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As keystone structures in urban ecosystems, trees are critical to addressing many of the current livability, health, and environmental challenges facing cities. Every day, trees are removed from urban landscapes as part of routine management. These tree removals are an opportunity for implementing manipulative experiments to directly measure the social and ecological functions of trees. Here we review the kinds of tree removals that commonly occur in cities, assess the relevant opportunities that arise for research–practice partnerships, and discuss the challenges posed when implementing experiments of this nature. We argue that experimental studies on the routine removal of urban trees will improve and expand the mechanistic understanding of how trees support biodiversity and human well‐being in cities beyond current knowledge, which is largely based on correlative studies. Finally, we highlight the opportunity for experiments to be co‐designed by scientists and urban land managers, and how "learning while doing" can generate tangible research impacts and improve urban forest decision making. [ABSTRACT FROM AUTHOR]

We present a binary hypothesis test for detecting clear sky in auroral all‐sky images based on single‐wavelength keograms. The coefficient of variation c, the ratio of the sample standard deviation to the mean over elevation angle along the meridian, is the test statistic. After image‐correcting keograms and excluding dark sky intervals, detection performance is compared to true conditions as determined by Advanced Very High Resolution Radiometer satellite imagery. The cloud mask, an index of cloud cover, is selected at the corresponding nearest time and location to the site of a meridian spectrograph at Poker Flat Research Range. With training data from 2014 to 2016, theoretical Rayleigh distributions fit to the histograms show a decision threshold of 0.40 could yield an accuracy of about 80%. Separately, we numerically compute the false alarm and missed detection statistics of the greenline 557.7 nm emission and of the redline 630.0 nm emission. We find a threshold of 0.25 for the greenline c maximizes the percent of events correctly identified at 76%. Applied to testing data from 2015 to 2017, the 0.25 threshold yields an accuracy of 68%. Diffuse aurora can have coefficient of variation around 0.2 to 0.5, which would be included by the numerical minimum, but partly excluded by the theoretical model obtained. Numerical results are a few percent worse for the redline emission. Plain Language Summary: Clouds in the sky are a problem for scientists trying to view space beyond. For upper atmospheric scientists, clouds can obscure or scatter auroral light in scientific auroral cameras, making it hard to identify, locate, and track auroral shapes. This paper shows a way to simply and automatically detect clouds using a north‐to‐south line scan of a single color of light from the sky over time, known as a keogram. We compute the ratio of the variation in pixel intensity to the average pixel intensity, for each north‐to‐south scan. Excluding dark sky periods, a large ratio means that the sky is clear, and a small ratio that the sky is cloudy. We find the method works with about a 70% correct rate using red or green auroral light. With this method we can eliminate data during cloudy conditions for any auroral studies that require clear sky conditions. Key Points: Auroral keogram coefficient of variation is used to decide if the sky is cloudy or clear, verified with NOAA satellite data from 2014 to 2017The coefficient of variation distinguishes clear from cloudy auroral conditions with as much as 75% accuracyThis method is intended for subsequent automation of auroral all‐sky image analysis [ABSTRACT FROM AUTHOR]

Experimentally manipulating urban tree abundance and structure can help explore the complex and reciprocal interactions among people, biodiversity and the services urban forests provide to humans and wildlife.In this study we take advantage of scheduled urban tree removals to experimentally quantify the benefits that urban trees provide to humans and wildlife. Specifically, we aim to understand how trees affect: (1) bird and mammal abundance and diversity, as well as an ecological process (predation); and (2) people's perception responses, such as the importance that people assign to the trees, wildlife and the site.We designed two independent Before‐after‐control‐impact (BACI) experiments based on two sites where tree removals were occurring (impact sites): an urban park and a residential street, both located in the Greater Melbourne Area, Australia. We selected three control sites for each impact site, or four per experiment. Ecological data were collected through field surveys, and social data on people's perceptions through intercept questionnaires among park and street users. Data were analysed using a GLMMs to determine the combined effect of time (before and after) and treatment (impact and controls).At the urban park, the abundance of nectarivorous birds and possums both declined by 62% following tree removal, while invertebrate predation increased by 82.1%. The level of importance people assigned to the urban park and to the trees at the site decreased after tree removal, and people's attitudes towards tree planting became more positive, meaning more people wanted to plant more trees at the site.None of these changes were observed in the street experiment where fewer and smaller trees were removed, suggesting that effects may be highly specific to context, where factors such as tree volume, diversity and arrangement influence the magnitude of social–ecological effects observed.By demonstrating the social–ecological effect of removing urban trees, we provide evidence that urban trees provide critical habitat to urban wildlife and are perceived as an important aspect of the human experience of urban nature. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]