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Methodology workshop focused on technology for identifying marine habitats Trine Bekkby Workshop at NIVA, Oslo, May 29-30 2007. Presenting Norwegian Institute for Water Research. District offices and daughter companies. Daughter companies:. NIVA group: 250 employees. District offices: - PowerPoint PPT Presentation
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Methodology workshop focused on
technology for identifying marine habitats
Trine Bekkby
Workshop at NIVA, Oslo, May 29-30 2007
Presenting
Norwegian Institute for Water Research
District offices and daughter companies
District offices:
- Trondheim- Hamar - Bergen - Grimstad
Solbergstrand Marine Research Station
Trondheim
Daughter companies:
Min office
NIVA group: 250 employees
Categories of work
5 %5 %
30 %20 %
20 %20 %Technical services
Research(basic and applied)
Development
MonitoringCounciling
Knowledge communication
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
Experience from more than 70 countries…
Presenting
Oslo Centre for Interdisciplinary Environmental and Social Research
Tandbergbygget på BrekkeTandbergbygget på Brekke
Area: 14 000 m2
Employees: 500Cost: 270 mill. krStarted: April 2005Inhabited: Oct. 2006
CIENS partners:► NIBR ► NINA► NILU► NIVA► TØI► UiO
► met.no
► CICERO+ NVE
Heat from the ground coversHeat from the ground covers90% of the cooling and 90% of the cooling and 60% of the heating60% of the heating
The biggest solar panel in NorwayThe biggest solar panel in Norway
Presenting
ICZM&P in Norway
ICZM&P 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
ICZM&P 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)
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)
ICZM&P in Norway – Management and planning
Water types according to the WDF work
ICZM&P 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
ICZM&P in Norway – ”All” collected data
Reference and trend monitoring stations (WFD) suggested
Presenting
different projects
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
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
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
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
Data
Field workInput models
Statistical model building, analyses, model selection
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
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
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
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
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
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
Presenting
equipment and methods for sampling
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
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
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)
Field work
Similarities and differencesNorway - Poland
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?
Presentingthe modelling approach in more detail
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?
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)
Isæus (2004)
Modelled wave exposure
Depth
Slope
Curvature
Modelled current
Light - % of surface level
Light – related to optimal slope and aspect
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)
Seabed substrate
Binomial seabed substrate modelling
Probability seabed substrate modelling
Probability soft seabed sediment modelling
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
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
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)
Binomial modelling of eelgrass (Zostera marina)
In shallow (down to 7 m depth), relatively flat (<7 degrees) and sheltered and moderately exposed areas
Predictions – habitat modelling
Green: modelled kelp forest
Pink: modelled eelgrass
Yellow: modelled shell sand
Turquoise: modelled Pecten maximus
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
< 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
> 2000000 Ekstremt eksponert
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
Laminaria hyperborean kelp forest
Seagrass (Zostera marina) meadows
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)
Three traditions
1. Frequentism (p-values)
2. Likelihood (AIC)
3. 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
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
One example on “neck-down” models
Kelp forest presence (P) is determined by wave exposure (WE) onlyP is determined by light attenuation (LA) onlyP is determined by sea bed substrate (SS) only
P is determined by WE and LAP is determines by WE and SSP is determined by LA and SEP is determined by WE, LA and SE
P is determined by WE, LA and WE*LAP is determined by WE, SS and WE*SSP is determined by LA, SE and LA*SE
P is determined by WE, LA, SE and WE*LAP is determined by WE, LA, SE and WE*SEP is determined by WE, LA, SE and LA*SEP is determined by WE, LA, SE and WE*LA*SE
”Neck-up” choice of hypotheses and
models is essential
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
“All models are wrong,
but some are useful”
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
Some more math
Model Log(L) K AIC AICc Delta Exp(-0.5*Delta) Wi1 -66.21 2 136.4200 136.4237 25.4800 0.0000 0.00002 -57.77 5 125.5400 125.5587 14.6000 0.0007 0.00043 -59.43 6 130.8600 130.8862 19.9200 0.0000 0.00004 -60.98 6 133.9600 133.9862 23.0200 0.0000 0.00005 -49.47 6 110.9400 110.9662 0.0000 1.0000 0.66436 -49.47 7 112.9400 112.9750 2.0000 0.3679 0.24447 -49.46 8 114.9200 114.9650 3.9800 0.1367 0.0908
1.5053
• The smaller the AIC value, the better the model fit• The delta value shows the difference between the best and the alternative models
Delta<=2: the alternative model has good supportDelta 4-7: the alternative model has low supportDeltaZ10: 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”, Wi=0.66 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
Model Log(L) K AIC AICc Delta Exp(-0.5*Delta) Wi1 -66.21 2 136.4200 136.4237 25.4800 0.0000 0.00002 -57.77 5 125.5400 125.5587 14.6000 0.0007 0.00043 -59.43 6 130.8600 130.8862 19.9200 0.0000 0.00004 -60.98 6 133.9600 133.9862 23.0200 0.0000 0.00005 -49.47 6 110.9400 110.9662 0.0000 1.0000 0.66436 -49.47 7 112.9400 112.9750 2.0000 0.3679 0.24447 -49.46 8 114.9200 114.9650 3.9800 0.1367 0.0908
1.5053
So, what if more than one model is good
1. Describe them all, but choose one for your predictions2. Model averaging (=multi model inference”), models are weigh using the Wi
value. Is most often recommended
GRASP for GIS prediction – comments and concerns
1. Uses GAM (Generalised Additative Models) to build models2. Uses AIC to select the models
3.Concerns4. The AIC algorithm used in GRASP only applies to large datasets, ad additional 5. algorithm should be added to correct for this6. GRASP does not allow for model averaging
Model validation using field data
1. 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.
2. 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
Habitat valorisation
We haven’t 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