Monday, August 13, 2018

Social Biology: Questions For Research after Sutherland WJ et al. (2013) [Clara B. Jones]

Social Biology: Questions for research based upon Sutherland WJ et al. (2013) Identification of 100 fundamental ecological questions. J Ecology 101, 58-67. This list is not intended as a substitute for the original questions nor does it represent an exhaustive treatment of the ways that the original list can be interpolated, interpreted, or used by Social Biologists and Behavioral Ecologists. The following list interpolates questions primarily related to individual and population levels of analysis, with some questions, additionally, pertaining to Community and Ecosystem levels. However, researchers are encouraged to interpolate other questions in the original list, e.g., those devoted to Disease Ecology. As well, important methodological questions relevant to Social Biology and Behavioral Ecology are presented in the original paper. Overall, the fundamental “decision rule” is to interpret and interpolate questions as they may pertain to Social Biology as per all levels and features of group-living taxa. Clearly, questions might be interpolated relative to "Behavior" per se for broad treatment by Behavioral Ecologists.  Submitted by Clara B. Jones: @cbjones1943 [Twitter]

  • What are the evolutionary consequences of group-living populations becoming less connected through fragmentation?
  • How local is adaptation in group-living populations?
  • For group-living taxa, what are the ecological causes and consequences of epigenetic variation?
  • For group-living taxa, what are the relative contributions of different levels of selection (gene, individual, group) to life-history evolution and the resulting population dynamics?
  • What selective forces cause sex differences in life-history and what are their consequences for population dynamics of group-living taxa?
  • How do the strength and form of density-dependence influence feedbacks between population dynamics and life-history evolution of group-living taxa?
  • How does phenotypic plasticity influence evolutionary trajectories of social traits?
  • How variable are social traits across taxa?
  • What are the genetic and physiological bases of life-history tradeoffs in group-living taxa?
  • What are the evolutionary and ecological mechanisms governing range margins of group-living populations?
  • How can we upscale detailed processes at the level of individuals into patterns at the population scale in group-living taxa?
  • What are the heritability/genetic bases of dispersal and movement behavior[s] in group-living taxa?
  • How do group-living organisms make movement decisions in relation to dispersal, migration, foraging, or mate search?
  • For group-living taxa, do different demographic rates vary predictably over different spatial scales, and how do they then combine to influence spatio-temporal population dynamics?
  • For group-living taxa, how does demographic and spatial structure modify the effects of environmental stochasticity on population dynamics?
  • For group-living taxa, how do environmental stochasticity and environmental change interact with density-dependence to generate population dynamics and distributions?
  • For group-living taxa, to what degree do trans-generational effects on life histories, such as maternal effects, impact on population dynamics?
  • For group-living taxa, how does covariance among life-history traits affect their contributions to population dynamics?
  • What is the relative importance of direct (consumption, competition) vs. indirect (induced behavioral change) interactions in determining the effect of one group-living population upon another?
  • For group-living taxa, how important is individual variation to population, community, and ecosystem dynamics?
  • For group-living taxa, what demographic traits [genetic, individual, group, population] determine the resilience of natural populations to disturbance and perturbation [“stress”]?
  • How well can community properties and responses to environmental change be predicted from the distribution of social traits?
  • Thinking of group-living taxa, how do social traits influence ecological network structure?
  • How many group-living taxa can coexist in a given area?
  • Thinking of group-living taxa, how do resource pulses affect resource use and interactions between individuals and groups?
  • How important are group-living taxa in the functioning of ecological communities?
  • Thinking of group-living taxa, which taxa are most sensitive to to changes in community composition?
  • What are the relative contributions of [different levels of] group-living taxa to biodiversity and ecosystem functioning?
  • Thinking of group-living taxa, how does nutrient input and output affect productivity in ecosystems?
  • Which, if any, group-living taxa are functionally redundant in the context of stochastic or directional environmental changes?
  • What unexploited theories used by other disciplines could inform Social Biology and Behavioral Ecology?

Monday, June 11, 2018

Clara B. Jones, Fieldwork, Costa Rica, 1973 (with Norm Scott, US Fish & Wildlife)

My 45 years as a Behavioral Ecologist...(1973-->)

"Maybe this is just convenient for me, but I never thought of being a mother as an accomplishment. At the same time, loneliness, independence, solitude--it's heavy." Claire Denis (The New Yorker, 2018)

My 45 years as a Behavioral Ecologist...#womeninscience #womeninbehavioralecology #womeninEcoEvo

1. The Science culture that I experienced 45 years ago might be called a "Brigade System"--hard-nosed, mostly male, rigid, rigorous--with no hand-holding. It was understood that many wouldn't survive the regime--we took this for granted--bad experiences were just part of the obstacle course. This system motivated me to be the best scientist I could be--emulating the work and standards of the premier Behavioral Ecologists of that time [especially, John Hurrell Crook (birds, primates), Stephen C. Emlen (birds, humans, one of my professors), Jack Bradbury (mammals, one of my professors), Ruth Buskirk (spiders, baboons, one of my professors), & Sandy Vehrencamp (birds, bats, a fellow graduate student)].

2. Having said the above, we had mutual respect among all deserving parties, whatever their rank, and had a lot of fun.

3. A necessary and sufficient component of my own motivation was falling in love with fieldwork in 1973 [I was 30--a "late-bloomer"] after which nothing ever competed with my work/career. Another factor important to my progress was relieving myself of most caretaking responsibilities [in 1979].

4. At one point during my graduate training, I asked my undergraduate advisor, Harry Levin, what was necessary to be successful as a scientist. He replied, "Learn to cope with humiliation." Afterwards, it occurred to me that I would need to have a "thick skin." It was, also, clear that I would have to make it work for me, by myself, on my own, but on others' terms--the terms set by those at the top of my fields--Behavioral Ecology, Social Biology. Serious Scientists would let me know--straightforwardly--when they thought I was not "measuring up." I could leave Science, or, I would need to find a way to "measure up." Combined with the ability to "hear" critical and negative feedback, I, eventually, enjoyed the challenges inherent to intense competition. At another point--after  completing my Ph.D.--my major advisor, Ethologist (birds), William C. Dilger, told me, "You have done less with more than any other graduate student I've had." This feedback shook me; however, the lesson was clear--it is very difficult, indeed, to earn the right to be taken seriously by a serious Scientist. Indeed, in graduate school, it was standard not to call ones-self "Scientist" until a recognized scientist had labeled you "Scientist." Dilger's comment reinforced that I needed to take myself and my aspirations seriously if I, and, more importantly, my work, were to earn the opportunity to be taken seriously. A consequence of this experience has been that I consider it a female's responsibility--to herself, more, even, than to others-- to find a way to develop her potential to the fullest, relative to the highest standards of her field. These words of wisdom & feedback from two highly-regarded scientists were instrumental in motivating me to be my best while understanding realities of the academic/professional/research landscape. The path is difficult, and there are no guarantees.

5. When I found my path in Behavioral Ecology to be difficult, I reminded myself that, if I didn't find a way to make it work, there was always another female breathing down my back who was not defeated by trying or who was making it work.

6. It is central to who I am as a Scientist to view myself having a role comparable to a Judge of the Court. My colleagues and I, if taken seriously, get to "weigh in" on difficult decisions, using critical thinking, data, and expertise.

7. It is important to me that I never used a sex/gender card, a race card, a class card, or a disability card.

8. I consider myself a feminist in the molds of Simone de Beauvoir and, especially, Francoise Giroud whose autobiography, I Give You My Word, which I read early in graduate school, changed my life forever. Everything changed after I read that book--combined with my first field season in 1973.

9. I free myself; others do not free me.

10. So-called "imposter syndrome" represented important feedback to me that something needed to be corrected. I did not deny my gut. I figured it out. I recalled, there was always another female prepared to take my place. That female would have dealt with her sense of imposition and would have self-corrected.

11. I think I have few, if, any, complaints as a woman of color in Science because: [1] I almost always see the glass as half-full rather than half-empty; [2] I take almost nothing personally--what another person does or says reflects on them, not me; [3] I have an uncanny ability to focus without distraction; [4] I try, and, mostly, succeed, at not multitasking, and [5] I, simply, kept my nose to the grindstone.

12. For other insights about me, including, many of my views about women, see:

Wednesday, April 11, 2018

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)

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 (;, 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.----

Literature cited
Altmann, S.A. 1962: A field study of the sociobiology of rhesus monkeys, Macaca mulatta. Ann. NY Acad. Sci. 102, 338-435.
Bell, A.M. & Robinson, G.E.R. 2011: Behavior and the dynamic genome. Science 332, 1161-1162.
Bradbury, J.W. & Vehrencamp S.L. 1977. Social organization and foraging in emballonurid bats III: Mating systems. Beh Ecol Sociobiol 2, 1-17.
Brown, J.L. 1975: The Evolution of Behavior. Norton, New York.
Burt, A. & Trivers, R.L. 2006: Genes in Conflict. Harvard University Press, Cambridge, MA.
Crespi, B.J. 1994: Three conditions for the evolution of eusociality: Are they sufficient? Insectes Sociaux 41, 395-400.
Crespi, B.J. 1996: Comparative analysis of the origins and losses of eusociality: Causal mosaics and historical uniqueness. In: Phylogenies and the Comparative Method in Animal Behavior (Martins, E. ed). Oxford University Press, Oxford, UK, pp 253-287.
Crespi, B.J. 2005: Social sophistry: Logos and mythos in the forms of cooperation. Ann. Zool. Fennici 42, 569-572.
Crespi, B.J. 2007: Comparative evolutionary ecology of social and sexual systems: Water-breathing insects come of age. In: Evolutionary Ecology of Social and Sexual Systems: Crustaceans as Model Organisms (Duffy, J.E. & Thiel, M. eds). Oxford University Press, Oxford, UK, pp 442-460.
Crespi, B.J. 2009: Social conflict resolution, life history, and the reconstruction of skew. In: Reproductive Skew in Vertebrates: Proximate and Ultimate Causes (Hager, R. & Jones, C.B. eds). Cambridge University Press, Cambridge, UK, 480-507.
Crespi, B.J. & Choe, J.C. 1997: Explanation and evolution of social systems. In: The Evolution of Social Behavior in Insects and Arachnids (Choe, J.C. & Crespi, B.J. eds). Cambridge University Press, New York, pp 499-524.
Crook, J.H. 1965: The adaptive significance of avian social organization. Symp. Zool. Soc. Lond. 14, 181-218.
Crook, J.H. 1970: Social behaviour and ethology. In: Social Behaviour in Birds and Mammals (Crook, J.H., ed). Academic Press, London, pp xxi-2.
Crook, J.H. 1977: On the integration of gender strategies in mammalian social systems. In: Reproductive Behavior and Evolution (Rosenblatt, J.S. & Komisaruk, B.R., eds). Plenum Press, New York, pp 17-38.
Crook, J.H., Ellis, J.D., Goss-Custard, J.D. 1976: Mammalian social systems: Structure and function. Anim. Behav. 24, 261-274.
Dunbar, R.I.M. 1996: Determinants of group size in primates: A general model. In: Evolution of Social Behaviour Patterns in Primates and Man (Runciman, W.G., Maynard Smith, J., Dunbar, R.I.M., eds). Oxford University Press, Oxford, UK, pp 33-58.
Eibl-Eibesfeldt, I. 2007: Human Ethology. Aldine Transactions (Aldine De Gruyter), Piscataway, NJ.
Eisenberg, J.F. 1981: The Mammalian Radiations. University of Chicago Press, Chicago, IL.
Emlen, S.T. 1991: The evolution of cooperative breeding in birds and mammals. In: Behavioural Ecology: An Evolutionary Approach (Krebs, J.R. & Davies, N.B., eds). Blackwell, Oxford, UK, pp 301-337.
Emlen, S.T. & Oring, L.W. 1977: Ecology, sexual selection, and the evolution of mating systems. Science 197, 215-223.
Emmons, L.H. 2000: Tupai: A Field Study of Bornean Treeshrews. University of California Press, Berkeley.
Ewer, R.F. 1960: Natural selection and neoteny. Acta Biotheoretica 13, 161-184.
Fischman, B.J., Woodard, S.H., Robinson, G.E. 2011: Molecular evolutionary analyses of insect societies. Proc. Nat. Acad. Sci. doi/10.1073/pnas.1100301 108
Frank, S.A. 1995: Mutual policing and repression of competition in the evolution of cooperative groups. Nature 377, 520-522.
Frank, S.A. 1998: Foundations of Social Evolution. Princeton University Press, Princeton, NJ.
Frank, S.A. 2006: Social selection. In: Evolutionary Genetics: Concepts and Case Studies (Fox, C.W. & Wolf, J.B., eds). Oxford University Press, Oxford, UK, pp 350-363.
Garamszegi, L.Z. & Møller, A.P. 2011: Non-random variation in within-species sample size and missing data in phylogenetic comparative studies. Syst. Biol. doi: 10.1093/sysbio/syr060
Hager, R. & Jones, C.B. 2009: Reproductive Skew in Vertebrates: Proximate and Ultimate Causes. Cambridge University Press, Cambridge, UK.
Hamilton, W.D. 1964: The evolution of social behavior. J. Theor. Biol. 7, 1-52.
Hamilton, M.J., Davidson, A.D., Sibly, R.M., Brown, J.H. 2011: Universal scaling of production rates across mammalian lineages. Proc. Roy. Acad. Lond. B, 278, 560-566.
Helms Cahan, S., Blumstein, D.T., Sundström, L., Liebig, J., Griffin, A. 2002: Social trajectories and the evolution of social behavior. Oikos 96, 206-216.
Heinze, J. & Keller, L. 2000: Alternative reproductive strategies: a queen perspective in ants. Trends. Ecol. Evol. 15, 508-512.
Hewitt, G.M. & Butlin, R.K. 1997: Causes and consequences of population structure. In: Behavioural Ecology: An Evolutionary Approach (Krebs, J.R. & Davies, N.B., eds). Blackwell, Oxford, UK, 350-372.
Holbrook, C.T., Barden, P.M., Fewell, J.H. 2011: Division of labor increases with colony size in the harvester ant Pogonomyrmex californicus. Behav. Ecol. doi: 10.1093/beheco/arr075
Holland, B. & Rice, W.R. 1999: Experimental removal of sexual selection reverses intersexual antagonistic coevolution and removes a reproductive load. Proc. Nat. Acad. Sci. USA 96, 5083-5088.
Hrdy, S.B. 1974: Male-male competition and infanticide among the langurs (Presbytis entellus) of Abu, Rajasthan. Folia. Primatol. 22, 101-158.
Hrdy, S.B. 2009: Mothers and Others. Harvard University Press, Cambridge, MA.
Jarvis, J.U.M. 1978: Energetics of survival in Heterocephalus glaber (Rüppell), the naked mole-rat (Rodentia: Bathyergidae). Bull. Carnegie Mus. Nat. Hist. 6, 81-87.
Jerison, H.J. 1983: The evolution of the mammalian brain as an information-processing system. In: Advances in the Study of Mammalian Behavior (Eisenberg, J.F. & Kleiman, D.G., eds). American Society of Mammalogists, Shippensburg, PA, 113-146.
Jetz, W. & Rubenstein, D.R. 2011: Environmental uncertainty and the global biogeography of cooperative breeding in birds. Curr. Biol. 21, 72-78.
Jones, C.B. 1980: The functions of status in the mantled howler monkey (Alouatta palliata Gray): intraspecific competition for group membership in a folivorous Neotropical primate. Primates 21, 389-405.
Jones, 1996: Temporal division of labor in a primate: age-dependent foraging behavior. Neotrop. Primates 4, 50-53.
Jones, C.B. 1997: Life history patterns of howler monkeys in a time-varying environment. Boletin Primatologico Latinoamericano 6, 1-8.
Jones, C.B. 2005: Behavioral Flexibility in Primates: Causes and Consequences. Springer, New York.
Jones, C.B. 2007: Orgasm as a post-copulatory display. Archiv. Sex. Behav. 36, 633-636.
Jones, C.B. 2008a: Ethology, neuroethology, and evolvability in vertebrates: a brief review and prospectus. Primate Report 75, 41-62.
Jones, C.B. 2009: The effects of heterogeneous regimes on reproductive skew in eutherian mammals. In: Reproductive Skew in Vertebrates: Proximate and Ultimate Causes (Hager, R. & Jones, C.B., eds). Cambridge University Press, Cambridge, UK, pp 84-113.
Jones, C.B. 2011: Are humans cooperative breeders? A call for research. Archiv. Sex. Behav. 40, 479-481.
Jones, C.B. & Agoramoorthy, G. 2003: Alternative reproductive behaviors in primates: towards general principles. In: Sexual Selection and Reproductive Competition in Primates: New Perspectives and Directions (Jones, C.B., ed). American Society of Primatologists, Norman, OK, pp 103-139.
Jones, C.B., Milanov, V., Hager, R. 2008: Predictors of male residence patterns in groups of black howler monkeys. J Zool 275, 72-78.
Knowlton, N. 1979: Reproductive synchrony, parental investment, and the evolutionary dynamics of sexual selection. Anim. Behav. 27, 1022-1033.
Lovegrove, B.J. & Wissel, C. 1988: Sociality in mole-rats: metabolic scaling and the role of risk sensitivity. Oecologia 74, 600-606.
McComb, K., Shannon, G., Durant, S.M., Sayialel, K., Slotwo, R., Poole, J., Moss, C. 2011: Leadership in elephants: the adaptive value of age. Proc. Roy. Soc. Lond. B doi: 10.1098/rspb.2611.0168
McNab, B.K. 2006: The energetics of reproduction in endotherms and its implication for their conservation. Integrative and Comparative Biology 46, 1159-1168.
Morris, D. 1956: The feather postures of birds and the problem of the origin of social signals. Behaviour 9, 6-113.
Nygard, S., Zhang, G., Schiø, H.M., Li, C., Wurm, Y., Hu, H., Zhou, J., Ji, L., Qiu, F., Rasmussen, M., Pan, H., Hauser, F., Krogh, A., Grimmel, Khuijzen, J.P., Wang, J., Boomsma, J.J. 2011: The genome of the leaf-cutting ant Acromyrmex echinatior suggests key adaptations to advanced social life and fungus farming. Genome Res doi: 10.1101/gr.121392.111
Pigliucci, M. & Müller, G.B. 2010: Evolution: The Extended Synthesis. MIT Press, Cambridge, MA.
Pulliam, H.R. & Caraco, T. 1984: Living in groups: Is there an optimal group size? In: Behavioural Ecology: An Evolutionary Approach, 2nd ed. (Krebs, J.R. & Davies, N.B., eds). Sinauer, Sunderland, MA, 122-147.
Rice, W.R. 2000: Dangerous liaisons. Proc. Nat. Acad. Sci. 97, 12953-12955.
Rose, K.D. & Archibald, J.D. (eds). 2005: The Rise of Placental Mammals. Johns Hopkins University Press, Baltimore, MD.
Russell, A.F., Sharpe, L.L., Brotherton, P.N.M., Clutton-Brock, T.H. 2003: Cost minimization by helpers in cooperative vertebrates. Proc. Nat. Acad. Sci. USA 100, 3333-3338.
Schoener, T.W. 1971: Theory of feeding strategies. Ann. Rev. Ecol. Syst. 2, 369-404.
Taborsky, M. 2009: Reproductive skew in cooperative fish groups: virtue and limitations of alternative modeling approaches. In: Reproductive Skew in Vertebrates: Proximate and Ultimate Causes (Hager, R. & Jones, C.B. eds). Cambridge University Press, Cambridge, UK, pp 265-304.
Thibert-Plante, X. & Hendry, P. 2009: Five questions on ecological speciation addressed with individual-based simulations. J. Evol. Biol. 22, 109-123.
Tinbergen, N. 1952: Derived activities: their causation, biological significance, origin, and emancipation during evolution. Quart. Rev. Biol. 27, 1-32.
Toth, A.L., Varala, K., Newman, T.C., Miguez, F.E., Hutchison, S.K., Willoughby, D.A., Simons, J.F., Egholm, M., Hunt, J.H., Hudson, M.E., Robinson, G.E. 2007: Wasp gene expression supports an evolutionary link between maternal behavior and eusociality. Sciencexpress,, 10.1126/science.1146647, 1-4.
Trivers, R.L. 1971: The evolution of reciprocal altruism. Q. Rev. Biol. 46, 35-57.
Trivers, R.L. 1972: Parental investment and sexual selection. In: Sexual Selection and the Descent of Man, 1871-1971 (Campbell, B., ed). Aldine, New York, 136-179.
Trivers, R.L. 1974: Parent-offspring conflict. Am. Zool. 14, 249-264.
Vehrencamp, S.L. 1979: The roles of individual, kin, and group selection in the evolution of sociality. In: Handbook of Behavioral: Social Behavior and Communication (Marler, P. & Vandenbergh, J., eds). Plenum Press, New York, pp 351-394.
Vehrencamp, S.L. 1983: A model for the evolution of despotic versus egalitarian societies. Anim. Behav. 31, 667-682.
Vehrencamp, S.L. 2000: Evolutionary routes to joint-female nesting in birds. Behav. Ecol. 11, 334-344.
Wang, G., Hobbs, N.T., Boone, R.B. 2006: Spatial and temporal variability modify density-dependence in populations of large herbivores. Ecology 87, 95-102.
Weinreich, D.M., Delaney, N.F., De Prisot, M.A., Hartl, D.L. 2006: Darwinian evolution can follow only very few mutational paths to fitter proteins. Science 312, 111-114.
Weiss, P. 1941a: Autonomous versus reflexogenous activity of the central nervous system. Proc. Am. Phil. Soc. 84, 53-64.
Weiss, P. 1941b: Self-differentiation of the basic patterns of coordination. Compar. Psychol. Monog. 17, 1-96.
West, S.A., Pen, I., Griffin, A.S. 2002: Cooperation and competition between relatives. Science 296, 72-75.
West-Eberhard, M.J. 1979: Sexual selection, social competition, and evolution. Proc. Am. Phil. Soc. 123, 222-234.
West-Eberhard, M.J. 1980: Toward a unified theory of social insect caste: Caste and Ecology in the Social Insects by George F. Oster and Edward O. Wilson (Book Review). Quart. Rev. Biol. 54, 430-433.
West-Eberhard, M.J. 2003: Developmental Plasticity and Evolution. Oxford University Press, Oxford, UK.
West-Eberhard, M.J. 2005: Phenotypic accommodation: adaptive innovation due to developmental plasticity. J. Exp. Zool. 304B, 610-618.
Whitfield, C.W., Cziko, A.-M., Robinson, G.E. 2003: Gene expression profiles in the brain predict behavior in individual honey bees. Science 302, 296-299.
Whitfield, C.W., Ben-Shahar, Y., Brillet, C., Leoncini, I., Crauser, D., Le Conte, Y., Rodriguez-Zas, S., Robinson, G.E. 2006: Genomic dissection of behavioral maturation in the honeybee. Proc. Nat. Acad. Sci. USA 103, 16068-16075.
Wilson, E.O. 1971: The Insect Societies. Belknap, Cambridge, MA.
Wilson, E.O. 1975: Sociobiology: The New Synthesis. Belknap, Cambridge, MA.
Wittenberger, J.F. 1980: Group size and polygamy in social mammals. Am. Nat. 115, 197-222.
Wong, M.Y.L. 2011: Group size in animal societies: the potential role of social and ecological limitations in the group-living fish, Paragobiodon xanthosomus. Ethology 117, 1-7.

Tuesday, April 10, 2018

Fake Orgasm In [Human] Females (FOF) (Clara B. Jones, 2013)

Is fake orgasm in [Human] females (FOF) a dishonest signal? (Clara B. Jones, 2013)

For a detailed discussion of "dishonest signaling" see Dawkins &Guilford (1991 Anim. Behav. 41:5 ). Stereotyped and ritualized behaviors in humans have been documented and discussed by Eibl-Eibesfeldt (2007), in particular, the unambiguous “eyebrow-flash” motor pattern (stereotyped lifting of the eyebrows). The latter author conducted cross-cultural research including cryptic filming of the eye-flash, demonstrating that, in cultures throughout the world, the eye-flash is most likely to occur between males and females while flirting and in apparent courtship, and, other, “bonding” situations, suggesting regulation of differential fitness optima x contexts. Eibl-Eibesfeldt (2007) concluded that, with the exception of discrete vocal displays (Marler 1976), stereotyped and ritualized action patterns are rare in the human behavioral repertoire, and it is assumed in this section that the latter condition obtains because, compared to other organisms, phenotypes of Homo sapiens are shaped to a large degree by learning. Among large animals, learning mechanisms are thought to have evolved in response to rapidly changing (“stochastic”) environments favoring flexible, resilient, and “plastic”behavior, effects with the potential for rapid adjustment of the phenotype to environmental heterogeneity (e.g., Mazur 2004, Jones 2012, West-Eberhard 2003; see Proulx 2001), possibly decreasing the selective advantage of some hard-wired responses such as those dedicated by the process of ritualization to stereotypy over time and space. In the present section, a variable, “dishonest”, behavioral pattern is discussed. This response, “fake” orgasm in human females (hereafter, “dishonest orgasm”), consciously mimics involuntary, “honest”orgasm by combining and recombining discrete (usually, vocal) and graded components of the latter, autonomic response.

Relative to the theoretical and empirical literature on the structure(s) and function(s) of “honest” male orgasms, the literature on “honest” or “face” orgasm by human females is limited (but, see Komisaruk et al. 2006). In 2007a, Jones (Mialon 2012) suggested that dishonest, fake orgasm by human females (FOF) might be viewed in the context of Signaling Theory (Fig. 5.1). Following the general schema advanced by Maynard Smith and Harper (2003), a partial conceptual framework is proposed for the study of dishonest orgasms displayed by female Homo sapiens. As a simplifying assumption, dishonest orgasm is assessed herein as a straightforward manifestation of Signaler-Receiver dynamics between two adults ("action-response games") because: (1) human sexual acts may be analyzed as discrete sequences in time and space ("context-dependent" behavior), with a discriminable beginning and end; and, (2) behavioral sequences involving 1 or >1 acts of sexual congress entails reciprocal ("back-and-forth", not, necessarily, "tit-for-tat") interactions between members of a dyad. In the present treatment, dishonest orgasms are considered to represent intentional, flexible, possibly, learned responses that appear not to represent a “ritualized" or polymorphic display. However, motor patterns characteristic of FOF may be stereotyped, in particular, articulation of femur and acetabulum permitting “axial skeleton and lower limb movement”.

Following the schema of Maynard Smith and Harper (2003), dishonest orgasms are best understood to function as (1) mimicry of honest orgasm and (2) exploitation (manipulation) of the sexual partner(s). Continuing to employ the system in Maynard Smith and Harper (2003), dishonest orgasm, as mimicry, represents an "unreliable signal..., believed because it resembles a reliable cue or signal" (in the present case, honest orgasm). Dishonest orgasm may be viewed as a manifestation of proximate conflict or reactions to exogenous stimuli (alarm?, fear?, discomfort?) between sexual partners, a condition analogous to an evolutionary "arms-race" (“sexual conflict”: Rice 2000, Fricke et al. 2010). Related to the latter suggestion, some (condition-dependent) cases of dishonest orgasm may result from exogenous, aversive stimuli (disgust, such as, by a male's tactile, auditory, olfactory responses during sexual congress), or, from a female's endogenous re-actions (alarm, fear). A byproduct or goal of dishonest orgasm presenting daunting empirical challenges is the possibility that the intentional display reinforces a male's feelings of, or, his actual, dominance, control, power, possibly, inducing aggression in some situations. Following ethological theory outlined above, honest, stereotyped, involuntary signals and displays are expected to represent “true communication” and to decrease likelihoods of aggression (Tinbergen 1952, Enquist et al. 2010).

According to Maynard Smith and Harper's (2003) system, dishonest orgasm would be classified as an "icon,...a signal whose form is similar to its meaning" (similar to honest orgasm). Systematic studies of dishonest orgasm are needed to address the aforementioned suggestions, and, others. For example: How detectable are dishonest from honest orgasms? Do dishonest orgasms incorporate components of honest orgasms? What are the differential costs and benefits of dishonest compared and contrasted to honest displays of orgasm or no display? What are the ancestral (genetic, physiological) origins of dishonest and honest orgasm, and do they differ?
Dishonest orgasm apparently represents an example of an exaggerated, compound (multi-component) display whose stereotyped features derive from its similarity to honest orgasm. Benefits from dishonest orgasm may sometimes outweigh costs, sometimes not. Females are expected to be differentially skilled at faking orgasm, and, likelihoods of aggression may vary with expertise.

For example, the learned elements of dishonest orgasm may have required modifications in the genetic and physiological substrates of honest orgasm. As a likely product of directional (sexual) selection, honest orgasm, on average, is expected to respond to a narrower range of endogenous and exogenous stimuli compared to dishonest orgasm, possibly, restricting the utility of the latter response in some regimes (stable conditions). The previous rationale may represent one of several proximate benefits of flexible tactics and strategies, including, social learning via familial or other social conventions, such as, cultural traditions. Learned mechanisms are thought to minimize potential costs in heterogeneous, stressful, unstable, or“rapidly changing” conditions (Proppe et al 2011, Mazur 1986), with effects more difficult to predict or control than those attendant to honest displays of orgasm. Nonetheless, a possibly advantageous tradeoff to dishonest orgasm would be that the signaler is likely to have more control over energy expenditure compared to contexts in which involuntary, honest orgasm is expressed. This possibility suggests that measuring energetic variables is one methodological approach to studying the two forms of female orgasm empirically. Such research programs have the potential to unify studies of intra- and inter-specific social competition (West-Eberhard 1979, Tobias and Seddon 2009) with those of “rapid” evolution (West-Eberhard 2003, Hairston et al. 2005), including, differential intensities of selection.

Focal Tree Method (FTM) of Observational Study (Clara B. Jones, 1976)

Focal Tree Method (FTM) of Observational Study (Clara B. Jones, 1976)

A standard procedure in studies of plant ecology and entomology is the use of the "focal tree" method (FTM) to obtain data on the behavior of trees themselves (e.g., phenophase and its variability through T, flower-opening T, changes over T in fruit, flower, or new leaf mass) and/or of insect density, abundance, and behavior in relation to tree behavior over T (and, sometimes, S). In general, trees in a given plot or area are sampled on some schedule, preferably, though not necessarily, random. The FTM is best employed whenever the distribution, abundance, behavior, etc. of the plant (tree, shrub, epiphyte, etc.) is expected to be an independent variable inducing dependent responses in other organisms [most commonly insects: e.g. Frankie et al., 1976; I learned the technique watching Gordon Frankie & his assistant, Bill Haber and transferred the method to mantled howler monkeys (Alouatta palliata Grey)]. Scientists have been slow to apply the FTM to vertebrates, perhaps because research on vertebrates generally entails following animals over T and S to record their behavioral interactions with conspecifics (social behavior) and making the focal animal him/herself the target of observation. While this research strategy may yield important information about foraging and other behaviors by individuals and groups, target animals and variations in their behaviors are the primary focus of data-collection, minimizing the influence of variations in plant behavior on animals as well as quantifiable events ongoing in plant food resources (e.g., variations in animal behavior as a function of tree size, species, and phenophase, variations in animal behavior as a function of competition with other organisms for plant tissues and other products such as nectar and pollen). In 1983, using the FTM, Jones reported selectivity of legume flowers (Pithecolobium saman: see image of flower) at flower-opening time by mantled howler monkeys. In 2005, the same author published results for selectivity by mantled howlers for legume flowers at anthesis with the FTM. In another research project (Jones, 1976, unpublished), the FTM was used to quantify howler density and order of entry into trees x monkey age and sex as a function of tree size, phenophase, species, and habitat (tropical dry forest riparian or deciduous: Frankie et al., 1976). One or more observers may be employed with the FTM, the latter approach used in studies recently reported by Vogel and Janson (e.g., 2011). Depending on the precise design of studies employing the FTM, data are amenable to mathematical simulation or other mathematical modeling after data are collected. Alternatively, the Vogel and Janson report cited uses a quantitative model to evaluate aggressive behavior in capuchins as a function of plot size. The success of the studies discussed herein and the rich information they provide highlight the value of the FTM for research with vertebrates using plants for food.

Frankie, G.W. et al. 1976. Foraging behavior of solitary bees: implications for outcrossing of a Neotropical tree species. J. Ecol. 64: 1049-1057.

Jones, C.B. 1983. Do howler monkeys feed upon legume flowers preferentially at flower-opening time? Brenesia 21: 41-46.

Jones, C.B. 2005. Discriminative feeding on legumes by mantled howler monkeys (Alouatta palliata) may select for persistence. Neotropical Primates 13(1): 3-8.

Vogel, E.R. & Janson, C.H. 2011. Quantifying primate food distribution and abundance for socioecological studies: an objective consumer-centered model. Int. J. Primatol. DOI: 10: 1077/s10764-011-9498-7

Experiment...Food Dispersion...Hacienda La Pacifica [1976] (Clara B. Jones)

Experiment: Hacienda La Pacifica, Cañas, Costa Rica [1976] (Clara B. Jones, Ph.D.)

WHO: This post describes an unsuccesful attempt to manipulate food dispersion (distribution of food in time and space) using a Neotropical primate. The target species was the mantled howler monkey (Alouatta palliata Gray), a predominately arboreal monkey that is exclusively herbivorous, preferring new leaves, flowers, and fruit. The diet of mantled howlers, also, includes mature leaves of many plant species (mostly tree and some shrubs and vines); as well, old leaves may be eaten in due course as well as "fallback" foods, eaten when preferred food items are not available, rare in time and space, or dispersed in a manner making foraging for them energetically and/or temporally expensive, ceteris paribus. As described by Milton in her 1980 book, the foraging behavior of mantled howlers is "rule-governed", and the method described here is probably most useful with animals whose foraging behavior is tactical and strategic (e.g., animals following particular routes depending upon distribution, abundance, and/or quality of food) rather than opportunistic or "random". The method described herein should apply to animals feeding on food occurring in discrete packages (e.g., trees, termite mounds, carcasses) and/or in patches. In general, the method has utility with non-volant and non-aquatic animals.

WHAT: Foraging behavior of one mantled howler group in Costa Rican tropical dry forest was followed before manipulation for 3 d in dry season. Dry season was selected as the time of year when many preferred foods are most likely to flower and fruit, and the particular procedure employed (see below), required the absence of rain. A medium-sized, relatively abundant tree (Tabebuia neocrysantha: see image) was flowering at its peak during the study week and was selected as the target food item for logistic and practical reasons. In addition, the manipulation was performed in a relatively small patch of forest on the monkeys' home range (see below) to allow for selected post-manipulation data collection. The 3 d window of observation was selected to minimize the chance that flower quality would deteriorate, decreasing salience of the food item for the animals.

DESIGN AND APPARATUS: Two T. neocrysantha trees were selected for experimental manipulation. Close observation of the animals' foraging behavior in the days prior to the manipulation permitted confident knowledge of the group's location relative to the test site and relatively confident prediction that the group would utilize the trees selected as well as the approximate time of day of feeding. The objective of this field experiment was to record group movement(s), including routes taken, and feeding behavior(s) before and after manipulation, in particular, "decisions" regarding food type (flowers, fruit, new leaves, and/or mature leaves), distance traveled from feeding site of origin, route taken to next feeding station, etc. The manipulation entailed spraying the target trees with a liquid substance gustatorially, and, possibly, olfactorily, aversive to the animals. Based upon the suggestion of a rancher, quinine (Qualaquin, see link) was selected as the substance employed because of its low cost, because of the low likelihood that it would harm the animals, and because it was water-soluble. Furthermore, in Costa Rica, quinine is available "across the counter". The particular ratio of quinine to water should be as high as possible to ensure its effectiveness as a deterrent/avoidant substance to the animals from the food source; however, the particular ratio of aversive substance to water will be a function of body size, type of aversive product, and, possibly, other factors. The vehicle for delivery of the liquid substance was an inexpensive, plastic spray container generally employed for delivery of insecticide.

OUTCOME: The success of the project descrtbed was limited as a completed study primarily because of the small number of field assistants used with whom to divide tasks, an obvious contingency unfortunately not considered in sufficient detail before beginning what must be termed a pre-test.

BENEFITS AND COSTS: Each researcher must determine for her/himself the relative benefits and costs of the design described here. However, inherent to any experiment, whether field or laboratory, is the requirement to stress organisms in order to obtain veridical results/data. This principle applies, also, to human research.

1. travel efficiency/costs pre- and post-manipulation
2. movements in relation to cognitive complexity requiring evaluation of foraging tactics/strategies
3. assesment of possible decision hierarchy regarding food selectivity and is pre- and post-manipulation foraging "rule-governed
4. assessment of consequences of manipulation as ecological constraint (e.g., does manipulation induce fissioning or other changes in social organization)
5. does manipulation increase/decrease competition/aggression
6. which, if any subject, emerges as leader to alternative food station(s) (e.g., topics related to coordination and control at individual, sub-group, and group levels)
7. do temporal and/or spatial (e.g., detours, alternate routes) patterning of movements change from pre- to post-manipulation
8. do animals continue to utilize or reject food item(s); if reject, for how long; if reject, what stimuli salient (color, food type, etc.); do they generalize these cues to other food items

The attached link displays a published report of a foraging experiment using two baboon groups as subjects: