crazyben

Hey!, whoever may read this... Here is the paper I wrote for GEO 6116, Perspectives on Environmental Thought. I think it makes for a good read, hopefully my prof. will too. Aaah, damn my eyes are burning...

Behavioral Ecology and the De-Animalization of Nature


Introduction: the Problem of De-animalization

Beginning in the early 1960s, the work of behavioral ecologists (behavioral ecology is the study of ecosystems to understand how they should affect the behavior of individual organisms) took a turn toward more abstract methods in their approaches to problem solving. The work of W.D. Hamilton (Hamilton 1964, I & II; Axelrod & Hamilton 1981), R. Trivers (1971; 1974; Trivers & Willard 1973), Lord Robert May (1973), John Maynard-Smith (Maynard-Smith & Price 1973; Maynard-Smith 1979) and others provided the impetus for a drastic reorganization of ecological knowledge. This new approach was based on the idea of modeling natural systems from the ground up, using a sort of building block approach (e.g., see Ginzburg 1986; Hamilton 1964, I & II; Axelrod & Hamilton 1981; May 1973; Maynard-Smith and Price 1973; Maynard-Smith 1979; Trivers 1971; Trivers 1974; and Trivers & Willard 1973 for some examples of the new paradigm). With the rise in ecological modeling using mathematical approaches such as game theory, as opposed to the old paradigm of patient observation of ecosystems and their suspected key variables in order to ascertain relationships of causality in ecosystem evolution and change, came a new power to model traits, behaviors and evolutionary trajectories of genetic change (e.g., see Hamilton 1964, I & II; Axelrod & Hamilton 1981; Maynard-Smith & Price 1973; Maynard-Smith 1979; Trivers 1971; Trivers 1974; Trivers & Willard 1973 for example) previously unmatched. Problems such as the problem of how genes for altruism could spread were now pronounced "solved."
With these brilliant solutions, however, came a problem that I call "de-animalization." The problem arises from the trend in behavioral ecology of assuming that only mathematical models and computer simulations can yield valuable insights into how nature "really" functions. True, ethology has not died as a field of inquiry but it is not considered as important a mode of study as is mathematical modeling and then comparing how well the observations stack up against the models. This is evidenced by the great profusion of, for example, game theory models in behavioral ecology, as well as more abstract models (using non-linear differential equations for example). Behavioral ecologists are often told, after having given some verbal explanation for a behavior's likely causes, to "quantify it and make it a serious explanation" (D.L. Cassill, personal communication). For examples of de-animalization, one has only to peruse the pages of academic journals such as Journal of Theoretical Biology (Ginzburg 1986; Demetrius et al 2007, for very recent work), Evolution (Orr 2007), Evolutionary Ecology (Biernaskie & Elle 2007), The American Naturalist (Pepper & Smuts 2002), Nature (Wolf et al 2007), Proceedings of the National Academy of Sciences (U.S.A.) (Tilman 2004), Proceedings of the Royal Society of London (B) (Grafen 1999) and Trends in Ecology and Evolution (Tomkins & Hazel 2007) (to name several prominent journals) to discover mathematical models that often make no pretense of describing animal behaviors as they will be found in the field. I argue that behavioral ecology's emphasis on mathematical models and computer simulations has "de-animalized" nature: they have taken wild life behavior out of their models. Each paper or piece of literature that I reference is, I believe, partially guilty of de-animalization, though each has been extensively cited and is regarded as a classic of contemporary behavioral ecology. These papers have no doubt contributed to a genuine new understanding of biology and ecology and their role in behavior, but each, I argue, has contributed to de-animalization. Some papers, such as Axelrod & Hamilton (1981), Hamilton (1964, I & II) and Trivers (1971 and 1974) are amongst the most cited papers in all of biology, with thousands of citations apiece, while others are merely considered modern classics that pointed us toward the new way of thinking about ecosystems as large computers composed of many interlocking, individually comprehensible parts (e.g., May 1973; Maynard Smith & Price 1973). Throughout, I do a close reading of selected texts to demonstrate to the reader my point that nature itself is increasingly replaced by game theory models in this way of thinking. The literature of behavioral ecology is vast, the literature of game theory and its applications doubly so; hence, my close reading is restricted to three of the most cited papers in the history of biology: W.D. Hamilton's (1964) "The Genetical Evolution of Social Behaviour (I & II)," J. Maynard-Smith's & G. R. Price's (1973) "The Logic of Animal Conflict" and R.L. Trivers' (1971) "The Evolution of Reciprocal Altruism."

Hamilton's Gene-centered Approach to Behavioral Ecology

I believe that much of the trouble of de-animalization in evolutionary biology and ecology stems from Hamilton's 1964 (I & II) paper on the evolutionary origins of altruism, especially his wildly successful theory of population genetics (often dubbed "kin selection" in the literature). Hamilton (1964, I & II) argued that there could be selective forces that would promote the spread of altruistic behaviors in a population. I treat the assumptions of his model in some depth here, to show that the beginning of a trend that appears to be continuing unabated_that of de-animalization_has its origins here. Hamilton (1964, I & II), in developing his model, did gain some insight into how genes for altruism could spread, but since then biological theorists have mistaken their model genes for real genes and model behaviors, with their limiting dichotomies, for real animal behavior (e.g., animal behavior in the wild). Hamilton's main interest in the problem of altruism was the same as it was (and still is) for many biologists: if natural selection dictates that an individual must out-reproduce his or her competitors, then how could behaviors that appear altruistic evolve? In biology, any behaviors that lower the reproductive odds ("success" in evolutionary terms is measured in terms of how many offspring one has) of one organism but appear to increase those of another are called altruistic behaviors (Hamilton 1964, I & II; Trivers 1971). Such "helping" behaviors should be rare but in fact are common (for example, amongst Belding's ground squirrels (Sherman 1977); baboons, macaques and vervet monkeys (Silk 2002), wood ducks (Nielsen et al 2006), tiger salamanders (Pfennig et al 1999) and wild turkeys (Krakauer 2005) to name a few cases). Hamilton's (1964) theory, while making some simplifying assumptions, seems to provide a partial explanation: our genes help themselves by causing us to help others that are statistically likely to share copies of at least some genes (Hamilton 1964, I & II).
But let us now look at the assumptions that Hamilton's (1964) model makes: one is that an organism reproduces once only in its lifetime, probably near the end of its life (Hamilton 1964, I & II). Many animals and other organisms reproduce well before the end of their lives. Further, the model considers only what could happen if the organism has different odds of sharing the same genes as another randomly encountered organism of the same species (Hamilton 1964, I & II). However, in social organisms, one could argue that "random" encounters don't really happen. For example, think of bees, ants, wasps and naked mole rats: they all tend to live in colonies, not separately. Moreover, it is also well established that whales, dolphins, manatees, many species of fish and many species of primates (other than humans), to name a few more examples, all live in social groups that tend to be stable over spatial and temporal scales. Finally, we must consider the fact that all of Hamilton's "genotypic arithmetic" is done with the idea that each organism in his model has only one possible unit of reproduction (e.g., shall have, at most, one offspring) (Hamilton 1964, I & II). Of course, there are a vast number of species in which the mother organism has at least one offspring. All of the above assumptions thus cast some doubt on the plausibility of Hamilton's starting assumptions.
The genius of Hamilton's approach was that he went on to show that if the cost to the "donor" in his model was less than the benefit the "recipient" received, the gene both organisms share then benefits, because it does not experience a true decrease in the total proportion of the population it represents (Hamilton 1964, I & II). Thus, Hamilton's rule of thumb, now called Hamilton's Rule, predicted that relatives should help one another because they are likely share some of the same genes (when compared with non-relatives). This conclusion is reached despite the apparently faulty assumptions of his model and I show below that Hamilton is not alone in ignoring animal behavior in his quest to model animal behavior. I do not intend to cast doubt on the correctness of Hamilton's conclusion (that it pays relatives to help one another), as kin selection has been abundantly studied in the intervening years, but only to show that Hamilton's approach, in modeling abstract "organisms" without reference to observed behavior, is not an isolated phenomena in biology.

"Hawk & Dove" and the "Games" Animals Play

I now turn to the 1973 Maynard-Smith & Price article "The Logic of Animal Conflict." Another seminal piece of theory, it provides a partial solution to the problem of why animals hardly ever indulge in lethal within-group conflicts and illustrates my thesis that contemporary behavioral ecology, in its quest to model behavior, tends to ignore it or dramatically simplify behavioral possibilities. True, animals are killed by predators a fair percentage of the time at any given time of the year, and others are killed during predation whilst defending themselves, their mate and/or their offspring, but what biologists had wondered for some time was why within-species resource competitions (e.g. for mates, habitation sites, food sources, etc.) that result in outright aggression between group members hardly ever turn lethal. Maynard-Smith & Price (1973) argued that it was because many large mammals and ungulates seem to use some sort of innate schema in their brains to decide when to escalate aggression and when to back down, and most of the time the schema said something along the lines of: "back down to avoid getting seriously hurt." In their view, it is the evolution of "limited war" strategies (as they dubbed such behaviors) that prevent within-group conflicts from becoming lethal (Maynard-Smith & Price 1973). Maynard-Smith and Price mention the image of mountain sheep circling each other and looking each other up and down the whole time without going for each other's throats, when they could quite easily maim or kill their opponent as an example (Maynard-Smith & Price 1973).
In the model Maynard-Smith & Price (1973) used, each player was considered to have a series of "moves," or behavioral strategies, available to it at each stage of conflict. This type of model is called a game theory model, where each organism is a "player" and what is competed for (food resources for example) is the "pay off." The assumptions of the particular model that Maynard-Smith & Price (1973) model used are that there are two major types of strategies we are likely to see. There is the use of "conventional" tactics on the part of the "players," tactics that are unlikely to cause serious or life-threatening injuries (Maynard-Smith & Price 1973. There are also "dangerous" tactics, tactics that if employed by one, or both, players, would result in serious injury or death to one, or both, players (Maynard-Smith & Price 1973). Each animal would be likely, in their view, to play a given move only based on the opponent's previous move (Maynard-Smith & Price 1973). This all hinges on one very important point: that each organism has no memory of previous encounters with a given organism beyond the very last move, something that is now known to be untrue. A variety of animals, such as olive baboons (Packer 1977), tree swallows (Lombardo 1985), guppies (Dugatkin & Alfieri 1991) and rats (Rutte & Taborsky 2007), for example, all have some memory of past interactions with conspecifics, beyond just the previous move of each "game."
Maynard-Smith & Price (1973) then deduce, using some elementary probability, that there are several distinct types of strategies available to the players: mouse, hawk, dove, bully, retaliator and prober-retaliator (Maynard-Smith & Price 1973). I will not bore the reader with the details of each strategy, but note that the names "hawk", "dove," and "mouse" for three of the strategies were intended to refer to real-life animal behavior that the animals named are commonly supposed to exhibit in competitive interactions (the model's name "hawk & dove" is taken from the two most likely to be played strategies). This model, using the stereotypes of the "peaceful dove," "warlike hawk" and "meek mouse", embodies them as empirical guides to animal behavior. "The Logic of Animal Conflict" is often considered as much of a watershed event in behavioral ecology as Hamilton's (1964) paper, and both illustrate my thesis that observed behaviors are first simplified or ignored to reach conclusions about what we should expect see on a behavioral level.

Trivers on the Origin of Goodness

The final of the three articles chosen by me as representative examples of de-animalization is "The Evolution of Reciprocal Altruism" (Trivers 1971). The theory of reciprocal altruism was coined by Robert L. Trivers to explain cases of altruism that kin selection could not. Reciprocal altruism was devised, in particular, to account for the evolution of symbiotic relationships, those where we seem to have altruistic acts that benefit one party to the detriment of another, for example, between members of two different species. As Trivers says, "Models that attempt to explain altruistic behavior in terms of natural selection are models designed to take the altruism out of altruism" (Trivers 1971: 18). Trivers' model showed how natural selection could still favor altruistic behavior even when the likelihood of genetic relatedness between multiple individuals are so low as to make kin selection virtually useless, for example, in symbiotic relationships. Dawkins (1989; e.g., chapter 10) summarizes the theory of reciprocal altruism with "you scratch my back, and I'll ride on yours." It is an apt description, for Trivers argued that natural selection would favor altruistic behavior in, for example, highly social groups of organisms if there was likely to be future reciprocation of some sort. Trivers looked at 3 possible scenarios of how altruistic acts could be distributed in nature: randomly, by reference to degree of relatedness, or by reference to altruistic tendencies of the beneficiary. In the case of randomly distributed acts of altruism, Trivers deduces that eventually altruists will go extinct because the benefits of their altruism will not be greater than the cost of their altruistic acts. In the second case, kin selection indeed would dictate, as (Hamilton had supposed) that one solid trajectory altruistic genes could take to propagate themselves at the expense of non-altruistic genes would be if they were kind to relatives and indifferent to non-relatives.
However, concerning the third case Trivers makes a compelling argument that if an altruist can recognize others who are likely to be altruistic, or who have been in the past, than the altruistic genes can still propagate at higher rates than the non-altruistic genes. In this case, the rule can be stated as: "be kind to kind organisms, and be indifferent to non-kind organisms." In this third scenario, it is the exchange itself that makes the act valuable to both organisms (even when between different species), not the probability of there being a copy of some gene or genes found in both organisms. The case of starving vampire bats receiving thrown-up blood from their fellow vampire bats is perhaps the most spectacular example of reciprocal altruism in action (Wilkinson 1984; DeNault & McFarlane 1995). And as Trivers (1971) predicted, individuals that don't reciprocate "lose": in this case they die because individuals that try to get "free" blood are left to starve the next time around.
Some would say that Trivers' model is more realistic than Hamilton's in several key respects (Trivers 1971). For one thing, Trivers assumes (e.g., concerning Hamilton's famous "drowning man" example) that the odds of a would-be rescuer drowning are not always the same, and will in fact change over the lifetime of an organism, the odds tending to be higher in post-reproductive life stages. Another assumption that Trivers makes is there will be a germane cost-benefit analysis regardless of how related the victim is to the possible rescuer, as well as the odds of the victim later reciprocating (Trivers 1971). Trivers was aware that "cheats" could be selected for, as well as those would help other kind persons, particularly those who have helped one in the past. However, the only thing necessary to prevent selection for widespread cheating is selection for t he ability to recognize individual conspecifics. Finally, Trivers believed that natural selection ought to favor the ability to learn about others and this may just be the most realistic assumption of his model (Trivers 1971). With kin selection effects, learning isn't necessary, but with non-kin selection effects, learning to recognize cheaters should be of paramount importance.
One quite problematic assumption of Trivers' model is that the genetic effects of altruism do not depend only on some "altruistic" gene found at one locus. In fact, Trivers' theory allows them to be found at multiple loci (points on the genome) in the genotypes of different agents. So long as there is an assumption of the individual as maximizing agent (e.g., see Grafen 1999 for the full implications of this idea in biology and economics), selection will favor reciprocal altruism amongst highly social organisms with a low rate of dispersion (i.e., they don't go far after birth), long life spans and high degrees of mutual dependence, as well as in species with long periods of parental investment in and care for offspring (Trivers 1971). There is of course the question of how genes for one behavior could recognize that same "behavior" expressed in the phenotype of the other gene promoting that same behavior (Trivers 1971). Dawkins (1989) called it the "green beard" effect: suppose one noticed that individuals with green beards were always nice, well than it would pay one to also always be nice to any green bearded individuals they encountered, as your genes and their genes (including the mysterious "green beard" gene) would continue to spread in the population. This assumption is perhaps what makes the model so difficult to test, for there have been few cases of reciprocal altruism observed in nature, though those that have been are quite unambiguous. Another problem is what sort of behavioral response is likely to be considered approximately equal to any previous action that a "donor" has benefited from, and what types of behaviors biologists should in fact look for when trying to decide if a series of social exchanges is in fact reciprocal altruism in action.
"The Evolution of Reciprocal Altruism" is innovative because it employed both cost-benefit analysis and a game theory model based on genetics principles (Trivers 1971). However, in addition to the problems with his assumptions, Trivers has another problem: that of how to quantify the benefits received by donor species that are actually different species than the beneficiary species in an altruistic interaction. For example, Trivers used the example of cleaner fish (Trivers 1971). It is clear that the large fish being cleaned get the benefit of a low parasite load. It is not clear, however, that the fish which do the cleaning thereby substantially benefit by having a steady supply of food than they would in simply foraging around reef ecosystems. It is also unclear, how either the donor or beneficiary species' reproductive fitness are affected as well, and reproductive success is the true measure of evolutionary fitness. Trivers' style of thinking is often dubbed "bioeconomics" for its use of principles taken from human economics (particularly cost-benefit analysis), but it is not clear how human economics can apply to animal behavior in the wild per se, nor that they should. Lastly, I will mention that though Trivers gave a few examples of cases of potential reciprocal altruism, it is still not clear to behavioral ecologists how many different species could develop the ability to catalogue and remember repeated social interactions, though as claimed above (in the section about "The Logic of Animal Conflict"), it does seem possible.
With all of the above in mind, it is not hard to see why Trivers' theories, like Hamilton's and Maynard-Smith's, are only occasionally confirmed correct. I do not at all intend to cast doubt on Trivers' idea that sometimes perhaps we help one another because we tend to interact with the same social actors over and over, and they will probably be more likely to help us in a time of need, but simply to show that his models, like the others, have the shortcoming of being simplified to be able to describe a large variety of animal behavior, without giving us insight into the behavior of individual animals.

Animal Bodies, Child-Like Minds?

We now turn to Masson & McCarthy (1995) to show that it appears likely that: a) other species, particularly primates, demonstrate complex patterns of cultural behavior (such as learning by both imitation and experimentation), and b) many different species of animals and birds are now known to exhibit complex behaviors and emotions that go far beyond tasks such as mate seeking and food provisioning (Masson & McCarthy 1995). Many animals, in fact, appear to have complex emotional lives. Below, I also provide some details from Van Schaik et al (2003) to further the idea that non-humans are capable of developing complex, emotionally rich individual lives and cultural groups that must be taken account of by scientists wishing to gain further insight into animal behavior.
In Masson & McCarthy's excellent (1995) survey article they document a variety of instances where biologists have observed behaviors in animals that can only be described as akin to grieving, as well as other complex emotions. For example, they mention the story of "Arthur," a male peregrine falcon who returned one day from hunting without his companion, "Jenny" (Masson & McCarthy 1995). For several days, Arthur looked nervously around the nest to make sure Jenny was there or not, and didn't eat or do anything but look depressed (Masson & McCarthy 1995). Finally, when it became clear that Jenny was gone, he set to work feeding their young nestlings, three of whom had died of starvation (Masson & McCarthy 1995).
Another example involves two huskies, "Misha" and "Maria," and Maria's reaction when Misha was given away: she sat motionless by a window for several days, seemingly hoping that Misha would return (Masson & McCarthy 1995). He didn't and Maria never again seemed as happy as she'd been with Misha. In another case, a female horse named "Alle" was stabled with "Ackman" for several years (Masson & McCarthy 1995). Their keepers had no idea they were "together" until Ackman died suddenly. Alle spent several days whinnying continuously and refused to eat or sleep. Finally, two months later she died, though there appeared to be nothing physiologically wrong with her, she had apparently died of grief (Masson & McCarthy 1995).
A fourth case involved two dolphins in a marine park in Hawaii, "Kiko" and "Hoku" (Masson & McCarthy 1995). These two had been "partners" for several years when Kiko died suddenly. Hoku then refused to eat and spent hours a day swimming in circles with his eyes closed. Though the true significance of, or motivation for, his activity may never be known, it seems undeniable that he was grieving his loss (Masson & McCarthy 1995). Elephants are also known for offering examples of the experience of grief and sadness at the death of both mates and offspring, and even other relatives (Masson & McCarthy 1995). It is true, as Masson & McCarthy (1995) point out, that if all animals reacted like this to the death of a loved one, it would spell extinction for their species, but the fact that many do and that their behavior serves no apparent survival purpose would seem to hint that the ability to maintain a complex, emotional life must serve some deep evolutionary purpose (Masson & McCarthy (1995). Masson & McCarthy (1995) also document other interesting behaviors such as: apparent loneliness (in beavers, elephants, European wildcats and fishing cats) and frustration and loneliness at living in captivity (in lions, gorillas, pilot whales, orcas and Hawaiian monk seals), as well as depression and "learned helplessness" (in rhesus monkeys, dogs, cats and rats). They even document cases of apparent tears of sadness in animals, though it is not always clear why the animals are weeping when not obviously injured (such as wolves, coyotes, horses, parrots, seals, elephants and beavers) (Masson & McCarthy 1995).
Van Schaik et al (2003) documented a different kind of complex behavior that humans tend to assume is unique to us: that of cultural learning (e.g., transmission of behaviors by learning and imitation, rather than through a genetic basis). In their article, they documented behaviors that show geographic variation and are unique to different social groups of orangutans, even when ecosystems appear to be similar (Van Schaik et al 2003). Particularly, Van Schaik et al (2003) showed that orangutans appear to exhibit three of the four behaviors that indicate social learning: they use labels to mark food preferences and recognize predators, they use socially transmitted signals such as song dialects and, finally, they use tools to procure food and move objects (Van Schaik et al 2003).
To reiterate, all of the anecdotal and empirical evidence assembled here points to the idea that many animals lead emotionally rich, complex lives. Until mathematical modeling can take account of such information, it will always be at a loss to describe how animals make decisions and solve the problems of their survival and reproduction.

Conclusion

I argue that, from what you have read of mathematical modeling in behavioral ecology, as well as animal behavior in the wild, we need a different approach to the study of animal behavior that utilizes a more holistic method, based on the idea that animals are not just "others," but often are emotionally complex beings with sophisticated minds. Furthermore, we must understand that animals are often faced with very difficult problems that involve strategizing and making plans. A caveat, however, is in order, and that is that I have largely ignored the mathematics itself in order to prevent the reader from getting bogged down in trying to make sense of the models, though each of the models presented above is based on rigorous mathematical principles (by rigorous I mean that the modelers are using theorems and predictions derived from applications of the theorems that are considered to be "proven" according to the standards of mainstream mathematicians).
Nonetheless, I find it prudent to point out that the gene-centered view of evolution, with its attention to game theory models and cost-benefit analyses that relate only to the plausibility of single genes that code for single behaviors spreading or not spreading, has its detractors. The Israeli animal behavior experts Eytan Avital and Eva Jablonka argue in Avital & Jablonka (2000) that the gene-centered view of evolution ignores a very important factor in social evolution: the role of cultural inheritance. In particular they are talking about animals that teach their kids (Avital & Jablonka 2000). Dugatkin (2000) also argues that behavioral ecology is incomplete without an understanding of animal learning, especially by imitation. Zahavi & Zahavi (1997) also believe that the gene-centered view of behavioral evolution misses something important: that some animals appear to persevere in spite of, and because of, their physical handicaps. They call their theory the "Handicap principle." The theory claims that there can be natural selection for behaviors that allow an organism specifically to advertise its excellent fitness, whether that behavior is learned or directly inherited by as a genetic predisposition (Zahavi & Zahavi 1997). Francis Odling-Smee, Kevin Laland and Marcus Feldman (all Cambridge University biologists) also believe that the gene-centered view of evolution misses an important facet of animal behavior: behaviors that allow animals to modify their physical habitats and thus increase their own chances of survival (Odling-Smee et al 2003). They refer to this process as "niche-construction" and argue that until we can model learned behavior's effects on genotypic selection through the mechanism of physical habitat modification, we will not truly understand the ways that animals' behavior affects their environment (Odling-Smee et al 2003). H.C. Plotkin (Plotkin 1988) believes that animal behavior must take account of the fact that animals' social behavior may affect their evolution as much as their phenotype. Lev Ginzburg (Ginzburg & Colyvan 2004), speaking from his distinguished career in biological modeling, says that "theoreticians often get so tied up in their mathematical models that they lose touch with the ecological system being studied" (Ginzburg 2004: 105). This despite being one of the early experts in applying mathematics to ecology problems (i.e., see Ginzburg 1986).
I believe that we won't understand behavior until we begin to appreciate how animals solve their own problems, let alone problems devised by lab technicians. An approach that utilizes my idea is called "cognitive ethology." Cognitive ethologists study animal behavior in the field using protocols developed by both animal behaviorists and comparative psychologists (Griffin 1978; Allen & Bekoff 1997). Allen and Bekoff, two prominent specialists in this emerging field, believe it is a given that at least some species of animals (besides humans) have mental states similar to those of humans, and that behavioral ecology will still be a science in its infancy until scientists are able to figure out the role of those mental states in animal behavior (Allen & Bekoff 1997). De-animalization need not occur, and where it does scientists must always keep in mind that mathematical models have their roots in complex ecosystems, made up of sophisticated animals.

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