Data importance and biases Flashcards
What type of data is needed?
Biological/ecological
Threats
Actions
Socio-economic
Context
Where does data come from ?
- Field studies - biological/environmental and socio-economic
- Remote sensing - environmental and socio-economic
- Countries/governments - mostly socio-economic
- Citizen science/community science - mostly biological/environmental
- Indigenous knowledge - ecological and socio-economic
Data biases
Spatial
Taxonomic
Proximal
Sampling biases shape our view of the natural world
742 million records of over 300,000 species from Global Biodiversity Information Facility (GBIF)
Only 6.74% of the globe sampled
Disproportionately poor tropical sampling and at high elevations and in the deep seas
Over 50% of records in most groups account for under 2% of species
Biases can intersect with conservation relevant factors
Taxonomic biases in biodiversity data and societal preferences
626 million records from the Global Biodiversity Information Facility (GBIF)
Bias towards more charismatic groups
Bias increased over time
A good biological indicator
A species, group of species, or biological process that responds predictably to environmental changes, providing measurable information beyond the indicator itself (fx ecosystem health, pollution levels, or biodiversity trends)
Ideal indicators are sensitive to specific stressors, easy to monitor, and have well-understood ecological roles, making them valuable for conservation and management decisions
Proximal bias
When data is used to approximate a specific ‘construct’ that might not accurately (or only partly) approximate
Empty forest syndrome
Refers to a phenomenon where a forest appears structurally intact but has lost much of its wildlife due to overhunting, habitat fragmentation, or disease.
While trees and vegetation may still thrive, the absence of key animals - such as large herbivores, predators, and seed dispersers - disrupts ecosystem functions.
This leads to long-term ecological imbalances, potentially altering forest regeneration
Functions:
- seed dispersal
- pollination
- herbivory
- interactions more broadly
- nutrient cycling
- fire regulations
- water regulation
UNDPs Human Development Index
Dimensions:
- Long and healthy life
- Knowledge
- A decent standard of living
Indicators:
- life expectancy at birth
- expected years of schooling and mean years of schooling
- GNI per capita
Dimension index:
- Life expectancy index
- education index
- GNI index
GDP
Per capita Gross Domestic Production
Data justice/environmental justice
Fairness in the way people are made visible, represented and treated as a result of their production of digital data
Data justice
Data composition (who are made visible/hidden and what are the biases)
Data control (who funds it, who can control content and use/share)
Data access (who has access and who can profit)
Data processing and use (who use the data, how do they use it)
Data consequences (who can make choices based on data. What is the impact and who has the benefits)
Do we have to not act if our data isn’t good enough?
Make sure we understand the biases
Examine the consequences of biases on conclusions
Ask who is affected by the data and biases
