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The natural capital indicator framework (NCIF) offers an entry‐level approach to natural capital reporting using existing data sources and indicators. Here, we test the NCIF for the first time in the megadiverse Pantanal biome in Brazil. We compile publicly available indicators on the cattle, soy, fishing, and nature‐based tourism industries alongside indicators on the state of the biome and its ecosystem services. We show that the NCIF is quick to implement using existing environmental and socioeconomic indicators produced by public and NGO statistics communities. However, we identify significant gaps in indicators on the condition and economic value of the biome, the human investment required to derive benefits from the biome, emissions produced by the industries that exploit the biome, and the ecosystem services that maintain the functioning of the biome, such as nutrient cycling and biodiversity. Existing initiatives in Brazil could fill natural capital reporting gaps, including Brazil's experimental natural capital accounts. The NCIF provides a structured approach to highlight gaps in natural capital reporting and guide decision‐makers to prioritize investment in filling data and reporting gaps. Systematic, transnational monitoring must fill gaps in natural capital data to inform decision‐making in the megadiverse Pantanal biome. [ABSTRACT FROM AUTHOR]

Cities support unique and valuable ecological communities, but understanding urban wildlife is limited due to the difficulties of assessing biodiversity. Ecoacoustic surveying is a useful way of assessing habitats, where biotic sound measured from audio recordings is used as a proxy for population abundance and/or activity. However, existing algorithms systematically over and underestimate measures of biotic activity in the presence of typical urban non‐biotic sounds in recordings.We develop CityNet, a deep learning system using convolutional neural networks (CNNs), to measure audible biotic (CityBioNet) and anthropogenic (CityAnthroNet) acoustic activity in cities. The CNNs were trained on a large dataset of annotated audio recordings collected across Greater London, UK. Using a held‐out test dataset, we compare the precision and recall of CityBioNet and CityAnthroNet separately to the best available alternative algorithms: four Acoustic Indices: Acoustic Complexity Index, Acoustic Diversity Index, Bioacoustic Index, and Normalised Difference Soundscape Index, and a state‐of‐the‐art bird call detection CNN (bulbul). We also compare the effect of non‐biotic sounds on the predictions of CityBioNet and bulbul. Finally we apply CityNet to describe acoustic patterns of the urban soundscape in two sites along an urbanisation gradient.CityBioNet was the best performing algorithm for measuring biotic activity in terms of precision and recall, followed by bulbul, whereas the Acoustic Indices performed worst. CityAnthroNet outperformed the Normalised Difference Soundscape Index, but by a smaller margin than CityBioNet achieved against the competing algorithms. The CityBioNet predictions were impacted by mechanical sounds, whereas air traffic and wind sounds influenced the bulbul predictions. Across an urbanisation gradient, we show that CityNet produced realistic daily patterns of biotic and anthropogenic acoustic activity from real‐world urban audio data.Using CityNet, it is possible to automatically measure biotic and anthropogenic acoustic activity in cities from audio recordings. If embedded within an autonomous sensing system, CityNet could produce environmental data for cites at large‐scales and facilitate investigation of the impacts of anthropogenic activities on wildlife. The algorithms, code and pretrained models are made freely available in combination with two expert‐annotated urban audio datasets to facilitate automated environmental surveillance in cities. [ABSTRACT FROM AUTHOR]