chen et al. 2019) This leaves little to no room for programmed germline mutations. Although this study did not specifically investigate adaptation in different environments, its findings and others not mentioned here do raise some doubts for me about this second hypothesis.

Avenues for Research

Key questions to differentiate among these hypotheses include: Can we identify a sensing + signaling mechanism that drives changes in the guppies? For example, are there pheromones that fish could be sensing to perceive their population density? Are transposable elements playing any role in germline mutations in guppies? If not, then can we discover how the genetic information is preserved to enable “standing genetic diversity”? Why doesn’t the information get diluted or lost after many generations in one environment vs. the other? What happens if guppies without the adaptive alleles are transplanted upstream? Answering these questions will provide further insight into the guppies’ adaptation process.

What Separates Design/Engineering from Darwinian Macroevolution?

Some unique aspects which separate the design/engineering model from the evolutionary one are:

• Adaption is expected to be tightly regulated, targeted, and sometimes reversible, just like human-engineered tracking systems. Because of these features, adaptation is expected to be predictable.
• The organism, not the environment, exerts control over its adaptation.
• The design of the organism determines what environmental stimuli the organism tracks and responds to.
• Environments do not possess agent-like capabilities of “selecting” which organisms will breed. The traits of organisms themselves are responsible.
• Organisms are problem-solving entities not passive objects being shaped by the environment.
• Organism operational parameters have a limited range. 

Expanding that last point, for the guppy and all the other listed examples, the main design architecture of the organism remains unchanged despite the fact that a trait’s variability can be adjusted for optimum performance. The guppy can change and adapt like a well-engineered system, but at the end of the day, it is still a guppy.

Looking to the Future

The latest findings by Reznick et al. on guppies do not attribute its evolution to random mutation. Instead, recent research on the guppy has revealed rapid, repeatable, reproducible allele shifts across multiple independent populations where credit is given to standing genetic variation and density-dependent selection for the phenotypic differences in the life history of the guppy. 

If a sensing + change mechanism is discovered to be responsible then we are looking at a complex design pattern. Such a result would not be evidence for RM/NS but rather more work for RM/NS to do as now a route must be identified for RM/NS not just to make the guppy, but also make the guppy capable of sensing and changing based on the environment.

On the other hand, if there is no sensing + change mechanism, then RM/NS still has more work to do. Now instead of just generating one type of guppy, RM/NS must generate different ways to make guppies and preserve all of them (“standing genetic variation”) so they can flip flop as the environment changes. Either way, this recent research reveals harder problems for RM/NS.

References

• Chen, Nancy, Ivan Juric, Elissa J. Cosgrove, Reed Bowman, John W. Fitzpatrick, Stephan J. Schoech, Andrew G. Clark, and Graham Coop. 2019. “Allele Frequency Dynamics in a Pedigreed Natural Population.” Proceedings of the National Academy of Sciences of the United States of America 116 (6): 2158–64.
• Haskins, C. P., E. G. Haskins, J. J. A. McLaughlin, and R. E. Hewitt. 1961. “Polymorphism and Population Structure in Lebistes Reticulatus, An Ecological Study.” Vertebrate Speciation, no. Austin: University Texas Press: 320–95.
• Lin, Yuying, Iulia Darolti, Wouter van der Bijl, Jake Morris, and Judith E. Mank. 2023. “Extensive Variation in Germline de Novo Mutations in.” Genome Research 33 (8): 1317–24.
• Miller, Kenneth R. 2007. Finding Darwin’s God: A Scientist’s Search for Common Ground Between God and Evolution. HarperCollins.
• Reznick, David N., and Joseph Travis. 2019. “Experimental Studies of Evolution and Eco-Evo Dynamics in Guppies (Poecilia Reticulata).” Annual Review of Ecology, Evolution, and Systematics 50 (1): 335–54.
• Science News Staff. 1997. “Guppy Evolution Fast Forwards.” Science, March 28, 1997. https://doi.org/10.1126/article.40366.
• Whiting, James R., Josephine R. Paris, Mijke J. van der Zee, and Bonnie A. Fraser. 2022. “AF‐vapeR: A Multivariate Genome Scan for Detecting Parallel Evolution Using Allele Frequency Change Vectors.” Methods in Ecology and Evolution / British Ecological Society 13 (10): 2167–80.
• Zee, Mijke J. van der, James R. Whiting, Josephine R. Paris, Ron D. Bassar, Joseph Travis, Detlef Weigel, David N. Reznick, and Bonnie A. Fraser. 2022. “Rapid Genomic Convergent Evolution in Experimental Populations of Trinidadian Guppies (Poecilia Reticulata).” Evolution Letters 6 (2): 149–61.