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Abstract Contributory science—including citizen and community science—allows scientists to leverage participant‐generated data while providing an opportunity for engaging with local community members. Data yielded by participant‐generated biodiversity platforms allow professional scientists to answer ecological and evolutionary questions across both geographic and temporal scales, which is incredibly valuable for conservation efforts.The data reported to contributory biodiversity platforms, such as eBird and iNaturalist, can be driven by social and ecological variables, leading to biased data. Though empirical work has highlighted the biases in contributory data, little work has articulated how biases arise in contributory data and the societal consequences of these biases.We present a conceptual framework illustrating how social and ecological variables create bias in contributory science data. In this framework, we present four filters—participation,detectability,samplingandpreference—that ultimately shape the type and location of contributory biodiversity data. We leverage this framework to examine data from the largest contributory science platforms—eBird and iNaturalist—in St. Louis, Missouri, the United States, and discuss the potential consequences of biased data.Lastly, we conclude by providing several recommendations for researchers and institutions to move towards a more inclusive field. With these recommendations, we provide opportunities to ameliorate biases in contributory data and an opportunity to practice equitable biodiversity conservation. Read the freePlain Language Summaryfor this article on the Journal blog. 

Data that influence policy and major investment decisions risk entrenching social and political inequities 

Abstract There is a lack of data on resources used and food produced at urban farms. This hampers attempts to quantify the environmental impacts of urban agriculture or craft policies for sustainable food production in cities. To address this gap, we used a citizen science approach to collect data from 72 urban agriculture sites, representing three types of spaces (urban farms, collective gardens, individual gardens), in five countries (France, Germany, Poland, United Kingdom, and United States). We answered three key questions about urban agriculture with this unprecedented dataset: (1) What are its land, water, nutrient, and energy demands? (2) How productive is it relative to conventional agriculture and across types of farms? and (3) What are its contributions to local biodiversity? We found that participant farms used dozens of inputs, most of which were organic (e.g., manure for fertilizers). Farms required on average 71.6 L of irrigation water, 5.5 L of compost, and 0.53 m² of land per kilogram of harvested food. Irrigation was lower in individual gardens and higher in sites using drip irrigation. While extremely variable, yields at well-managed urban farms can exceed those of conventional counterparts. Although farm type did not predict yield, our cluster analysis demonstrated that individually managed leisure gardens had lower yields than other farms and gardens. Farms in our sample contributed significantly to local biodiversity, with an average of 20 different crops per farm not including ornamental plants. Aside from clarifying important trends in resource use at urban farms using a robust and open dataset, this study also raises numerous questions about how crop selection and growing practices influence the environmental impacts of growing food in cities. We conclude with a research agenda to tackle these and other pressing questions on resource use at urban farms. 

Abstract Environmental merits are a common motivation for many urban agriculture (UA) projects. One powerful way of quantifying environmental impacts is with life cycle assessment (LCA): a method that estimates the environmental impacts of producing, using, and disposing of a good. LCAs of UA have proliferated in recent years, evaluating a diverse range of UA systems and generating mixed conclusions about their environmental performance. To clarify the varied literature, we performed a systematic review of LCAs of UA to answer the following questions: What is the scope of available LCAs of UA (geographic, crop choice, system type)? What is the environmental performance and resource intensity of diverse forms of UA? How have these LCAs been done, and does the quality and consistency allow the evidence to support decision making? We searched for original, peer-reviewed LCAs of agricultural production at UA systems, and selected and evaluated 47 papers fitting our analysis criteria, covering 88 different farms and 259 production systems. Focusing on yield, water consumption, greenhouse gas emissions, and cumulative energy demand, using functional units based on mass of crops grown and land occupied, we found a wide range of results. We summarized baseline ranges, identified trends across UA profiles, and highlighted the most impactful parts of different systems. There were examples of all types of systems—across physical set up, crop type, and socio-economic orientation—achieving low and high impacts and yields, and performing better or worse than conventional agriculture. However, issues with the quality and consistency of the LCAs, the use of conventional agriculture data in UA settings, and the high variability in their results prevented us from drawing definitive conclusions about the environmental impacts and resource use of UA. We provided guidelines for improving LCAs of UA, and make a strong case that more research on this topic is necessary to improve our understanding of the environmental impacts and benefits of UA. 

Despite extensive literature on the socio-cultural services of urban open spaces, the role of food-producing spaces has not received sufficient attention. This hampers advocacy for preserving and growing urban agricultural activities, often dismissed on justifications that their contributions to overall food supply are negligible. To understand how the social benefits of urban agriculture have been measured, we conducted a systematic review of 272 peer-reviewed publications, which drew on insights from urban agriculture sites in 57 different countries. Through content analysis, we investigated socio-cultural benefits in four spheres: engaged and cohesive communities, health and well-being, economic opportunities, and education. The analysis revealed growth in research on the social impacts of gardens and farms, with most studies measuring the effects on community cohesion and engagement, followed by increased availability and consumption of fruits and vegetables associated with reduced food insecurity and better health. Fewer studies assessed the impact of urban farming on educational and economic outcomes. Quantifying the multiple ways in which urban agriculture provides benefits to people will empower planners and the private sector to justify future investments. These findings are also informative for research theorizing cities as socio-ecological systems and broader efforts to measure the benefits of urban agriculture, in its many forms. 

ABSTRACT MotivationSNAPSHOT USA is an annual, multicontributor camera trap survey of mammals across the United States. The growing SNAPSHOT USA dataset is intended for tracking the spatial and temporal responses of mammal populations to changes in land use, land cover and climate. These data will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, as well as the impacts of species interactions on daily activity patterns. Main Types of Variables ContainedSNAPSHOT USA 2019–2023 contains 987,979 records of camera trap image sequence data and 9694 records of camera trap deployment metadata. Spatial Location and GrainData were collected across the United States of America in all 50 states, 12 ecoregions and many ecosystems. Time Period and GrainData were collected between 1st August and 29th December each year from 2019 to 2023. Major Taxa and Level of MeasurementThe dataset includes a wide range of taxa but is primarily focused on medium to large mammals. Software FormatSNAPSHOT USA 2019–2023 comprises two .csv files. The original data can be found within the SNAPSHOT USA Initiative in the Wildlife Insights platform.