27 research outputs found
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
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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Monitoring phenology in US national parks through citizen science: Some preliminary lessons and prospects for protected areas
Phenology—the timing of seasonal events such as flower production, insect emergence, bird migrations, and snowmelt—has profound significance for people and ecosystems. Many US national parks monitor phenology through citizen science projects that use tools developed by the USA National Phenology Network. We summarize the scope of such efforts conducted over the past decade and identify some preliminary lessons and recommendations for others who wish to develop new projects. Successes include an enormous wealth of data relevant to resource management and park operations, and attainment of goals for resource management, education, and public engagement. Challenges include long-term sustainability, limited capacity to analyze data, and the ongoing demands of matching volunteer interest and capacity with the geography and natural history of studied species. Practical recommendations pertain to project planning, design, and volunteer engagement, and highlight the need for working and communicating across organizational and disciplinary boundaries. With careful planning and awareness of opportunities and pitfalls, citizen science-based phenology monitoring can benefit any protected area
Recommended from our members
Monitoring phenology in US national parks through citizen science: Some preliminary lessons and prospects for protected areas
Phenology—the timing of seasonal events such as flower production, insect emergence, bird migrations, and snowmelt—has profound significance for people and ecosystems. Many US national parks monitor phenology through citizen science projects that use tools developed by the USA National Phenology Network. We summarize the scope of such efforts conducted over the past decade and identify some preliminary lessons and recommendations for others who wish to develop new projects. Successes include an enormous wealth of data relevant to resource management and park operations, and attainment of goals for resource management, education, and public engagement. Challenges include long-term sustainability, limited capacity to analyze data, and the ongoing demands of matching volunteer interest and capacity with the geography and natural history of studied species. Practical recommendations pertain to project planning, design, and volunteer engagement, and highlight the need for working and communicating across organizational and disciplinary boundaries. With careful planning and awareness of opportunities and pitfalls, citizen science-based phenology monitoring can benefit any protected area
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How well do the spring indices predict phenological activity across plant species?
The spring indices, models that represent the onset of spring season biological activity, were developed using a long-term observational record from the mid-to-late twentieth century of three species of lilacs and honeysuckles contributed by volunteer observers across the nation. The USA National Phenology Network (USA-NPN) produces and freely delivers maps of spring index onset dates at fine spatial scale for the USA. These maps are used widely in natural resource planning and management applications. The extent to which the models represent activity in a broad suite of plant species is not well documented. In this study, we used a rich record of observational plant phenology data (37,819 onset records) collected in recent years (1981-2017) to evaluate how well gridded maps of the spring index models predict leaf and flowering onset dates in (a) 19 species of ecologically important, broadly distributed deciduous trees and shrubs, and (b) the lilac and honeysuckle species used to construct the models. The extent to which the spring indices predicted vegetative and reproductive phenology varied by species and with latitude, with stronger relationships revealed for shrubs than trees and with the Bloom Index compared to the Leaf Index, and reduced concordance between the indices at higher latitudes. These results allow us to use the indices as indicators of when to expect activity across widely distributed species and can serve as a yardstick to assess how future changes in the timing of spring will impact a broad array of trees and shrubs across the USA.12 month embargo; published online: 27 February 2020This 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]
The results of applying t-SNE on contextual information.
<p>The transformed contextual information for (A) cloned lilac and (B) common lilac.</p
The geographic distribution of the clusters in context condition of common lilac.
<p>The geographic distribution of the clusters in context condition of common lilac.</p
Geographic distribution of species <i>Ă—</i> year data points used in constructing 107 unique species <i>Ă—</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
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