Slajd 1

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Methodology workshop focused on technology for
identifying marine habitats Trine
Bekkby Workshop at NIVA, Oslo, May 29-30 2007
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PresentingNorwegian Institute for Water Research
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District offices and daughter companies
Daughter companies
NIVA group 250 employees
District offices - Trondheim- Hamar - Bergen
- Grimstad
Trondheim
Min office
Solbergstrand Marine Research Station
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Categories of work
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Most important areas of research

• Water resource management
• Taxonomy and biodiversity
• Physiology and ecotoxology
• Physical processes and modelling
• Geochemistry
• Cleaning and transport of drinking and bilge
  water
• Water chemistry and chemical analyses

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Experience from more than 70 countries
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PresentingOslo Centre for Interdisciplinary
Environmental and Social Research
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Area 14 000 m2 Employees 500 Cost 270
mill. kr Started April 2005 Inhabited Oct.
2006
CIENS partners ? NIBR ? NINA ? NILU ?
NIVA ? TØI ? UiO ? met.no ? CICERO NVE
Tandbergbygget på Brekke
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Heat from the ground covers 90 of the cooling
and 60 of the heating
The biggest solar panel in Norway
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Presenting ICZMP in Norway
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ICZMP in Norway - Background

• Norway has complex terrain, with high mountains,
  deep fjords and a large archipelago. Hence, large
  marine areas are found within the baseline
• We have many rivers and large freshwater runoffs
  to the ocean, hence a large interaction across
  the coast line
• We have many water types, outer exposed coast,
  archipelago and inner sheltered areas.
• Because of all this, the habitats are many and
  complex and biodiversity often high

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ICZMP in Norway Management and planning
Norway is obliged to the Water Framework
directive WFD (because we are in the EEC), which
includes large marine areas (since we have such a
large archipelago) We are not obliged to the
Habitat Directive and Natura 2000 (because we are
not in the EU) We do not have any MPAs (marine
protected areas), only suggestions under
discussion We have Ramsar areas (for bird
protection), landscape protection areas, national
parks etc., but no true marine protection. We
have management plans for selected areas (e.g.
the Barents Sea), area defined as being of extra
value regarding biodiversity To fulfil the
requirement of the WFD, we have suggested areas
and stations for reference monitoring (i.e. they
are relatively pristine) and areas and stations
for trend monitoring (with pressures, not
pristine)
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Legal borders of Norway
Coastal areas (1 nm outside the base
line) Territorial waters (12 nm outside the base
line) Exclusive economic zone (200 nm outside
the base line, with exceptions)
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ICZMP in Norway Management and planning
Water types according to the WDF work
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ICZMP in Norway Management and planning
Reference areas according to the WFD Trend
monitoring areas according to the WFD Areas of
particularly interest when it comes to
biodiversity Suggestions for MPA
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ICZMP in Norway All collected data
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Reference and trend monitoring stations (WFD)
suggested
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Presenting different projects
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Presentation of selected projects
MarModell - finding criteria for habitat
modelling CoastScenes - modelling effects of
scenarios Dynamod developing models for the
Skagerrak area Sugar kelp modelling in the
Skagerrak area NorGIS - modelling habitats at
the Nordic level MarNatur - The national
program for mapping and modelling of marine
habitats. Balance Others
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Presentation of selected projects
MarModell - finding criteria for habitat
modelling CoastScenes - modelling effects of
scenarios Dynamod developing models for the
Skagerrak area Sugar kelp modelling in the
Skagerrak area NorGIS - modelling habitats at
the Nordic level MarNatur - The national
program for mapping and modelling of marine
habitats. Balance Others
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The aim of MarModell

• Study the relationship between environmentla
  factors and the distribution and abundance of
  marine coastal habitats
• Develop methodology for habitat modelling
• Study the effects of scale
• The link geology-biology crucial

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Predictors and responses

• Bathymetry and terrain (depth, slope, curvature)
• Wave exposure at different scales
• Tidal current (together with UiO)
• Light exposure
• Light
• Presence/absence
• Coverage

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Input models
Field work
Statistical model building, analyses, model
selection
Data
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Presentation of selected projects
MarModell - finding criteria for habitat
modelling CoastScenes - modelling effects of
scenarios Dynamod developing models for the
Skagerrak area Sugar kelp modelling in the
Skagerrak area NorGIS - modelling habitats at
the Nordic level MarNatur - The national
program for mapping and modelling of marine
habitats. Balance Others
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The aim of Coast-scenes

• Study the relationship between environmentla
  factors and the distribution and abundance of
  marine coastal habitats develop model
• Define the natural conditions of the area at the
  site of a fish farm, compare with the existing
  conditions
• Analyse/model the effect of scenarios for human
  acticity development

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Presentation of selected projects
MarModell - finding criteria for habitat
modelling CoastScenes - modelling effects of
scenarios Dynamod developing models for the
Skagerrak area Sugar kelp modelling in the
Skagerrak area NorGIS - modelling habitats at
the Nordic level MarNatur - The national
program for mapping and modelling of marine
habitats. Balance Others
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The aim of Dynamod

• Develop methodology for modelling of marine
  substrate and habitats, both rocky and soft
  seabed
• Developing base models, i.e. light models
• Comparing wave exposure models
• Developing current models
• Separating rocks from soft sediment
• Separating different soft sediment classes
• Modelling ecological status?
• Modelling rocky shore macroalgaes

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Presentation of selected projects
MarModell - finding criteria for habitat
modelling CoastScenes - modelling effects of
scenarios Dynamod developing models for the
Skagerrak area Sugar kelp modelling in the
Skagerrak area NorGIS - modelling habitats at
the Nordic level MarNatur - The national
program for mapping and modelling of marine
habitats. Balance Others
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Presentation of selected projects
MarModell - finding criteria for habitat
modelling CoastScenes - modelling effects of
scenarios Dynamod developing models for the
Skagerrak area Sugar kelp modelling in the
Skagerrak area NorGIS - modelling habitats at
the Nordic level MarNatur - The national
program for mapping and modelling of marine
habitats. Balance Others
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Presenting equipment and methods for sampling
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Equipment and methods for sampling (sample
design, equipment, sampling)
Sample design Preliminary model as basis for
selecting stations We need to cover the range of
predictor (depth, slope, terrain, wave exposure,
currents etc.) Stations are randomly selected
within the study area
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Equipment and methods for sampling (sample
design, equipment, sampling)
Equipment in the field ROV Pico-ROV (small
portable camera, may be operated by
hand) Singlebeam echosounder for recording of
depth in the field, used together with
pico-ROV Multibeam echosounder, used at selected
locations Sediment profile Image (SPI) camera
for sediment penetration depth and ecological
status Grab (sediment samples) FerryBox
(recording equipment on ferries) Divers
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Equipment and methods for sampling (sample
design, equipment, sampling)

• Recorded in the field
• Usually we use small boats and record
• Depth (from the echosounder)
• Substrate (visually), presence/absence and
  coverage
• Habitat presence and absence
• Habitat coverage
• If larger boats, then some of the following are
  recorded
• Substrate classified based on multibeam on
  selected locations
• Penetration depth (using SPI)
• Redox depth (from SPI or other equipment)
• Grain size (from grab)
• Species composition (from grab of sediment or
  diving on rocky substrate)
• Environmental state (from SPI pictures)

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Field work
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Similarities and differencesNorway - Poland
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Similarities and differences compared with Polish
conditions
Poland is in the EU and is obliged to both the
Water Framework and the Habitat Directive. Norway
is not in the EU and is only obliged to the EFD
(because we are in the EEC) Norway and Poland
has different bathymetry and topography, the
terrain variability is less in Poland than in
Norway The exposure levels are higher and more
variable in Norway than in Poland The number of
habitats differ between the two countries The
pressures are different (?). In Norway, the
pressures are mainly fishing, fish farms, kelp
harvesting, waterfall regulations and, in some
areas, changing of habitats for recreational
purposes. In Poland ? More?
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Presentingthe modelling approach in more detail
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The basic idea
Terrain structures and environmental factors
determines the distribution of marine habitats
But what kind and how? And how to make good
predictions?
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Modelling in more detail the Norwegian approach
(geophysical factors, substrate habitat
Geophysical base models Depth model (25 m
resolution for the whole of Norway, better in
selected areas), includes some land data to
ensure good models in the coastal zone Wave
exposure model (25 m resolution for the whole of
Norway, 10 m in selected areas) Terrain models
for selected areas (e.g. slope, curvature,
basins, tops) Current circulation models for
selected areas Light percentage models for
selected areas ( of surface light reaching the
seabed, depends on secchi depth) Light exposure
models (an index based on optimal slope and
aspect)
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Modelled wave exposure

• Isæus (2004)

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Depth
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Slope
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Curvature
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Modelled current
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Light - of surface level
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Light related to optimal slope and aspect
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Modelling in more detail the Norwegian approach
(geophysical factors, substrate habitat
Substrate Binomial models separating rocks from
sediment based on slope and curvature Probability
model separating rocks from sediment Probability
model separating sand from softer sediment
(based on data on penetration depth)
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Seabed substrate
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Binomial seabed substrate modelling
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Probability seabed substrate modelling
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Probability soft seabed sediment modelling
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Modelling in more detail the Norwegian approach
(geophysical factors, substrate habitat
Habitat Kelp forest - binomial models for
Norway Zostera meadows binomial models for
Norway EUNIS classes binomial models to level
2 for Norway Large shallow inlets and bays
(Natura 2000 habitat) binomial models for
selected areas Kelp probability models for
selected areas Zostera meadows - probability
models for selected areas
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Modelling approach methodology, some examples

• Binomial modelling pros and cons
• Uses empirical data to find max and min values
• Uses expert judgement to set borders
• Provides modelled areas on maps that may be
  measured (area)
• Absolute borders, easy to miscommunicate
• The uncertainty in the models not included, no
  probability measures

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Binomial modelling of kelp forest
Skagerrak In exposed and moderately exposed
areas down to 20 m depth North Sea In exposed
areas down to 25 m depth and moderately exposed
areas down to 20 m depth Norwegian Sea to
South-Trøndelag as in the North Sea Norwegian
Sea from to the Barents Sea Exposed areas down
to 25 m (moderately exposed areas are grazed by
sea urchins)
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Binomial modelling of eelgrass (Zostera marina)
In shallow (down to 7 m depth), relatively flat
(lt7 degrees) and sheltered and moderately exposed
areas
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Predictions habitat modelling
Green modelled kelp forest Pink modelled
eelgrass Yellow modelled shell sand Turquoise
modelled Pecten maximus
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Binomial modelling of EUNIS classes
Based on the data available for the whole of
Norway, it has been possible to model EUNIS down
to level 2, using wave exposure and depth
classes. The depth classes are 0-30 m, 30-50,
50-100, 100-200, 200-500, 500-700 and deeper than
700 m. Wave exposure classes are
Wave exposure (SWM) EUNIS class
lt 1200 Ultra beskyttet
1200 4000 Ekstremt beskyttet
4000 10000 Svært beskyttet
10000 100000 Beskyttet
100000 500000 Moderat eksponert
500000 1000000 Eksponert
1000000 2000000 Svært eksponert
gt 2000000 Ekstremt eksponert
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Modelling approach methodology, some examples
Probability modelling pros and cons Uses
empirical data to find max and min values
Includes the uncertainty of the data in the
models, has probabilities Probabilities makes
it possible to select different approaches,
overestimate (precautionary) or underestimate
(e.g. for time-efficient searching) More
intuitive, easier to explain discrepancies from
observations - Can not include expert
judgement - Depends a lot on the empirical data
set, an insufficient data set will give a bad
model
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Laminaria hyperborean kelp forest
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Seagrass (Zostera marina) meadows
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Analyses separating the information from the
noise

• Integrating data in a GIS
• Linking data for analyses
• Predictor data (depth, slope, wave exposure,
  currents etc)
• Response data (habitat presence/absence,
  coverage etc)
• Analyses and model building
• Finding significant factors (traditional H0
  testing with p-values) OR
• Build different alternative models and use model
  selection techniques (e.g. AIC)

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Three traditions

• Frequentism (p-values)
• Likelihood (AIC)
• Bayesian IC
• Frequentism
• H0 hypothesis testing, p-values, significance
• Akaikes Information Criterion (AIC)
• Testing the models (and the hypotheses) relative
  to each other
• Finding the model that looses the least
  information
• Bayes
• Often called BIC, but it has noting to do with
  information theory, not as well founded on theory
  as AIC
• Often gets none or very large effects
• Regarded as better that frequentism, but not as
  good as AIC

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Traditional H0 testing or AIC model selection
techniques

• Finding significant factors (traditional H0
  testing with p-values)
• Did we believe in the H0 in the first place?
• What does significant p really mean?
• We test the H0(not the H1), as accept the H1
  because of the rejection
• Build different alternative models and use model
  selection techniques (AIC)
• My models are my hypotheses and model selection
  is hypothesis selection
• All hypothesis are formulated as models, a
  priori neck-up-thinking is essential
• Testing the models (and the hypotheses) relative
  to each other
• AIC finds the model that looses the least
  information
• AIC weights the benefit of a better and more
  complicated model against the cost of
• including more factors

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One example on neck-down models

• Kelp forest presence (P) is determined by wave
  exposure (WE) only
• P is determined by light attenuation (LA) only
• P is determined by sea bed substrate (SS) only
• P is determined by WE and LA
• P is determines by WE and SS
• P is determined by LA and SE
• P is determined by WE, LA and SE
• P is determined by WE, LA and WELA
• P is determined by WE, SS and WESS
• P is determined by LA, SE and LASE
• P is determined by WE, LA, SE and WELA
• P is determined by WE, LA, SE and WESE
• P is determined by WE, LA, SE and LASE
• P is determined by WE, LA, SE and WELASE

Neck-up choice of hypotheses and models is
essential
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More about AIC Akaike Information Criterion

• AIC finds the model that looses the least
  information
• AIC weights the benefit of a better and more
  complicated model against the cost of
• including more factors
• A bit of introduction to the math
• A maximum likelihood estimate (MLE) or RSS
  (residual sum of squares from Lest
• square estimate, LSE) value for each
  hypothesis based model are needed
• (obtained from e.g. an ANOVA)
• MLE maximises the likelihood, LES minimises the
  sum of squares of error
• ML or RSS RSS assumes normal, independent data
  and linear relationships, often
• this is not the case with ecological data. ML
  is most often the best choice.
• AIC -2log(L) 2K ? -2log(L) is the deviance,
  i.e. the measure of lack of fit.
• This is linked to the Chi square analysis
  (ChiSq-2log(La/Lb)
• The model fit often gets better with more
  factors, but you are punished for
• complicating the model (2K), i.e. a
  cost-benefit approach

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All models are wrong, but some are useful
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A bit of math

• AIC -2log(L) 2K
• ? -2log(L) is the deviance, i.e. the measure of
  lack of fit.
• ? K is the number of parameters in the model
• This is linked to the Chi square analysis
  (ChiSq-2log(La/Lb)
• The model fit often gets better with more
  factors, but you are punished for
• complicating the model (2K), i.e. a
  cost-benefit approach

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Some more math

• The smaller the AIC value, the better the model
  fit
• The delta value shows the difference between the
  best and the alternative models
• Deltalt2 the alternative model has good support
• Delta 4-7 the alternative model has low support
• DeltaZ10 the alternative model has no support
• Wi the Akaike weight, the probability that the
  model in fact is the best, how many ticket do I
  have in the lottery, Wi0.66 means 66 chance
  that the model is best.
• To know if the best model is fact is good (not
  only the best of the bad), combine AIC with
• adjusted R2 and residual plotting

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• So, what if more than one model is good
• Describe them all, but choose one for your
  predictions
• Model averaging (multi model inference), models
  are weigh using the Wi value. Is most often
  recommended

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GRASP for GIS prediction comments and concerns

1. Uses GAM (Generalised Additative Models) to
   build models
2. Uses AIC to select the models
3. Concerns
4. The AIC algorithm used in GRASP only applies to
   large datasets, ad additional
5. algorithm should be added to correct for this
6. GRASP does not allow for model averaging

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Model validation using field data

• Cross validation re-using the data from the
  predictive modelling
• No point when using AIC, because in the Akaike
  development of the Kullback-Leiber methodology
  into AIC, the expectation of the cross validation
  ends up as the same or similar to the expectation
  of the AIC. So cross validation adds nothing.
• Validation using fresh data
• From the predictions, you get probabilities of
  finding a habitat at a certain site (pixel)
• Collecting data in the field (e.g.
  presence/absence data), you get binomial data (0s
  or 1s) that can be compared with modelled values
  using logistic regression.
• Look at the R2 and the residual plot

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Habitat valorisation

• We havent come too far, due to lack of
  information on habitat distribution and function
  (e.g. little knowledge on rare and threatened
  species). The national program for mapping of
  marine habitats has established some criteria for
  nationally very important (A), regionally
  important (B) and locally important (C)
  occurences.
• Ecological criteria
• Ecological function (richness, size, age,
  production rate, functionally close to natural
  state
• Rareness (rare both regionally and nationally,
  close to natural state when it comes to
  biodiversity
• Threatenedness (small occurrences, vulnerable,
  reducing in abundance
• Cultural criteria
• Aesthetics
• Use (provides understanding of nature, important
  for recreation, teaching, research, long time
  series and knowledge of trends)
• A includes the categories critically and
  strongly threatened and vulnerable
• B includes close threatened