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The global lockdown to mitigate COVID-19 pandemic health risks has altered human interactions with nature. Here, we report immediate impacts of changes in human activities on wildlife and environmental threats during the early lockdown months of 2020, based on 877 qualitative reports and 332 quantitative assessments from 89 different studies. Hundreds of reports of unusual species observations from around the world suggest that animals quickly responded to the reductions in human presence. However, negative effects of lockdown on conservation also emerged, as confinement resulted in some park officials being unable to perform conservation, restoration and enforcement tasks, resulting in local increases in illegal activities such as hunting. Overall, there is a complex mixture of positive and negative effects of the pandemic lockdown on nature, all of which have the potential to lead to cascading responses which in turn impact wildlife and nature conservation. While the net effect of the lockdown will need to be assessed over years as data becomes available and persistent effects emerge, immediate responses were detected across the world. Thus, initial qualitative and quantitative data arising from this serendipitous global quasi-experimental perturbation highlights the dual role that humans play in threatening and protecting species and ecosystems. Pathways to favorably tilt this delicate balance include reducing impacts and increasing conservation effectiveness.

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.

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.

Science projects, which engage non-professional scientists in one or more stages of scientific research, have been gaining popularity; yet maintaining participants' activity level over time remains a challenge. The objective of this study was to evaluate the potential for a short-term, focused campaign to increase participant activity in a national-scale citizen science program. The campaign that we implemented was designed to answer a compelling scientific question. We invited participants in the phenology-observing program, "Nature's Notebook," to track trees throughout the spring of 2012, to ascertain whether the season arrived as early as the anomalous spring of 2010. Consisting of a series of six electronic newsletters and costing our office slightly more than 1 week of staff resources, our effort was successful; compared with previous years, the number of observations collected in the region where the campaign was run increased by 184%, the number of participants submitting observations increased by 116%, and the number of trees registered increased by 110%. In comparison, these respective metrics grew by 25, 55, and 44%, over previous years, in the southeastern quadrant of the United States, where no such campaign was carried out. The campaign approach we describe here is a model that could be adapted by a wide variety of programs to increase engagement and thereby positively influence participant retention.

The USA National Phenology Network was established in 2007 to formalize standardized phenology monitoring across the country. The aims of the network are to collect, store, and share phenology data and information to support scientific discovery, decision-making, an appreciation for phenology, and equitable engagement within the network. To support these aims, the network launched Nature's Notebook, a rigorous platform for monitoring plant and animal phenology, in 2009. Since the launch of Nature's Notebook, participants across the country have contributed over 30 million phenology records. The participants range from backyard observers with an interest in nature to researchers and natural resource managers asking specific questions. We survey the breadth of studies and applied decisions that have used Nature's Notebook and the consequent data. The dimensionality of the data set maintained by the network is a function of Nature's Notebook users; this insight is key to shaping the network's future data collection activities. [ABSTRACT FROM AUTHOR]

Insect pests cost billions of dollars per year globally, negatively impacting food crops and infrastructure, and contributing to the spread of disease. Timely information regarding developmental stages of pests can facilitate early detection and control, increasing efficiency and effectiveness. In 2018, the U.S. National Phenology Network (USA-NPN) released a suite of 'Pheno Forecast' map products relevant to science and management. The Pheno Forecasts include real-time maps and short-term forecasts of insect pest activity at management-relevant spatial and temporal resolutions and are based on accumulated temperature thresholds associated with critical life-cycle stages of economically important pests. Pheno Forecasts indicate, for a specified day, the status of the insect's target life-cycle stage in real time across the contiguous United States. The maps are available for 12 pest species including the invasive emerald ash borer (Agrilus planipennis Fairmaire [Coleoptera: Buprestidae]), hemlock woolly adelgid (Adelges tsugae Annand), and gypsy moth (Lymantria dispar Linnaeus [Lepidoptera: Erebidae]). Preliminary validation based on in-situ observations for hemlock woolly adelgid egg and nymph stages in 2018 indicated the maps to be ≥93% accurate depending on phenophase. Since their release in early 2018, these maps have been adopted by tree care specialists and foresters across the United States. Using a consultative mode of engagement, USA-NPN staff have continuously sought input and critique of the maps and delivery from end users. Based on feedback received, maps have been expanded and modified to include additional species, improved descriptions of the phenophase event of interest, and e-mail-based notifications to support management decisions. [ABSTRACT FROM AUTHOR]

Purpose: 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. Methods: 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. Results: 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 (Amelanchier canadensis-breaking leaf buds) to 1,300 GDDs (Prunus serotina-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. Implications: 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Recent improvements in online information communication and mobile location-aware technologies have led to the production of large volumes of volunteered geographic information. Widespread, large-scale efforts by volunteers to collect data can inform and drive scientific advances in diverse fields, including ecology and climatology. Traditional workflows to check the quality of such volunteered information can be costly and time consuming as they heavily rely on human interventions. However, identifying factors that can influence data quality, such as inconsistency, is crucial when these data are used in modeling and decision-making frameworks. Recently developed workflows use simple statistical approaches that assume that the majority of the information is consistent. However, this assumption is not generalizable, and ignores underlying geographic and environmental contextual variability that may explain apparent inconsistencies. Here we describe an automated workflow to check inconsistency based on the availability of contextual environmental information for sampling locations. The workflow consists of three steps: (1) dimensionality reduction to facilitate further analysis and interpretation of results, (2) model-based clustering to group observations according to their contextual conditions, and (3) identification of inconsistent observations within each cluster. The workflow was applied to volunteered observations of flowering in common and cloned lilac plants (Syringa vulgaris and Syringa x chinensis) in the United States for the period 1980 to 2013. About 97% of the observations for both common and cloned lilacs were flagged as consistent, indicating that volunteers provided reliable information for this case study. Relative to the original dataset, the exclusion of inconsistent observations changed the apparent rate of change in lilac bloom dates by two days per decade, indicating the importance of inconsistency checking as a key step in data quality assessment for volunteered geographic information. Initiatives that leverage volunteered geographic information can adapt this workflow to improve the quality of their datasets and the robustness of their scientific analyses. [ABSTRACT FROM AUTHOR]

Agriculture has long been a part of the urban landscape, from gardens to small scale farms. In recent decades, interest in producing food in cities has grown dramatically, with an estimated 30% of the global urban population engaged in some form of food production. Identifying and managing the insect biodiversity found on city farms is a complex task often requiring years of study and specialization, especially in urban landscapes which have a complicated tapestry of fragmentation, diversity, pollution, and introduced species. Supporting urban growers with relevant data informs insect management decision-making for both growers and their neighbors, yet this information can be difficult to come by. In this study, we introduced several web-based citizen science programs that can connect growers with useful data products and people to help with the who, what, where, and when of urban insects. Combining the power of citizen science volunteers with the efforts of urban farmers can result in a clearer picture of the diversity and ecosystem services in play, limited insecticide use, and enhanced non-chemical alternatives. Connecting urban farming practices with citizen science programs also demonstrates the ecosystem value of urban agriculture and engages more citizens with the topics of food production, security, and justice in their communities. [ABSTRACT FROM AUTHOR]

The dataset is comprised of leafing and flowering data collected across the continental United States from 1956 to 2014 for purple common lilac (Syringa vulgaris), a cloned lilac cultivar (S. x chinensis 'Red Rothomagensis') and two cloned honeysuckle cultivars (Lonicera tatarica 'Arnold Red' and L. korolkowii 'Zabeli'). Applications of this observational dataset range from detecting regional weather patterns to understanding the impacts of global climate change on the onset of spring at the national scale. While minor changes in methods have occurred over time, and some documentation is lacking, outlier analyses identified fewer than 3% of records as unusually early or late. Lilac and honeysuckle phenology data have proven robust in both model development and climatic research.