The frailty of adaptive hypotheses for the origins of organismal complexity

Abstract

The vast majority of biologists engaged in evolutionary studies interpret virtually every aspect of biodiversity in adaptive terms. This narrow view of evolution has become untenable in light of recent observations from genomic sequencing and populationgenetic theory. Numerous aspects of genomic architecture, gene structure, and developmental pathways are difficult to explain without invoking the nonadaptive forces of genetic drift and mutation. In addition, emergent biological features such as complexity, modularity, and evolvability, all of which are current targets of considerable speculation, may be nothing more than indirect by-products of processes operating at lower levels of organization. These issues are examined in the context of the view that the origins of many aspects of biological diversity, from gene-structural embellishments to novelties at the phenotypic level, have roots in nonadaptive processes, with the populationgenetic environment imposing strong directionality on the paths that are open to evolutionary exploitation.adaptation ͉ genome evolution ͉ evolvability ͉ modularity ͉ population genetics

Impact of long‐term chromosomal shuffling on the multispecies coalescent analysis of two anthropoid primate lineages

“…Both methods were statistically consistent even in cases where the distribution of gene tree topologies was anomalous (e.g., with 100 bp). In this sense, MSC methods are preferable if the researcher wishes to adopt an inferential framework nested within the foundations of population genetics, allowing for a theoretical unity between different areas of evolutionary genetics (Edwards et al., 2016; Lynch, 2007). Moreover, the efficacy of both MSC and concatenation methods in estimating the species tree, even with gene fragments as small as 200 bp, is operationally relevant because current genome sequencing technologies frequently produce short sequence reads that, according to our analysis, could be readily used in phylogenetic inference.…”

Embodied artificial life at an impasse can evolutionary robotics methods be scaled?

“…Human cognition, intuition and linguistic issues may play a significant role in introducing methodological and theoretical difficulties into the design of goal-driven artificial evolutionary systems [15,16,25,74]. For example, [15] points out that there is little evidence or theory to connect evolutionary processes to increasing complexity, but that this concept remains a fixture in descriptions of natural selection.…”

“…The problem is that the general class of cognitive behaviors, especially those that require complex nested information manipulation, likely do not have simple solutions in any domain. Further, many evolutionary biologists do not consider natural evolution to be a goal-driven process, so that the generative power of natural evolution cannot be related to the emergence of any particular predefined complex capability [11,15,16,25].…”

“…In the larger field of Artificial Life (ALife) there has been a move toward characterizing the conditions under which open-ended evolution [12] can occur [6,13,14]. This in turn has added steam to a growing realization that the fitness or objective functions used to drive the artificial evolution of agents (robots in the case of ER) may be incompatible with open-ended evolution [8,15,16]. Although evolution is popularly characterized by the phrase "survival of the fittest", it has been pointed out by theoreticians and researchers that conceptualizing fitness as a driving force does not fully describe natural evolution [7,15,16].…”

“…This in turn has added steam to a growing realization that the fitness or objective functions used to drive the artificial evolution of agents (robots in the case of ER) may be incompatible with open-ended evolution [8,15,16]. Although evolution is popularly characterized by the phrase "survival of the fittest", it has been pointed out by theoreticians and researchers that conceptualizing fitness as a driving force does not fully describe natural evolution [7,15,16]. Natural evolution is a goalless process, in the most general sense, but local adaptation can often be modeled satisfactorily in terms of genotype or phenotype features and reproductive efficiency as formalized by a fitness landscape, as per Sewall Wright [17,18].…”