Quantifying the Impact of Linear Regression Model in Deriving Bio-Optical Relationships: The Implications on Ocean Carbon Estimations.

Linear regression is widely used in applied sciences and, in particular, in satellite optical oceanography, to relate dependent to independent variables. It is often adopted to establish empirical algorithms based on a finite set of measurements, which are later applied to observations on a larger scale from platforms such as autonomous profiling floats equipped with optical instruments (e.g., Biogeochemical Argo floats; BGC-Argo floats) and satellite ocean colour sensors (e.g., SeaWiFS, VIIRS, OLCI). However, different methods can be applied to a given pair of variables to determine the coefficients of the linear equation fitting the data, which are therefore not unique. In this work, we quantify the impact of the choice of "regression method" (i.e., either type-I or type-II) to derive bio-optical relationships, both from theoretical perspectives and by using specific examples. We have applied usual regression methods to an in situ data set of particulate organic carbon (POC), total chlorophyll-a (TChla), optical particulate backscattering coefficient (bbp), and 19 years of monthly TChla and bbp ocean colour data. Results of the regression analysis have been used to calculate phytoplankton carbon biomass (Cphyto) and POC from: i) BGC-Argo float observations; ii) oceanographic cruises, and iii) satellite data. These applications enable highlighting the differences in Cphyto and POC estimates relative to the choice of the method. An analysis of the statistical properties of the dataset and a detailed description of the hypothesis of the work drive the selection of the linear regression method.

Bight of Benin: a Maternal Perspective of Four Beninese Populations and their Genetic Implications on the American Populations of African Ancestry.

The understanding of the first movements of the ancestral populations within the African continent is still unclear, particularly in West Africa, due to several factors that have shaped the African genetic pool across time. To improve the genetic representativeness of the Beninese population and to better understand the patterns of human settlement inside West Africa and the dynamics of peopling of the Democratic Republic of Benin, we analyzed the maternal genetic variation of 193 Beninese individuals belonging to Bariba, Berba, Dendi, and Fon populations. Results support the oral traditions indicating that the western neighbouring populations have been the ancestors of the first Beninese populations, and the extant genetic structure of the Beninese populations is most likely the result of admixture between populations from neighbouring countries and native people. The present findings highlight how the Beninese populations contributed to the gene pool of the extant populations of some American populations of African ancestry. This strengthens the hypothesis that the Bight of Benin was not only an assembly point for the slave trade during the Trans-Atlantic Slave Trade but also an important slave trapping area.

The sxt Gene and Paralytic Shellfish Poisoning Toxins as Markers for the Monitoring of Toxic Alexandrium Species Blooms.

Paralytic shellfish poisoning (PSP) is a serious human illness caused by the ingestion of seafood contaminated with saxitoxin and its derivatives (STXs). These toxins are produced by some species of marine dinoflagellates within the genus Alexandrium. In the Mediterranean Sea, toxic Alexandrium spp. blooms, especially of A. minutum, are frequent and intense with negative impact to coastal ecosystem, aquaculture practices and other economic activities. We conducted a large scale study on the sxt gene and toxin distribution and content in toxic dinoflagellate A. minutum of the Mediterranean Sea using both quantitative PCR (qPCR) and HILIC-HRMS techniques. We developed a new qPCR assay for the estimation of the sxtA1 gene copy number in seawater samples during a bloom event in Syracuse Bay (Mediterranean Sea) with an analytical sensitivity of 2.0 × 10° sxtA1 gene copy number per reaction. The linear correlation between sxtA1 gene copy number and microalgal abundance and between the sxtA1 gene and STX content allowed us to rapidly determine the STX-producing cell concentrations of two Alexandrium species in environmental samples. In these samples, the amount of sxtA1 gene was in the range of 1.38 × 10(5) - 2.55 × 10(8) copies/L and the STX concentrations ranged from 41-201 nmol/L. This study described a potential PSP scenario in the Mediterranean Sea.

Evaluating sea cucumbers as extractive species for benthic bioremediation in mussel farms.

Filter-feeding mussels blend suspended particles into faeces and pseudo-faeces enhancing organic matter flows between the water column and the bottom, and strengthening benthic-pelagic coupling. Inside operating farms, high bivalve densities in relatively confined areas result in an elevated rate of organic sinking to the seabed, which may cause a localized impact in the immediate surrounding. Deposit-feeding sea cucumbers are potentially optimal candidates to bioremediate mussel organic waste, due to their ability to process organic-enriched sediments impacted by aquaculture waste. However, although the feasibility of this polyculture has been investigated for a few Indo-Pacific species, little is known about Atlanto-Mediterranean species. Hence, for the first time, in the present study, we conducted a comparative investigation on the suitability of different Mediterranean sea cucumber species, to be reared in Integrated Multitrophic Aquaculture (IMTA) with mussels. A pilot-scale experiment was accomplished operating within a mussel farm where two sea cucumbers species, Holothuria tubulosa and Holothuria polii, were caged beneath the long-line mussel farm of Mytilus galloprovincialis. After four months, H. tubulosa showed high survivorship (94%) and positive somatic growth (6.07%); conversely H. polii showed negative growth (- 25.37%), although 92% of specimens survived. Furthermore, sea cucumber growth was size-dependent. In fact, smaller individuals, independently from the species, grew significantly faster than larger ones. These results evidenced a clear difference in the suitability of the two sea cucumber species for IMTA with M. galloprovincialis, probably due to their different trophic ecology (feeding specialization on different microhabitats, i.e. different sediment layers). Specifically, H. tubulosa seems to be an optimal candidate as extractive species both for polycultures production and waste bioremediation in M. galloprovincialis operating farms.

A Baseline for Skeletal Investigations in Medaka (Oryzias latipes): The Effects of Rearing Density on the Postcranial Phenotype.

Oryzias latipes is increasingly used as a model in biomedical skeletal research. The standard approach is to generate genetic variants with particular skeletal phenotypes which resemble skeletal diseases in humans. The proper diagnosis of skeletal variation is key for this type of research. However, even laboratory rearing conditions can alter skeletal phenotypes. The subject of this study is the link between skeletal phenotypes and rearing conditions. Thus, wildtype medaka were reared from hatching to an early juvenile stage at low (LD: 5 individuals/L), medium (MD: 15 individuals/L), and high (HD: 45 individuals/L) densities. The objectives of the study are: (I) provide a comprehensive overview of the postcranial skeletal elements in medaka; (II) evaluate the effects of rearing density on specific meristic counts and on the variability in type and incidence of skeletal anomalies; (III) define the best laboratory settings to obtain a skeletal reference for a sound evaluation of future experimental conditions; (IV) contribute to elucidating the structural and cellular changes related to the onset of skeletal anomalies. The results from this study reveal that rearing densities greater than 5 medaka/L reduce the animals' growth. This reduction is related to decreased mineralization of dermal (fin rays) and perichondral (fin supporting elements) bone. Furthermore, high density increases anomalies affecting the caudal fin endoskeleton and dermal rays, and the preural vertebral centra. A series of static observations on Alizarin red S whole mount-stained preural fusions provide insights into the etiology of centra fusion. The fusion of preural centra involves the ectopic formation of bony bridges over the intact intervertebral ligament. An apparent consequence is the degradation of the intervertebral ligaments and the remodeling and reshaping of the fused vertebral centra into a biconoid-shaped centrum. From this study it can be concluded that it is paramount to take into account the rearing conditions, natural variability, skeletal phenotypic plasticity, and the genetic background along with species-specific peculiarities when screening for skeletal phenotypes of mutant or wildtype medaka.

Modeling Posidonia oceanica shoot density and rhizome primary production.

Posidonia oceanica meadows rank among the most important and most productive ecosystems in the Mediterranean basin, due to their ecological role and to the goods and services they provide. Estimations of crucial ecological process such as meadows productivity could play a major role in an environmental management perspective and in the assessment of P. oceanica ecosystem services. In this study, a Machine Learning approach, i.e. Random Forest, was aimed at modeling P. oceanica shoot density and rhizome primary production using as predictive variables only environmental factors retrieved from indirect measurements, such as maps. Our predictive models showed a good level of accuracy in modeling both shoot density and rhizome productivity (R2 = 0.761 and R2 = 0.736, respectively). Furthermore, as shoot density is an essential parameter in the estimation of P. oceanica productivity, we proposed a cascaded approach aimed at estimating the latter using predicted values of shoot density rather than observed measurements. In spite of the complexity of the problem, the cascaded Random Forest performed quite well (R2 = 0.637). While direct measurements will always play a fundamental role, our estimates could support large scale assessment of the expected condition of P. oceanica meadows, providing valuable information about the way this crucial ecosystem works.

Chemical signatures of femoral pore secretions in two syntopic but reproductively isolated species of Galápagos land iguanas (Conolophus marthae and C. subcristatus).

The only known population of Conolophus marthae (Reptilia, Iguanidae) and a population of C. subcristatus are syntopic on Wolf Volcano (Isabela Island, Galápagos). No gene flow occurs suggesting that effective reproductive isolating mechanisms exist between these two species. Chemical signature of femoral pore secretions is important for intra- and inter-specific chemical communication in squamates. As a first step towards testing the hypothesis that chemical signals could mediate reproductive isolation between C. marthae and C. subcristatus, we compared the chemical profiles of femoral gland exudate from adults caught on Wolf Volcano. We compared data from three different years and focused on two years in particular when femoral gland exudate was collected from adults during the reproductive season. Samples were processed using Gas Chromatography coupled with Mass Spectrometry (GC-MS). We identified over 100 different chemical compounds. Non-Metric Multidimensional Scaling (nMDS) was used to graphically represent the similarity among individuals based on their chemical profiles. Results from non-parametric statistical tests indicate that the separation between the two species is significant, suggesting that the chemical profile signatures of the two species may help prevent hybridization between C. marthae and C. subcristatus. Further investigation is needed to better resolve environmental influence and temporal reproductive patterns in determining the variation of biochemical profiles in both species.

An ecologically constrained procedure for sensitivity analysis of Artificial Neural Networks and other empirical models.

Sensitivity analysis applied to Artificial Neural Networks (ANNs) as well as to other types of empirical ecological models allows assessing the importance of environmental predictive variables in affecting species distribution or other target variables. However, approaches that only consider values of the environmental variables that are likely to be observed in real-world conditions, given the underlying ecological relationships with other variables, have not yet been proposed. Here, a constrained sensitivity analysis procedure is presented, which evaluates the importance of the environmental variables considering only their plausible changes, thereby exploring only ecological meaningful scenarios. To demonstrate the procedure, we applied it to an ANN model predicting fish species richness, as identifying relationships between environmental variables and fish species occurrence in river ecosystems is a recurring topic in freshwater ecology. Results showed that several environmental variables played a less relevant role in driving the model output when that sensitivity analysis allowed them to vary only within an ecologically meaningful range of values, i.e. avoiding values that the model would never handle in its practical applications. By comparing percent changes in MSE between constrained and unconstrained sensitivity analysis, the relative importance of environmental variables was found to be different, with habitat descriptors and urbanization factors that played a more relevant role according to the constrained procedure. The ecologically constrained procedure can be applied to any sensitivity analysis method for ANNs, but obviously it can also be applied to other types of empirical ecological models.

A model predicting the PSP toxic dinoflagellate Alexandrium minutum occurrence in the coastal waters of the NW Adriatic Sea.

Increased anthropic pressure on the coastal zones of the Mediterranean Sea caused an enrichment in nutrients, promoting microalgal proliferation. Among those organisms, some species, such as the dinoflagellate Alexandrium minutum, can produce neurotoxins. Toxic blooms can cause serious impacts to human health, marine environment and economic maritime activities at coastal sites. A mathematical model predicting the presence of A. minutum in coastal waters of the NW Adriatic Sea was developed using a Random Forest (RF), which is a Machine Learning technique, trained with molecular data of A. minutum occurrence obtained by molecular PCR assay. The model is able to correctly predict more than 80% of the instances in the test data set. Our results showed that predictive models may play a useful role in the study of Harmful Algal Blooms (HAB).

Cascaded neural networks improving fish species prediction accuracy: the role of the biotic information.

Species distribution is the result of complex interactions that involve environmental parameters as well as biotic factors. However, methodological approaches that consider the use of biotic variables during the prediction process are still largely lacking. Here, a cascaded Artificial Neural Networks (ANN) approach is proposed in order to increase the accuracy of fish species occurrence estimates and a case study for Leucos aula in NE Italy is presented as a demonstration case. Potentially useful biotic information (i.e. occurrence of other species) was selected by means of tetrachoric correlation analysis and on the basis of the improvements it allowed to obtain relative to models based on environmental variables only. The prediction accuracy of the L. aula model based on environmental variables only was improved by the addition of occurrence data for A. arborella and S. erythrophthalmus. While biotic information was needed to train the ANNs, the final cascaded ANN model was able to predict L. aula better than a conventional ANN using environmental variables only as inputs. Results highlighted that biotic information provided by occurrence estimates for non-target species whose distribution can be more easily and accurately modeled may play a very useful role, providing additional predictive variables to target species distribution models.

Composition of arthropod species assemblages in Bt-expressing and near isogenic eggplants in experimental fields.

The environmental impact of genetically modified (GM) plants in experimental fields has been examined in several ways, in particular with respect to the dynamics of specific nontarget organisms. The approach of sampling for biodiversity in agroecosystems to compare complex patterns could also be useful in studying potential disruptions caused by GM crops. In this study, we set up replicated field plots of Bt-expressing eggplants and near isogenic untransformed eggplants as a control. We monitored the presence and abundance of herbivore and predator arthropods in weekly visual samplings of the plant canopy for three growing seasons (2001-2003). Insect species were pooled in organismal taxonomic units (OTUs); three multivariate methods were used to compare species assemblage as an estimate of insect biodiversity. This multistep statistical approach proved to be efficient in recognizing association patterns, as evidenced by the data for the target species Leptinotarsa decemlineata Say (Coleoptera: Chrysomelidae) clearly showing a significant association with the control plots. All the analyses indicate a comparable species assemblage between transgenic and near isogenic eggplant areas. Our results suggest that some taxa may warrant more specific study. For example, Alticinae beetles (Coleoptera: Chrysomelidae) were alternatively more abundant in either of the two treatments, and their overall abundance was significantly higher on transgenic eggplants. In light of these results and because of their taxonomic proximity to the target species, these herbivores may represent an important nontarget group to be further studied. Moreover, some sap feeders (e.g., Homoptera: Cicadellidae) were more abundant on Bt-expressing plants in some samples in all 3 yr.

Analysis of phytoplankton assemblage structure in the Mediterranean Sea based on high-throughput sequencing of partial 18S rRNA sequences.

Studying taxonomic and ecological diversity of phytoplankton assemblages is often difficult because morphological analysis cannot provide a complete description of their composition. Therefore, more robust and feasible approaches have to be chosen to elucidate the interactions between environmental and human pressures and phytoplankton assemblages. The Ocean Sampling Day (OSD) allowed collecting seawater samples from a wide range of oceanic regions including the Mediterranean Sea. In this study, a total of 754,167 V4-18S ribosomal DNA (rDNA) metabarcodes derived from 20 plankton samples collected at 19 sampling sites across the coastal areas of the Mediterranean Sea were analyzed to explore the relationships between phytoplankton assemblages' composition, sub-regional environmental features and human pressures. We reduced the whole set of autotroph plankton (1398 OTUs) to a smaller number of ecologically relevant entities (205 taxa) and used the latter for analysing the structure of phytoplankton assemblages. Chaetoceros was the only genus occurring in all the samples, while the number of taxa was maximum in the W Mediterranean. Based on the assigned OTUs, the structure of E Mediterranean phytoplankton was the most homogeneous. Further, phytoplankton assemblages from the three Mediterranean sub-regions (Western, Adriatic and Eastern) were significantly different (R=0.25, p=0.0136) based on Jaccard similarity. We also observed that phytoplankton diversity and human impact on marine ecosystems were not significantly related to each other based on Mantel's test.

ACQUA: Automated Cyanobacterial Quantification Algorithm for toxic filamentous genera using spline curves, pattern recognition and machine learning.

Toxigenic cyanobacteria are one of the main health risks associated with water resources worldwide, as their toxins can affect humans and fauna exposed via drinking water, aquaculture and recreation. Microscopy monitoring of cyanobacteria in water bodies and massive growth systems is a routine operation for cell abundance and growth estimation. Here we present ACQUA (Automated Cyanobacterial Quantification Algorithm), a new fully automated image analysis method designed for filamentous genera in Bright field microscopy. A pre-processing algorithm has been developed to highlight filaments of interest from background signals due to other phytoplankton and dust. A spline-fitting algorithm has been designed to recombine interrupted and crossing filaments in order to perform accurate morphometric analysis and to extract the surface pattern information of highlighted objects. In addition, 17 specific pattern indicators have been developed and used as input data for a machine-learning algorithm dedicated to the recognition between five widespread toxic or potentially toxic filamentous genera in freshwater: Aphanizomenon, Cylindrospermopsis, Dolichospermum, Limnothrix and Planktothrix. The method was validated using freshwater samples from three Italian volcanic lakes comparing automated vs. manual results. ACQUA proved to be a fast and accurate tool to rapidly assess freshwater quality and to characterize cyanobacterial assemblages in aquatic environments.

Dataset exploited for the development and validation of automated cyanobacteria quantification algorithm, ACQUA.

The estimation and quantification of potentially toxic cyanobacteria in lakes and reservoirs are often used as a proxy of risk for water intended for human consumption and recreational activities. Here, we present data sets collected from three volcanic Italian lakes (Albano, Vico, Nemi) that present filamentous cyanobacteria strains at different environments. Presented data sets were used to estimate abundance and morphometric characteristics of potentially toxic cyanobacteria comparing manual Vs. automated estimation performed by ACQUA ("ACQUA: Automated Cyanobacterial Quantification Algorithm for toxic filamentous genera using spline curves, pattern recognition and machine learning" (Gandola et al., 2016) [1]). This strategy was used to assess the algorithm performance and to set up the denoising algorithm. Abundance and total length estimations were used for software development, to this aim we evaluated the efficiency of statistical tools and mathematical algorithms, here described. The image convolution with the Sobel filter has been chosen to denoise input images from background signals, then spline curves and least square method were used to parameterize detected filaments and to recombine crossing and interrupted sections aimed at performing precise abundances estimations and morphometric measurements.

[Fish-based assessment methods for the ecological status of aquatic systems]. / I pesci nella valutazione dello stato ecologico.

A short overview of fish-based assessment methods for aquatic systems is presented. Multimetric indices, as, e.g., the index of biotic integrity (IBI), firstly developed in USA and later adapted for European river basins and other countries, are shortly described. Non-multimetric indices are also discussed, e.g. the ichthyological index (II) and the index of ecological status of fish communities (ISECI), both proposed for monitoring Italian rivers. Moreover, statistical and predicting methods based on machine learning techniques are described. Finally, a new approach for developing standardised fish-based methods useful to assess ecological status of Italian rivers is proposed. Although rather complex, the use of bony fish in biomonitoring is promising and requires a multidisciplinary approach to be adopted.

An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models.

We investigated 32 net primary productivity (NPP) models by assessing skills to reproduce integrated NPP in the Arctic Ocean. The models were provided with two sources each of surface chlorophyll-a concentration (chlorophyll), photosynthetically available radiation (PAR), sea surface temperature (SST), and mixed-layer depth (MLD). The models were most sensitive to uncertainties in surface chlorophyll, generally performing better with in situ chlorophyll than with satellite-derived values. They were much less sensitive to uncertainties in PAR, SST, and MLD, possibly due to relatively narrow ranges of input data and/or relatively little difference between input data sources. Regardless of type or complexity, most of the models were not able to fully reproduce the variability of in situ NPP, whereas some of them exhibited almost no bias (i.e., reproduced the mean of in situ NPP). The models performed relatively well in low-productivity seasons as well as in sea ice-covered/deep-water regions. Depth-resolved models correlated more with in situ NPP than other model types, but had a greater tendency to overestimate mean NPP whereas absorption-based models exhibited the lowest bias associated with weaker correlation. The models performed better when a subsurface chlorophyll-a maximum (SCM) was absent. As a group, the models overestimated mean NPP, however this was partly offset by some models underestimating NPP when a SCM was present. Our study suggests that NPP models need to be carefully tuned for the Arctic Ocean because most of the models performing relatively well were those that used Arctic-relevant parameters.

Applications of self-organizing maps for ecomorphological investigations through early ontogeny of fish.

We propose a new graphical approach to the analysis of multi-temporal morphological and ecological data concerning the life history of fish, which can typically serves models in ecomorphological investigations because they often undergo significant ontogenetic changes. These changes can be very complex and difficult to describe, so that visualization, abstraction and interpretation of the underlying relationships are often impeded. Therefore, classic ecomorphological analyses of covariation between morphology and ecology, performed by means of multivariate techniques, may result in non-exhaustive models. The Self Organizing map (SOM) is a new, effective approach for pursuing this aim. In this paper, lateral outlines of larval stages of gilthead sea bream (Sparus aurata) and dusky grouper (Epinephelus marginatus) were recorded and broken down using by means of Elliptic Fourier Analysis (EFA). Gut contents of the same specimens were also collected and analyzed. Then, shape and trophic habits data were examined by SOM, which allows both a powerful visualization of shape changes and an easy comparison with trophic habit data, via their superimposition onto the trained SOM. Thus, the SOM provides a direct visual approach for matching morphological and ecological changes during fish ontogenesis. This method could be used as a tool to extract and investigate relationships between shape and other sinecological or environmental variables, which cannot be taken into account simultaneously using conventional statistical methods.

Skeletal anomaly monitoring in rainbow trout (Oncorhynchus mykiss, Walbaum 1792) reared under different conditions.

The incidence of skeletal anomalies could be used as an indicator of the "quality" of rearing conditions as these anomalies are thought to result from the inability of homeostatic mechanisms to compensate for environmentally-induced stress and/or altered genetic factors. Identification of rearing conditions that lower the rate of anomalies can be an important step toward profitable aquaculture as malformed market-size fish have to be discarded, thus reducing fish farmers' profits. In this study, the occurrence of skeletal anomalies in adult rainbow trout grown under intensive and organic conditions was monitored. As organic aquaculture animal production is in its early stages, organic broodstock is not available in sufficient quantities. Non-organic juveniles could, therefore, be used for on-growing purposes in organic aquaculture production cycle. Thus, the adult fish analysed in this study experienced intensive conditions during juvenile rearing. Significant differences in the pattern of anomalies were detected between organically and intensively-ongrown specimens, although the occurrence of severe, commercially important anomalies, affecting 2-12.5% of individuals, was comparable in the two systems. Thus, organic aquaculture needs to be improved in order to significantly reduce the incidence of severe anomalies in rainbow trout.

Environmental conditioning of skeletal anomalies typology and frequency in gilthead seabream (Sparus aurata L., 1758) juveniles.

In this paper, 981 reared juveniles of gilthead seabream (Sparus aurata) were analysed, 721 of which were from a commercial hatchery located in Northern Italy (Venice, Italy) and 260 from the Hellenic Center for Marine Research (Crete, Greece). These individuals were from 4 different egg batches, for a total of 10 different lots. Each egg batch was split into two lots after hatching, and reared with two different methodologies: intensive and semi-intensive. All fish were subjected to processing for skeletal anomaly and meristic count analysis. The aims involved: (1) quantitatively and qualitatively analyzing whether differences in skeletal elements arise between siblings and, if so, what they are; (2) investigating if any skeletal bone tissue/ossification is specifically affected by changing environmental rearing conditions; and (3) contributing to the identification of the best practices for gilthead seabream larval rearing in order to lower the deformity rates, without selections. The results obtained in this study highlighted that: i) in all the semi-intensive lots, the bones having intramembranous ossification showed a consistently lower incidence of anomalies; ii) the same clear pattern was not observed in the skeletal elements whose ossification process requires a cartilaginous precursor. It is thus possible to ameliorate the morphological quality (by reducing the incidence of severe skeletal anomalies and the variability in meristic counts of dermal bones) of reared seabream juveniles by lowering the stocking densities (maximum 16 larvae/L) and increasing the volume of the hatchery rearing tanks (minimum 40 m(3)). Feeding larvae with a wide variety of live (wild) preys seems further to improve juvenile skeletal quality. Additionally, analysis of the morphological quality of juveniles reared under two different semi-intensive conditions, Mesocosm and Large Volumes, highlighted a somewhat greater capacity of Large Volumes to significantly augment the gap with siblings reared in intensive (conventional) modality.

"Right" or "wrong"? Insights into the ecology of sidedness in european flounder, Platichthys flesus.

Sidedness polymorphism in flatfish has been linked to ecological selection between morphs. However, the alternate hypothesis that morphological differences between right- and left-sided forms may be due to errors during development, as a consequence of disturbed homeostasis, which still remains largely unexplored. Here, we examined the case of Platichthys flesus (flounder), a polymorphic flatfish exhibiting large and clinal variation in the frequency of the left-sided morph, which is the reversed condition in this generally right-sided species. An integrated approach consisting of the analyses of shape variation, stomach contents, and skeletal anomalies was used. Morphological differences were observed between morphs, which are in agreement with previous findings in a congeneric species (Platichthys stellatus). In parallel, significant differences in feeding choices were detected, suggesting a coherent association between subtle morphological differences between morphs and their use of trophic resources. Skeletal anomalies and meristic counts did not corroborate the hypothesis that morphometric divergence in reversed individuals may be caused or reinforced by developmental instability.