No abstract

Shark populations have declined by more than 70% over the past 50 years. These declines have largely been attributed to increases in fishing efforts. Despite increased public awareness surrounding the conservation of sharks, three-quarters of all oceanic shark species are currently considered at risk of extinction. Here, we use DNA barcoding to identify shark DNA found in pet food purchased within Singapore. We identified a number of sharks that have some degree of control over their trade exerted under the auspices of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES), or through their classification as threatened by the International Union for Conservation of Nature (IUCN). The most commonly identified shark was the blue shark, Prionace glauca, a species that is not listed in CITES or classified as threatened by the IUCN, but one which scientific evidence suggests is overexploited and should have its catch regulated. The second most commonly encountered species was the CITES Appendix II listed silky shark, Carcharhinus falciformis. None of the products specifically listed shark as an ingredient, listing only generic terms, such as “ocean fish,” “white fish,” and “white bait.” The vague terminology used to describe pet food ingredients, and in some cases, the mislabeling of contents, prevents consumers – in this case, pet owners – from making informed and environmentally conscious decisions; consequently, pet owners and animal lovers may unwittingly be contributing to the overfishing of endangered sharks.

Cities have become primary actors on climate change and are increasingly setting goals aimed at net-zero emissions, which warrants closer examination to understand how they intend to meet these goals. The incomplete and heterogeneous nature of city climate policy documents, however, has made systemic analysis challenging. We analyze 318 climate action documents from cities with net-zero targets using machine learning-based natural language processing (NLP) techniques. We aim to accomplish two goals: (1) determine text patterns that predict ‘ambitious’ net-zero targets; and (2) perform a sectoral analysis to identify patterns and trade-offs in climate action themes. We find that cities with ambitious climate actions tend to emphasize quantitative metrics and specific high-emitting sectors in their plans. Cities predominantly emphasize energy-related actions in their plans, but often at the expense of other sectors, including land-use and climate impacts. The method presented in this paper provides a replicable, scalable approach to analyzing climate action plans and a first step towards facilitating cross-city learning.