1. Extracting time series from occurrence records Nick Isaac Cross-taxa analysis of community dynamics: 4/11/15

2. Environmental Monitoring in the UK Intensity Volume Sentinel sites National surveys National Monitoring Schemes Unstructured occurrence records

3. = terrestrial ECN sites = ECN river sites = ECN lake sites Environmental Change Network Sentinel sites Multi-metric integrated monitoring: Meteorology Atmospheric deposition Bulk soil analysis Soil chemistry Runoff chemistry Vegetation quadrats Butterfly, bird & bat transects Moth light traps Beetle pitfall traps Morecroft et al (2009) Biol Conserv

4. Countryside Survey National professional surveys in 1978, 1984, 1990, 2000, 2007 Stratified random sampling with 1km 2 grain size Vegetation plots Landscape features National Sample based Freshwater macrophytes Soils Norton et al. (2012) J Env Manag 113: 117-127

5. National Monitoring Schemes Birds: BBS, WeBS, WBS, WBBS …. Butterflies: UKBMS, WCBS Moths: RIS Bats: NMRS Some terrestrial mammals Plants: NPMS Volunteers collect, professionals curate Partnerships with NGOs are crucial

6. UK Butterfly Monitoring Scheme WCBS Pollard walks

7. Severe droughts impacts on butterflies Managing landscapes (e.g. reducing habitat fragmentation), the probability of persistence until mid-century improves from around zero to between 6 and 42% Figure: a.Low fragmentation level b. & c. Mid d.High fragmentation level e.Current situation Oliver et al. (2015). Nature Climate Change doi:10.1038/nclimate2746

8. Unstructured Occurrence Records

9. 1960-2002 Shift between 1960-2002 Key Publications: Hickling et al. 2006 Global Change Biology; Chen et al. 2011 Science

10. What are national schemes and societies?

12. Time series from occurrence records Recording is biased Occupancy models as a solution Applications Broad patterns in biodiversity Resilience of ecosystem services Trends among British bees Biodiversity Indicators Drivers of biodiversity change

13. Problem: ad hoc recording is biased in time in space detectability effort per visit Effort Number of Species Isaac & Pocock (2015) Biol J Linn Soc 115: 522-531

14. Occupancy models: a solution? WildAboutBritain.co.uk van Strien et al (2011) Ecol Appl 21: 2510–2520 Bayesian Occupancy-Detection (BOD) models van Strien and others have argued that BOD provides a framework for modelling unstructured occurrence records van Strien et al (2010) Basic & Appl. Ecol. 11: 495-503 van Strien et al (2011) Ecol. Appl. 21: 2510-2520 van Strien et al (2103) Biodiversity & Conserv 22: 673-686 van Strien et al (2013) J. Appl. Ecol. 50: 1450-1458 Observer model: (p D |z) ~ ListLength

15. Occupancy models are robust to biases BOD 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: 1052-1060

16. Defaunation Review Dirzo et al (2014) Science, 345: 401–406

17. Species trends and ecosystem functions Turnover is higher among species that deliver pollination and pest control services These functions may be less resilient to change Oliver et al (in review)

18. Occupancy models for British bees Nick Owens Bombus bohemicus

19. Indicator of Pollinating Insects from Occupancy models for 216 bee species

20. Drivers of change: Invasive ladybird Declines in native ladybirds are attributable to the arrival of the invasive Harlequin ladybird Similar patterns across 8 native species in both GB & Belgium Roy et al (2012) Diversity & Distributions, 18: 717–725 Mike Majerus davidkennardphotography.com 1990 2000 2010

21. Declines in native ladybirds

22. Where next? The ideal dataset for studying global change has broad taxonomic and spatial coverage with fine spatio-temporal resolution No dataset has all these properties Beck et al (2012) Ecography 35: 1-11

23. Existing datasets are complementary What is the best way to model biodiversity responses to environmental change? TraitEnv Change Network Countryside Survey Monitoring schemes Occurrence Records ProtocolsDetailed SimpleNo Stratified designNoYesmixedNo Co-locationYesNolimitedNo Taxonomic breadthlimited high Temporal precisionhighlowMostly goodhigh Spatial coveragelowhighmixed Spatial extentNational International

24. Model datasets together It’s not an either-or question Each is a separate realization of the same underlying state Borrow strength across datasets

25. Conclusions UK has a wealth and diversity of biodiversity data Some are scalable across Europe Opportunistic records can provide valuable insights – climate change – ecosystem services – Invasive species Best value from combining datasets – New hybrid schemes are under development

26. Acknowledgments Tom August, Gary Powney, David Roy, Charlie Outhwaite Arco van Strien, Marnix de Zeeuw Colin Harrower, Helen Roy, Michael Pocock, Oli Pescott, Stephen Freeman Mark Hill, Chris Preston @drnickisaac