Challenges of working in the governmental sector: from science to judgements Natural Heritage Trends Des Thompson and Iain MacGowan Policy and Advice Directorate

Challenges of working in the governmental sector: from science to judgements

Natural Heritage Trends

Des Thompson and Iain MacGowan

Policy and Advice Directorate

Scottish Natural Heritage


The Natural Heritage (Scotland) Act 1991

the natural heritage

… includes the flora and fauna of Scotland,its geological and physiographical features,

its natural beauty and amenity …

Scottish Natural Heritage

general aims and purposes: - secure conservation and enhancement of nature - foster understanding and facilitate enjoyment of nature and outdoors


the problem

issues

diversity ………. remit

variability …….... natural & social systems

complexity ….…… environmental responses

requirements

evidence ……….. data

change …..…… direction, rate, forecast

credibility ……….. statistics


top 10 statistical problems

1. Sample size – insufficient / governed by resources

2. Sampling design – inefficient / stratification

3. Data – examine prior to analysis for peculiar values / important relationships

4. Statistical assumptions – normal / groups compared have similar variance

5. Sampling – non-random / systematically located

6. Pseudo-replication – within a large plot / small number of large plots

7. Misinterpretation – e.g. sampling errors do exist within permanent plots

8. Correction / calibration factor – difficult to measure / at one location / time

9. Complex 'black box' methods – can be sensitive to assumptions / parameters

10. Correlation – between successive values in a time-series / non-existent trends


statistical methods

1) Planning - method selection, experimental design, sampling strategy,

data analysis and evaluation

2) Habitats - survey, evaluation and monitoring methods

3) Species - general & specific methods of survey and monitoring for the

major taxonomic groups


land cover change

National Countryside Monitoring Scheme

1947, 1973, 1988

Countryside Survey

1978, 1984, 1990, 1998, 2007

stratified random samples

1 Biodiversity – action plan outcomes 2 Protected Areas – site condition 3 Habitat condition – botanical indices 4 Landscape – spatial analysis 5 Habitat networks – spatial modelling 6 Information – direct & indirect provision 7 Climate – phenology 8 Recreation – activities & impediments 9 Greenspace – spatial analysis 10 Satisfaction – attitudes & participation 11 Birds – terrestrial, water, seabirds 12 Fresh water – ecological status 13 Marine – trophic indices 14 Tourism – natural heritage 15 Development – spatial analysis 16 Positive management – land and sea


indicators


recreation

longitudinal tracking of trends

- statutory right of access under part 1 of the Land Reform (Scotland) Act 2003

- participation in outdoor recreation

- awareness of Scottish Outdoor Access Code (SOAC)

Scottish Recreation Survey

- ScRS is undertaken through Scottish Opinion Survey – a monthly omnibus

- representative sample of the Scottish adult population ~ 1000/month

- interviewing in the home with Computer Assisted Personal Interviewing (CAPI)

- survey managed by TNS market information company


recreation

- quota sampling provides the most cost-effective approach

- confidence intervals can only be estimated

- sample size ~ 1,000 = confidence bands of +/-5% at the 95% confidence interval

non-probability, quota sampling

- stratified by geographical distribution of Scottish population

- 42 sample points of 25 interviews per point

- different sampling points (random) each month ~ 500 points / annum

- quotas for gender, age, socio-economic group and working status

- results weighted by demographic profile from National Readership Survey (NRS)


marine

plankton

- Greek, meaning ‘wanderer’ or ‘drifter’

- micro-organisms of both plant (phyto-) and animal (zoo-) components

- spend all / part of their life cycle suspended in the water column (pelagic)

- phytoplankton are at the base of the marine food web - primary producers

- upon which all higher trophic-level organisms in the sea depend

- plankton ecosystems play an important role in biochemical cycles

- important to larval stages of commercially important fish – e.g. cod and herring


marine

- trends x bio-geographical areas & key components of the plankton community

a) phytoplankton biomass

b) cold-water zooplankton / warmer-water zooplankton / Calanus abundance

c) seabed organisms (larvae in the water-column)

Continuous Plankton Recorder

- survey of seas around UK and North Atlantic Ocean over the past c. 50 years

- plankton abundance & community composition throughout pelagic ecosystem

- may be linked to environmental conditions - e.g. sea temperatures / pollution

loading


marine

Year

1960 1970 1980 1990 2000

Ind

ex

(19

58

= 1

00

) L

og

Sca

le

10

100

1000 Calanus finmarchicusDecapodaPCICalanus helgolandicusTotal Calanus

All Scotland

phytoplankton biomass


marine

statistical questions

b) if the spatial distribution of sampling locations has changed over time

- temporal trends reported may in-part reflect the changing sampling

a) data behind the annual averages needs to be described

- if in some years all observations were high / low then outliers are true

- alternatively, possible errors in observations contributing to the mean

c) trends

- index approach good but average the start-point over (say) first 10 years

- check diagrams carefully to ensure that running means are correct


marine

simple linear regression

- expect temporal autocorrelation in the residuals

- coefficients of the line will be poorly estimated

- standard errors will be too low

- tests against a null hypothesis (no change) will reject null hypotheses too often

- data often do not seem to follow a linear trend

- log-transformation may be more appropriate - scattering of high observations


birds

- relatively well-studied…lot of volunteers contribute data

- popular and resonant

- adopted as indicators of biodiversity and sustainable development

characteristics

- short-lived, mobile, productive breeders

- respond quickly through breeding output, survival, dispersal

- near top of food web, widespread, easy to identify

- range & abundance may reflect less-evident underlying conditions


seabirds

- modelling uses observed SMP counts to predict counts at un-surveyed colonies

- counts & imputed counts from each colony summed to estimate each species

- annual species abundance x region, feeding guild, nest type

- sub-sets of species with similar ecological traits point to factors responsible

data characteristics

- 24 species of breeding seabird from UK census c. 1970, 1985, 2000

- annual SMP survey (Seabird Monitoring Programme) commenced 1986

- SMP and census data are consistent at the UK level


seabirds

- the statistical problem:

only a sample of population counted each year, not all colonies counted

annually

need to estimate time-series trends from an incomplete time-series of counts

sampling

- whole-colony counts & sub-sections (plots)

- sample plots selected randomly & birds counted several times during season

- takes account of counting error and variation in birds present at any given time

- species differences = loss of information about variation within & between plots

- of 24 species recorded in SMP, data were sufficient for 13 species


seabirds

- chaining discards non-consecutive data that have taken time & effort to collect

- poor use of auxiliary plot data = unnecessarily high variability within indices

- assumptions not transparent regarding biases, variability & uncertainty

chain index of abundance

- compares data from only those sites counted in consecutive years

index in year x = 100 * (index in year x – 1)

* (abundance in year x / abundance in year x-1)

- index for first year of the time series (x = 0) is conventionally set to 100%

- easy to implement and understand & past years are unaffected by new data


seabirds

- initial development of an appropriate model is difficult & time consuming

- intellectually and computationally

Bayesian approach

- explicit statistical model for each seabird species at each colony

- estimation of unknown parameters by Bayesian inference

- makes more effective and efficient use of the available data

- scientific assumptions underpinning the model used to estimate parameters

- fully quantifies levels of variability for colony, region and Scotland


seabirds

- observation model accounts for uncertainty in counting birds (recording error)

- latent model describes the trend over time in the true number of birds

- no / weak assumption of linearity or that trends across sites were synchronous

hierarchical model

- modelled seabird abundance at colonies were combined to region & Scotland

- assumption: counts were due to an observation and a hidden (latent) process

- both involved a random component

- a hierarchical approach linked plot & colony counts to underlying trends

- assumption: seabird populations change in a relatively smooth way


seabirds

- valid for 8 species (fitting algorithm for 50,000 iterations + 10,000 ‘burn-in’)

- relevant parameters converge to their equilibrium distribution

- chaining method was applied to 5 species that violated the model assumptions

annual abundance trend 1986 - 2004

- Bayesian inference - repeated simulation to generate plausible parameter values

- repetition quantifies uncertainty about the values of the parameters

- models are more realistic than traditional statistical approaches

- computationally intensive Markov chain Monte Carlo approach (LinBUGS)


seabirds

- not all species declined - gains as well as losses

- various causes – food availability, predation, weather, disease

- interactions between the causes – e.g. phenology

annual abundance trend 1986 - 2004

- aggregate (& individual) trend of breeding abundance of 13 species

- uncertainty bands equivalent to 95% confidence intervals

- sandeel-specialist seabirds predominantly in decline

60

70

80

90

100

110

120

1986 1988 1990 1992 1994 1996 1998 2000 2002 2004

Year

Ind

ex

of

ab

un

da

nc

e

So…what’s the challenge?

• Robust science• Understanding data and information

limitations• Having clear questions• Making judgements on the available science• Being careful when straying from science

into policy, opinion, advice or recommendations


snh.org.uk/trends

Documents

Challenges of working in the governmental sector: from science to judgements Natural Heritage Trends Des Thompson and Iain MacGowan Policy and Advice Directorate