A COMMUNITY BASED PROCEDURE FOR THE ASSESSMENT OF ENVIRONMENTAL QUALITY IN MEDITERRANEAN BENTHIC ECOSYSTEMS M. Scardi, E. Fresi and M. Penna Dept. of Biology,

A COMMUNITY BASED PROCEDURE FOR THE ASSESSMENT OF ENVIRONMENTAL QUALITY IN MEDITERRANEAN BENTHIC ECOSYSTEMS

M. Scardi, E. Fresi and M. Penna

Dept. of Biology, University of Rome “Tor Vergata”via della Ricerca Scientifica – 00133 Rome, Italy

E-mail: [email protected]: http://www.mare-net.com/mscardi/

Defining ecosystem quality Biotic Integrity: the ability to support and

maintain a “balanced, integrated, adaptative community of organisms having a species composition, diversity, & functional organization comparable to that of natural habitat of the region” (Karr & Dudley, 1981).

Ecological Status: “…quality expression of the aquatic ecosystems structure & functioning, associated with superficial water bodies...” (WFD,2000)

Tools

Expert judgement

Biotic indices

Comparison with reference community

The quickest solu-tion. But you have to find an …expert!

Based on subjective assumptions, they need consensus.

Sounds good. But we have to define a reference community.

Reference community structure Experts can assist in defining what is

“reference” and they can certainly provide useful insights, but their opinions are just as subjective as indices.

Species distribution models? They can be a good solution, but we don’t have enough data for good generalization right now.

So, let’s the data tell their story…

Data sets We collected data from about 2200

macrozoobenthic samples (0-100 m). Sampling depth and grain size information

was only available for ¼ of the samples (n=553).

This data subset included 823 taxa, but most of them were very rare (27% found only once, 48% no more than three times)

Only those taxa whose % of occurrence was > 5% were included in the final data subset (534 samples, 89 taxa).

Sampling sites

Yellow sites range from pristine to moderately disturbed conditions (non-point sources).

Red sites are disturbed by point sources:

CaCO3 discharge (fine grain size)

Former industrial area and harbour

Heavy organic pollution (and deeper than other sites)

Our subset of species

Occurence > 5% Diogenes pugilator Lumbrineris emandibulata mabiti Prionospio caspersiDiplocirrus glaucus Lumbrineris latreilli Prionospio malmgreni

Abra alba Dosinia lupinus Magelona minuta Prionospio multibranchiataAbra nitida Drilonereis filum Magelona papillicornis Processa macrophtalmaAmpelisca brevicornis Echinocardium cordatum Melinna palmata Pseudoleiocapitella fauveliAmpelisca diadema Echinocyamus pusillus Micronephtys mariae Scolaricia typicaAmpelisca sarsi Euclymene oerstedi Monticellina dorsobranchialis Scolelepis tridentataAmpelisca typica Eunice vittata Nassarius incrassatus Sigalion mathildaeAmpharete acutifrons Eunoe nodosa Nematonereis unicornis Sigambra tentaculataAmphipholis squamata Galathowenia oculata Nephtys hombergi Spio decoratusAmphiura chiajei Glycera alba Nephtys incisa Spiophanes bombyxAnapagurus bicorniger Glycera rouxi Nephtys kersivaliensis Spiophanes kroyeri reyssiAphelochaeta marioni Glycera unicornis Notomastus aberans Spisula subtruncataAponuphis bilineata Goneplax rhomboides Notomastus latericeus Sternaspis scutataApseudes acutifrons Goniada maculata Nucula nitidosa Tellina donacinaApseudes echinatus Harpinia crenulata Ophiura texturata Tellina pulchellaAricidea assimilis Heteromastus filiformis Owenia fusiformis Terebellides stroemi

Aricidea fragilis mediterranea Hippomedon massiliensis Paralacydonia paradoxa Thyasira flexuosaAutonoe spiniventris Laonice cirrata Paraprionospio pinnata Turritella communisChaetozone setosa Leucothoe incisa Photis longicaudata Urothoe intermediaChone duneri Levinsenia gracilis Phtisica marina Urothoe pulchellaClymenura clypeata Loripes lacteus Pilargis verrucosa Westwoodilla rectirostrisCorbula gibba Lucinella divaricata Poecilochaetus fauchaldi

Defining reference conditions

• We used Self-Organizing Maps (SOM) for recognizing common patterns in community structure

• Environmental variables were then visualized onto the SOM

Samples Op O1 O2 O3

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. . .

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Oi

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• Inizialization (random values)

O

O O

O

O

O

O O

O O

• Training (iterative)

• Real samples are then projected onto the closest SOM unit

O

O

O

- A sample is randomly selected

- The “best matching unit” (BMU) is detected

SOM units (=virtualspecies lists)

- The BMU and the neighbouring units are updated

Species

sp1

sp2

spn

.

.

.

Projecting samples onto the SOM

Sample S1

Sp. 1 1Sp. 2 0Sp. 3 1

…

Sp. s 1

Using binary input data, each SOM unit is a list of values in the [0,1] range (they can be regarded as probabilities of occurence)

All the samples are projected onto the closest SOM unit [i.e. looking for min(D)]

S1SOM unit

Sp. 1 0.102Sp. 2 0.923Sp. 3 0.793

…

Sp. s 0.007

SOM unit

Sp. 1 0.092Sp. 2 0.043Sp. 3 0.931

…

Sp. s 0.927

SOM unit

Sp. 1 0.952Sp. 2 0.072Sp. 3 0.889

…

Sp. s 0.978

SOM unit

Sp. 1 0.052Sp. 2 0.172Sp. 3 0.876

…

Sp. s 0.098

D=min(Di)

SOM unit

Sp. 1 0.797Sp. 2 0.975Sp. 3 0.076

…

Sp. s 0.298

Inside our SOM

Similar units are close to each other on the SOM, but the opposite isn’t true, so neighbouring units may be quite different from each other.

It is possible to visualize these features, but we’re in a hurry, so more next time…

Clustering SOM units(“natural” communities?)

Test statistic: T = -79.654Observed delta = 11.951Expected delta = 24.641

Chance-corrected within-group agreement, R = 0.515Probability of a smaller or equal delta, p < 0.001

R = 1 - (observed delta/expected delta)Rmax = 1 when all items are identical within groups (delta=0)R = 0 when heterogeneity within groups equals expectation by chanceR < 0 with more heterogeneity within groups than expected by chance

MRPP

In other words, in these clusters of SOM units within-group distances are smaller than expected in case the groups were randomly defined.

Optimal non-hierarchical partition: n=13

Characteristic species

Indicator Species AnalysisI.V. p

Abra alba 68.1 0.001Loripes lacteus 51.7 0.001Corbula gibba 34.5 0.001Diogenes pugilator 34.8 0.001Nassarius incrassatus 29.0 n.s.Aricidea assimilis 24.3 0.001… … …

Typical biocenoses(sensu Peres & Picard)

Similarity to SFBC: min max

SFBC

Relationships withenvironmental variables (1)

Depth: min max

Relationships withenvironmental variables (2)

Silt and clay: min max

And so on with other grain sizes…

The result is that each SOM unit is now associated with a vector of values for environmental variables.

Assessing ecological status

• Find the best matching SOM unit, given environmental info (grain size and depth)

• Measure distance from that unit to the observed community structure

• If distance is greater than 95% of the distances between SOM units, then the community structure is probably perturbed

Measuring environmental distance (1)

0%

10%

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90%

100%

-2 -1 0 1 2 3 4+

f

27 m

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-2 -1 0 1 2 3 4+

f

28 m

Measuring environmental distance (2)

0%

10%

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100%

-2 -1 0 1 2 3 4+

f

28.2 m

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-2 -1 0 1 2 3 4+

f

27 m

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-2 -1 0 1 2 3 4+

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28 m

Best matchingunit (BMU)

4

22

1

2

1

i

SOMi

obsi

max

SOMobs

z

zzD ff

Measuring coenotic distance (1)

Species p

Abra alba 0.077Abra nitida 0.777Ampelisca brevicornis <0.001Ampelisca diadema 0.258Ampelisca sarsi 0.518 … … … …Thyasira flexuosa 0.857Turritella communis 0.830Urothoe intermedia <0.001Urothoe pulchella <0.001Westwoodilla rectirostris 0.002

The BMU is associated to a list of species, i.e. to a virtual community


Species p


0 0.5 1

Abra alba

Ampelisca brevicornisAmpelisca diadema

Amphiura chiajei………

Sternaspis scutataTellina donacina

Terebellides stroemi

Urothoe intermediaUrothoe pulchella

Westwoodilla rectirostris

probability of occurence

Abra nitida

Ampelisca sarsiAmpelisca typica

Ampharete acutifronsAmphipholis squamata

Tellina pulchella

Thyasira flexuosaTurritella communis

0 0.5 1

Abra alba








Abra nitida



Tellina pulchella



Species p


0 0.5 1

Abra alba








Abra nitida



Tellina pulchella


0 0.5 1

Abra albaAbra nitida


Ampelisca sarsiAmpelisca typicaAmpharete acutifronsAmphipholis squamata



Tellina pulchellaTerebellides stroemi





Abra nitida



Tellina pulchella


Expected Observed[ros_165a]

• Disturbance isproportional todistance betweenexpected and observedcommunity structure.

• Ecological status depends ondisturbance.

• Distance from expected community is a measure for ecological status.

From distance to ecological status

Sample ros_165aEuclidean distance to BMU = 5.45

Speciesexpected

[BMU]observed

[ros_165a]observed [ros_166a]

Abra alba 0.077 0 0

Abra nitida 0.777 1 0

Ampelisca brevicornis 0.000 0 0

Ampelisca diadema 0.258 0 0

Ampelisca sarsi 0.518 1 1

Ampelisca typica 0.078 1 1

Ampharete acutifrons 0.730 1 0

… … … …

Tellina pulchella 0.000 1 1

Terebellides stroemi 0.427 0 0

Thyasira flexuosa 0.857 1 1

Turritella communis 0.830 1 1

Urothoe intermedia 0.000 0 0

Urothoe pulchella 0.000 0 0

Westwoodilla rectirostris 0.002 0 0

Sample ros_166aEuclidean distance to BMU = 5.47

1 2 3 4 5 60.0

0.2

0.4

0.6

0.8

1.0

95t

h pe

rcen

tile

= 5

.091

86

Distribution of within-SOM distancesDistance to BMU is larger than expectedDistance may depend on disturbanceLarge distance to BMU: poor ecological status

Other test sites

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 >5.75

distance to BMU

reference sites

test sites

Large distances to BMU are more frequent in test (perturbed) sites than in reference sites.

Summarizing our approach…

A. Defining reference conditions1. Find common patterns in community structure

using the available data (i.e. train a SOM).2. Define relationships between environmental

variables and those patterns.

B. Assessing ecological status1. Given environmental info, look up SOM units for

the expected community structure (BMU).2. Measure the distance between observed and

expected community structure (BMU).3. Define ecological status as a function of the

distance to BMU.

The bottom line We are proposing a methodological

framework, not a turnkey solution! More work is needed (as usual!):

we’re able to recognize perturbed communites, but now we want to rank them according to disturbances.

Next step: selecting suitable metrics (euclidean distance is not adequate)

Let the data tell their story!

E-mail: [email protected]: http://www.mare-net.com/mscardi/

Special thanks to Bioservice s.c.r.l, for providing a lot of data.

Are you interested in A.I. and Machine Learning applications to Ecology? Have a look at: www.isei3.org www.isei4.org http://www.waite.adelaide.edu.au/ISEI/

Documents

A COMMUNITY BASED PROCEDURE FOR THE ASSESSMENT OF ENVIRONMENTAL QUALITY IN MEDITERRANEAN BENTHIC ECOSYSTEMS M. Scardi, E. Fresi and M. Penna Dept. of Biology,