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Aquatic Research Infrastructure Network
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Pretending to be Socially Responsible?
Publication . Catalão-Lopes, Margarida; Pina, Joaquim P.; Costa, Ana S.; MARE - Centro de Ciências do Mar e do Ambiente; DCSA - Departamento de Ciências Sociais Aplicadas; Alexandru Ioan Cuza - University of Iasi
Extant evidence on corporate social responsibility (CSR) shows that consumers are willing to pay a premium if they infer that the firm is truly "prosocial" (i.e if it is altruistic), but their valuation of the product will not increase as much (and may even decrease) if they believe the company has an ulterior motive for CSR (i.e. if the firm is opportunistic). We pose that the CSR level of investment can be strategically used as a signalling tool to help consumers identify the true nature of the firm and solve this incomplete information problem. Using a signalling game, where altruistic firms want to express their nature and opportunistic ones want to conceal it, we explore the relative effectiveness of consumers’ premiums and penalties (expressed as demand increases or decreases, respectively) in the promotion of corporate truth-revealing behaviour. We also characterize the conditions for market equilibria in which altruistic firms are distinguished from opportunistic ones, allowing consumers to solve the information asymmetry and, with that, influence firms’ profits. Contrary to what might be expected, we show that rewards for altruistic CSR and penalties for opportunistic CSR are not symmetrically effective. Our results help companies to improve their CSR decisions, by understanding how consumers solve the information asymmetry regarding the true nature of the CSR investments. Especially for altruistic firms, this may be important to guarantee that CSR effort and expenses are not just a cost but turn into higher revenues and profits.
Seagrasses benefit from mild anthropogenic nutrient additions
Publication . Vieira, Vasco M. N. C. S.; Lobo-Arteaga, Jorge; Santos, Rafael; Leitão-Silva, David; Veronez, Arthur; Neves, Joana M.; Nogueira, Marta; Creed, Joel C.; Bertelli, Chiara M.; Samper-Villarreal, Jimena; Pettersen, Mats R. S.; MARE - Centro de Ciências do Mar e do Ambiente; Frontiers Media
Seagrasses are declining globally, in large part due to increased anthropogenic coastal nutrient loads that enhance smothering by macroalgae, attenuate light, and are toxic when in excessive concentrations of inorganic nitrogen and phosphorus. However, as sanitation is improved many seagrass meadows have been observed to recover, with a few studies suggesting that they may even benefit from mild anthropogenic nutrient additions. Monitoring seagrass demography and health has faced difficulties in establishing the adequate variables and metrics. Such uncertainty in the methods has caused uncertainty of the significance of results presented and compromised extrapolations to other seasons, areas, or species. One solution has come from within the plant self-thinning theories. During the 1980s, an interspecific boundary line (IBL) was determined as the upper limit of the combination of plant density and above-ground biomass for any stand on Earth, setting their maximum possible efficiency in space occupation. Recently, two meta-analyses to determine specific IBLs for algae and for seagrasses have been performed. The recently updated seagrass dataset comprises 5,052 observations from 78 studies on 18 species. These IBLs opened new perspectives for monitoring: the observed distance of a stand to the respective IBL (i.e., each stand’s relative efficiency of space occupation) was demonstrated to be a valuable indicator of a population’s health. Thus, this metric can be used to determine the impact of nutrients and pollutants on algae and seagrass populations. Furthermore, because the IBLs are common to all species, they may be used to compare all species from any location worldwide. This novel approach showed that Halodule wrightii, Halodule beaudettei, Halophila baillonii, Zostera marina, and Zostera noltei meadows benefit from anthropogenic additions of nitrogen and phosphorus, as long as these additions are moderate. In fact, the healthier Z. noltei meadows in Portugal (and among the healthiest meadows worldwide) were the ones exposed to effluents from wastewater treatment plants (WWTP) and a food factory. We conclude that those effluents are providing water with enough quality and that their optimal management should coordinate the technological solutions of the WWTP with the natural potential of seagrass meadows as water purifiers and biomass producers.
A first assessment of ERA5 and ERA5-Land reanalysis air temperature in Portugal
Publication . Almeida, Manuel; Coelho, Pedro; MARE - Centro de Ciências do Mar e do Ambiente; Royal Meteorological Society
This study evaluates the reliability of ERA5 and ERA5-Land reanalysis datasets in describing the mean daily air temperature of four climate domains in mainland Portugal. The reanalysis datasets were compared with ground observations from 94 meteorological stations (1980–2021). Overall, the results demonstrated a good degree of correlation between the observed and reanalysis data on both a daily and seasonal scale. Both the latitudinal distribution of the air temperature and the moderating effect of the Atlantic Ocean are well described. However, in the case of Portugal, the ERA5-Land was shown to be considerably more effective at describing the mean daily air temperature than ERA5. The results also indicated that, in general, the reanalysis methodologies perform better when applied to air temperature simulation in flatter regions as opposed to regions with high-altitude and complex terrain. The study further suggests that ERA5 and ERA5-Land reanalysis should be used with caution in the case of short-term environmental studies. In fact, relevant differences were shown to exist between the reanalyses and the observed daily mean air temperature datasets for certain specific years. Overall, considering the RMSE between the ERA5-Land reanalysis datasets for mean daily air temperature and the observed datasets there is a 28% probability of locally having a mean RMSE <1.5°C, 52% probability of having a mean RMSE >1.5°C and <2.0°C, and 16% probability of having a RMSE >2.0°C and <3.0°C. These conclusions will hopefully contribute to improving our understanding of the uncertainty sources in relation to ERA5 and ERA5-Land reanalysis data for different climate domains.
Improving Whole Biodiversity Monitoring and Discovery With Environmental DNA Metagenomics
Publication . Curto, Manuel; Veríssimo, Ana; Riccioni, Giulia; Santos, Carlos D.; Ribeiro, Filipe; Jentoft, Sissel; Alves, Maria Judite; Gante, Hugo F.; MARE - Centro de Ciências do Mar e do Ambiente; DCEA - Departamento de Ciências e Engenharia do Ambiente; Faculdade de Ciências e Tecnologia (FCT); Blackwell Publishing Ltd
Environmental DNA (eDNA) metagenomics sequences all DNA molecules present in environmental samples and has the potential of identifying virtually any organism from which they are derived. However, due to unacceptable levels of false positives and negatives, this approach is underexplored as a tool for biodiversity monitoring across the tree of life, particularly for non-microscopic eukaryotes. We present SeqIDist, a framework that combines multilocus BLAST matches against several reference databases followed by an analysis of sequence identity distribution patterns to disentangle false positives while revealing new biodiversity and increasing the accuracy of metagenomic approaches. We tested SeqIDist on an eDNA metagenomic dataset from a riverine site and compared the results to those obtained with an eDNA metabarcoding approach for benchmarking purposes. We start by characterising the biological community (~2000 taxa) across the tree of life at low taxonomic levels and show that eDNA metagenomics has a higher sensitivity than eDNA metabarcoding in discovering new diversity. We show that limited representation of whole genome sequences in reference databases can lead to false positives. For non-microscopic eukaryotes, eDNA metagenomic data often consist of a few sparse, anonymous sequences scattered across the genome, making metagenome assembly methods unfeasible. Finally, we infer eDNA source and residency time using read length distributions as a measure of decay status. The higher accuracy of SeqIDist opens the discussion of the potential of eDNA metagenomics for archived samples and its implementation in long-term biodiversity monitoring at a planetary scale.
Modeling river water temperature with limiting forcing data
Publication . Almeida, Manuel C.; Coelho, Pedro S.; MARE - Centro de Ciências do Mar e do Ambiente; Copernicus Publications
The prediction of river water temperature is of key importance in the field of environmental science. Water temperature datasets for low-order rivers are often in short supply, leaving environmental modelers with the challenge of extracting as much information as possible from existing datasets. Therefore, identifying a suitable modeling solution for the prediction of river water temperature with a large scarcity of forcing datasets is of great importance. In this study, five models, forced with the meteorological datasets obtained from the fifth-generation atmospheric reanalysis, ERA5-Land, are used to predict the water temperature of 83 rivers (with 98% missing data): three machine learning algorithms (random forest, artificial neural network and support vector regression), the hybrid Air2stream model with all available parameterizations and a multiple regression. The machine learning hyperparameters were optimized with a tree-structured Parzen estimator, and an oversampling-undersampling technique was used to generate synthetic training datasets. In general terms, the results of the study demonstrate the vital importance of hyperparameter optimization and suggest that, from a practical modeling perspective, when the number of predictor variables and observed river water temperature values are limited, the application of all the models considered in this study is crucial. Basically, all the models tested proved to be the best for at least one station. The root mean square error (RMSE) and the Nash-Sutcliffe efficiency (NSE) values obtained for the ensemble of all model results were 2.75±1.00 and 0.56±0.48°C, respectively. The model that performed the best overall was random forest (annual mean - RMSE: 3.18±1.06°C; NSE: 0.52±0.23). With the application of the oversampling-undersampling technique, the RMSE values obtained with the random forest model were reduced from 0.00% to 21.89% (μ=8.57%; σ=8.21%) and the NSE values increased from 1.1% to 217.0% (μ=40%; σ=63%). These results suggest that the solution proposed has the potential to significantly improve the modeling of water temperature in rivers with machine learning methods, as well as providing increased scope for its application to larger training datasets and the prediction of other types of dependent variables. The results also revealed the existence of a logarithmic correlation among the RMSE between the observed and predicted river water temperature and the watershed time of concentration. The RMSE increases by an average of 0.1°C with a 1h increase in the watershed time of concentration (watershed area: μ=106km2; σ=153).
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Fundação para a Ciência e a Tecnologia
Programa de financiamento
6817 - DCRRNI ID
Número da atribuição
LA/P/0069/2020