Blanco Garrido, Francisco

Hermoso López, Virgilio

Prenda Marín, José

Arias Montano: Institutional Repository of the University of Huelva

THE ASSESSMENT OF ECOLOGICAL STATUS OF MEDITERRANEAN RIVERS BASED ON FISH COMMUNITIES USING A RECENTLY DEVELOPED PREDICTIVE METHODHermoso V., Blanco-Garrido F. and Prenda J.Biología de las Aguas EpicontinentalesDpto. Biología Ambiental y S.P., Universidad de Huelva, Spain E-MAIL: jprenda@uhu.esINTRODUCTIONThe use of predictive models is a widespread and powerful tool to asses the ecological status of river systems. This method provide ideal expected conditions (E) to be compared with those really observed (O). The deviation O/E represents a measure of the true ecological status of a particular site. This approach has been previously applied in several widely accepted formulations, such as the Index of Biotic Integrity or RIVPACS. Here, we present some preliminary results obtained from the adaptation of the recently developed ANNA (Assesment by Near Neighbour Analysis) predictive method (Linke, et al; 2005) to estimate the fish community in Mediterranean rivers. We predict the optimal community from a particular site directly from the community composition found in the environmentally nearest sites. So whatever intermediate artificial classification is avoided.THE MODELSTUDY AREA AND DATA COLLECTIONThe study was carried out in 3 south-western Iberian Peninsula basins (Guadiana, Guadalquivir and Odiel basins) with typical  mediterranean regimen. Fish community was sampled by electrofishing. The sixty three studied sites were inhabited by 19 fish species. From them only the 6 most widespread native species were finally included in the analysis.At the same time a total of 12 environmental variables, both at reach and basin scale, were measured from each sampled site. All of them were more or less free from  human influence: stream order, basin surface, evapotranspiration, rainfall, air temperature, slope, source and mouth distance and relative position along the watercourse. With these data we developed the models to predict the expected communities not affected by anthropogenic influences.##S#S#S#S#S#S#S#S#S#S#S #S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S #S #S#S#S#S#S #S#S#S #S#S#S#SGUADIANAGUADALQ UIVIRTI NTOODIELATLANTIC OCEAN#SFig. 1. Map of the study area. Sampled locations are in blue.REFERENCE: Linke, S., Norris, R. H., Faith, D. P. & Stockwell D. (2005). ANNA: A new prediction method for bioassessment programs. FreshwaterBiology, 50: 147-158.Matrix of presence-absence of species Ordination of sites in a three-dimensional space defined by the first three axesChoice of environmental variables as predictorsExpected taxonomic composition derived from the nearest localities63 sites1 Model construction (30 sites)“Perturbed” sites (>5% abundance of exotic species)Reference sites (<5% abundance of exotic species)2 Model validation (15 sites)Correspondence Analysis (CA)If slope is not different from 1 and intercept not from 0Model validatedCalculation of total 0/E for each species and plot all of themEnvironmental variablesa) Evaluation of optimal cut-off point to establish the presence-absence of speciesb) Evaluation of optimal number of neighbour localities to use as predictor∑∑===niijijni iddXp1111Formula.1. Assessment of probability of presence for each specie in each point. Xi=1 if the specie is present at the neighbour locality and Xi=0 if it is absent.Localization of a point in the three-dimensional space defined in the fish CAAxis 1Axis 2qik- qjkqik- qjkdij∑=−=312Kjkikij qqd )(Fig. 4. Assessment of modified E clidean distance from a reference point and the nearest neighbour ones. Total distance in the three-dimensional space is obtained combining the weights on the three axes by the formula showed in the down-right corner.0.0040.23-0.5*DISTANCE TO SOURCE + 0.16830.0040.29-0.47*TEMPERATURE - 0.37*DISTANCE TO SOURCE + 2.9882<0.0010.42-0.37*ALTITUDE + 0.43*PRECIPITATION - 0.35*SLOPE - 1.2771pR2Multiple regression modelAxesTabl  Multiple regression models for each of the axes obtained in the Correspondence Analysis. 01 02 03 04 05 06 07 08 09 01 0 00 ,1 0 ,2 0 ,3 0 ,4 0 ,5 0 ,6 0 ,7 0 ,8 0 ,9PCCR (%)Fig. 2. Correct classification rates (CRR) for the model  at all possible cut-off points at 0.1 intervals. The blue line represents presences and red absences.Ob s/ Exp ratio-1 -0,500,5GD14GQ19O07O0 2GD23O09O03GD26O06GQ20GD01GQ02GD0 9GD02GD19Fig.5. Deviation of O/E values from 1. There was no significant difference from 0 (t-student= 0.62, p>0.05). It is an additional prove of validationCalculation of 0/E from observed and expected communities at perturbed sites3 Model application (18 sites)Fig. 6. Set of reference (yellow) and “perturbed” (orange) localities (A) and results from application of the O/E evaluation over the perturbed sites (B). Points in red show real perturbed places, while green point out not perturbed ones. The Q10 of E/O values from reference sites was taken as perturbation threshold.#S#S #S#S#S#S#S#S#S#S#S#S#S#S #S#S#SGUADIANAGUADALQ UIVIRTI NTOODIELATLANTIC OCEAN#S#S#S#S#S#S#S#S#S#S#S #S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S#S #S #S#S#S#S#S #S#S#S #S#S#S#SGUADIANAGUADALQ UIVIRTI NTOODIELATLANTIC OCEANA BMODEL OPTIMISATIONObtaining awere provided with meaning throughPearson Correlation Multiple regression+in order toThrough regression equations obtained above for each axesAssessment of Euclidean distance to the 6 nearest pointsis transformed in probability of occurrence throughThe model was valid (slope of O/E plot not different from 1 and intercept not from 0). It showed that some localities initially considered as perturbed  were not seriously affected by exotic degradation, since their species richness were not different from the expected (O/E values not different from 1).Only a few localities were badly perturbed (8/63). They appeared specially in middle-downstream reaches, where the more perturbed habitats allowed exotic species to establish permanent populations. A wide range of habitat types were accounted in the model construction, but the high abundance of exotic species, specially in downstream reaches, made difficult to obtain enough data from there. Additional studies are needed to solve this problem.Applying the optimal cut-off point obtainedFig. 3. Odds ratio values for each of the models obtained increasing the number of neighbour localities to predict de presence of species.012330 28 26 24 22 20 18 16 14 12 10 8 6 4 2Number  of neighbour  places included in each modelOdds ratio valuewas run trough aTo improve the predictive power of the model, two steps were followedThe model optimisation showed the ideal cut-off point to consider the presences or absences of species and the optimal number of neighbour localities to include in the prediction to improve its efficiency.e 1.u

The assessment of ecological status of Mediterranean rivers based on fish communities using a recently developed predictive method

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The assessment of ecological status of Mediterranean rivers based on fish communities using a recently developed predictive method

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