A stratified model for the assessment of meteorologically adjusted trends of surface ozone

We consider the problem of assessing long-term trends of ozone concentrations measured on a single site located in an urban area. Among the many methods proposed in the literature to eliminate the confounding effect of changing weather conditions, we employ a stratification of daily maxima based on regression trees. Within each stratum conditional independence and Weilbull distribution are assumed for maxima. Long-term trend is defined non-parametrically by the sequence of yearly medians. Models are estimated following the Bayesian approach. The alternative assumptions of common and stratum specific trends are compared and a model with common trend for all strata is selected for the analyzed real dataset. The conditional independence assumption is checked by the comparison with a model including an autoregressive component.     

Concordance of genetic and fin photo identification in the great white shark, Carcharodon carcharias, off Mossel Bay, South Africa

Visual identification of naturally acquired marks has been a popular if subjective method of animal identification and population estimation over the last 40 years. Molecular genetics has also independently developed objective individual marking techniques during the same period. Here, we assess the concordance of individual great white shark (Carcharodon carharias) dorsal fin recognition and identification, using seven microsatellite loci as the independent unbiased arbiter, over a period of 5 years. As a monitoring technique, fin photographs offer a very good individual identification key for white sharks over a relatively short period of time (5 years), matching with genetic data in about 85% of cases, whilst caution and a continuously updated database is required for animal recognition over a longer period.     

How to address data privacy concerns when using social media data in conservation science

Social media data are being increasingly used in conservation science to study human–nature interactions. User-generated content, such as images, video, text, and audio, and the associated metadata can be used to assess such interactions. A number of social media platforms provide free access to user-generated social media content. However, similar to any research involving people, scientific investigations based on social media data require compliance with highest standards of data privacy and data protection, even when data are publicly available. Should social media data be misused, the risks to individual users' privacy and well-being can be substantial. We investigated the legal basis for using social media data while ensuring data subjects’ rights through a case study based on the European Union's General Data Protection Regulation. The risks associated with using social media data in research include accidental and purposeful misidentification that has the potential to cause psychological or physical harm to an identified person. To collect, store, protect, share, and manage social media data in a way that prevents potential risks to users involved, one should minimize data, anonymize data, and follow strict data management procedure. Risk-based approaches, such as a data privacy impact assessment, can be used to identify and minimize privacy risks to social media users, to demonstrate accountability and to comply with data protection legislation. We recommend that conservation scientists carefully consider our recommendations in devising their research objectives so as to facilitate responsible use of social media data in conservation science research, for example, in conservation culturomics and investigations of illegal wildlife trade online.     

Natural organic matter controls metal speciation and toxicity for marine organisms: a review

Environmental Chemistry Letters - Heavy metals are occurring in the aquatic environment as the result of natural or anthropogenic inputs, and depending on concentration, availability and resilience...     

An integrated air quality forecast system for a metropolitan area

Air quality forecasting is an important issue in environmental research, due to the effects that air pollutants have on population health. To deal with this topic, in this work an integrated modelling system has been developed to forecast daily maximum eight hours ozone concentrations and daily mean PM10 concentrations, up to two days in advance, over an urban area. The presented approach involves two steps. In the first step, artificial neural networks are identified and applied to get point-wise forecasting. In the second step, the forecasts obtained at the monitoring station locations are spatially interpolated all over the domain using the cokriging technique, which allows to improve the spatial interpolation in the absence of densely sampled data. The integrated modelling system has been then applied to a case study over Northern Italy, performing a validation over space and time for the year 2004 and analyzing if the limit values for the protection of human health set by the European Commission are respected. The presented approach represents a fast and reliable way to provide decision makers and the general public with air quality forecasting, and to support prevention and precautionary measures.     

Field evaluation of a passive sampler of polycyclic aromatic hydrocarbons (PAHs) in an urban atmosphere (Bologna, Italy)

A simple device consisting of a Petri dish containing cellulose paper soaked with paraffin oil (hereinafter defined "artificial leaf", AL) was deployed in a typical urban site and partitioning of a set of PAHs between the atmosphere and AL was investigated. Gas-phase PAHs rapidly equilibrate with AL, thus rendering it a promising cost-effective tool for spatial and temporal trends studies of air quality.     

Maternal effects on begging behaviour: an experimental demonstration of the effects of laying sequence, hatch order, nestling sex and brood size

Differential resource allocation by females across the laying sequence has been hypothesised as a mechanism through which females could either compensate nestlings that hatch last in asynchronous broods or promote brood reduction. In this study we artificially incubated eggs and cross-fostered offspring to manipulate nestlings’ position in the hatching order, to identify whether the competitive ability of nestlings is dependent on position in the laying sequence. In both control and experimentally reversed broods, first hatched chicks had a higher survival than last hatched siblings. Yet, nestlings that hatched from eggs laid in the second half of a clutch begged with a greater intensity than nestlings hatched from eggs laid in the first half of a clutch. In natural broods, the greater begging competitiveness of nestlings from later-laid eggs led to a moderation of sibling competition and these nestlings achieved the same body size and weight as nestlings from eggs laid in the first half of the clutch. The lack of a substantial difference in the size and condition of surviving nestlings in respect to laying order suggests that differential resource allocation across the egg-laying sequence partially compensates for hatching last in asynchronous broods and reduces the negative effects of the nestling size hierarchy. The effect of laying order, brood size and experimental treatment also differed for male and female nestlings. Our study highlights the need to be aware of the complex and subtle effects of nestling sex and laying sequence when investigating genetic and environmental influences on individual fitness.     

A framework for investigating illegal wildlife trade on social media with machine learning

Article impact statement: Machine learning can be used to automatically monitor and assess illegal wildlife trade on social media platforms.