Evolution by natural selection has been extended to several supraorganismic levels, but whether it can apply to ecosystems remains controversial on two main counts. First, local ecosystems are loosely individuated, so that it is unclear how they manifest heredity and fitness. Second, even if they did, the meta-ecosystem formed by this population of local ecosystems will also suffer from a very low degree of cohesion, which will jeopardize any ENS. We suggest a way to overcome both issues, focusing on ecosystem (...) properties rather than on ecosystemic individuals. First, we follow recent theoretical developments which deny the centrality of reproduction in ENS. This leads us to merge heredity and fitness in a single process, in which a local ecosystem causally influences its neighbors, in a way responsible for their phenotypic similarity. Second, we suggest that the resulting meta-ecosystem need not meet the criteria of individuality. Instead, the evolving meta-ecosystem is best understood as a homeostatic property cluster, i.e., a set of phenotypes that together evolve by ENS at the ecosystem level. We illustrate this conceptual framework with a hypothetical example of local ecosystems that vary in terms of stability, productivity, diversity, and complementarity between species. Finally, we stress that our property-based account of ecosystemic ENS can help understand evolution at other biological levels, such as early life evolution and holobionts. (shrink)

We develop an account of laboratory models, which have been central to the group selection controversy. We compare arguments for group selection in nature with Darwin's arguments for natural selection to argue that laboratory models provide important grounds for causal claims about selection. Biologists get information about causes and cause-effect relationships in the laboratory because of the special role their own causal agency plays there. They can also get information about patterns of effects and antecedent conditions in nature. But to (...) argue that some cause is actually responsible in nature, they require an inference from knowledge of causes in the laboratory context and of effects in the natural context. This process, cause detection, forms the core of an analogical argument for group selection. We discuss the differing roles of mathematical and laboratory models in constructing selective explanations at the group level and apply our discussion to the units of selection controversy to distinguish between the related problems of cause determination and evaluation of evidence. Because laboratory models are at the intersection of the two problems, their study is crucial for framing a coherent theory of explanation for evolutionary biology. (shrink)

In recent years, quite a few evolutionary psychologists have come to embrace a heuristic interpretation of the discipline. They claim that, no matter how methodologically incomplete, adaptive thinking works fine as a good heuristic that effectively reduces the hypothesis space by generating novel and promising hypotheses that can eventually be empirically tested. The purpose of this article is to elucidate the use of heuristics in evolutionary psychology, thereby clarifying the role adaptive thinking has to play. To that end, two typical (...) heuristic interpretations—Machery’s "bootstrap strategy" and Goldfinch’s heuristically streamlined evolutionary psychology—are examined, focusing on the relationship between adaptive thinking and heuristics. The article draws two primary conclusions. The first is that the reliability of the heuristic hypothesis generation procedure should count no less than the conclusiveness of the final testing procedure in establishing scientific facts; nature does not always get the last word. Philosophy also counts. The second is that adaptive thinking constitutes a core heuristic in evolutionary psychology that provides the discipline with its raison d'être, but this is only possible when adaptive thinking is substantiated with sufficient historical underpinnings. (shrink)

In The Levels of Selection (Brandon, 1984), Robert Brandon provides a suggestive but ultimately unsuccessful attempt to use the probabilistic notion ofscreening off in providing a schema for dealing with an aspect of the units of selection question in the philosophy of biology. I characterize that failure, and suggest a revision and expansion of Brandon's account which addresses its key shortcoming.

Genic selectionism holds that all selection can be understood as operating on particular genes. Critics (and conventional biological wisdom) insist that this misrepresents the actual causal structure of selective phenomena at higher levels of biological organization, but cannot convincingly defend this intuition. I argue that the real failing of genic selectionism is pragmatic – it prevents us from adopting the most efficient corpus of causal laws for predicting and intervening in the course of affairs – and I offer a Pragmatic (...) account of causation itself which ultimately bears out the claim that genic selectionism misrepresents the causal structure of selective contexts. (shrink)

Most attempts to answer the question of whether populations of groups can undergo natural selection focus on properties of the groups themselves rather than the dynamics of the population of groups. Those approaches to group selection that do emphasize dynamics lack an account of the relevant notion of equivalent dynamics. I show that the theory of ‘dynamical kinds’ I proposed in Jantzen :3617–3646, 2014) can be used as a framework for assessing dynamical equivalence. That theory is based upon the notion (...) of a dynamical symmetry, a transformation of a system that commutes with its evolution through time. In the proposed framework, structured sets of dynamical symmetries are used to pick out equivalence classes of systems. These classes are large enough to encompass the range of phenomena we associate with natural selection, yet restrictive enough to guarantee a sort of causal homogeneity. By characterizing dynamical kinds via symmetry structures in this way, the question of levels of selection becomes a precise question about which populations respect the dynamical symmetries of Darwinian evolution. Standard population genetic models suggest that populations undergoing evolution by natural selection are partially characterized by a group of fitness-scaling symmetries. I demonstrate conditions under which these symmetries may be satisfied by populations of individuals, populations of groups of individuals, or both simultaneously. (shrink)

Determining whether a homoplastic trait is the result of convergence or parallelism is central to many of the most important contemporary discussions in biology and philosophy: the relation between evolution and development, the importance of constraints on variation, and the role of contingency in evolution. In this article, I show that two recent attempts to draw a black-or-white distinction between convergence and parallelism fail, albeit for different reasons. Nevertheless, I argue that we should not be afraid of gray areas: a (...) clarified version of S. J. Gould's earlier account, based on a separation of underlying developmental mechanisms from the realized trait, still represents a useful approach. (shrink)

Genic selectionists (Williams 1966; Dawkins 1976) defend the view that genes are the (unique) units of selection and that all evolutionary events can be adequately represented at the genic level. Pluralistic genic selectionists (Sterelny and Kitcher 1988; Waters 1991; Dawkins 1982) defend the weaker view that in many cases there are multiple equally adequate accounts of evolutionary events, but that always among the set of equally adequate representations will be one at the genic level. We describe a range of cases (...) all involving stable equilibria actively maintained by selection. In these cases genotypic models correctly show that selection is active at the equilibrium point. In contrast, the genic models have selection disappearing at equilibrium. For deterministic models this difference makes no difference. However, once drift is added in, the two sets of models diverge in their predicted evolutionary trajectories. Thus, contrary to received wisdom on this matter, the two sets of models are not empirically equivalent. Moreover, the genic models get the facts wrong. (shrink)