Background and Definition
Behavioral ecology studies ecological dynamics that drive behavioral adaptations within evolutionary context. This approach was developed from research of ethologists, such as Lorenz and Tinbergen (Krebs and Davies 1997). Lorenz and Tinbergen examined animal behavior in their natural environment in which the focus of the research was determining the function and fruition of the behaviors. Another contributor to the development of behavioral ecology was the field of sociobiology (Wilson 1975). Sociobiology “is a field of study in which principles of population biology and evolutionary theory are applied to social organizations” (Olmstead and Kuhlmeier 2015, p. 17). Behavioral ecology emerged from research within ethology and sociobiology. Questions in the field of behavioral ecology initially explored were related to predictions of survival and reproductive success. More specifically, how is the behavior traditionally used and does it increase survival? General topics of interest included fitness (e.g., behavior), the selection of behaviors, and genetic distinction, which if given enough time and raw material, selection led to optimal designs (behavior). Over time, behavioral ecologists expanded their understanding of processes to other areas such as perception and learning (Shettleworth 2010).
Measures and Examples
Behavioral ecologists utilize several types of measures: behavioral observations, correlations of inheritance, experimental studies, and optimality models. Based on Tinbergen’s questions regarding proximate and ultimate causes, behavioral ecologists used observational research to explore the possible context of adaptiveness and evolutionary history. This approach was a useful way to examine the conditions of the environment and behaviors that could lead to investigating specific patterns. One way this technique has been utilized is by identifying the dominance hierarchy within a social group. In this case, identifying the hierarchy position of an individual animal is achieved by detecting the animal’s observed size and various behaviors (Hausfater 1975). For example, Altmann and Alberts (2005) used this method to identify the maternal characteristics of baboons. This approach was then used to assess the impact of maternal characteristics on the growth of the offspring. Correlations of inheritance are used to examine patterns of similarities or differences between parents and offspring. For example, do they both show similar migration patterns? Experimental studies explore questions related to the mechanisms behind a pattern of behavior or the environmental conditions that facilitate a particular behavioral outcome. One example is a phenotype manipulation experiment. Saino et al. (2003) examined the influence of the concentration of a compound (carotenoid), inside the egg of a Barn Swallow, on the growth and immune system of the offspring. Another type of experiment is a selection experiment. Wilkinson and Reillo (1994) examined the difference in male eye spans of flies and if those with long or short eye spans would influence the mate choice by a female fly. Both of these types of experiments specifically examined the characteristics that play a role in survival and reproduction. Finally, optimality models explore the historical function within the context of mathematical programming. More specifically, to study “decision problems in which one seeks to minimize (min) or maximize (max) a function by systematically choosing the values of variables within their allowed sets” (Zhang et al. 2015, p. 26). This model focuses on decision, result and uncontrolled variables.
Applications and Future Research
The research in the field of behavioral ecology is important to animal conservation in regards to a variety of species and environment situations. This is especially important with threatened species and significant ecological occurrences such as alterations in sex ratio and ecological parameters (e.g., temperature, resource availability). Bradbury and Vehrencamp (2014) propose that future research in behavioral ecology should utilize the complexity theory. Within this theory, topics such as communication, social coordination, cooperation, and personality (individual differences) can be explored. In doing so, they suggested that behavioral ethologists could still maintain their emphasis on linear interactions and dyadic behavior but should start exploring the complex nonlinear interactions.
A Variation: Human Behavioral Ecology
Background and Definition
Behavioral ecology was extended to humans shortly after its emergence in the animal behavior field in the 1960s and 1970s. With objectives to explain the evolution of human behavioral patterns and the selection of specific characteristics and behaviors that maximized fitness in an environment, human behavioral ecology concentrated on ultimate causes including the function of different behavioral patterns and the environmental pressures that produced them. Humans adapt to new environmental niches very quickly – faster than genetics can evolve. Interested in variation and diversity, human behavioral ecologists seek to determine the mechanisms that lie behind this diverse behavioral plasticity. Through their review, Nettle et al. (2013) argue that the plasticity of the processes used by individuals to independently and socially learn underlies their abilities to adapt to novel environments quickly. The role of cultural transmission is part of this adaptation based on social learning and is a frequent question in the study of human behavioral ecology. Human behavioral ecologists focus on traditional behavioral ecology questions involving the evolution of foraging, mating, social living, cooperation, among other social practices as they have evolved in humans while also examining the current evolutionary process. Gurven (2006) provides a very detailed summary of each of these major topics and the assumptions underlying the behavioral ecology approach.
Like behavioral ecology, human behavioral ecology uses a mix of theoretical and data-driven approaches. The use of models derived from various theories (optimization models, game-theoretic models) approaches the study of some aspect of human behavior from a series of logical assumptions with various hypothetical outcomes. Data-driven ethnographic approaches of traditional societies, experimental hypothetical situations, and data from large, culturally diverse datasets examine questions of fitness based on patterns observed based on the approach utilized. More current reproductive success is used to measure fitness rather than lifetime and overall offspring.
Key Examples and Applications
As summarized by Gurven (2006), “Behavioral ecologists evaluate the adaptiveness of behavior given the set of constraints and options in the natural, social, and cultural environment. There has been a renewed appreciation for the ways in which cultural history can provide insight into the content and form of these constraints, and help to explain behavioral variation among peoples living in similar ecologies.” (p. 5). Since this statement, research in the traditional areas of behavioral ecology, foraging approaches, mating strategies, reproductive success including fertilization and offspring care, and life history continue to promote productive research although topics such as foraging elicit less research today. More current topics such as social living, urbanization, effectiveness of public policies and interventions, and other culturally transmitted beliefs are experiencing increased attention, especially in an effort to explain diverse human societies.
One concern about the field is its relative independence from behavioral ecology in general, such that although many questions of interest overlap between human behavioral ecology and behavioral ecology, human behavioral ecology tends to consider only the human perspective instead of a cross-species approach, which is reflected by the primary journals publishing human behavioral ecology research (Nettle et al. 2013).
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- Wilson, E. O. (1975). Sociobiology: The new synthesis. Cambridge, MA: Harvard University Press.Google Scholar
- Zhang, G., Lu, J., & Gao, Y. (2015). Multi-level decision making: Models, methods and applications (Vol. 82). New York: Springer.Google Scholar