In this episode of K9 Conservationists, Kayla speaks with Dr. Charles Van Rees about data. They discuss the ins and outs of working with data and how to turn data into information scientists can use.Episode suggestion: Take the time to see, smell, and notice things outside!
Call to action: Check out ebird, inaturalist or seek!
What is/are data?
- Information that we can use for inference and for learning things
- Data doesn’t necessarily “teach you things” but it helps you learn an answer to something you’re looking for
Examples of data for conservation science
- Occurrence data: evidence of the occurrence of a species in a specific area
- Abundance data: identifying and counting every individual of every species in a given sector
- Time series data: a series of data points indexed in time order
- Monitoring: the process of watching the changes and patterns in the species
Challenges in data collection
- Biases in both humans vs dogs
- Dogs have less of a visual bias than humans as they rely on olfactory vs sight to find data
- Important to consider if the questions you are asking from the data are going to be worth it to have dogs involved.
- Sample size: you have to have enough data in order to be able to work with the data
- Trying to collect data in a cost effective way
- You can’t control the data you collect
What happens to the life-cycle of data?
- Analysis
- Publishing
- FAIR principles
- Meta-analyses and reviews
- Policy!
- Or if not, advocacy! Action! Management!
What are ways to collect data?
- Conservation detection dogs
- Radio collars
- Camera traps
- eDNA
- Drones
- Audio surveys
- Netting
Links Mentioned in the Episode:
- Mirror test (Kayla got this slightly wrong – dolphins spent extra time head circling, which may indicate recognition)
- Dogs self-recognition with scent
- Wolverine Way book
- What the Dog Knows
- Inside of a Dog
- Are We Smart Enough to Know How Smart Animals Are?
- Chicken Wire Gauge preference study
- Nathan Hall
Where to find Charles: Website | Twitter | Instagram | Nature Guys
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