A comparison of dominance rank metrics reveals multiple competitive landscapes in an animal society: Dominance rank & competitive landscapes

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Study Justification:
– Dominance hierarchies in group-living animals lead to disparities in resource access, health outcomes, and reproductive performance.
– Previous studies have used different dominance rank metrics without considering the underlying competitive processes.
– This study aims to compare two dominance rank metrics (simple ordinal rank and proportional rank) to predict traits in a wild baboon population.
– Understanding how different rank metrics predict traits can help distinguish between different competitive processes in animal societies.
Study Highlights:
– Comparison of two dominance rank metrics (simple ordinal rank and proportional rank) in predicting 20 traits in a wild baboon population.
– 75% of traits showed better prediction by one rank metric over the other.
– Male traits were best predicted by simple ordinal rank, while female traits were evenly split between proportional and simple ordinal rank.
– Male traits are shaped by density-dependent resource access, while female traits are shaped by both density-independent and density-dependent resource access.
Study Recommendations:
– Consider the use of different dominance rank metrics when studying the effects of dominance hierarchies on traits in animal societies.
– Recognize that different competitive processes may shape male and female traits differently.
– Further investigate the specific mechanisms underlying the observed patterns in dominance rank and trait prediction.
Key Role Players:
– Researchers and scientists specializing in animal behavior and dominance hierarchies.
– Field assistants and data collectors for long-term data collection in the baboon population.
– Statistical analysts to analyze and interpret the data.
– Policy makers and conservationists interested in understanding social dynamics and resource allocation in animal populations.
Cost Items for Planning Recommendations:
– Research funding for long-term data collection, including fieldwork expenses, equipment, and salaries for researchers and field assistants.
– Statistical analysis software and expertise.
– Communication and dissemination of research findings through publications and conferences.
– Collaboration and coordination with other research institutions and conservation organizations.
– Potential costs for implementing conservation measures based on the study’s findings (if applicable).

Across group-living animals, linear dominance hierarchies lead to disparities in access to resources, health outcomes and reproductive performance. Studies of how dominance rank predicts these traits typically employ one of several dominance rank metrics without examining the assumptions each metric makes about its underlying competitive processes. Here, we compare the ability of two dominance rank metrics – simple ordinal rank and proportional or ‘standardized’ rank – to predict 20 traits in a wild baboon population in Amboseli, Kenya. We propose that simple ordinal rank best predicts traits when competition is density-dependent, whereas proportional rank best predicts traits when competition is density-independent. We found that for 75% of traits (15/20), one rank metric performed better than the other. Strikingly, all male traits were best predicted by simple ordinal rank, whereas female traits were evenly split between proportional and simple ordinal rank. Hence, male and female traits are shaped by different competitive processes: males are largely driven by density-dependent resource access (e.g. access to oestrous females), whereas females are shaped by both density-independent (e.g. distributed food resources) and density-dependent resource access. This method of comparing how different rank metrics predict traits can be used to distinguish between different competitive processes operating in animal societies.

The Amboseli Baboon Research Project is a long-term study of a natural population of savannah baboons located in Kenya’s Amboseli basin. Data collection began in 1971 and continues today [49]. The population consists primarily of yellow baboons (Papio cynocephalus) that experience some naturally occurring admixture with olive baboons (P. anubis) [50–52]. The number of social groups under observation at any given time has ranged from 1 to 6, varying as a result of logistical considerations or group fissions and fusions. All individuals in study groups are visually recognized based on morphological and facial features. Near-daily demographic, environmental and behavioural data have been collected throughout the study, and paternity data (beginning around 1995) and endocrinological data (beginning around 2000) have been collected for part of the study. We routinely calculate both simple ordinal ranks and proportional ranks for males and females on a monthly basis. Only adult ranks are considered in this analysis. For traits measured in immature individuals, maternal dominance rank is used as the predictor variable. Dominance ranks are determined by assigning wins and losses in dyadic agonistic interactions between same-sex individuals. Data on agonistic interactions are collected ad libitum during daily data collection, typically while the observer is simultaneously carrying out random-order focal animal sampling [53]. This sampling procedure ensures that observers continually move to new locations within the social group and observe focal individuals on a regular rotating basis. An individual is considered to win an agonistic interaction if they displace another individual, or if they give only aggressive or neutral gestures while their opponent gives only submissive gestures. All agonistic outcomes are entered into sex-specific dominance matrices (i.e. adult males are ranked separately from adult females). Individuals are placed in order of descending, sex-specific rank so as to minimize the number of entries that fall below the diagonals of the matrices [37,54]. Simple ordinal ranks are produced by numbering individuals according to the order in which they occur in the monthly matrix (1, 2, 3 … n, where n = hierarchy size), with 1 being the highest-ranking male or female in the hierarchy and n being the lowest. Proportional ranks are computed as 1−((simple ordinal rank−1)/(hierarchy size−1)) to produce ranks that fall in the range of [0,1] for every hierarchy, with 1 being the highest-ranking male or female in the hierarchy and 0 being the lowest. We aimed to test whether 20 different sex- and age-class-specific traits were better predicted by simple ordinal rank or proportional rank in the Amboseli baboon population. We first identified previous publications from the Amboseli Baboon Research Project that reported statistically significant effects of rank on various traits. For a complete list of re-analyses performed, see electronic supplementary material, table S1. Our methods of re-analysis followed three steps:

Based on the provided information, it seems that the research project is focused on studying dominance rank and its impact on various traits in a baboon population. The study compares two dominance rank metrics – simple ordinal rank and proportional rank – to determine which metric better predicts different traits. The research aims to understand the competitive processes operating in animal societies.

In terms of innovations to improve access to maternal health, the information provided does not directly address this topic. It is focused on studying dominance rank in baboons. To provide recommendations for improving access to maternal health, we would need additional information or a different context related to maternal health specifically.
AI Innovations Description
Based on the provided description, the recommendation to improve access to maternal health would be to conduct further research and analysis on the relationship between dominance rank and maternal health outcomes in animal societies. This can be done by comparing different dominance rank metrics and examining their ability to predict maternal health traits.

Specifically, the recommendation would involve:

1. Collecting data on dominance rank and maternal health outcomes in a population of animals, such as baboons.
2. Using different dominance rank metrics, such as simple ordinal rank and proportional rank, to analyze the data.
3. Comparing the performance of these rank metrics in predicting maternal health traits.
4. Identifying the dominant rank metric that best predicts maternal health outcomes.
5. Drawing conclusions about the competitive processes that influence maternal health based on the results.

By implementing this recommendation, researchers can gain a better understanding of the relationship between dominance rank and maternal health, which can inform the development of innovative strategies to improve access to maternal health in human populations.
AI Innovations Methodology
Based on the provided description, it seems that you are looking for innovations to improve access to maternal health. However, the text you provided is unrelated to maternal health or innovations in that field. Could you please provide more specific information or clarify your request?

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