Methodology workshop focused on technology for identifying marine habitats Trine Bekkby Workshop at...

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)


Water types according to the WDF work


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

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








“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








“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








“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 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


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

Curvature

Modelled current

Light - % of surface level

Light – related to optimal slope and aspect


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


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

Methodology workshop focused on technology for identifying marine habitats Trine Bekkby Workshop at...

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