Multilevel Natural Selection: Bibliometric Review Reveals Abundant Empirical Evidence of Evolution on Multiple Levels

Understanding Multilevel Selection Theory

Imagine evolution not as a solitary race where only the strongest individual survives, but as a complex team effort where success depends on coordination across genes, cells, organisms, and even entire groups. This is the essence of multilevel selection (MLS) theory, a framework in evolutionary biology that posits natural selection acts simultaneously at multiple hierarchical levels of biological organization. Unlike the classic 'survival of the fittest' view focused primarily on individuals or genes, MLS recognizes that traits can be favored if they benefit the group, even at a cost to the individual within it.

To grasp this, consider the historical context. Charles Darwin himself hinted at group-level dynamics in Descent of Man (1871), suggesting primitive human societies could advance through moral qualities that aid collective survival. However, post-1960s, gene-centric views popularized by George Williams' Adaptation and Natural Selection (1966) largely dismissed group selection as implausible, arguing selfish genes would undermine it. The tide turned in the 1970s with Michael Wade's pioneering experiments and David Sloan Wilson's mathematical models, reviving MLS as a rigorous, testable approach.

MLS uses tools like the multilevel Price equation to partition selection into within-group (individual-level) and between-group components. If between-group variance exceeds within-group competition—often via limited migration or group reproduction—group-favoring traits evolve. This explains altruism in eusocial insects like ants, where sterile workers sacrifice reproduction for colony success, or human cooperation in tribes facing scarcity.

For aspiring biologists, mastering MLS opens doors to understanding major evolutionary transitions, from single cells to multicellularity. Those exploring research jobs in evolutionary ecology will find MLS central to modern debates.

📊 The Landmark Bibliometric Review

A groundbreaking study published in February 2026 in Frontiers in Ecology and Evolution has cataloged abundant empirical support for MLS, shattering lingering skepticism. Lead author César Marín, alongside Anne B. Clark, Conner S. Philson, Omar Tonsi Eldakar, and Michael J. Wade, conducted a systematic bibliometric review following BIBLIO guidelines. They scoured Scopus, Web of Science, and Google Scholar for terms like 'multilevel selection' and 'group selection' from 1900 to 2024, yielding 2,950 candidate articles.

After rigorous screening—removing duplicates, non-biological papers, models, and reviews—280 studies emerged with direct empirical evidence of selection at multiple levels. Strikingly, 100 were in situ observations in natural settings, and 180 were controlled laboratory experiments. Publications surged post-2012, with 199 studies since then, peaking at 22 in 2019.

This review, detailed in the full paper, underscores MLS's ubiquity, countering claims of evidential scarcity.

Diverse Taxa and Biological Levels Explored

The 280 studies span an astonishing breadth of life forms, from viruses and microbes to humans. Over 90% focused on selection among organismal groups like demes (small local populations, 71% or 198 studies), colonies (31 studies), and aggregates (24 studies). The remaining 9.6% targeted sub-organismal levels (cells, nuclei, genetic elements) or supra-organismal ones (multispecies communities).

• Eusocial insects and farm animals: Dominant, comprising about 65% of studies, highlighting cooperation in hives and pens.
• Humans: Cultural and social traits under MLS.
• Microbes and viruses: Yeast, bacteria, Caenorhabditis elegans, showing rapid evolution in populations.
• Wild species: Plants, algae, birds, mammals, fish, amphibians, invertebrates like spiders and polychaetes.
• Crops and fungi: Applied breeding contexts.

Such diversity proves MLS is no niche idea but a general principle. For instance, in microbiomes, bacterial cooperation aids host health, while viral traits balance within-host replication and between-host transmission.

Iconic Empirical Examples Across Kingdoms

Concrete cases illuminate MLS's power. Wade's 1976 PNAS experiment with flour beetles (Tribolium castaneum) epitomizes early evidence. By propagating from smallest or largest groups, Wade evolved cannibalistic tendencies in small-group lines—adaptive at group level via reduced competition, despite individual costs.

In agriculture, William Muir's chicken breeding (1990s) contrasted individual vs. group selection. Individual-selected 'super hens' pecked peers to death in groups, yielding low productivity. Group-selected cage-mates, bred holistically, boosted egg output 160% over five generations, fostering docility and survival. This practical triumph informs modern livestock genetics.

Among microbes, Ratcliff et al. (2012) showed yeast evolving multicellular clusters under predation, with group-level sedimentation favoring faster-sinking aggregates. In humans, contextual analyses reveal group effects on fitness via social networks or cultural norms, as in Soltis et al. (1995) on ethnic voting patterns.

Cancer exemplifies conflict: cells cooperate within tumors for metastasis but compete between tumors for hosts. Diseases like myxoma virus in rabbits evolved virulence moderation via host-group dynamics.

These span lab (e.g., C. elegans indirect genetic effects) to field (wild bird flocks), affirming MLS's versatility. Details in Binghamton's coverage here.

Analytical Methods Powering MLS Insights

Researchers deploy diverse tools: contextual analysis (26 studies dissected, with regression betas showing comparable individual/group effects); indirect genetic effects (IGEs, quantifying social partner influences); breeding designs; and artificial selection. Over half of in situ studies used IGEs or contextual methods, revealing selection directions—aligned, opposing, or varying by context.

The Price equation's multilevel extension formalizes this: total change = within-group + between-group covariance. Tools like Lande-Arnold regressions quantify phenotypic selection gradients across levels.

For students, these methods are teachable via simulations, preparing for faculty positions in quantitative biology.

Implications for Biology, Medicine, and Society

MLS reframes evolution as hierarchical teamwork, explaining eusociality, multicellularity, and human prosociality. In medicine, it models antibiotic resistance as bacterial group arms races or cancer progression via cellular collectives. Agriculture leverages it for sustainable breeding, reducing aggression while boosting yields.

Societally, it informs policy: rewarding individual competition may erode group cohesion, as in hyper-competitive classrooms stifling collaboration. Anne Clark notes: "Multilevel selection complicates the picture... selection on one level headed differently than another."

A separate 2025 review in Ecology and Evolution laments MLS's marginal textbook presence, urging balanced curricula. Access it here.

Future Research and Academic Opportunities

With MLS resurgent, gaps beckon: more in situ human studies, multispecies communities, and integration with genomics. Zenodo databases from the review enable meta-analyses.

Academics, pursue MLS via postdoc opportunities or professor jobs at institutions like Binghamton University, evolutionary biology hubs. Rate evolution courses on Rate My Professor to guide peers.

Photo by Krists Luhaers on Unsplash

• Model opposing selections quantitatively.
• Apply to climate-impacted metapopulations.
• Explore cultural MLS in globalized societies.

Wrapping Up: Evolution's Multilevel Symphony

This bibliometric tour de force affirms MLS as indispensable for holistic evolution. From viral quasispecies to human tribes, selection orchestrates across scales. Aspiring researchers, dive into higher ed jobs and research assistant jobs to contribute. Share insights via comments, rate professors teaching MLS on Rate My Professor, and advance your career with higher ed career advice from AcademicJobs.com.