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!