Studies on Cichlid Fish Demonstrate the Predictive Power of Engineering Models for Adaptation
In an article yesterday, I described how studies of labrid fish validate the operational gravity well model (OGWM) for adaptation. The model is further validated by studies on cichlid fish. In addition, cichlid studies demonstrate the predictive power of the tracking model for adaptation.
Predictions of the Tracking Model
The tracking model predicts that variation in a population will to a large extent result from adaptive mechanisms engineered to direct predefined anatomical and physiology changes. These modifications help a population to effectively respond to environmental challenges or opportunities. Some useful variation might exist due to random mutations, but nearly all such cases would correspond to degradations of genes (i.e., the loss of information) or to trivial modifications that could never accumulate to significantly alter any trait (here, here).
The model also predicts that the most substantive genetic diversity will often result from natural genetic engineering that drives targeted modifications such as increased mutation rates at specific locations. And variation will almost entirely correspond to a few adjustable parameters that allow a population to fine-tune the species’ design plan or architecture without fundamentally altering it. These predictions conflict with those of the standard evolutionary model, and they perfectly match the results of research on cichlid adaptation.
OGWM predictions for variation in cichlid feeding were confirmed by Cooper et al. in a 2010 study. The investigators used a principal component analysis (PCA) to quantify differences in feeding structures for cichlid populations in Lakes Tanganyka, Malawi, and Victoria. This technique creates composite variables that capture for each individual how the value of one variable (e.g., head width) correlates with another variable (e.g., jaw length) or variables. The analysis revealed that variation in all populations predominately resided along the same two composite variables (aka PC axes).
Kara Feilich in a 2016 study performed a PCA on cichlid body and fin structures, and she also found that the same variation occurred repeatedly in separate populations:
Body shape disparity among the cichlid fishes has been studied extensively, repeatedly demonstrating common axes of diversification across many lineages, including the tropheines (Wanek and Sturmbauer 2015), geophagines (Astudillo-Clavijo et al. 2015), and others (Clabaut et al. 2007; Muschick et al. 2012).
Feilich noted that changes in traits were highly correlated to optimize hydrodynamics and maneuvering. Of key importance, the underlying design architecture never changed, but it was constantly fine-tuned to best perform in the immediate environmental conditions. This observation matches the predictions of both engineering models for adaptation.
Cichlid research has also confirmed other tracking model predictions. Multiple studies have demonstrated that the variation in cichlids results largely from phenotypic plasticity where adaptive mechanisms direct targeted changes in response to environmental conditions. Mazzarella et al. in a 2015 study raised cichlids in water with different salinity levels. Greater salinity resulted in adult fish displaying shallower bodies and longer jaws.
As a second example, Parsons et al. in a 2016 study fed two separate groups of young juvenile fish different diets. The two groups developed into adults with distinctly different head-jaw structures that were tailored to forage for the available food most effectively. Navon et al. in 2021 reported on a similar experiment that confirmed the Parsons et al. results. The investigators also demonstrated diet-induced adaptive changes to body shape and fin-ray number. The observed dissimilarity between the two diet groups mimicked the differences between distinct cichlid species in the wild.
Researchers increasingly recognize that the most significant variation in cichlid fish results from internal adaptive mechanisms. As Parsons et al. stated:
…there is an emerging view that additive genetic variation accounts for a relatively small percentage of phenotypic variation and rather it’s the context in which traits develop that determines their final form (Hendrikse et al. 2007, Jamniczky et al. 2010, Pfennig et al. 2010, Hallgrimsson et al. 2014).
Natural Genetic Engineering
Other investigators discovered that much of the genetic variation resulted not from random mutations but from natural genetic engineering (NGE). To name but one example, Carleton et al. in a 2020 study demonstrated that NGE accounts for the most important variation in the genetics underlying vision:
One of their [cichlids] most variable phenotypes is visual sensitivity, with some of the largest spectral shifts among vertebrates. These shifts arise primarily from differential expression of seven cone opsin genes. By mapping expression quantitative trait loci (eQTL) in intergeneric crosses of Lake Malawi (LM) cichlids, we have thus far identified four causative genetic variants that correspond to indels in the promoters of either key transcription factors or of the opsin gene itself. Here we show that these indels are caused by the movement of transposable elements (TEs).
Indels are insertions of nucleotide sequences, such as TEs, into DNA or deletions of sequences from DNA. They are often initiated by environmental cues (here, here) and targeted to specific regions of DNA to drive rapid adaptation. Carleton et al. stated:
These precise indels are not found outside of LM, suggesting that these TEs are recently active and are segregating within the Malawi cichlid lineage. A similar indel has arisen independently outside of LM at one locus, suggesting that some locations are primed for TE insertion and the resulting indels.
Overall, our study suggests that TEs may contribute to key regulatory changes, and may facilitate rapid phenotypic change and possibly speciation in African cichlids.
Future research will undoubtedly continue to demonstrate that cichlid variation did not primarily originate from random mutations but from systems engineered to drive targeted modifications.