Hunt Prices for North American Mammals
Investigating Costly Signaling Theory
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About this dataset
This dataset of 721 guided hunts for fifteen North American large mammal species is a powerful tool in exploring costly signaling theory. Costly signaling is an evolutionary psychology explanation for certain behaviors that involve the investing of resources disproportionate to their return in terms of nutrition; guided hunting is one example. With this dataset, we can gain insight into the motivations underlying the choice to hunt a particular type or rarity of large mammal, as well as examine correlations with variables such as body size, carnivory and classification, conservation status, and geographical location. See how our choices are shaped by these factors and how they relate to an animal's price tag!
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How to use the dataset
This dataset provides detailed information on 721 guided hunts for fifteen different North American large mammal species. It is an invaluable resource in understanding costly signaling theory, which posits that expenditure on resources and services disproportionate to the nutritional yield can provide advantages through increased fitness and survival benefits.
To make the most of this dataset, users should focus first on exploring how factors such as rarity, status, body size, carnivory, and classification affect hunt prices. To do this effectively it is important to start by first getting familiar with the data: explore each field listed in the columns to see what kind of information it contains and how they relate to one another. By comparing data points across various factors you can better understand consumer behavior when it comes to guided hunting trips, as well as gain insight into potential strategies for maximizing profits or conservation efforts related to those services.
Once comfortable with individual fields from the dataset itβs time get creative and start exploring correlations between multiple variables at once! For example; does a higher SCI score lead to higher prices? Are hunt lengths longer for rarer species? Does higher latitude lead significantly larger bodies sizes or vice versa? All these questions are answerable with this one powerful dataset β all you need is a bit of imagination!
Research Ideas
- Analyzing the correlation between the species' conservation status and the cost of hunting them, as hunters willing to pay a higher price are likely helping to conserve endangered species.
- Investigating whether hunters have a preference towards larger mammal species by examining how average body mass relates to hunt prices.
- Correlating hunt prices with Safari Club International (SCI) score ranking, which would suggest an economic incentive for pursuing rarer beasts while also revealing hunter preferences for particular rarity levels when budgeting their money
Acknowledgements
If you use this dataset in your research, please credit the original authors.
Data Source
License
License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.
Columns
File: Mihalikdata.csv
Column name |
Description |
Species |
The name of the species of mammal being hunted. (Text) |
Province/State |
The province or state in which the hunt took place. (Text) |
Price |
The total cost of the hunt. (Numerical) |
Number of days |
The number of days the hunt lasted. (Numerical) |
Day price |
The cost per day of the hunt. (Numerical) |
S Rank |
The rarity of the species being hunted. (Numerical) |
Status |
The conservation status of the species being hunted. (Text) |
Classification |
The classification of the species being hunted (carnivore, omnivore, or herbivore). (Text) |
Avg Mass |
The average mass of the species being hunted. (Numerical) |
SCI |
The Safari Club International score of the species being hunted. (Numerical) |
Latitude |
The latitude of the location where the hunt took place. (Numerical) |
Acknowledgements
If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .