community science
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2022 ◽  
Vol 12 ◽  
Rachel A. Reeb ◽  
Naeem Aziz ◽  
Samuel M. Lapp ◽  
Justin Kitzes ◽  
J. Mason Heberling ◽  

Community science image libraries offer a massive, but largely untapped, source of observational data for phenological research. The iNaturalist platform offers a particularly rich archive, containing more than 49 million verifiable, georeferenced, open access images, encompassing seven continents and over 278,000 species. A critical limitation preventing scientists from taking full advantage of this rich data source is labor. Each image must be manually inspected and categorized by phenophase, which is both time-intensive and costly. Consequently, researchers may only be able to use a subset of the total number of images available in the database. While iNaturalist has the potential to yield enough data for high-resolution and spatially extensive studies, it requires more efficient tools for phenological data extraction. A promising solution is automation of the image annotation process using deep learning. Recent innovations in deep learning have made these open-source tools accessible to a general research audience. However, it is unknown whether deep learning tools can accurately and efficiently annotate phenophases in community science images. Here, we train a convolutional neural network (CNN) to annotate images of Alliaria petiolata into distinct phenophases from iNaturalist and compare the performance of the model with non-expert human annotators. We demonstrate that researchers can successfully employ deep learning techniques to extract phenological information from community science images. A CNN classified two-stage phenology (flowering and non-flowering) with 95.9% accuracy and classified four-stage phenology (vegetative, budding, flowering, and fruiting) with 86.4% accuracy. The overall accuracy of the CNN did not differ from humans (p = 0.383), although performance varied across phenophases. We found that a primary challenge of using deep learning for image annotation was not related to the model itself, but instead in the quality of the community science images. Up to 4% of A. petiolata images in iNaturalist were taken from an improper distance, were physically manipulated, or were digitally altered, which limited both human and machine annotators in accurately classifying phenology. Thus, we provide a list of photography guidelines that could be included in community science platforms to inform community scientists in the best practices for creating images that facilitate phenological analysis.

Land ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 137
Aireona B. Raschke ◽  
Jeny Davis ◽  
Annia Quiroz

Land managers are currently faced with a nexus of challenges, both ecological and social, when trying to govern natural open spaces. While social media has led to many challenges for effective land management and governance, the technology has the potential to support key activities related to habitat restoration, awareness-raising for policy changes, and increased community resilience as the impacts of increased use and climate change become more apparent. Through the use of a case study examining the work of the Central Arizona Conservation Alliance’s social media ambassadorship and its app-supported community science projects, we examine the potential and realized positive impact that technology such as social media and smartphone apps can create for land managers and surrounding communities.

2022 ◽  
Paige E. Howell ◽  
Patrick K. Devers ◽  
Orin J. Robinson ◽  
J. Andrew Royle

Hydrology ◽  
2022 ◽  
Vol 9 (1) ◽  
pp. 11
Ingrid Luffman ◽  
Daniel Connors

Volunteered Geographic Information, data contributed by community scientists, is an increasingly popular tool to collect scientific data, involve the community in scientific research, and provide information and education about a prominent issue. Johnson City, Tennnessee, USA has a long history of downtown flooding, and recent redevelopment of two land parcels has created new city parks that mitigate flooding through floodwater storage, additional channel capacity, and reduced impervious surfaces. At Founders Park, a project to collect stage data using text messages from community scientists has collected 1479 stage measurements from 597 participants from May 2017 through July 2021. Text messages were parsed to extract the stage and merged with local precipitation data to assess the stream’s response to precipitation. Of 1479 observations, 96.7% were correctly parsed. Only 3% of observations were false positives (parser extracted incorrect stage value) or false negatives (parser unable to extract correct value but usable data were reported). Less than 2% of observations were received between 11 p.m. and 7 a.m., creating an overnight data gap, and fewer than 7% of observations were made during or immediately following precipitation. Regression models for stage using antecedent precipitation explained 21.6% of the variability in stream stage. Increased participation and development of an automated system to record stage data at regular intervals will provide data to validate community observations and develop more robust rainfall–runoff models.

2022 ◽  
pp. 440-448
Dumisani Chirambo

Climate change is likely to exacerbate inequality and poverty in Global South cities despite the presence of international agreements and conventions to enhance sustainable development such as the Paris Agreement and the Sustainable Development Goals (SDGs). Moreover, replicating Global North development models in the Global South might not be sufficient to address the climate change and development aspirations in the context of Asia; hence, Global North innovation capabilities might not be sufficient to address Global South climate change challenges. This paper provides an inductive analysis of the innovations and policies that could facilitate improved climate change mitigation and adaptation in the context of developing Asian cities. The paper concludes that innovative climate change policies should utilise emerging climate finance mechanisms such as South-South climate finance modalities to promote community science/citizen science and social innovation rather than building hard infrastructure as this could improve the governance and distribution of resources in cities.

2022 ◽  
Vol 48 (1) ◽  
pp. 27-43
Ryan Schmidt ◽  
Brianna Casario ◽  
Pamela Zipse ◽  
Jason Grabosky

Background: With the creation of photo-based plant identification applications (apps), the ability to attain basic identifications of plants in the field is seemingly available to anyone who has access to a smartphone. The use of such apps as an educational tool for students and as a major identification resource for some community science projects calls into question the accuracy of the identifications they provide. We created a study based on the context of local tree species in order to offer an informed response to students asking for guidance when choosing a tool for their support in classes. Methods: This study tested 6 mobile plant identification apps on a set of 440 photographs representing the leaves and bark of 55 tree species common to the state of New Jersey (USA). Results: Of the 6 apps tested, PictureThis was the most accurate, followed by iNaturalist, with PlantSnap failing to offer consistently accurate identifications. Overall, these apps are much more accurate in identifying leaf photos as compared to bark photos, and while these apps offer accurate identifications to the genus-level, there seems to be little accuracy in successfully identifying photos to the species-level. Conclusions: While these apps cannot replace traditional field identification, they can be used with high confidence as a tool to assist inexperienced or unsure arborists, foresters, or ecologists by helping to refine the pool of possible species for further identification.

2022 ◽  
Vol 134 ◽  
pp. 108451
Wendy Estes-Zumpf ◽  
Brett Addis ◽  
Brenna Marsicek ◽  
Mason Lee ◽  
Zoe Nelson ◽  

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