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Alternative reproductive tactics (ARTs) evolve when there is strong intra-sexual competition between conspecifics for access to mates. Typically, larger “bourgeois” males reproduce by securing the access to reproductive resources while smaller “parasitic” males reproduce by stealing fertilizations from larger males. A number of factors can influence the reproductive success of each tactic, including intrinsic (e.g. size) and extrinsic (e.g. tactic relative frequency) variables. An example where plastic ARTs occur is the peacock blenny Salaria pavo, with large males reproducing by defending nests and attracting females (bourgeois tactic) and small males reproducing by achieving sneaked fertilizations (parasitic tactic). In this study, we conducted field observations on individually tagged animals to determine their social network and collected eggs from 11 nests to determine the fertilization success of each male tactic. Paternity estimates for 550 offspring indicated an average fertilization success for nest-holder males of 95%. Nest-holder male morphological traits and social network parameters were tested as predictors of fertilization success, but only the number of sneakers present in the nest-holder’s social networks was found to be a predictor of paternity loss. Although male morphological traits had been previously found to be strongly correlated with reproductive success of nest-holder males, as measured by the number of eggs collected in the male’s nest, no correlation was found between any of the measured morphological traits and fertilization success for these males. The results suggest a stronger influence of the social environment than of morphological variables in the proportion of lost fertilizations by nest-holder males of this species.

Distinct patterns of gene expression often underlie intra- and intersexual differences, and the study of this set of coregulated genes is essential to understand the emergence of complex behavioural phenotypes. Here, we describe the development of a de novo transcriptome and brain gene expression profiles of wild-caught peacock blenny, Salaria pavo, an intertidal fish with sex-role reversal in courtship behaviour (i.e., females are the courting sex) and sequential alternative reproductive tactics in males (i.e., larger and older nest-holder males and smaller and younger sneaker males occur). Sneakers mimic both female's courtship behaviour and nuptial coloration to get access to nests and sneak fertilizations, and later in life transition into nest-holder males. Thus, this species offers the unique opportunity to study how the regulation of gene expression can contribute to intersex phenotypes and to the sequential expression of male and female behavioural phenotypes by the same individual. We found that at the whole brain level, expression of the sneaker tactic was paralleled by broader and divergent gene expression when compared to either females or nest-holder males, which were more similar between themselves. When looking at sex-biased transcripts, sneaker males are intersex rather than being either nest-holder or female-like, and their transcriptome is simultaneously demasculinized for nest-holder-biased transcripts and feminized for female-biased transcripts. These results indicate that evolutionary changes in reproductive plasticity can be achieved through regulation of gene expression, and in particular by varying the magnitude of expression of sex-biased genes, throughout the lifetime of the same individual.

The levels of air pollution in Macao often exceeded the levels recommended by WHO. In order for the population to take precautionary measures and avoid further health risks under high pollutant exposure, it is important to develop a reliable air quality forecast. Statistical models based on linear multiple regression (MR) and classification and regression trees (CART) analysis were developed successfully, for Macao, to predict the next day concentrations of NO2, PM10, PM2.5, and O3. All the developed models were statistically significantly valid with a 95% confidence level with high coefficients of determination (from 0.78 to 0.93) for all pollutants. The models utilized meteorological and air quality variables based on 5 years of historical data, from 2013 to 2017. Data from 2013 to 2016 were used to develop the statistical models and data from 2017 was used for validation purposes. A wide range of meteorological and air quality variables was identified, and only some were selected as significant independent variables. Meteorological variables were selected from an extensive list of variables, including geopotential height, relative humidity, atmospheric stability, and air temperature at different vertical levels. Air quality variables translate the resilience of the recent past concentrations of each pollutant and usually are maximum and/or the average of latest 24-h levels. The models were applied in forecasting the next day average daily concentrations for NO2 and PM and maximum hourly O3 levels for five air quality monitoring stations. The results are expected to be an operational air quality forecast for Macao.

Statistical methods such as multiple linear regression (MLR) and classification and regression tree (CART) analysis were used to build prediction models for the levels of pollutant concentrations in Macao using meteorological and air quality historical data to three periods: (i) from 2013 to 2016, (ii) from 2015 to 2018, and (iii) from 2013 to 2018. The variables retained by the models were identical for nitrogen dioxide (NO2), particulate matter (PM10), PM2.5, but not for ozone (O3) Air pollution data from 2019 was used for validation purposes. The model for the 2013 to 2018 period was the one that performed best in prediction of the next-day concentrations levels in 2019, with high coefficient of determination (R2), between predicted and observed daily average concentrations (between 0.78 and 0.89 for all pollutants), and low root mean square error (RMSE), mean absolute error (MAE), and biases (BIAS). To understand if the prediction model was robust to extreme variations in pollutants concentration, a test was performed under the circumstances of a high pollution episode for PM2.5 and O3 during 2019, and the low pollution episode during the period of implementation of the preventive measures for COVID-19 pandemic. Regarding the high pollution episode, the period of the Chinese National Holiday of 2019 was selected, in which high concentration levels were identified for PM2.5 and O3, with peaks of daily concentration exceeding 55 μg/m3 and 400 μg/m3, respectively. The 2013 to 2018 model successfully predicted this high pollution episode with high coefficients of determination (of 0.92 for PM2.5 and 0.82 for O3). The low pollution episode for PM2.5 and O3 was identified during the 2020 COVID-19 pandemic period, with a low record of daily concentration for PM2.5 levels at 2 μg/m3 and O3 levels at 50 μg/m3, respectively. The 2013 to 2018 model successfully predicted the low pollution episode for PM2.5 and O3 with a high coefficient of determination (0.86 and 0.84, respectively). Overall, the results demonstrate that the statistical forecast model is robust and able to correctly reproduce extreme air pollution events of both high and low concentration levels.

The levels of air pollution in Macao often exceeded the levels recommended by WHO. In order for the population to take precautionary measures and avoid further health risks under high pollutant exposure, it is important to develop a reliable air quality forecast. Statistical models based on multiple regression (MR) analysis were developed successfully for Macao to predict the next day concentrations of PM10, PM2.5, and NO2. All the developed models were statistically significantly valid with a 95% confidence level with high coefficients of determination (from 0.89 to 0.92) for all pollutants. The models utilized meteorological and air quality variables based on five years of historical data, from 2013 to 2017. The data from 2013 to 2016 were used to develop the statistical models and data from 2017 were used for validation purposes. A wide range of meteorological and air quality variables were identified, and only some were selected as significant dependent variables. Meteorological variables were selected from an extensive list of variables, including geopotential height, relative humidity, atmospheric stability, and air temperature at different vertical levels. Air quality variables translate the resilience of the recent past concentrations of each pollutant and usually are maximum and/or the average of latest 24-hour levels. The models were applied in forecasting the next day average daily concentrations for PM10, PM2.5, and NO2 for the air quality monitoring stations. The results are expected to be an operational air quality forecast for Macao.

Air pollution is a major concern issue on Macao since the concentration levels of several of the most common pollutants are frequently above the internationally recommended values. The low air quality episodes impacts on human health paired with highly populated urban areas are important motivations to develop forecast methodologies in order to anticipate pollution episodes, allowing establishing warnings to the local community to take precautionary measures and avoid outdoor activities during this period. Using statistical methods (multiple linear regression (MLR) and classification and regression tree (CART) analysis) we were able to develop forecasting models for the main pollutants (NO2, PM2.5, and O3) enabling us to know the next day concentrations with a good skill, translated by high coefficients of determination (0.82–0.90) on a 95% confidence level. The model development was based on six years of historical data, 2013 to 2018, consisting of surface and upper-air meteorological observations and surface air quality observations. The year of 2019 was used for model validation. From an initially large group of meteorological and air quality variables only a few were identified as significant dependent variables in the model. The selected meteorological variables included geopotential height, relative humidity and air temperature at different altitude levels and atmospheric stability characterization parameters. The air quality predictors used included recent past hourly levels of mean concentrations for NO2 and PM2.5 and maximum concentrations for O3. The application of the obtained models provides the expected daily mean concentrations for NO2 and PM2.5 and maximum hourly concentrations O3 for the next day in Taipa Ambient air quality monitoring stations. The described methodology is now operational, in Macao, since 2020.

Fishes show remarkably diverse aggressive behaviour. Aggression is expressed to secure resources; adjusting aggression levels according to context is key to avoid negative consequences for fitness and survival. Nonetheless, despite its importance, the physiological basis of aggression in fishes is still poorly understood. Several reports suggest hormonal modulation of aggression, particularly by androgens, but contradictory studies have been published. Studies exploring the role of chemical communication in aggressive behaviour are also scant, and the pheromones involved remain to be unequivocally characterized. This is surprising as chemical communication is the most ancient form of information exchange and plays a variety of other roles in fishes. Furthermore, the study of chemical communication and aggression is relevant at the evolutionary, ecological and economic levels. A few pioneering studies support the hypothesis that aggressive behaviour, at least in some teleosts, is modulated by “dominance pheromones” that reflect the social status of the sender, but there is little information on the identity of the compounds involved. This review aims to provide a global view of aggressive behaviour in fishes and its underlying physiological mechanisms including the involvement of chemical communication, and discusses the potential use of dominance pheromones to improve fish welfare. Methodological considerations and future research directions are also outlined.