General Principles of Social Ethology (Clara B. Jones, 2013)

General Principles of Socio-sexual Ethology and Organization: A Likely or Unlikely Prospect?

Clara B. Jones (2013)

Introduction

Among terrestrial animals, including humans, socio-sexual ethology and organization (SSEO) has the potential to evolve wherever limiting resources are clumped in time and space. The existence of general, synthetic principles or laws of SSEO, however, remains an unresolved and controversial topic. On the one hand, some researchers are actively engaged in theoretical and empirical programs to detect, describe, analyze, and model patterns among the diverse forms of SSEO within and between taxa (e.g., Emlen & Oring 1977; Bradbury & Vehrencamp 1977; Helms Cahan et al. 2002; Bell & Robinson 2011; Fischman et al. 2011). Other investigators have been cautious in their assessments of attempts advancing unifying properties of SSEO, and some authors have suggested that general principles of SSEO are unlikely to be formulated or that synthetic models are likely to be limited to closely related and convergent taxa (e.g., Crespi 2007; Crespi 1994, 1996, 2005, 2009; Crespi & Choe 1997; Taborsky 2009). Classical ethology has historically emphasized species-typical, discrete (ritualized) signals and displays (“fixed-action-patterns”) responsive to predictable environmental (sign) stimuli (e.g., Tinbergen 1952; Eibl-Eibesfeldt 2007) rather than graded, variable motor patterns favored in heterogeneous regimes (e.g., Jones 2005; Jones & Agoramoorthy 2003). Behavioral and ontogenetic plasticity entailing the study of polymorphisms (genotypically-induced and/or regulated alternative responses) and polyphenisms (environmentally switched alternatives), however, has gained central ground in ethology (the biology of behavior) as a result of renewed interest in the ways that behavioral responses, modified by environmental stimuli, can induce genetic and phenotypic variability (e.g., West-Eberhard 1979, 2003, 2005; Jones, 2005, 2008a; Pigliucci & Muller 2009).

Highlighting SSEO, the present paper assesses ongoing programs actively engaged in a search for general patterns and principles, in particular, unifying models of the diversity of SSEO within and between taxa across space and time. Another objective of the current treatment is to evaluate some researchers’ claims that a few predictive parameters underlie ethological patterns and processes. After arguing for the utility of an integrated search for and formalization of general principles of SSEO, this paper addresses the controversial and unresolved issues surrounding what data, methodological tools, and research designs are required in order to provide robust data for analyses and tests of hypotheses. An important component of this project will be to determine whether the required data and techniques are currently available to investigators. Helms Cahan et al. (2002) suggest that a sufficient database exists to comprehensively search for general ethological principles using character traits. Notwithstanding these authors’ optimism, no consensus regarding terminology, questions, and other requirements has been reached among ethologists studying social biology. This article concludes by considering limitations of current treatments, outstanding questions, and future prospects and directions. In this essay, no attempt is made to review all mainstream efforts to characterize and formalize patterns of SSEO, quantitatively/mathematically or empirically. Instead, I highlight programs of research appearing to me to be clear representatives of different strategies currently employed to discover patterns across and to express synthetic statements about the variability of SSEO within and across species, from supra-solitary (e.g., Emmons, 2000), to cooperative breeders (Emlen 1991), to quasi- or primitively-eusocial (e.g., Jones 2011; Jones 1996; McComb et al. 2011); to eusocial (e.g., Wilson 1971; Jarvis 1978).

General agreement among ethologists about patterning of population structure relative to environment

Ethologists generally agree about overall associations between environmental features and population structure. Vertebrate adaptations have been driven by environmental stochasticity, in particular, variability in food dispersion and quality (Emlen & Oring 1977; Eisenberg 1981; Jones 1980, 1997, 2005, 2009). In brief, first principles of ecology indicate that the size and composition of groups change in response to temporal environmental heterogeneity (e.g., climate) with subsequent consequences for the survival and fecundity of organisms (Pulliam & Caraco 1984; Jones 1997; Wang et al., 2006). Population abundance and structure (e.g., Wilson 1975; Pulliam & Caraco 1984; Wong 2011) through time is an attribute of resource predictability (e.g., Emlen & Oring 1977; Bradbury & Vehrencamp 1977). High resource predictability and high resource quality, relatively homogeneous spatial dispersion of resources combined with resource tracking by the animal population are expected to favor resource defense (e.g., contest competition or territoriality) by individuals or small groups, ceteris paribus. However, low resource predictability and large distance or high variation in distance between resource patches may make resources indefensible (not monopolizeable), yielding large average group size (Schoener 1971; Emlen & Oring 1977; Pulliam & Caraco 1984). Since temporal unpredictability of resources may be positively correlated with spatial uncertainty (“patchiness”), foraging in groups may reduce average search time per individual group member. Thus, environmental predictability will be inversely correlated with group size (Wittenberger 1980; Pulliam & Caraco 1984), reflecting the “environmental potential” of local regimes.

Population structure or socio-sexual organization has significant consequences for genes and the individuals carrying them (Hewitt & Butlin 1997). Population structure may be evident as subdivision into demographic subunits or groups representing an evolutionary compromise among those parameters yielding optimal inclusive fitness to individuals (Wilson 1975; Wittenberger 1980; Pulliam & Caraco 1984; Dunbar 1996) or, more realistically, “best of a bad job” (e.g., Austad and “bet-hedging” (e.g., Jones 1997) tactics and strategies. As Wilson (1975) pointed out, the frequency distribution of group sizes in a population will be a function of those phenomena leading individuals to join and to leave groups combined with the selection pressures on individual responses to these forces (cum stressors). The parameters determining modal group size in a population, thus, are ultimately expressed as adaptations of individuals to local conditions (Pulliam & Caraco 1984; Wilson 1975; Brown 1975; Wittenberger 1980; Dunbar 1996; also see, West et al. 2002).

Have the fundamental parameters of social evolution been specified?

As noted, the overall schema relating local conditions to population structure is not in particular dispute; however, parameters, traits, mechanisms, functions, and adaptive values associated with the template are controversial, especially, the role of predation in structuring populations (see Wilson 1975; Brown 1975). Most contemporary attempts to define and generate unifying models of social ethology, implicitly or explicitly, follow from Emlen & Oring’s (1977) verbal model based primarily on empirical results from avian and amphibian field studies; notwithstanding this restricted database, the paper implied that its formulations were general ones. Emlen and Oring (1977) advanced a synthetic, organizational framework for the evolution of socio-sexual architecture, proposing three predictive parameters: (1) dispersion of limiting resources, (2) the operational sex ratio (OSR:-----), and (3) synchrony of female reproductive cycles. One or more of these parameters has been empirically evaluated and broadly supported for a wide range of plants and animals (both invertebrates and vertebrates), taxa exhibiting virtually every described socio-sexual system and environmental regime.  The present paper’s treatments refer to theoretical work and animal, including human, studies on the evolution of socio-sexual diversity, the latter emphasis consistent with the essential focus of ethology. 

Important research preceded and, subsequently, expanded Emlen and Oring’s (1977) schema. For example, Hawkins (1966, quoted in West-Eberhard 1980), addressing insect sociality, advanced ideas resonant of the later OSR formulation as did Schoener (1971) with his theoretical treatment of sexual dimorphism in the energetics of foraging. Others, (e.g., West-Eberhard 1979, 2003, 2005; Crespi 1996; Frank 1995, 1998, 2006) have provided seminal perspectives on aspects of evolution related to SSEO. Crook, recognized as the inceptor of ecological ethology, conducted classic studies on weaver birds (Crook 1965) and mammals (Crook et al. 1976), the first systematic attempts to correlate socio-sexual organization and ecological heterogeneity, particularly dispersion of limiting food resources. Altmann (1962) and Bradbury & Vehrencamp (1977) addressed temporal and spatial correlates of SSBE, including the spatiotemporal distribution of females as factors influencing the ability of males to monopolize the opposite sex. Unlike Emlen & Oring (1977), however, neither of these papers overtly identified and organized specific parameters within a synthetic conceptual framework. In 1979, Knowlton presented a theoretical model evaluating the spatial and temporal patterning of reproductive synchrony as influences upon socio-sexual variables, particularly, parenting effort. Her treatment, while focusing on a factor, parenting effort, not advanced as a fundamental predictive parameter by Emlen & Oring (1977), showed, importantly, that reproductive synchrony of the sex with greater parental investment rather than female reproductive synchrony, per se, was a definitive predictive variable, revising a feature of the 1977 verbal model. Questions remaining unresolved subsequent to Knowlton’s (1979) work concern how to evaluate differential degrees of bi-parental investment across taxa, and how to assess the relative significance to SSBO of this component of Emlen & Oring’s (1977) propositions. In 2002, Helms Cahan et al. highlighted three reproductive “trajectories” (dispersal, breeding, and alloparental care) as fundamental parameters for investigations of social evolution. Jones et al. (2008), studying mammals with quantitative models, concluded that group size and group sex ratio would predict variations in socio-sexual organization "wherever males compete directly for females."

The empirical and theoretical treatments so far mentioned in this article, as well as numerous other studies, have evaluated the utility of Emlen & Oring's (1977) verbal model, including certain of its limitations and need for refinement. Clearly, many relevant issues remain to be evaluated such as the condition-dependence, tradeoffs, thresholds, costs and benefits, and differential significance of Emlen & Oring's (1977) three fundamental parameters. As empirical research continues to identify patterns and mechanisms of socio-sexual ethology at all levels of biological organization, it is important to emphasize that, despite widespread support for the robustness of the 1977 formulations, mathematical treatments are required to demonstrate that the proposed parameters provide a firm basis, within and between taxa, for fundamental, unifying, predictive principles of variations in SSEO in nature.

Devising research programs to identify principles of socio-sexual evolution within and between taxa

In 1964, Hamilton advanced a general theoretical formulation ("Hamilton's Rule) of social behavior, termed "kin selection" or inclusive-fitness maximizing, that is widely, though not unanimously, accepted to be a general model of inter-individual interactions (see, especially, West et al. 2002). Trivers’ work (e.g., 1971, 1972, 1974; Burt & Trivers 2006) has, also, generated synthetic models of several topics, in particular, parental manipulation, sex-ratio selection, and genetic conflict. Rice’s (e.g., 2000; Holland & Rice 1999) treatments of sexual conflict have provided unifying schemas for co-evolution between the sexes over evolutionary scales. All of these research programs have proven to be rich sources of new hypotheses and investigations, including theoretical and empirical work. The contributions of these and other authors (e.g., Hrdy 1974; Vehrencamp 1983), while synthetic statements, address particular mechanisms of inter-individual interactions rather than parameters hypothesized to predict variations in SSEO over time and space. The fundamental assumption underlying these research programs is that, ceteris paribus, organisms have "solved" similar environmental problems in similar ways (Weinreich et al. 2006), supporting the idea that social taxa have converged on “a similar suite of traits” comprising a “genetic toolkit” (Fischman et al. 2011; Toth et al. 2007; Nygaard et al. 2011).

Although mature theoretical formulations and the new cohort of analytical tools were not available to early ethologists, researchers such as Weiss (1941a, b), Morris (1956), and Ewer (1960) emphasized the importance of understanding mechanisms underlying and regulating action and motor patterns. Contemporary investigations of SSEO utilize sets of data based on environmental, phenotypic, and/or genotypic features. In the simplest case (e.g., Helms Cahan et al. 2002), selected phenotypic character characters (e.g., extracted from ethograms), comparing these within and between taxa (e.g., insects, amphibians, birds, mammals), first qualitatively by “eye-balling” and, subsequently, by methods of correlated trait analysis (see Garamszegi & Møller 2011). These methods have the potential to reveal similar and different patterns of phenotypic characters and to permit inferences about origins and evolutionary “trajectories” of social traits. Correlated trait analyses, thus, do not provide information about causes of patterns detected or their underlying mechanisms. The primary utility of these procedures is the relatively straightforward manner in which preliminary speculations about alternative predictive parameters might be evaluated (see Helms Cahan et al. 2002, Table 1); however, I am not aware of any theoretical or empirical tests of the 2002 schema.

Multi-level studies such as those by Jetz & Rubenstein (2011) achieve a higher level of data integration by mapping variations in environmental or ecological variables (climate stochasticity) to variations in socio-sexual architectures (cooperatively breeding birds), analyzing these results with multi-factorial techniques. Jetz & Rubenstein (2011), for example, were able to determine that climate was more significant than phylogeny as a predictor of worldwide distribution patterns for cooperative-breeding birds. An advantage of this method is its inclusion of a variable (climate) exogenous to phenotypes and potentially significant as a selective force. This approach, like that of Helms Cahan et al. (2002), permits within and between taxa comparative analyses, and it is my understanding that Rubenstein, and colleagues are in the process of incorporating data for cooperative-breeding mammals into their program. A limitation of the work by Jetz & Rubenstein (2011) is that, though environmental heterogeneity is widely understood to be an important factor in the evolution of sociality, local (e.g., resource dispersion) rather than global (e.g., climate) features of the environment are expected to differentiate among SSEO (e.g., Wilson 1975; Brown 1975; Emlen & Oring 1977; Jones l997; West et al. 2002; Jones & Agoramoorthy 2003). “Mapping” spatial distributions of environmental, socio-sexual and/or other features (e.g., genomic characters) is amenable to multi-level geospatial modeling (http://web.cs.dal.ca/~sbrooks/; http://www.proteus.co.nz), and individual-based models (e.g., Thibert-Plante & Hendry 2011) should, also, be helpful utilities for quantitative treatments of some synthetic databases developed to explore the evolution of SSEO. The previously discussed research programs address phenotype or environment--phenotype levels of organization with verbal and correlation analyses. Integrated and complete formulations of SSEO, however, require knowledge of gene/genome---phenotype---environment--- effects.

Studying social insects, Robinson and members of his laboratory (e.g., Whitlock et al. 2003; Whitlock et al. 2006; Toth et al. 2007; Fischman et al. 2011) analyzed molecular pathways of primitively social and eusocial taxa in order to dissect social evolution. This precise though tedious approach requires significant genomic resources, including knowledge of the effects of genes on phenotypes. These investigators’ genomic methods permit within- and between-taxa comparisons; however, knowledge of gene function(s) at the species level is limited for social insects (Fischman et al. 2011) and other groups. Although microarray (gene ontology) analyses do not permit tests of causation, they yield cladograms (Fischman et al. 2011) amenable to quantitative modeling. In addition, knowledge of gene function(s), in particular, the effects of molecular changes, provides information about alternative molecular routes associated with SSEO, permitting inferences about differential evolutionary pathways and constraints, including ecological ones (Fischman et al. 2011), and the latter variable may be the parameter of greatest importance included in the insightful treatment by Emlen (---; Emlen & Oring 1977). Whitfield et al. (2006) and Fischman et al. (2011) provide further discussion of the problems encountered with these techniques, including the contingent nature of inferences about specifics of gene action (e.g., epistasis, pleiotropy) and comparative supra-genomic analyses. The issues discussed in these papers should apply, as well, to other synthetic initiatives addressing the analysis of character traits from the genome level.

Discussion and Conclusions

The more general a model (the more phenomena encompassed), the less realistic it will be. The most parsimonious and comprehensive models of SSEO advanced to date express social traits as functions of organisms’ energetic properties. This approach has a long history, initiated in Oster & Wilson’s (1978, cited in West-Eberhard 1980) “ergonomics” concept whereby group efficiency or output is measured in terms of optimal allocation of energy for survival and reproduction (see, also, Wilson 1971, 1975). Although models of reproductive skew (e.g., Veherencamp 1983) have not been explicitly stated as energetic models, original definitions of differential skew within groups characterized the concept as relative monopolization by one or more group members of total reproductive output of a group (a reproductive unit). Following the perspective of ergonomics, reproductive skew might be formulated as differential energy-investment by group members in (direct) reproductive effort. As a potential synthetic model, reproductive skew is controversial, having received intense scrutiny (e.g., Reeve 200????) since its initial proposal; nonetheless, theoretical and empirical evaluations of the concept’s utility are ongoing (e.g., Hager & Jones 2009).

A recent paper reported that, for social insects, division of labor scales with group size (Holbrook et al. 2011) and, one would add, group density (a measure that should correlate highly with variations in interaction rates). This quantitative treatment is important but highly reductionistic in scope, and many vertebrate researchers are likely to be skeptical that variations in SSEO can be expressed so minimally. Importantly, the new findings are consistent with Emlen & Oring’s (1977) parameterization of ecological factors since these variables determine in large part a local landscape’s potential for sociality via differential dispersion in time and space of limiting resources, measures reflecting relative environmental stochasticity. The findings of Holbrook et al. (2011) are also supported by Wong’s (2011) study showing that group size (and, group density) significantly influences individual survival and reproductive success, leading to differential “decisions” by individuals in response to social, inter-individual stress (c.f. social competition and social selection: Crook 1970, 1977; West-Eberhard 1979; Frank 2006). These developments reinforce the idea that more than one synthetic model of SSEO will be advanced, depending, among other factors, on the nature of phenomena (e.g., energy, phenotypic character states) and level(s) of organization addressed. At least one caveat indicates that the construction of highly reductionistic models may be more complex than it appears on surface based on the work of Hamilton et al. (2011) who found that rules for allocation of energy are effectively equivalent across all mammalian species. This report suggests, then, that principles of scaling may differ for different classes of animals (as a function of body size?).

Notwithstanding the need for further investigation, the SSEO literature provides numerous indicators that energetic factors, in particular, energy savings, are of general import for the evolution of SSEO (e.g., Shoener 1971; Jarvis 1978; Jerison 1983; Lovegrove & Wissel 1988; Heinze & Keller 2000; Jones & Agoramoorthy 2003; Russell et al. 2003; Jones 2005, 2009; Whitfield et al. 2006; Toth et al. 2007; also see Vehrencamp 1983). Following these treatments, it, additionally, seems likely that information about social genetic/genomic pathways sensitive to energy-maximization/optimization and/or energy savings can be expressed synthetically (e.g., Schoener, 1971; Fischman et al. 2011). Other research programs may explore the utility of expressing variations in SSEO as functions of body size (e.g.; Wong 2011) and one or more additional factors (e.g., ecological constraints, life history schedules, phenotypic plasticity).

Data relevant to genomic treatments are in the very early stage of collection for vertebrates, increasing vertebrate ethologists’ reliance upon less ambitious approaches for detection of variations and patterns of SSEO. It seems likely that attempts to express social evolution as general principles will yield more than one model depending upon the level(s) of organization addressed and emphasized by different researchers. Conceivably, different synthetic formulations will be derived for energetic, molecular, genetic, epigenetic, developmental, physiological, and/or phenotypic variables; though, one might speculate that, as Emlen & Oring’s (1977) formulation advances, some ecological measure, in their view, resource dispersion, must be integrated into any predictive schema. It also seems reasonable to conclude that the three parameters suggested by Emlen & Oring (1977) to have general predictive power, may not be the only combination of variables with utility for synthetic expression even though their importance has been ubiquitously demonstrated by empirical research. For example, Frank’s (1998) theoretical work on social evolution led him to highlight three “measures of value”: reproductive value, coefficients of relatedness, and marginal value (and, generation time?). These or other combinations of variables may provide robust models of variations in SSEO across space, perhaps reflecting the complex, multi-determinate nature of sociality or the ability of different metrics to represent assays of fundamental parameters. Finally, the emphasis….throughout this essay on the dependence of variations in SSEO upon variations in ecological, genetic, and/or other limiting factors discounts claims that traits characteristic of SSEO are species-typical (e.g., Hrdy 2009; see Jones 2011), as documented in the technical literature since Crook’s (1965; Crook et al. 1976) fundamental and pathbreaking work.----

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