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

Transcription

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

2 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?

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

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

5 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

6 Published Atlases

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

8 Repeat atlases: plants & birds, butterflies

9 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),

10 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

11 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 Mike Majerus Roy, HE, Adriaens, T, Isaac, NJB et al. (2012). Invasive alien predator causes rapid declines of native European ladybirds. Diversity and Distributions, 18(7),

12 Past, present and future Describing change Attributing change Biodiversity Indicators

13 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 Biodiversity Indicator

14 Recording intensity varies among taxa

15 Number of records Extracting trends from biological records Year

16 Recording intensity has increased over time Number of records Butterflies Bryophyte Orthoptera Myriapod Isopods Coleoptera Moths Bees Wasps Ants Hoverflies Odonata

17 Telfer s Change Index Compares two time-periods that differ in recording intensity &/or geographic coverage 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),

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

19 Proportion of all visits Most lists are incomplete For most groups, ~50% of visits produce incidental records 100% 90% 80% 70% 60% 50% 40% 30% > 3 species 3 species 2 species Single species 20% 10% 0%

20 Proportion of incidental records Lists lengths are not constant over time Bryophyte Isopods Coleoptera Moths Ants Hoverflies

21 Proportion Mixed model Year

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

23 Spatial pattern of recording behaviour Orthoptera : top 4 recorders made 14% of all visits

24 Hill s Frescalo method Frescalo estimates the recording intensity of each grid cell Red = under-recorded White = well-recorded 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),

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

26 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),

27 Occupancy modelling: a panacea? Gateshead birders 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),

28 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 Biodiversity Indicator

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

30 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

31 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

32 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

33 Type I Error Rates

34 Type I Error Rates A Even Recording Change Index nrecords Visit Rate MM2sp MM3sp MM4sp Frescalo 0.040

35 Type I Error Rates A Even Recording B Increasing Intensity C1 Incomplete even C2 Incomplete increasing Change Index

36 Type I Error Rates A Even Recording B Increasing Intensity C1 Incomplete even C2 Incomplete increasing nrecords

37 Type I Error Rates A Even Recording B Increasing Intensity C1 Incomplete even C2 Incomplete increasing Visit Rate

38 Type I Error Rates A Even Recording B Increasing Intensity C1 Incomplete even C2 Incomplete increasing MM2sp MM3sp MM4sp

39 Type I Error Rates A Even Recording B Increasing Intensity C1 Incomplete even C2 Incomplete increasing Frescalo

40 Type I Error Rates A Even Recording B Increasing Intensity C1 Incomplete even C2 Incomplete increasing Change Index nrecords Visit Rate MM2sp MM3sp MM4sp Frescalo

41 Power to detect a genuine decline A Even Recording Change Index nrecords Visit Rate MM2sp MM3sp MM4sp Frescalo 0.612

42 Power to detect a genuine decline A Even Recording B Increasing Intensity C1 Incomplete even C2 Incomplete increasing Change Index nrecords Visit Rate MM2sp MM3sp MM4sp Frescalo

43 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

44 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 Biodiversity Indicator

45 Odonata trends Broad agreement between methods 14/32 species show significant increases under both methods 2/32 show significant decreases under both methods

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

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

48 Odonata Indicator

49 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

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