1. What can Statistics do for me? Marian Scott Dept of Statistics, University of Glasgow Statistics course, September 2007

2. Outline of presentation Why would or indeed should an environmental scientist need to know any statistics? Illustration: environmental change- one of the most enduring features with – Links to research, policy, policy effectiveness evaluation,policy and management

3. Why quantify? Quantification is an essential part of most scientific activities For the environment, quantification must account – for inherent variability of the process or – for lack of precise knowledge of it and is needed for resolving many of the environmental issues of today Decision making- Which areas should be restricted? Prediction-What is the trend in temperature? Predict its level in 2050? Decision making-is it safe to eat fish? Regulatory- Have emission control agreements reduced air pollutants? Understanding -when did things happen in the past

4. Quantification is model and observation based Questions about the model Is it valid? Are the assumptions reasonable? Does the model make sense based on best scientific knowledge? Is the model credible? Do the model predictions match the observed data? How uncertain are the results? Questions we ask about data Do they result from observational or designed; laboratory or field experiments? What scale are they collected over (time and space)? Are they representative? Are they qualitative or quantitative? How are they connected to processes, how well understood are these connections? How uncertain are they?

5. Example: are atmospheric SO 2 concentrations declining? Measurements made at a monitoring station over a 20 year period Complex statistical model developed to describe the pattern, the model portions the variation to trend, seasonality, residual variation

8. Comments on the issue Lots of variation Variation may make the pattern more difficult to see (signal to noise ratio) There may be small numbers of unusual observations There may be distinct changes (discontinuities)

9. Example: particles on a beach Discovery of radioactive particles on the foreshore of a nuclear facility since 1983 Is the rate of finds falling off? Are the particle characteristics changing with time? Why?, there have been a number of campaigns to recover the particles

10. Log activity and trend

11. Trend in number of finds

12. Example: how is Cs-137 distributed over a large area of SW Scotland? Aerial survey of the area (detectors mounted in helicopters) How to design the flight pattern (straight lines separated by 250m)? How to match and then calibrate the results to ground based measurements?

13. 137Cs deposition maps in SW Scotland prepared by different European teams (ECCOMAGS, 2002)

14. Lochs in area Y

15. Comments Spatial variation is clear There is variation amongst the measurement techniques There are many ways of exploring the important features There is uncertainty about the spatial extent

16. NERC priorities climate change and more generally environmental change; earths life support system; sustainable economies and environment and human health where some of the fundamental research questions associated with each of these priorities require quantitative skills involving:

17. Statistics might be needed where? the use of complex computer models to simulate the whole earth system (e.g. climate change and the carbon cycle); uncertainty, model evaluation the analysis of observational records, (e.g. past climate indicators, water quality, pollutant trends); trends, spatio-temporal modelling, dealing with variation the study and modelling of extreme events (e.g. sea levels, flood prediction) for prediction and management of future occurrences; extremes the evaluation and quantification of risk and uncertainty (e.g. volcanic or earthquake prediction);uncertainty, prediction

18. Statistics might be needed where? designing and evaluation monitoring and sampling networks; sampling strategies the analysis of observational records, (e.g. past climate indicators, water quality, pollutant trends); trends, spatio-temporal modelling, dealing with variation the study and modelling of extreme events (e.g. sea levels, flood prediction) for prediction and management of future occurrences; extremes, risk modelling, uncertainty evaluating the state of the environment;trends, uncertainty, prediction

19. Some examples of current environmental issues……. Climate change Biodiversity Arctic ice cover Water quality Extreme weather

20. Direct Observations of Recent Climate Change Gobal mean temperature Global average sea level Northern hemisphere Snow cover

21. Warming in the Arctic is double that for the globe from 19 th to 21 st century and from late 1960s to present. Warmth 1925 to 1950 in Arctic was not as widespread as recent global warmth. Note different scales Arctic vs Global annual temperature anomalies (°C)

22. Trends in seasons over Europe (Global Change Biology, 2006) 21 countries, 125,000 studies, 542 plant and 19 animal species, 1971-2000 Spring is on average 6 to 8 days earlier than it was 30 years ago Analysis of 254 national time series, pattern of observed change in spring matches measured national warming (correlation coefficient –0.69, P<0.001)

23. Observed temperature trend in Europe (EEA signals 2004). Global average temp increased by 0.7 0.2°C over the past 100 years Change in different periods of the year may have different effects, – start of the growing season determined by spring and autumn temps, – changes in winter important for species survival.

24. What is the state and trend in biodiversity (EEA CSI 009) Populations of common and widespread farmland bird species in 2003 are only 71% of their 1980 levels. Key message: Butterfly and bird species across Europe show population declines of between -2% and -37% since the early 1970s. What can we do? Biodiversity convention

25. Spatial patterns of change Spatial patterns of change may be important Changes in the start and end of the growing season between two years (1961, 2004) – heterogeneous

26. Visualisation

27. Time series plots Bar charts Smoothers Linear regression Space and time Pairs of images

28. Statistical modelling

29. Smoothers Linear regression Additive models

30. Evidence-base for policy and management The integration of experience, judgement and expertise with the best available external evidence from systematic research (Davis, 1991) ….opinions and judgements of experts that constitute high quality, valid and reliable evidence Modernising government requires to produce policies that really deal with problems: that are forward looking and shaped by the evidence……, that tackle causes not symptoms Cabinet Office, 1999 Much policy making happens in response to very short-term pressures –Timely findings of practical relevance

31. Effects of policy How much or how little we know about the links between environmental policy measures and their actual impact in the environment much of the information gathered is of limited use in assessing the impact of environmental measures ( Nigel Haigh, foreword of Environmental Issues, Report 25/EC ) Agencies are data rich and information poor. Good policy needs a foundation in good science. ( Margot Wallstrom, European Environment state and outlook report, 29 Nov 2005 )

32. Statistics and the environment Appropriate statistical models can give – added value to routine monitoring data, – better descriptions of complex change behaviour and – begin to tease out climate change driven effects in environmental quality – handle natural variation. Greater, innovative statistical analysis needed for environmental science

33. Statistics and the environment As environmental scientists, we need to try and ensure that: data are gathered under good statistical principles and that they are not left in the filing cabinet. We need to ensure that Good environmental science is served by good statistical science. Environmental science should be Data and information rich