Masters of Science / African brown house snakes (Boaedon capensis) are widely thought to be dietary specialists that predominantly consume rodents. Given their ubiquitous distribution, these snakes potentially play an important role in controlling rodent populations throughout their range. However, the full extent of the diet of this species remains poorly quantified, and the proportional importance of mammals to their diet is speculative. Moreover, little is known regarding intraspecific dietary variation of these snakes. In recent years, a dearth of reports of B. capensis feeding, particularly from novel information-sharing streams available through social media, suggests that the diet of these snakes may be broader than previously thought. B. capensis are not rodent specialists as amphibians (3%), birds (12%), and reptiles (38%) collectively comprise a significant proportion of their diet. There was no evidence for sexual size dimorphism or dietary variation between adult males and females. However, significant differences in morphology and prey utilisation between adults and juveniles indicate an ontogenetic shift in diet from small lizards to larger, and more diverse prey. Variation in climate and time of year did not affect diet. Importantly, the diet of B. capensis as presented by museum data vastly differed compared to literature and community science reports. In particular, the proportional use of reptiles differed across sources (museum data = 6%; community science data = 27%). Overall, my findings reiterate the importance of examining multiple sources of information when characterizing the diet of species, as these multiple sources provide different result outcomes.

Harmful Algal Blooms (HABs) occur due to the excessive growth of algal in waterbodies such as lakes, rivers, and ponds. The cyanotoxins produced by HABs are harmful to wildlife, animals, and humans when ingested or exposed. Due to the toxic and rapid growth of HABs, it is essential to assess potential causes of HABs over broad geographical scales. This observational study aims to understand the spatiotemporal patterns and drivers of HABs across the State of Illinois using both regular environmental monitoring and citizen science datasets from the Illinois Environmental Protection Agency (IEPA). The Ambient Lake Monitoring Program and the Illinois Clean Lakes Program regularly conduct chlorophyll-a measurements, collectively referred to as the ALMP + ICLP dataset. Similarly, the Volunteer Lake Monitoring Program of the Illinois Environmental Protection Agency (IEPA) organizes volunteer citizens to collect Secchi-disk measurements, known as the VLMP dataset. Machine learning algorithms including Random Forest, Artificial Neural Network, and Support Vector Machine are used to evaluate HABs and trophic states of HABs based on nine meteorological variables, six lake morphological variables, and eight land use and land cover variables. The data characteristics found the Cook county area consisted of over half of the total VLMP observations. The meteorological variables were most important for accuracy and classification in the Random Forest modeling, and the VLMP dataset performed the best at trophic state classification, and the Random Forest model performed the best overall compared to the other machine learning models. This study concludes that the VLMP is a beneficial and comparable tool when coupled with the ALMP + ICLP data for HAB monitoring in Illinois.

Unpaved forest roads are adversely affecting coldwater streams through excessive erosion and the subsequent sedimentation of adjacent waterways. To help identify areas of concern, Trout Unlimited (TU) in the Southern Appalachian region developed a Community Science initiative to gather data on sediment sources and stream-road crossings. Volunteers were recruited and trained to monitor road and trail conditions and collect and submit data using a Survey123 application on their cell phones. Analysis of the contributed data reveals statistical connections between drainage type and both erosion level and stream sedimentation. The contributed data were also included as a calibration for the lite version of the Geomorphic Road Analysis and Inventory Package (GRAIP-Lite), a GIS-based road sediment contribution model. The analysis found statistically significant differences between Basic and Calibrated models at one of two sites, and substantial increases in sediment delivery from the Alternate model at both sites.

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.

Indiana University-Purdue University Indianapolis (IUPUI) / Rise in air pollution resulting in negative health externalities for humans has created an urgent need for cities and communities to monitor it regularly. At present we have insufficient ground passive and active monitoring networks in place which presents a huge challenge. Satellite imagery has been used extensively for such analysis, but its resolution and methodology present other challenges in estimating pollution burden. The objective of this study was to propose three low-cost methods to fill in the gaps that exist currently. First, EPA grade sensors were used in 11 cities across the U.S. to examine NO2. This is a simplistic way to assess the burden of air pollution in a region. However, this technique cannot be applied to fine scale analysis, which resulted in the next two components of this research study. Second, a citizen science network was established on the east side of Indianapolis, IN who hosted 32 Ogawa passive sensors to examine NO2 and O3 at a finer scale. These low-cost passive sensors, not requiring power, and very little maintenance, have historically tracked very closely with Federal Reference Monitors. Third, a low-cost PurpleAir PA-II-SD active sensors measuring PM2.5 were housed with the citizen scientists identified above. This data was uploaded via Wi-Fi and available via a crowd sourced site established by PurpleAir. These data sets were analyzed to examine the burden of air pollution. The second and third research studies enabled granular analyses utilizing citizen science, tree canopy data, and traffic data, thus accommodating some of the present limitations. Advancement in low-cost sensor technology, along with ease of use and maintenance, presents an opportunity for not just communities, but cities to take charge of some of these analyses to help them examine health equity impacts on their citizens because of air pollution.

In this paper I examine the possibilities for researchers in the field of climate change in the arctic ecosystems to collaborate with people of the common public for monitoring or to answer scientific questions, sometimes called Citizen Science. I have done this by focusing on the research community of Abisko, northern Sweden. I present a number of examples of possible projects that I have collected through literature, interviews and field work with researchers. Furthermore, I argue that Citizen Science projects might be a tool for nature guides in reaching adventurous visitors searching for more understanding of climate change, ecosystems, and who also want to contribute to real science. However, Citizen Science can also be a part of the democratization of a community when local people get engaged. Thus different aspects of Citizen Science are suitable for different target groups.