Bias, Information, Signal and Noise in Citizen Science data Nick Isaac Phot ocredit: Rich

Defaunation in the Anthropocene Dirzo et al., (2014) Science, 345: 401–406

Biological Recording A rich history Millions of records Opportunistic recording is biased in time in space detectability effort per visit Effort Number of Species

The problem For any research question, how can we extract biological signal from noisy data?

Detecting signal amidst the noise Methods for trend estimation: Aggregation Data Selection methods Correction for sampling effort Bayesian Occupancy models (modelling the data collection process)

Statistics for Citizen Science Occupancy models are robust to several forms of biases in opportunistic data, and more powerful than other methods Isaac et al (2014) Methods in Ecology & Evolution 5:

Occupancy: modelling data collection Extant Extinct Occupancy (unobserved) Separation of “state” and “data generation” process Annual estimates of both occupancy & detection probabilities Observer model: p Detection ~ ListLength Observations Data generation process Year 1Year 2Year 4Year 3Year 5

Occupancy models for British bees Nick Owens Bombus bohemicus

Bias vs Information Isaac & Pocock (2015) Biol J Linn Soc 115: We can’t tell the difference between these ! The information content of a dataset is question-dependent and depends on survey effort

Do citizen scientists record assemblages? Biological recording is unstructured, but many citizen science projects have structure What is the assemblage?

Do citizen scientists record assemblages? Information about the data collection process (meta-data) is critical for making robust inferences from citizen science data What would happen if I treat the data as biological records?

What does this mean? More sophisticated models? Pagel et al. (2014) Methods in Ecology & Evolution 5: Your data will outlive your project! We need to invest in better systems: Meta-data Data standards Ontologies and controlled vocabularies We need to understand more about the behaviour, motivation and aptitude of citizen scientists

What have we learned? We have the tools to model biodiversity change using citizen science data We shouldn’t remove the bias but model it Occupancy models make this possible A little bit of meta-data would go a long way = a vast untapped resource (but it could be improved)

Acknowledgments Tom August, Arco van Strien, Marnix de Zeeuw, David Roy Michael Pocock Charlie Outhwaite, Gary Powney Colin Harrower, Helen Roy, Chris Preston, Mark