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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