Machine Learning Methods Applied to the Prediction of Pseudo-nitzschia spp. Blooms in the Galician Rias Baixas (NW Spain)

This work presents new prediction models based on recent developments in machine learning methods, such as Random Forest (RF) and AdaBoost, and compares them with more classical approaches, i.e., support vector machines (SVMs) and neural networks (NNs). The models predict Pseudo-nitzschia spp. blooms in the Galician Rias Baixas. This work builds on a previous study by the authors (doi.org/10.1016/j.pocean.2014.03.003) but uses an extended database (from 2002 to 2012) and new algorithms. Our results show that RF and AdaBoost provide better prediction results compared to SVMs and NNs, as they show improved performance metrics and a better balance between sensitivity and specificity. Classical machine learning approaches show higher sensitivities, but at a cost of lower specificity and higher percentages of false alarms (lower precision). These results seem to indicate a greater adaptation of new algorithms (RF and AdaBoost) to unbalanced datasets. Our models could be operationally implemented to establish a short-term prediction system

Remote sensing chlorophyll a of optically complex waters (rias Baixas, NW Spain): Application of a regionally specific chlorophyll a algorithm for MERIS full resolution data during an upwelling cycle

This study takes advantage of a regionally specific algorithm and the characteristics of Medium Resolution Imaging Spectrometer (MERIS) in order to deliver more accurate, detailed chlorophyll a (chla) maps of optically complex coastal waters during an upwelling cycle. MERIS full resolution chla concentrations and in situ data were obtained on the Galician (NW Spain) shelf and in three adjacent rias (embayments), sites of extensive mussel culture that experience frequent harmful algal events. Regionally focused algorithms (Regional neural network for rias Baixas or NNRB) for the retrieval of chla in the Galician rias optically complex waters were tested in comparison to sea-truth data. The one that showed the best performance was applied to a series of six MERIS (FR) images during a summer upwelling cycle to test its performance. The best performance parameters were given for the NN trained with high-quality data using the most abundant cluster found in the rias after the application of fuzzy c-mean clustering techniques (FCM). July 2008 was characterized by three periods of different meteorological and oceanographic states. The main changes in chla concentration and distribution were clearly captured in the images. After a period of strong upwelling favorable winds a high biomass algal event was recorded in the study area. However, MERIS missed the high chlorophyll upwelled water that was detected below surface in the ria de Vigo by the chla profiles, proving the necessity of in situ observations. Relatively high biomass “patches” were mapped in detail inside the rias. There was a significant variation in the timing and the extent of the maximum chla areas. The maps confirmed that the complex spatial structure of the phytoplankton distribution in the rias Baixas is affected by the surface currents and winds on the adjacent continental shelf. This study showed that a regionally specific algorithm for an ocean color sensor with the characteristics of MERIS in combination with in situ data can be of great help in chla monitoring, detection and study of high biomass algal events in an area affected by coastal upwelling such as the rias Baixas

Pseudo-nitzschia Blooms in a Coastal Upwelling System: Remote Sensing Detection, Toxicity and Environmental Variables

The NW coast of the Iberian Peninsula is dominated by extensive shellfish farming, which places this region as a world leader in mussel production. Harmful algal blooms in the area frequent lead to lengthy harvesting closures threatening food security. This study developed a framework for the detection of Pseudo-nitzschia blooms in the Galician rias from satellite data (MERIS full-resolution images) and identified key variables that affect their abundance and toxicity. Two events of toxin-containing Pseudo-nitzschia were detected (up to 2.5 μg L−1 pDA) in the area. This study suggests that even moderate densities of Pseudo-nitzschia in this area might indicate high toxin content. Empirical models for particulate domoic acid (pDA) were developed based on MERIS FR data. The resulting remote-sensing model, including MERIS bands centered around 510, 560, and 620 nm explain 73% of the pDA variance (R2 = 0.73, p < 0.001). The results show that higher salinity values and lower Si(OH)4/N ratios favour higher Pseudo-nitzschia spp. abundances. High pDA values seem to be associated with relatively high PO43, low NO3− concentrations, and low Si(OH)4/N. While MERIS FR data and regionally specific algorithms can be useful for detecting Pseudo-nitzschia blooms, nutrient relationships are crucial for predicting the toxicity of these blooms

Neural network estimation of chlorophyll a from MERIS full resolution data for the coastal waters of Galician rias (NW Spain)

In typical Case 2 waters, accurate remote sensing retrieval of chlorophyll a (chla) is still a challenging task. In this study, focusing on the Galician rias (ΝW Spain), algorithms based on neural network (NN) techniques were developed for the retrieval of chla concentration in optically complex waters, using Medium Resolution Imaging Spectrometer (MERIS) data. There is considerable interest in the accurate estimation of chla for the Galician rias, because of the economic and social importance of the extensive culture of mussels, and the high frequency of harmful algal events. Fifteen MERIS full resolution (FR) cloud-free images paired with in situ chla data (for 2002–2004 and 2006–2008) were used for the development and validation of the NN. The scope of NN was established from the clusters obtained using fuzzy c-mean (FCM) clustering techniques applied to the satellite-derived data. Three different NNs were developed: one including the whole data set, and two others using only points belonging to one of the clusters. The input data for these latter two NNs was chosen depending on the quality level, defined on the basis of quality flags given to each data set. The fitting results were fairly good and proved the capability of the tool to predict chla concentrations in the study area. The best prediction was given for the NN trained with high-quality data using the most abundant cluster data set. The performance parameters in the validation set of this NN were R2 = 0.86, mean percentage error (MPE) = − 0.14, root mean square error (RMSE) = 0.75 mg m− 3, and relative RMSE = 66%. The NN developed in this study detected accurately the peaks of chla, in both training and validation sets. The performance of the Case-2-Regional (C2R) algorithm, routinely used for MERIS data, was also tested and compared with our best performing NN and the sea-truthing data. Results showed that this NN outperformed the C2R, giving much higher R2 and lower RMSE values. This study showed that the combination of in situ data and NN technology improved the retrieval of chla in Case 2 waters, and could be used to obtain more accurate chla maps. A local-based algorithm for the chla retrieval from an ocean colour sensor with the characteristics of MERIS would be a great support in the quantitative monitoring and study of harmful algal events in the coastal waters of the Rias Baixas. The limitations and possible improvements of the developed chla algorithms are also discussed

Support Vector Machine-based method for predicting Pseudo-nitzschia spp. blooms in coastal waters (Galician rias, NW Spain)

Phytoplanktonic blooms in the coastal embayments (rias) at the NW part of Spain were mentioned for the first time in 1918 and since then they have been associated numerous times with negatives impacts to a very important economic activity in the area, mussel production. In this study, eight years of Pseudo-nitzschia spp. abundance and associated meteorological and oceanographic data were used to develop and validate support vector machine (SVM) models for the prediction of these diatoms. SVM were used to identify presence/below low detection limit, bloom/no bloom conditions of Pseudo-nitzschia spp. and finally to predict blooms due to these diatoms in the coastal systems of the Galician rias. The best SVM models were selected on the basis of C and γ parameters and their performance was evaluated in terms of accuracy and kappa statistics (κ). Regarding the presence/below low detection limit, bloom/no bloom models the best results in the validation dataset were achieved using all the variables: ria code, day of the year, temperature, salinity, upwelling indices and bloom occurrence in previous weeks. The best performing models were also tested in an independent dataset from the study area, where they showed high overall accuracy (78.53-82.18%), κ values (0.77-0.81) and true positive rates (62.60-78.18). In these models the bloom occurrence in previous weeks was identified as a key parameter to the prediction performance. In this paper, toxic Pseudo-nitzschia blooms could not be predicted due to limited information on toxin concentration and species composition. Nevertheless, this study demonstrates that the approach followed here is capable for high predictive performance which could be of great aid in the monitoring of algal blooms and offer valuable information to the local shellfish industry. The reliable prediction of categorical Pseudo-nitzschia abundances using variables that are operationally determined or short-term predicted could provide early warning of an impending bloom and could help to the development of strategies that could minimize the risks to human health and protect valuable economic resources

Multi-scale habitat preference analyses for Azorean blue whales

Blue whales are sighted every year around the Azores islands, which apparently provide an important seasonal foraging area. In this paper we aim to characterize habitat preferences and analyze the temporal distribution of blue whales around Sao Miguel Island. To do so, we applied Generalized Additive Models to an opportunistic cetacean occurrence dataset and remotely sensed environmental data on bathymetry, sea surface temperature, chlorophyll concentration and altimetry. We provide a brief description of the oceanography of the area, emphasizing its high spatio-temporal variability. In order to capture this dynamism, we used environmental data with two different spatial resolutions (low and high) and three different temporal resolutions (daily, weekly and monthly), thus accounting for both long-term oceanographic events such as the spring bloom, and shorter-term features such as eddies or fronts. Our results show that blue whales have a well-defined ecological niche around the Azores. They usually cross the archipelago from March to June and habitat suitability is highest in dynamic areas (with high Eddy Kinetic Energy) characterized by convergence or aggregation zones where productivity is enhanced. Multi-scale studies are useful to understand the ecological niche and habitat requirements of highly mobile species that can easily react to short-term changes in the environment

Spatiotemporal patterns of marine mammal distribution in coastal waters of Galicia, NW Spain

The spatial and seasonal distribution of cetaceans and possible links with environmental conditions were studied at the Galician continental shelf. Data were collected between February–August 2001 and June–September 2003 during opportunistic surveys onboard fishing boats. Seven species of cetaceans were identified from 250 sightings of 6,846 individuals. The common dolphin (Delphinus delphis) was by far the most frequently sighted and the most widely distributed species. Spatiotemporal trends in cetacean distribution and abundance, and their relationships with environmental parameters (sea depth, SST and chlorophyll-a) were quantified using generalised additive models (GAMs). Results for all cetaceans were essentially the same as for common dolphins alone. Modelling results indicated that the number of common dolphin sightings per unit effort was higher further south. The number of individual common dolphins seen per sighting of this species (i.e. group size) was however higher in the north and west of the study area, higher later in the year and higher in 2001 than in 2003. In contrast, the number of common dolphin calves seen (per sighting of this species) was higher in the south. Models including environmental variables indicated larger common dolphin group sizes in deeper waters and at higher chlorophyll concentrations (i.e. in more productive areas). There was also a positive relationship between survey effort and group size, which is probably an artefact of the tendency of the survey platforms (fishing boats) to spend most time in areas of high fish abundance. Numbers of common dolphin calves per sighting were found to be higher in shallower waters. The results are consistent with common dolphins foraging mainly in deeper waters of the Galician continental shelf, while more southern inshore waters may represent a nursery area