A new approach to generating research-quality data through citizen science: The USA National Phenology Monitoring System

Phenology is one of the most sensitive biological responses to climate change, and recent changes in phenology have the potential to shake up ecosystems. In some cases, it appears they already are. Thus, for ecological reasons it is critical that we improve our understanding of species’ phenologies and how these phenologies are responding to recent, rapid climate change. Phenological events like flowering and bird migrations are easy to observe, culturally important, and, at a fundamental level, naturally inspire human curiosity— thus providing an excellent opportunity to engage citizen scientists. The USA National Phenology Network has recently initiated a national effort to encourage people at different levels of expertise—from backyard naturalists to professional scientists—to observe phenological events and contribute to a national database that will be used to greatly improve our understanding of spatio-temporal variation in phenology and associated phenological responses to climate change.

Traditional phenological observation protocols identify specific dates at which individual phenological events are observed. The scientific usefulness of long-term phenological observations could be improved with a more carefully structured protocol. At the USA-NPN we have developed a new approach that directs observers to record each day that they observe an individual plant, and to assess and report the state of specific life stages (or phenophases) as occurring or not occurring on that plant for each observation date. Evaluation is phrased in terms of simple, easy-to-understand, questions (e.g. “Do you see open flowers?”), which makes it very appropriate for a citizen science audience. From this method, a rich dataset of phenological metrics can be extracted, including the duration of a phenophase (e.g. open flowers), the beginning and end points of a phenophase (e.g. traditional phenological events such as first flower and last flower), multiple distinct occurrences of phenophases within a single growing season (e.g multiple flowering events, common in drought-prone regions), as well as quantification of sampling frequency and observational uncertainties. These features greatly enhance the utility of the resulting data for statistical analyses addressing questions such as how phenological events vary in time and space, and in response to global change. This new protocol is an important step forward, and its widespread adoption will increase the scientific value of data collected by citizen scientists.
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A Bird’s- Eye View of the USA National Phenology Network: an off-the-shelf monitoring program

Phenology is central to the biology and ecology of organisms and highly sensitive to climate. Differential responses to climate change are impacting phenological synchrony of inter- acting species, which has been implicated in the decline of migratory birds that rely on seasonal resources. However, few studies explicitly measure phenology of seasonal habitat resources on the breeding and wintering grounds and at stopover sites. While avian monitoring methods are widely standardized, methods of monitoring resource phenology can be highly variable and difficult to integrate. The USA National Phenology Network (USA- NPN) has developed standardized plant and animal phenology protocols and a robust information management system to support a range of stakeholders in collecting, storing, and sharing phenology data, at the appropriate scale, to shed light on phenological synchrony. The USA-NPN’s Nature’s Notebook can be integrated into established research programs, ensuring that data will be comparable over time and across projects, taxa, regions, and research objectives. We use two case studies to illustrate the application of USA-NPN methods and protocols to established long- term landbird research programs. By integrating phenology into these programs, avian ecologists are increasing their ability to understand the magnitude and consequences of phenological responses to climate change

Contribution of citizen science towards international biodiversity monitoring

To meet collective obligations towards biodiversity conservation and monitoring, it is essential that the world's governments and non-governmental organisations as well as the research community tap all possible sources of data and information, including new, fast-growing sources such as citizen science (CS), in which volunteers participate in some or all aspects of environmental assessments. Through compilation of a database on CS and community-based monitoring (CBM, a subset of CS) programs, we assess where contributions from CS and CBM are significant and where opportunities for growth exist. We use the Essential Biodiversity Variable framework to describe the range of biodiversity data needed to track progress towards global biodiversity targets, and we assess strengths and gaps in geographical and taxonomic coverage. Our results show that existing CS and CBM data particularly provide large-scale data on species distribution and population abundance, species traits such as phenology, and ecosystem function variables such as primary and secondary productivity. Only birds, Lepidoptera and plants are monitored at scale. Most CS schemes are found in Europe, North America, South Africa, India, and Australia. We then explore what can be learned from successful CS/CBM programs that would facilitate the scaling up of current efforts, how existing strengths in data coverage can be better exploited, and the strategies that could maximise the synergies between CS/CBM and other approaches for monitoring biodiversity, in particular from remote sensing. More and better targeted funding will be needed, if CS/CBM programs are to contribute further to international biodiversity monitoring

Citizen science: a new approach to advance ecology, education, and conservation

Citizen science has a long history in the ecological sciences and has made substantial contributions to science, education, and society. Developments in information technology during the last few decades have created new opportunities for citizen science to engage ever larger audiences of volunteers to help address some of ecology’s most pressing issues, such as global environmental change. Using online tools, volunteers can find projects that match their interests and learn the skills and protocols required to develop questions, collect data, submit data, and help process and analyze data online. Citizen science has become increasingly important for its ability to engage large numbers of volunteers to generate observations at scales or resolutions unattainable by individual researchers. As a coupled natural and human approach, citizen science can also help researchers access local knowledge and implement conservation projects that might be impossible otherwise. In Japan, however, the value of citizen science to science and society is still underappreciated. Here we present case studies of citizen science in Japan, the United States, and the United Kingdom, and describe how citizen science is used to tackle key questions in ecology and conservation, including spatial and macro-ecology, management of threatened and invasive species, and monitoring of biodiversity. We also discuss the importance of data quality, volunteer recruitment, program evaluation, and the integration of science and human systems in citizen science projects. Finally, we outline some of the primary challenges facing citizen science and its future.Dr. Janis L. Dickinson was the keynote speaker at the international symposium at the 61th annual meeting of the Ecological Society of Japan. We appreciate the Ministry of Education, Culture, Sports, Science and Technology in Japan for providing grant to Hiromi Kobori (25282044). Tatsuya Amano is financially supported by the European Commission’s Marie Curie International Incoming Fellowship Programme (PIIF-GA-2011- 303221). The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the funding agencies or the Department of the Interior or the US Government.This is the final version of the article. It was first available from Springer via http://dx.doi.org/10.1007/s11284-015-1314-

USA National Phenology Network’s volunteer-contributed observations yield predictive models of phenological transitions

<div><p>Purpose</p><p>In support of science and society, the USA National Phenology Network (USA-NPN) maintains a rapidly growing, continental-scale, species-rich dataset of plant and animal phenology observations that with over 10 million records is the largest such database in the United States. The aim of this study was to explore the potential that exists in the broad and rich volunteer-collected dataset maintained by the USA-NPN for constructing models predicting the timing of phenological transition across species’ ranges within the continental United States. Contributed voluntarily by professional and citizen scientists, these opportunistically collected observations are characterized by spatial clustering, inconsistent spatial and temporal sampling, and short temporal depth (2009-present). Whether data exhibiting such limitations can be used to develop predictive models appropriate for use across large geographic regions has not yet been explored.</p><p>Methods</p><p>We constructed predictive models for phenophases that are the most abundant in the database and also relevant to management applications for all species with available data, regardless of plant growth habit, location, geographic extent, or temporal depth of the observations. We implemented a very basic model formulation—thermal time models with a fixed start date.</p><p>Results</p><p>Sufficient data were available to construct 107 individual species × phenophase models. Remarkably, given the limited temporal depth of this dataset and the simple modeling approach used, fifteen of these models (14%) met our criteria for model fit and error. The majority of these models represented the “breaking leaf buds” and “leaves” phenophases and represented shrub or tree growth forms. Accumulated growing degree day (GDD) thresholds that emerged ranged from 454 GDDs (<i>Amelanchier canadensis</i>-breaking leaf buds) to 1,300 GDDs (<i>Prunus serotina</i>-open flowers). Such candidate thermal time thresholds can be used to produce real-time and short-term forecast maps of the timing of these phenophase transition. In addition, many of the candidate models that emerged were suitable for use across the majority of the species’ geographic ranges. Real-time and forecast maps of phenophase transitions could support a wide range of natural resource management applications, including invasive plant management, issuing asthma and allergy alerts, and anticipating frost damage for crops in vulnerable states.</p><p>Implications</p><p>Our finding that several viable thermal time threshold models that work across the majority of the species ranges could be constructed from the USA-NPN database provides clear evidence that great potential exists this dataset to develop more enhanced predictive models for additional species and phenophases. Further, the candidate models that emerged have immediate utility for supporting a wide range of management applications.</p></div

Geographic distribution of species <i>×</i> year data points used in constructing 107 unique species <i>×</i> phenophase models.

<p>Black dots represent data points for all models (n = 10,604); green dots represent points used in constructing the 15 candidate models (n = 1,893).</p

Observation locations used to a) construct and b) test the thermal time model for <i>Cercis canadensis</i>-leaves.

<p>Point size and color represent the difference, in days, between the predicted and the observed day of year for leaf-out. Locations where the model predicted leaves earlier than observer reports are shown in orange; locations where the model predicted leaves later than observer reports are shown in blue.</p

Climate change is advancing spring onset across the U.S. national park system

Many U.S. national parks are already at the extreme warm end of their historical temperature distributions. With rapidly warming conditions, park resource management will be enhanced by information on seasonality of climate that supports adjustments in the timing of activities such as treating invasive species, operating visitor facilities, and scheduling climate-related events (e.g., flower festivals and fall leaf-viewing). Seasonal changes in vegetation, such as pollen, seed, and fruit production, are important drivers of ecological processes in parks, and phenology has thus been identified as a key indicator for park monitoring. Phenology is also one of the most proximate biological responses to climate change. Here, we use estimates of start of spring based on climatically modeled dates of first leaf and first bloom derived from indicator plant species to evaluate the recent timing of spring onset (past 10-30 yr) in each U.S. natural resource park relative to its historical range of variability across the past 112 yr (1901-2012). Of the 276 high latitude to subtropical parks examined, spring is advancing in approximately three-quarters of parks (76%), and 53% of parks are experiencing "extreme" early springs that exceed 95% of historical conditions. Our results demonstrate how changes in climate seasonality are important for understanding ecological responses to climate change, and further how spatial variability in effects of climate change necessitates different approaches to management. We discuss how our results inform climate change adaptation challenges and opportunities facing parks, with implications for other protected areas, by exploring consequences for resource management and planning.NPS landscape dynamics monitoring project, NPScape; NASA-NPS Landscape Climate Change Vulnerability Project (NASA Applied Sciences program) [10-BIOCLIM10-0034]; United States Geological Survey [G14AC00405]This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Details pertaining to candidate thermal time species × phenophase models that emerged from an evaluation of data maintained by the USA National Phenology Network, including mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe model efficiency (NSME).

<p>Details pertaining to candidate thermal time species × phenophase models that emerged from an evaluation of data maintained by the USA National Phenology Network, including mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe model efficiency (NSME).</p