Changes in phenology are often cited as a key biotic impact of climate change. Consequently, understanding the major environmental cues and responses to those cues in different species is important for making predictions about the future impacts and ecological implications of changing phenology. In this thesis, I set out to explore the phenological cues, mechanisms of response to temperature and the potential for interacting species to experience phenological mismatch in a range of UK plants. To do this, I utilised phenological records from two citizen science schemes; the well-established Nature’s Calendar, which collects observations for the UK Phenology Network (UKPN), and Track a Tree, a novel project I set up specifically to examine the phenology of interacting plant species in UK woodlands. I first assessed the ability of plasticity to track shifts in the optimum phenology for 22 plant species. I employed a statistical approach to estimate the plasticity and temperature sensitivity of the phenological optimum for leafing and flowering dates obtained from the UKPN. In identifying the most important cues I found that all species are sensitive to spring forcing temperatures, with plastic responses ranging from -3 to -8 days °C-1. Chilling temperatures in autumn/winter and photoperiod were important in species with early and late phenology, respectively. In seven species, plasticity was sufficient to track geographic variation in the optimum phenology. In four species, plasticity did not track the optimum, which is consistent with clinal local adaptation to temperature, and which could place phenology under directional selection in a changing climate. I then performed a phylogenetic comparative analysis on the median phenology and estimates of plasticity and local adaptation for the 22 species analysed previously. I found that phenological event (leafing or flowering) and growth form (woody or herbaceous perennial) predicted plasticity in phenological response. These traits may help inform future predictions of phenological responses to temperature. In contrast, the median date of phenology and clinal local adaptation over latitude were not predicted by any of the ecological traits considered. I next used records from the Track a Tree project to examine the relative phenology of canopy tree and understorey flowering species across UK woodlands. I found that first leafing and peak flowering of focal species pairs were correlated over space, and that the time between canopy leafing and the ground flora flowering (relative phenology) was spatially consistent. Relative phenology of two canopy tree species pairs was spatially consistent, but for a native versus non-native tree species pair the relationship varied over space (with a slope close to 0). If temperature-mediated plasticity determines these species’ phenology, my results suggest understorey flowering may be able to track canopy leafing in future, maintaining shading interactions. Finally, I used the Track a Tree data to partition the variance in phenology for seven tree species, and test what predicts variation in oak and birch. I found that the contributors to variance differ among tree species, with spatial variables important, and within site variance low, for all species except sycamore. The low intraspecific within-site variance suggests that some species may have a limited capacity for phenological buffering. These findings contribute to understanding what impacts on the phenological distribution of different species, an important requirement for assessing the phenological buffering of mismatch. In this thesis, I broadened the range of approaches that can be used to understand plant phenology in a changing climate. I demonstrated the value of employing novel statistical methods to analyse existing phenology data and the utility of hypothesis driven citizen science for predicting phenological shifts and the subsequent ecological implications for interacting species.

Environmental research is increasingly using citizen scientists in many aspects of projects, such as data collection and question design. To date, only a limited number of co-created citizen science projects where community members are involved in most or all steps of the scientific process have been completed, and few comparing community engagement methods and efficacy and learning outcomes across demo- and geographic data. This study compares two citizen science programs, evaluating what motivates citizen scientists to participate in environmental health research and whether participation affects scientific knowledge and environmental behavior and efficacy. Participants in the Gardenroots: A Citizen Science Garden Project completed sample collection training and submitted soil, water, vegetable, and dust samples for analysis and received their environmental monitoring results. In the Facilitating Community Action to Address Climate Change and Build Resiliency in Southern Metropolitan Tucson project, Spanish speaking community members of South Tucson underwent training in climate change and environmental quality and sample collection, and worked with families in the South Tucson community, collecting soil and water samples and providing environmental health education. For both projects, participants completed a pre- and post-survey with a variety of qualitative and quantitative questions. These survey instruments were used to evaluate differences in environmental self-efficacy and motivations. In addition, select Gardenroots participants were involved in focus groups and semi-structured interviews to understand and gauge changes in knowledge and to further explore changes in motivation and self-efficacy. The participants were primarily internally motivated and saw increases in both efficacy and knowledge as a result of participation in the program. This information is critical to moving citizen science efforts forward and determining whether such projects: 1) co-produce environmental monitoring, exposure assessment, and risk data in a form that will be directly relevant to the participant's lives, 2) increase the community’s involvement in environmental decision-making, and 3) improve environmental health education and literacy in underserved communities.

eBird, a citizen-science program developed by the Cornell Lab of Ornithology and National Audubon, allows users to enter bird sightings from around the world in order to develop a large scale data set for research. This study seeks to analyze eBird data and methods in order to determine if the data collected is robust enough to be usable as a basis for habitat management and, if so, to what extent. This is accomplished through a comparison of Piping Plover (Charadrius melodus) (a threatened shorebird of management concern) counts, trends, and methodologies made through a survey following a strict protocol versus data collected by eBird in three different areas (Bolivar Flats, Apfel Park, and San Luis Pass). Using descriptive statistics such as mean counts, counts adjusted for effort, and frequency, and confirming with Kruskal-Wallis tests, variation was found between eBird and survey data. eBird contained lower counts of Piping Plovers and a lower sighting frequency than survey data. When adjusting counts as a function of effort, similar results were found. Piping Plovers were found not to occur frequently at Bolivar Flats (9 birds over 2 surveys), while Apfel Park and San Luis Pass showed similar but inconclusive results. This study ultimately determined that, while of great use on large scales, use of eBird data on the local level, should be used with caution. Further study should be done to investigate sources of variation and methods to increase the effectiveness of eBird on small scales.

This research looks at some of the different methods of validation used in the growing phenomenon citizen science. Citizen science is discussed and a small range of different typologies is used to define it. To find some of the common themes of validation six case studies are performed. The case studies examine the following six citizen science projects: GLOBE at Night, eBird, Citclops, Foldit, Galaxy Zoo and EyeWire. These projects a divided equally in to two types based on previous typological research: data collection projects and analysis projects. All projects are international in scope but differ greatly in actions and so in what type of validation they use. It is showed that some validation is made in comparison to data made by professionals or machine data or some other external source. In two cases the results of the project are self-validating and one case validation is made by experts on data that seems out of the ordinary. A few projects use consensus data i.e. the average of observations or analyses made by the citizen scientists either for validation or as a measure of probable correctness. A short discussion of the results and some suggestions of future research finishes of this research.

Natural waterways form an integral part of the urban and rural environment. In New Zealand, their uses are generally related to agriculture, drainage, power generation and recreation, but their value also extends to providing ecological services that are vital to the maintenance of a fully functioning environment. In the areas of the world that are considered to be developed, several functions and services that waterway systems initially would have provided, have been degraded, or lost completely, due to water abstractions, altered flow regimes and input of pollutants. In New Zealand, and around the world, groups of volunteers give up their time in order to help monitor the quality and state of waterways. However, there remains a distrust of data generated by such groups throughout the scientific community. This concern is also voiced by members of these groups, querying what the point of their monitoring is, if the data has no real use. As a result of this uncertainty about the data quality and its subsequent uses, data is often just entered onto a database with little, or no, analysis conducted. The purpose of this research was to ascertain the quality of the data generated by volunteers groups in New Zealand by comparing it with data collected by professionals from city and regional councils. Volunteer monitoring methods and tools were also compared with those available to professionals in order to determine if any differences observed were a product of equipment, or other factors. However, data generation is not the only purpose of these volunteer groups. By being involved, volunteers are gaining education, practical skills and knowledge they may not have access to otherwise, and they are meeting people and strengthening community ties. Volunteers from each group therefore also completed a survey to determine their knowledge of the programme they participate in, of the environment and freshwater, and to collect some basic background information. The Styx Living Laboratory Trust (SLLT) in Christchurch, the Wakapuaka Rivercare Group in Nelson and Wai Care in Auckland were the three New Zealand community water monitoring groups chosen to be the subject of this study. Generally, the volunteer conductivity and pH data was significantly different from that of their professional counterparts, with large differences obvious in the data sets from all three groups. Water temperature was the only variable that was consistently similar for volunteer and professional data. Comparison of the SLLT’s methods with professional-level methods, however, revealed that differences in the data sets may be due to a combination of factors including equipment (e.g., use of pH colour strips instead of meters), and variation in the monitoring protocols, rather than a lack of quality in the volunteer data. However, new dissolved oxygen and nitrogen monitoring methods utilised by Wai Care did produce some promising results, with some of the comparisons unable to be statistically differentiated from the professional data set. Visual assessment of the SLLT data over time suggests seasonal patterns in pH and conductivity, and possible increases in water clarity over time. Statistical analysis of the individual variables of pH, water temperature, clarity and conductivity, in the SLLT data revealed several significant predictors and interactions, including time, date and pH among other things. However, the very small effect size and the large data set suggest this may just be a product of the large data set with very few of these variable interactions having any real meaning with regards to management. Volunteers were predominantly over the age of 40, and were generally either very new recruits to their monitoring programmes (<6 months) or had been involved for a reasonably long time (>5 years). There were differing patterns of involvement between the groups with the WRG having volunteers mainly involved for >10 years while the SLLT had a large number of new recruits. There were also varying reasons volunteers chose to become involved however, the predominant reason was concern for the environment. Approximately half of the volunteers surveyed proved to be very knowledgeable about their programme and understood the purposes of the monitoring programme, although most were associated with a science-related industry and therefore likely already had this knowledge. More education and training would be needed to bring all of the other volunteers up to this level. All volunteers had good knowledge of issues in New Zealand’s environment and freshwater currently face, with public apathy considered the most pressing issue. In summary, despite the lack of clear statistical similarities between volunteer and professional data sets for some variables, the data do not appear to be randomly inaccurate and could be corrected to be combined with professional data. The benefits the volunteers gain appear to outweigh any issues that may be present in the data, as long as the volunteers perceive the data to be ultimately useful. Volunteer-based water quality monitoring has proved to be a valuable way to gather environmental data, educate the community and improve their commitment to local waterways.

An increasingly popular method of collecting scientific data is to use citizen scientists in community-based monitoring programs. Numerous formal and community-based monitoring programs use butterflies as indicator species to detect and understand changes in ecosystems. A butterfly monitoring program was established with the City of Kitchener in 2012, in order to measure the effectiveness of citizen science observations in identifying butterfly assemblages. Two monitoring sites were used: Lakeside Park contained relatively simple butterfly habitats, and Huron Natural Area which included complex butterfly habitats. The program consisted of training volunteers to collect data on the butterfly assemblages. Volunteers were given the choice to monitor an established trail at either natural area once every two weeks from the beginning of May to the end of August using the modified Pollard method. It was important to train volunteers how to monitor butterflies, so they were required to attend an instruction workshop to learn the methods to be employed, identification of butterflies, use of the recording form, and proper net and butterfly handling techniques. Quality control measures were another important component of the program, and included comparing volunteer observations at each natural area to those collected by an expert. Each species observation was reviewed to determine the likelihood of the species observation. The species and its habitat were compared to the information and status recorded in The Butterflies of Waterloo Region. The data collected by the participants was analyzed separately by study site and included the calculation of species richness, Shannon-Wiener Diversity Index (H), evenness value, Simpson Index (D), and Simpson Reciprocal (1/D) values. A two-tailed t-test was conducted to compare the data (as represented by Shannon indexes) collected by volunteers and the expert. The species richness for Lakeside Park was 29, which was slightly higher than the 26 species identified at Huron Natural Area. However, the Shannon-Wiener Diversity Index (H), evenness value (E), Simpson Diversity Index (D), and Simpson Reciprocal Index (1/D) all demonstrated that there was a more even and diverse butterfly population at Huron Natural Area than at Lakeside Park. A high level of validity of volunteer observations was found during this study, as 93% of submitted observations at Lakeside Park and 94% of submitted observations at Huron Natural Area passed the review process. Based on the high level of validity of observations it was determined that the volunteers were successful in characterizing butterfly assemblages, and establishing baseline conditions at each site. The City of Kitchener will be collecting long-term butterfly data, which they can compare over time to provide insight into the diversity at these natural areas. This research program has contributed to the field of science and to the literature by establishing a review process for citizen science, particularly for butterfly programs. It has also provided further validation of citizen science data.

The purpose of this research was to develop, implement, and evaluate a citizen science program to survey and monitor for jaguars (Panthera onca) and ocelots (Leopardus pardalis) within key mountain ranges in southwest Arizona. I had three main objectives: 1.) develop and implement a citizen science program to train participants with little or no knowledge of wildlife conservation, 2.) identify program effectiveness in comparison with experts in terms of the quality of data gathered and the financial and administrative costs/benefits, 3.) understand what creates an effective citizen science program and how it can be replicated in the future. This research examined the efficacy of using citizen science as a tool to connect the public and scientific community through education and a research-based project. A network of citizen scientists were provided extensive education and training by field experts to participate in jaguar and ocelot monitoring in southern Arizona. The scale and scope of this project is unprecedented in the citizen science literature; participants drove and hiked off-trail monitoring wildlife cameras monthly for a minimum of one year in extremely rugged backcountry conditions in the mountains near the U.S./Mexico border. Citizen science participants were challenged by terrain, weather, and border issues during their time on this project. Despite these challenges, our group of nine citizen scientists successfully downloaded and sorted 28,637 photos from 22 cameras at 14 monitoring sites over 12 months. They logged a total of 327 hours of fieldwork including driving, hiking, and performing camera maintenance. After finishing a site visit, citizen scientists also sorted photos identifying wildlife, logging approximately 109 hours of data organization. In addition, 100% of our citizen scientists adhered to our protocols that we tested and implemented in the field over a one-year period. Citizen scientists continue to monitor remote wildlife cameras in the rugged mountainous regions of this southern Arizona study area. Citizen science is often criticized because of skepticism from the scientific community regarding data integrity, quality control, and potentially biased data. This research aimed to exam the data integrity of citizen scientists by comparing it to the data analyzed by experts. When comparing the ability of citizen scientists and field team experts in sorting and correctly identifying wildlife, I found strong positive correlations between the levels of data quality. These high positive correlations indicate that, with training, citizen scientists are capable of accurately identifying wildlife from camera data nearly as well as the field team experts, and can be an excellent surrogate for experts. Connecting volunteers to the natural landscape through hands-on science research has the potential to create many positive experiences. Citizen science can increase participant’s knowledge of science, build trust and foster understanding, and can create a more informed public. Despite these benefits, data integrity is the most important aspect of research and data collected by non-scientists remains heavily scrutinized. Collaboration amongst professionals, educators, program designers, and data managers is necessary to ensure that project goals are achieved while maintaining scientific integrity. Continuing to examine citizen science programs is important to advance the field of citizen science and foster meaningful relationships between the public and scientists. This study provided a unique opportunity to use non-scientists to augment data collection and assessment in the scientific workplace to advance jaguar and ocelot conservation. We strongly believe that citizen scientists remain an underutilized resource for helping scientists collect and analyze data in a climate of reduced funding and increasing need for long-term monitoring.

Master of Science / Department of Horticulture, Forestry, and Recreation Resources / Jeffrey Skibins / Citizen science is a method of carrying out scientific research with the help of untrained citizens. Citizen science carries multiple potential benefits for scientific inquiry, but in order to be effective must facilitate mutually beneficial settings. The most prevalent use of citizen science has been in ornithology. Bird based citizen science projects have been highly successful and have facilitated mutually beneficial projects. The field of citizen science is changing with the onset of new technologies. These technologies may expand the opportunities of citizen science, but it is important that a mutual benefit is maintained. This study uses self-determination theory, a theory of human psychological needs and motivations, to address motivational factors of bird based citizen science participation to provide a framework by which to maintain the necessary mutual benefit. This study consists of responses from an online survey administered to subscribers of birding listservs across the country. Analysis of the responses found that the need for relatedness is most consistently related to participants’ motivations. The suggestion is made that future citizen science efforts focus on the community building aspects of participation. Other nuances of the data and ideas for further research are discussed.

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.

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