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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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Collective intelligence facilitates emergent resource partitioning through frequency-dependent learning.

Mina Ogino1,2, Damien R Farine1,2,3

  • 1Department of Evolutionary Biology and Environmental Science, University of Zurich , Zurich Winterthurerstrasse 190, 8057, Switzerland.

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
|July 22, 2024
PubMed
Summary

Group-living animals better partition resources than solitary ones by pooling information, leading to reduced competition. Larger groups show enhanced resource partitioning, especially with more options, benefiting population-level foraging strategies.

Keywords:
collective decision-makingcollective sensingnegative frequency-dependent learningniche partitioningresource specializationspatial structure

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Area of Science:

  • Behavioral Ecology
  • Population Dynamics
  • Collective Intelligence

Background:

  • Foraging decisions require considering habitat quality and competitor presence.
  • Negative frequency-dependent learning helps individuals avoid exploited resources (indirect competition), promoting resource partitioning.
  • Sensing indirect competition cues can be challenging for individuals.

Purpose of the Study:

  • To investigate if collective decision-making in group-living animals enhances resource partitioning compared to solitary animals.
  • To determine the impact of group size on resource partitioning effectiveness.
  • To explore the role of information pooling in mitigating foraging competition.

Main Methods:

  • Agent-based modeling simulating individual foraging behavior based on recent success.
  • Simulating group foraging decisions using a majority rule mechanism.
  • Comparing resource partitioning in simulated solitary versus group-living populations.

Main Results:

  • Solitary animals exhibit partial avoidance of indirect competition via negative frequency-dependent learning.
  • Group-living animals demonstrate more effective resource partitioning than solitary individuals.
  • Larger group sizes lead to better resource partitioning, particularly in environments with diverse resources.

Conclusions:

  • Collective intelligence through group decision-making significantly improves resource partitioning.
  • Group living and larger group sizes are advantageous for managing foraging competition and promoting specialization.
  • Findings offer insights into the evolution of sociality, group size, and territorial behavior.