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Related Concept Videos

Evolutionary Psychology01:20

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Evolutionary psychology explores the origins of human behavior and mental processes by framing them within the context of natural selection, a theory famously propounded by Charles Darwin. This field asserts that many behaviors common across human societies — ranging from instinctive fear reactions to complex social interactions — arose as evolutionary adaptations. These adaptations enhanced the survival and reproductive success of our ancestors, thereby becoming embedded in the...
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Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
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Predators consume prey for energy. Predators that acquire prey and prey that avoid predation both increase their chances of survival and reproduction (i.e., fitness). Routine predator-prey interactions elicit mutual adaptations that improve predator offenses, such as claws, teeth, and speed, as well as prey defenses, including crypsis, aposematism, and mimicry. Thus, predator-prey interactions resemble an evolutionary arms race.
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Humans engage in aggression when they seek to cause harm or pain to another person. Aggression takes two forms depending on one’s motives: hostile or instrumental. Hostile aggression is motivated by feelings of anger with intent to cause pain; a fight in a bar with a stranger is an example of hostile aggression. In contrast, instrumental aggression is motivated by achieving a goal and does not necessarily involve intent to cause pain (Berkowitz, 1993); a contract killer who murders for...
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Related Experiment Video

Updated: Mar 20, 2026

Manipulation of Color Patterns in Jumping Spiders for Use in Behavioral Experiments
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Ecology and multilevel selection explain aggression in spider colonies.

Jay M Biernaskie1, Kevin R Foster2

  • 1Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, UK OX1 3RB, UK.

Ecology Letters
|June 7, 2016
PubMed
Summary

Aggression in social spiders is driven by within-group competition for resources, not just group adaptation. This selfish strategy impacts group efficiency and survival, highlighting the need for ecological data in sociobiology.

Keywords:
AICAdaptationAnelosimus studiosusanimal personalitycompetitiongroup selectioninclusive fitnessinformation theoretickin selectionmodel-based inference

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Area of Science:

  • Sociobiology
  • Behavioral Ecology
  • Evolutionary Biology

Background:

  • Sociobiology progress is impeded by theoretical debates on selection levels.
  • Empirical data and concrete examples are crucial for advancing sociobiology.
  • Previous studies suggested group-level adaptation explains aggression in social spider colonies.

Purpose of the Study:

  • To examine the role of within-group vs. between-group selection in social spider aggression.
  • To test the hypothesis of pure group-level adaptation using ecologically detailed models.
  • To integrate kin and multilevel selection frameworks with empirical data.

Main Methods:

  • Developed ecological models for social spider colonies.
  • Incorporated kin and multilevel selection principles.
  • Analyzed data on aggression levels in relation to competition and group outcomes.

Main Results:

  • Between-group selection partially influences aggression levels.
  • Within-group competition provides a strong fit to observed aggression data.
  • Pure group-level adaptation is inconsistent with the data.

Conclusions:

  • Aggression is primarily a selfish strategy for resource competition.
  • This strategy can decrease group foraging efficiency and increase extinction risk.
  • Sociobiology benefits from a pluralistic approach linking ecological models and data.