M. Scardi , E. Fresi and M. Penna

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

7. Our subset of species Occurence > 5% Diogenes pugilator Lumbrineris emandibulata mabiti Prionospio caspersi Diplocirrus glaucus Lumbrineris latreilli Prionospio malmgreni Abra alba Dosinia lupinus Magelona minuta Prionospio multibranchiata Abra nitida Drilonereis filum Magelona papillicornis Processa macrophtalma Ampelisca brevicornis Echinocardium cordatum Melinna palmata Pseudoleiocapitella fauveli Ampelisca diadema Echinocyamus pusillus Micronephtys mariae Scolaricia typica Ampelisca sarsi Euclymene oerstedi Monticellina dorsobranchialis Scolelepis tridentata Ampelisca typica Eunice vittata Nassarius incrassatus Sigalion mathildae Ampharete acutifrons Eunoe nodosa Nematonereis unicornis Sigambra tentaculata Amphipholis squamata Galathowenia oculata Nephtys hombergi Spio decoratus Amphiura chiajei Glycera alba Nephtys incisa Spiophanes bombyx Anapagurus bicorniger Glycera rouxi Nephtys kersivaliensis Spiophanes kroyeri reyssi Aphelochaeta marioni Glycera unicornis Notomastus aberans Spisula subtruncata Aponuphis bilineata Goneplax rhomboides Notomastus latericeus Sternaspis scutata Apseudes acutifrons Goniada maculata Nucula nitidosa Tellina donacina Apseudes echinatus Harpinia crenulata Ophiura texturata Tellina pulchella Aricidea assimilis Heteromastus filiformis Owenia fusiformis Terebellides stroemi Aricidea fragilis mediterranea Hippomedon massiliensis Paralacydonia paradoxa Thyasira flexuosa Autonoe spiniventris Laonice cirrata Paraprionospio pinnata Turritella communis Chaetozone setosa Leucothoe incisa Photis longicaudata Urothoe intermedia Chone duneri Levinsenia gracilis Phtisica marina Urothoe pulchella Clymenura clypeata Loripes lacteus Pilargis verrucosa Westwoodilla rectirostris Corbula gibba Lucinella divaricata Poecilochaetus 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 Oi O1 O2 O3 Op sp1 sp2 . . . Species . . . spn O O O O O O O O O SOM units (=virtualspecies lists) O O O O . . . . . . . . . . . . . . . • Inizialization (random values) • Training (iterative) - A sample is randomly selected - The “best matching unit” (BMU) is detected - The BMU and the neighbouring units are updated • Real samples are then projected onto the closest SOM unit

10. SOM 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 SOM unit Sp. 1 0.797Sp. 2 0.975Sp. 3 0.076… Sp. s 0.298 D=min(Di) Projecting samples onto the SOM Sample S1 Sp. 1 1Sp. 2 0Sp. 3 1… Sp. s 1 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)]

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. 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 chance R < 0 with more heterogeneity within groups than expected by chance MRPP Clustering SOM units(“natural” communities?) 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 alba 68.1 0.001 Loripes lacteus 51.7 0.001 Corbula gibba 34.5 0.001 Diogenes pugilator34.8 0.001 Nassarius incrassatus 29.0 n.s. Aricidea assimilis 24.3 0.001 … … …

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

15. Depth: min max Relationships withenvironmental variables (1)

16. Silt and clay: min max Relationships withenvironmental variables (2) 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 matchingunit (BMU)

20. Species pAbra 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 Measuring coenotic distance (1) The BMU is associated to a list of species, i.e. to a virtual community

21. Species pAbra 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 Measuring coenotic distance (2)

22. Species pAbra 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 Measuring coenotic distance (2) • Disturbance isproportional todistance betweenexpected and observedcommunity structure. • Ecological status depends ondisturbance. • Distance from expected community is a measure for ecological status. Expected Observed[ros_165a]

23. From distance to ecological status Sample ros_165a Euclidean distance to BMU = 5.45 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… • Defining reference conditions • Find common patterns in community structure using the available data (i.e. train a SOM). • Define relationships between environmental variables and those patterns. • Assessing ecological status • Given environmental info, look up SOM units for the expected community structure (BMU). • Measure the distance between observed and expected community structure (BMU). • 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.itURL: 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/