Nick Isaac Biological Records Centre Centre for Ecology & Hydrology

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 FERA Wikipedia Commons GBNSS 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 Describing change Attributing change Biodiversity Indicators

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. Frescalo estimates the recording intensity of each grid cell Red = under-recorded White = well-recorded

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

31. Simulations Aims: To compare the performance of different methods for estimating range change under realistic scenarios of recorder behaviour To discard methods that are inappropriate 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. Type I Error Rates

36. Type I Error Rates

37. Type I Error Rates

38. Type I Error Rates

39. Type I Error Rates

40. Type I Error Rates

41. Type I Error Rates

42. Power to detect a genuine decline

43. Power to detect a genuine decline

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 Small red-eyed Damselfly (Erythromma viridulum) Scarce chaser (Libellula fulva) Emperor Dragonfly (Anax imperator) Wikipedia Commons

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 21st 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