1. 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: mscardi@mclink.it URL: http://www.mare-net.com/mscardi/

2. 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)

3. 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.

4. 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…

5. 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).

6. Sampling sites Yellow sites range from pristine to moderately disturbed conditions (non- point sources). Red sites are disturbed by point sources: CaCO 3 discharge (fine grain size) Former industrial area and harbour Heavy organic pollution (and deeper than other sites)

7. Our subset of species Occurence > 5% Diogenes pugilatorLumbrineris emandibulata mabitiPrionospio caspersi Diplocirrus glaucusLumbrineris latreilliPrionospio malmgreni Abra albaDosinia lupinusMagelona minutaPrionospio multibranchiata Abra nitidaDrilonereis filumMagelona papillicornisProcessa macrophtalma Ampelisca brevicornisEchinocardium cordatumMelinna palmataPseudoleiocapitella fauveli Ampelisca diademaEchinocyamus pusillusMicronephtys mariaeScolaricia typica Ampelisca sarsiEuclymene oerstediMonticellina dorsobranchialisScolelepis tridentata Ampelisca typicaEunice vittataNassarius incrassatusSigalion mathildae Ampharete acutifronsEunoe nodosaNematonereis unicornisSigambra tentaculata Amphipholis squamataGalathowenia oculataNephtys hombergiSpio decoratus Amphiura chiajeiGlycera albaNephtys incisaSpiophanes bombyx Anapagurus bicornigerGlycera rouxiNephtys kersivaliensisSpiophanes kroyeri reyssi Aphelochaeta marioniGlycera unicornisNotomastus aberansSpisula subtruncata Aponuphis bilineataGoneplax rhomboidesNotomastus latericeusSternaspis scutata Apseudes acutifronsGoniada maculataNucula nitidosaTellina donacina Apseudes echinatusHarpinia crenulataOphiura texturataTellina pulchella Aricidea assimilisHeteromastus filiformisOwenia fusiformisTerebellides stroemi Aricidea fragilis mediterraneaHippomedon massiliensisParalacydonia paradoxaThyasira flexuosa Autonoe spiniventrisLaonice cirrataParaprionospio pinnataTurritella communis Chaetozone setosaLeucothoe incisaPhotis longicaudataUrothoe intermedia Chone duneriLevinsenia gracilisPhtisica marinaUrothoe pulchella Clymenura clypeataLoripes lacteusPilargis verrucosaWestwoodilla rectirostris Corbula gibbaLucinella divaricataPoecilochaetus fauchaldi

8. Defining reference conditions We used Self-Organizing Maps (SOM) for recognizing common patterns in community structure Environmental variables were then visualized onto the SOM

9. Samples O p O 1 O 2 O 3........................... O i...... 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 (=virtual species lists) - The BMU and the neighbouring units are updated Species sp 1 sp 2 sp n......

10. Projecting samples onto the SOM Sample S1 Sp. 11 Sp. 20 Sp. 31 … Sp. s1 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)] S1 SOM unit Sp. 10.102 Sp. 20.923 Sp. 30.793 … Sp. s0.007 SOM unit Sp. 10.092 Sp. 20.043 Sp. 30.931 … Sp. s0.927 SOM unit Sp. 10.952 Sp. 20.072 Sp. 30.889 … Sp. s0.978 SOM unit Sp. 10.052 Sp. 20.172 Sp. 30.876 … Sp. s0.098 D=min(D i ) SOM unit Sp. 10.797 Sp. 20.975 Sp. 30.076 … Sp. s0.298

11. 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…

12. Clustering SOM units (“natural” communities?) Test statistic: T = -79.654 Observed delta = 11.951 Expected delta = 24.641 Chance-corrected within-group agreement, R = 0.515 Probability 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 chance R < 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

13. Characteristic species Indicator Species Analysis I.V.p Abra alba68.10.001 Loripes lacteus51.70.001 Corbula gibba34.50.001 Diogenes pugilator34.80.001 Nassarius incrassatus29.0n.s. Aricidea assimilis24.30.001 ………

14. Typical biocenoses (sensu Peres & Picard) Similarity to SFBC: minmax SFBC

15. Relationships with environmental variables (1) Depth: minmax

16. Relationships with environmental variables (2) Silt and clay: minmax 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.

17. 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

18. Measuring environmental distance (1)

19. Measuring environmental distance (2) Best matching unit (BMU)

20. Measuring coenotic distance (1) Species p Abra alba 0.077 Abra nitida 0.777 Ampelisca brevicornis <0.001 Ampelisca diadema 0.258 Ampelisca sarsi 0.518 … … … … Thyasira flexuosa 0.857 Turritella communis 0.830 Urothoe intermedia <0.001 Urothoe pulchella <0.001 Westwoodilla rectirostris 0.002 The BMU is associated to a list of species, i.e. to a virtual community

21. Measuring coenotic distance (2) Species p Abra alba 0.077 Abra nitida 0.777 Ampelisca brevicornis <0.001 Ampelisca diadema 0.258 Ampelisca sarsi 0.518 … … … … Thyasira flexuosa 0.857 Turritella communis 0.830 Urothoe intermedia <0.001 Urothoe pulchella <0.001 Westwoodilla rectirostris 0.002

22. Measuring coenotic distance (2) Species p Abra alba 0.077 Abra nitida 0.777 Ampelisca brevicornis <0.001 Ampelisca diadema 0.258 Ampelisca sarsi 0.518 … … … … Thyasira flexuosa 0.857 Turritella communis 0.830 Urothoe intermedia <0.001 Urothoe pulchella <0.001 Westwoodilla rectirostris 0.002 Expected Observed [ros_165a] Disturbance is proportional to distance between expected and observed community structure. Ecological status depends on disturbance. Distance from expected community is a measure for ecological status.

23. From distance to ecological status Sample ros_165a Euclidean distance to BMU = 5.45 Species expected [BMU] observed [ros_165a] observed [ros_166a] Abra alba0.07700 Abra nitida0.77710 Ampelisca brevicornis0.00000 Ampelisca diadema0.25800 Ampelisca sarsi0.51811 Ampelisca typica0.07811 Ampharete acutifrons0.73010 ………… Tellina pulchella0.00011 Terebellides stroemi0.42700 Thyasira flexuosa0.85711 Turritella communis0.83011 Urothoe intermedia0.00000 Urothoe pulchella0.00000 Westwoodilla rectirostris0.00200 Sample ros_166a Euclidean distance to BMU = 5.47 Distribution of within- SOM distances Distance to BMU is larger than expected Distance may depend on disturbance Large distance to BMU: poor ecological status

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

25. Summarizing our approach… A.Defining reference conditions 1.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 status 1.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.

26. 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!

27. E-mail: mscardi@mclink.it URL: 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/