Nick Isaac Arco van Strien*, Tom August & David Roy Biological Records Centre, Centre for Ecology & Hydrology *Statistics Netherlands Extracting trends from citizen science #BES12
Biological Recording as Citizen Science Perring, F H, & Walters, S M, eds 1962 Atlas of the British Flora. Thomas Nelson & Sons, London
Atlases: Stock & change in distribution
Where are we now? Atlases provide a rather static view of biodiversity Increasing demand for quantitative information The unstructured nature of the data makes square counting unreliable New methods for estimating trends are being developed
Detecting and attributing change Trends in the distribution of 8 common ladybirds A majority show substantial negative response to arrival of Harlequin ladybird Similar patterns in GB & Belgium Roy, HE, Adriaens, T, Isaac, NJB et al. (2012). Invasive alien predator causes rapid declines of native European ladybirds. Diversity and Distributions, 18(7), 717–725 Mike Majerus
Talk outline Counting squares is unreliable Comparison of candidate methods Simulations of recording behaviour Which methods are useful for detecting trends? Applications: which species are declining?
Extracting trends from biological records
Recording intensity has increased over time
Most lists are incomplete For most groups, ~50% of lists are single species
Lists lengths are not constant over time
Recording is biased in space Orthoptera : top 4 recorders made 14% of all visits
Talk outline Counting squares is unreliable Comparison of candidate methods Simulations of recording behaviour Which methods are useful for detecting trends? Applications: which species are declining?
Simulations Aims: 1.To compare the performance of different methods for estimating range change under realistic scenarios of recorder behaviour 2.To discard methods that are inappropriate 3.To derive rules of thumb for when other methods are appropriate
Type I Error Rates Even Recording Change Index nRecords List Length Visit Rate MM2sp MM4sp Frescalo 0.023
Type I Error Rates Even Recording Increasing Intensity Change Index nRecords List Length Visit Rate MM2sp MM4sp Frescalo
Type I Error Rates Even Recording Increasing Intensity Incomplete even Incomplete increasing Change Index nRecords List Length Visit Rate MM2sp MM4sp Frescalo
Power to detect a genuine decline Even Recording Change Index0.574 nRecords0.642 List Length0.713 Visit Rate0.739 MM2sp0.665 MM4sp0.615 Frescalo0.612 A 30% decline is detectable in ~60% of datasets
Talk outline Counting squares is unreliable Comparison of candidate methods Simulations of recording behaviour Which methods are useful for detecting trends? Applications: which species are declining?
Odonata trends Broad agreement between methods 14/32 species show significant increases under both methods 2/32 show significant declines under both methods
Odonata trends: winners Wikipedia Commons Small red-eyed Damselfly (Erythromma viridulum) Scarce chaser (Libellula fulva) Emperor Dragonfly (Anax imperator)
Odonata trends: losers Variable damselfly (Coenagrion pulchellum) Blue-tailed Damselfly (Ischnura elegans) Common Blue Damselfly (Enallagma cyathigerum)
Past, present and future Attributing changeDescribing changeBiodiversity Indicators
Past, present and future Attributing changeDescribing changeBiodiversity Indicators
Conclusions We have the tools to model biodiversity change using citizen science data The simulation provides a framework for comparing methods under a range of recording scenarios We’re only just beginning to exploit the potential of these historical data
Acknowledgments Colin Harrower, Helen Roy, Michael Pocock, Gary Powney, Chris Preston Mark #BES12