1. Nick Isaac Biological Records Centre Centre for Ecology & Hydrology Interpreting biodiversity under diverse syndromes of recording behaviour

2. Nick Isaac Biological Records Centre Centre for Ecology & Hydrology Extracting trends from biological recording data

3. Is biological recording fit for purpose? What is the purpose? What data are available? What are the problem issues? What tools might provide a solution?

4. What is the purpose? Describing species’ distributions Detecting and attributing change over time Identifying novelties Is biological recording fit for purpose? Mike Majerus Wikipedia Commons FERA GBNSS

5. Biological records data How do we interpret the gaps? NBN lists 35 data sources: Individual records Regional recording projects Co-ordinated national surveys

6. Published Atlases The primary tool for understanding UK biodiversity Authoritative summary of the current state of knowledge A snapshot of species’ distributions Perring, F H, & Walters, S M, eds 1962 Atlas of the British Flora. Thomas Nelson & Sons, London

7. Published Atlases

8. Stock & change in distribution Repeat atlases allow an assessment of change over time Prickly Lettuce (Lactuca serriola) has expanded northwest since 1970

9. Repeat atlases: plants & birds, butterflies

10. Biodiversity change using atlases ‘Square counts‘ on repeat atlases reveal which species are increasing vs decreasing Greatest losses occurred among butterflies, then birds Thomas, JA et al. (2004). Comparative losses of British butterflies, birds, and plants and the global extinction crisis. Science, 303(5665), 1879–81

11. Where are we now? Atlases provide a rather static view of biodiversity The unstructured nature of the data makes square counting unreliable Increasing demand for quantitative information New methods for estimating trends are being developed

12. 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

13. Past, present and future Biodiversity IndicatorsAttributing changeDescribing change

14. Talk outline Extracting trends from Biological records data Problems & possible solutions Comparison of candidate methods Simulations of recording behaviour Which methods are useful for detecting trends? Applications: which species are declining? Trends in Odonata 1970-2011 Biodiversity Indicator

15. Recording intensity varies among taxa

16. Extracting trends from biological records

17. Recording intensity has increased over time

18. Telfer’s Change Index Telfer, MG, Preston, CD & Rothery, P (2002). A general method for measuring relative change in range size from biological atlas data. Biological Conservation, 107(1), 99–109 Compares two time-periods that differ in recording intensity &/or geographic coverage

19. Ball’s Visit Rate model Ball, S, Morris, R, Rotheray, G, & Watt, K (2011). Atlas of the Hoverflies of Great Britain (Diptera, Syrphidae).

20. Most lists are incomplete For most groups, ~50% of visits produce ‘incidental records’

21. Lists lengths are not constant over time

22. Mixed model

23. Most records come from a few recorders Bryophytes: 18 Myriapods: 11 Moths: 102 Orthoptera: 39

24. Spatial pattern of recording behaviour Orthoptera 1970-2011: top 4 recorders made 14% of all visits

25. Hill’s Frescalo method Hill, MO (2011). Local frequency as a key to interpreting species occurrence data when recording effort is not known. Methods in Ecology and Evolution, 3(1), 195–205. Red = under-recorded White = well-recorded Frescalo estimates the recording intensity of each grid cell

26. Hill’s Frescalo method By estimating recording intensity, Frescalo calculates the number of species that ‘should’ be in each grid cell.

27. Hill’s Frescalo method Hill, MO (2011). Local frequency as a key to interpreting species occurrence data when recording effort is not known. Methods in Ecology and Evolution, 3(1), 195–205.

28. Occupancy modelling: a panacea? van Strien, A, van Swaay, C, & Kéry, M (2011). Metapopulation dynamics in the butterfly Hipparchia semele changed decades before occupancy declined in the Netherlands. Ecological Applications, 21(7), 2510–2520 Gateshead birders

29. Talk outline Extracting trends from Biological records data Problems & possible solutions Comparison of candidate methods Simulations of recording behaviour Which methods are useful for detecting trends? Applications: which species are declining? Trends in Odonata 1970-2011 Biodiversity Indicator

30. Recorder behaviour Estimate trends Raw data Simulations How can we estimate trends?

31. 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

32. Simulation overview 1000 sites (no spatial information) 1 focal species + 25 others Focal species occupies 50% sites Impose different patterns of recording Run for 10 years Estimate trends using different methods

33. Simulation patterns of recording A: Control scenario: even recording Equal probability of sites being visited B: Increasing recording intensity Growth in number of visits C1: Incomplete recording (even) A fixed proportion of Visits produce short lists C2: incomplete recording (increasing) Proportion of short lists increases over time

34. Type I Error Rates

35. A Even Recording Change Index0.027 nRecords0.024 Visit Rate0.046 MM2sp0.061 MM3sp0.058 MM4sp0.058 Frescalo0.040

36. Type I Error Rates A Even Recording B Increasing Intensity C1 Incomplete even C2 Incomplete increasing Change Index0.0270.0260.0330.037

37. Type I Error Rates A Even Recording B Increasing Intensity C1 Incomplete even C2 Incomplete increasing nRecords0.0240.9930.0420.609

38. Type I Error Rates A Even Recording B Increasing Intensity C1 Incomplete even C2 Incomplete increasing Visit Rate0.0460.0600.0590.675

39. Type I Error Rates A Even Recording B Increasing Intensity C1 Incomplete even C2 Incomplete increasing MM2sp0.0610.0790.0530.195 MM3sp0.0580.0790.0600.089 MM4sp0.0580.0730.0660.049

40. Type I Error Rates A Even Recording B Increasing Intensity C1 Incomplete even C2 Incomplete increasing Frescalo0.0400.1640.0360.060

41. Type I Error Rates A Even Recording B Increasing Intensity C1 Incomplete even C2 Incomplete increasing Change Index0.0270.0260.0330.037 nRecords0.0240.9930.0420.609 Visit Rate0.0460.0600.0590.675 MM2sp0.0610.0790.0530.195 MM3sp0.0580.0790.0600.089 MM4sp0.0580.0730.0660.049 Frescalo0.0400.1640.0360.060

42. Power to detect a genuine decline A Even Recording Change Index0.574 nRecords0.642 Visit Rate0.739 MM2sp0.665 MM3sp0.649 MM4sp0.615 Frescalo0.612

43. Power to detect a genuine decline A Even Recording B Increasing Intensity C1 Incomplete even C2 Incomplete increasing Change Index0.5740.4610.370.316 nRecords0.64200.4490.979 Visit Rate0.7390.6060.5070.985 MM2sp0.6650.4240.3190.685 MM3sp0.6490.4080.2710.463 MM4sp0.6150.3630.2110.208 Frescalo0.6120.7680.340.308

44. Simulations: Conclusions The simulation provides a framework for comparing methods under a range of recording scenarios The Mixed model method performs best so far (Frescalo & Occupancy results pending) In the best recording scenario, a decline of 30% was detected in 60% of simulated datasets

45. Talk outline Extracting trends from Biological records data Problems & possible solutions Comparison of candidate methods Simulations of recording behaviour Which methods are useful for detecting trends? Applications: which species are declining? Trends in Odonata 1970-2011 Biodiversity Indicator

46. Odonata trends 1970-2011 Broad agreement between methods 14/32 species show significant increases under both methods 2/32 show significant decreases under both methods

47. Odonata trends: winners Wikipedia Commons Small red-eyed Damselfly (Erythromma viridulum) Scarce chaser (Libellula fulva) Emperor Dragonfly (Anax imperator)

48. Odonata trends: losers Variable damselfly (Coenagrion pulchellum) Blue-tailed Damselfly (Ischnura elegans) Common Blue Damselfly (Enallagma cyathigerum)

49. Odonata Indicator

50. Biological Recording for the 21 st Century We have the tools to model biodiversity change using unstructured biological records This is only possible if records continue to be submitted to the database! We could be smarter about data collection We’re only just beginning to exploit the potential of biological recording data Indicators, Red Listing, ecosystem service provision, targeting Agri-environment schemes

51. Acknowledgments Tom August Colin Harrower David Roy, Helen Roy, Michael Pocock, Gary Powney, Chris Preston Mark Hill Arco van Strien