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Dealing with observer bias when mapping species
distribution using citizen science data; an example
on the distribution of brown bears in Greece.
Anne-Sophie Bonnet-Lebrun, Alexandros A. Karamanlidis,
Miguel de Gabriel Hernando, Olivier Gimenez
INTRODUCTION – Citizen science
New technologies
Importance of mapping species distributions:
 Citizen science!
- Define priority areas for conservation
- Map problematic interactions
- Spatial information
- Increasingly connected world
INTRODUCTION – Citizen science
- Time and money
Citizen-science: pros and cons
BUT
- Quality
- Quantity
- Presence-only
- Observer bias
Sampling effort not evenly distributed
Impossible to evaluate detectability
- Large spatial cover
Greece
Threats:
- Habitat loss and fragmentation
- Human-bear conflicts
Map its distribution in Greece
Inform conservation strategies
INTRODUCTION – Monitoring brown bears
Conservation status:
- Globally: Least Concern (IUCN Red List status)
- Locally in Europe: small and isolated populations
Brown bears, like other large
carnivores, are difficult to monitor:
 Citizen science!
INTRODUCTION – Using citizen science to monitor brown bears
- Cryptic and solitary
- Low density in very large areas
INTRODUCTION – Species Distribution Models
+
Partial information on the
species’ presence
Environmental variables
Probabilities of presence in
the whole area of interest
Traditional methods to infer species distributions:
METHODS – Dealing with citizen-science data
-presence-only data
 inhomogeneous Poisson point process
(Warton & Shepherd 2010)
Homogeneous
Intensity = constant
- Intensity: average number of points per unit area
- Poisson point process: random process to generate
points scattered in space
Inhomogeneous
Intensity = f(spatial variable)
-opportunistic data
 Model observer bias (Warton et al. 2013)
METHODS – Dealing with citizen-science data
Make the difference between:
• ecological (forest cover, altitude, …) variables
• observer bias (distance to the roads, …) variables
- Affect the species’ presence
- Used for building AND projecting the model
- Affect the probability to detect the species
- Used only for building the model
(projection with a common level of bias)
Maps of estimated intensity (in presence points per square kilometre)
of Eucalyptus apiculata from three different models.
Ecological
variables only
Ecological +
observer bias
variables
Ecological + observer
bias variables,
conditioning on a
common level of bias
METHODS – Dealing with citizen-science data
Warton et al. 2013
METHODS – Environmental variables
Variables used in the model:
- Mean slope
- Altitude
- Density of rivers
- % Agricultural land
- % Forests
- Human population density
- Distance to roads
Ecological
Observer bias
RESULTS
Average expected number
of observations
Model based on
opportunistic data
Model based on
presence-absence data
Probabilities of presence
The results of the two
models seem coherent
RESULTS – Modelling observer bias
DISCUSSION
Distance to the roads
DISCUSSION
Ecological vs. observer bias variables:
the example of human population density
- : ecological variable
+ : observer bias variable
The more people, the
more likely they are to
detect a bear
Bears are likely to
avoid areas with a
lot of people
DISCUSSION
- Model opportunistic data with Poisson Point Processes?
- Deal with presence-only data
- Possibility to combine different sources of data
(Dorazio 2012, O’Hara 2014)
- Model observer bias?
- Ecological vs. observer bias variables
- Difficulty to find a relevant observer bias variable
- Really reflecting the spatial observer bias process
Model the citizen’s behaviour
Thank you for your attention!
Dealing with observer bias when mapping species distribution using citizen science data; an example on the distribution of brown bears in Greece.

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Dealing with observer bias when mapping species distribution using citizen science data; an example on the distribution of brown bears in Greece.

  • 1. Dealing with observer bias when mapping species distribution using citizen science data; an example on the distribution of brown bears in Greece. Anne-Sophie Bonnet-Lebrun, Alexandros A. Karamanlidis, Miguel de Gabriel Hernando, Olivier Gimenez
  • 2. INTRODUCTION – Citizen science New technologies Importance of mapping species distributions:  Citizen science! - Define priority areas for conservation - Map problematic interactions - Spatial information - Increasingly connected world
  • 3. INTRODUCTION – Citizen science - Time and money Citizen-science: pros and cons BUT - Quality - Quantity - Presence-only - Observer bias Sampling effort not evenly distributed Impossible to evaluate detectability - Large spatial cover Greece
  • 4. Threats: - Habitat loss and fragmentation - Human-bear conflicts Map its distribution in Greece Inform conservation strategies INTRODUCTION – Monitoring brown bears Conservation status: - Globally: Least Concern (IUCN Red List status) - Locally in Europe: small and isolated populations
  • 5. Brown bears, like other large carnivores, are difficult to monitor:  Citizen science! INTRODUCTION – Using citizen science to monitor brown bears - Cryptic and solitary - Low density in very large areas
  • 6. INTRODUCTION – Species Distribution Models + Partial information on the species’ presence Environmental variables Probabilities of presence in the whole area of interest Traditional methods to infer species distributions:
  • 7. METHODS – Dealing with citizen-science data -presence-only data  inhomogeneous Poisson point process (Warton & Shepherd 2010) Homogeneous Intensity = constant - Intensity: average number of points per unit area - Poisson point process: random process to generate points scattered in space Inhomogeneous Intensity = f(spatial variable)
  • 8. -opportunistic data  Model observer bias (Warton et al. 2013) METHODS – Dealing with citizen-science data Make the difference between: • ecological (forest cover, altitude, …) variables • observer bias (distance to the roads, …) variables - Affect the species’ presence - Used for building AND projecting the model - Affect the probability to detect the species - Used only for building the model (projection with a common level of bias)
  • 9. Maps of estimated intensity (in presence points per square kilometre) of Eucalyptus apiculata from three different models. Ecological variables only Ecological + observer bias variables Ecological + observer bias variables, conditioning on a common level of bias METHODS – Dealing with citizen-science data Warton et al. 2013
  • 10. METHODS – Environmental variables Variables used in the model: - Mean slope - Altitude - Density of rivers - % Agricultural land - % Forests - Human population density - Distance to roads Ecological Observer bias
  • 11. RESULTS Average expected number of observations Model based on opportunistic data Model based on presence-absence data Probabilities of presence The results of the two models seem coherent
  • 12. RESULTS – Modelling observer bias
  • 14. DISCUSSION Ecological vs. observer bias variables: the example of human population density - : ecological variable + : observer bias variable The more people, the more likely they are to detect a bear Bears are likely to avoid areas with a lot of people
  • 15. DISCUSSION - Model opportunistic data with Poisson Point Processes? - Deal with presence-only data - Possibility to combine different sources of data (Dorazio 2012, O’Hara 2014) - Model observer bias? - Ecological vs. observer bias variables - Difficulty to find a relevant observer bias variable - Really reflecting the spatial observer bias process Model the citizen’s behaviour
  • 16. Thank you for your attention!