Envisaging a global infrastructure to exploit the potential of digitised collections.

Tens of millions of images from biological collections have become available online over the last two decades. In parallel, there has been a dramatic increase in the capabilities of image analysis technologies, especially those involving machine learning and computer vision. While image analysis has become mainstream in consumer applications, it is still used only on an artisanal basis in the biological collections community, largely because the image corpora are dispersed. Yet, there is massive untapped potential for novel applications and research if images of collection objects could be made accessible in a single corpus. In this paper, we make the case for infrastructure that could support image analysis of collection objects. We show that such infrastructure is entirely feasible and well worth investing in.

Ten best practices for effective phenological research.

The number and diversity of phenological studies has increased rapidly in recent years. Innovative experiments, field studies, citizen science projects, and analyses of newly available historical data are contributing insights that advance our understanding of ecological and evolutionary responses to the environment, particularly climate change. However, many phenological data sets have peculiarities that are not immediately obvious and can lead to mistakes in analyses and interpretation of results. This paper aims to help researchers, especially those new to the field of phenology, understand challenges and practices that are crucial for effective studies. For example, researchers may fail to account for sampling biases in phenological data, struggle to choose or design a volunteer data collection strategy that adequately fits their project's needs, or combine data sets in inappropriate ways. We describe ten best practices for designing studies of plant and animal phenology, evaluating data quality, and analyzing data. Practices include accounting for common biases in data, using effective citizen or community science methods, and employing appropriate data when investigating phenological mismatches. We present these best practices to help researchers entering the field take full advantage of the wealth of available data and approaches to advance our understanding of phenology and its implications for ecology.

Citizen science needs a name change.

Amidst attention towards improving equality, inclusivity, and diversity, citizen science is woefully anachronistic in its name. There is a critical need for this field to distance itself from the exclusionary nature of the term 'citizen'. We provide reasoning for abandoning this term and an outline for adopting a new name.

Disorder or a new order: How climate change affects phenological variability.

Advancing spring phenology is a well documented consequence of anthropogenic climate change, but it is not well understood how climate change will affect the variability of phenology year to year. Species' phenological timings reflect the adaptation to a broad suite of abiotic needs (e.g., thermal energy) and biotic interactions (e.g., predation and pollination), and changes in patterns of variability may disrupt those adaptations and interactions. Here, we present a geographically and taxonomically broad analysis of phenological shifts, temperature sensitivity, and changes in interannual variability encompassing nearly 10,000 long-term phenology time series representing more than 1000 species across much of the Northern Hemisphere. We show that the timings of leaf-out, flowering, insect first-occurrence, and bird arrival were the most sensitive to temperature variation and have advanced at the fastest pace for early-season species in colder and less seasonal regions. We did not find evidence for changing variability in warmer years in any phenophase groups, although leaf-out and flower phenology have become moderately but significantly less variable over time. Our findings suggest that climate change has not to this point fundamentally altered the patterns of interannual phenological variability.

Conservation ethics in the time of the pandemic: Does increasing remote access advance social justice?

The COVID-19 pandemic is stimulating improvements in remote access and use of technology in conservation-related programs and research. In many cases, organizations have intended for remote engagement to benefit groups that have been marginalized in the sciences. But are they? It is important to consider how remote access affects social justice in conservation biology-i.e., the principle that all people should be equally respected and valued in conservation organizations, programs, projects, and practices. To support such consideration, we describe a typology of justice-oriented principles that can be used to examine social justice in a range of conservation activities. We apply this typology to three conservation areas: (1) remote access to US national park educational programs and data; (2) digitization of natural history specimens and their use in conservation research; and (3) remote engagement in conservation-oriented citizen science. We then address the questions: Which justice-oriented principles are salient in which conservation contexts or activities? How can those principles be best realized in those contexts or activities? In each of the three areas we examined, remote access increased participation, but access and benefits were not equally distributed and unanticipated consequences have not been adequately addressed. We identify steps that can and are being taken to advance social justice in conservation, such as assessing programs to determine if they are achieving their stated social justice-oriented aims and revising initiatives as needed. The framework that we present could be used to assess the social justice dimensions of many conservation programs, institutions, practices, and policies.

Digital Extended Specimens: Enabling an Extensible Network of Biodiversity Data Records as Integrated Digital Objects on the Internet.

The early twenty-first century has witnessed massive expansions in availability and accessibility of digital data in virtually all domains of the biodiversity sciences. Led by an array of asynchronous digitization activities spanning ecological, environmental, climatological, and biological collections data, these initiatives have resulted in a plethora of mostly disconnected and siloed data, leaving to researchers the tedious and time-consuming manual task of finding and connecting them in usable ways, integrating them into coherent data sets, and making them interoperable. The focus to date has been on elevating analog and physical records to digital replicas in local databases prior to elevating them to ever-growing aggregations of essentially disconnected discipline-specific information. In the present article, we propose a new interconnected network of digital objects on the Internet-the Digital Extended Specimen (DES) network-that transcends existing aggregator technology, augments the DES with third-party data through machine algorithms, and provides a platform for more efficient research and robust interdisciplinary discovery.

Plant and bird phenology and plant occurrence from 1851 to 2020 (non-continuous) in Thoreau's Concord, Massachusetts.

Concord, Massachusetts, USA has served as an active location for phenological observations since philosopher and naturalist Henry David Thoreau began recording plant and animal occurrence and phenology in 1851. Since that time, numerous naturalists, scientists, and researchers have continued this tradition, creating an invaluable time series of 758 species in a single location. In total, 13,441 phenological records, spanning 118 years, now exist, with observations of many species ongoing. Relative abundance data for an additional 200 plant species is also provided. Thoreau's published journals and records in Special Collections libraries at the Concord Free Public Library, Harvard University, Peabody Essex Museum, and Morgan Library and Museum provide insight into his methods of routinely walking around Walden Pond, through natural areas, and within the town of Concord, seeking the first leaf or flower on plants, seasonal observations of migratory birds, and fruit maturation times. Several amateur naturalists, and most recently the present research group, have followed this method of regularly searching Concord for the earliest signs of seasonal events, visiting many of the same locations including Walden Pond, the site made famous by Thoreau. While Thoreau's observations were initially made out of a curious desire to document the natural world, these data have led to dozens to contemporary studies, addressing timely issues such as climate change, conservation, ecology, and invasive species. This time series of data, initiated by Thoreau and continued by others, has resulted in dozens of peer-reviewed publications, a popular science book, and numerous educational and outreach opportunities. These data grow increasingly valuable with time and as new and creative studies are undertaken with Thoreau's historic records. No copyright restrictions apply to the use of this data set other than citing this publication.

Macrophenology: insights into the broad-scale patterns, drivers, and consequences of phenology.

Plant phenology research has surged in recent decades, in part due to interest in phenological sensitivity to climate change and the vital role phenology plays in ecology. Many local-scale studies have generated important findings regarding the physiology, responses, and risks associated with shifts in plant phenology. By comparison, our understanding of regional- and global-scale phenology has been largely limited to remote sensing of green-up without the ability to differentiate among plant species. However, a new generation of analytical tools and data sources-including enhanced remote sensing products, digitized herbarium specimen data, and public participation in science-now permits investigating patterns and drivers of phenology across extensive taxonomic, temporal, and spatial scales, in an emerging field that we call macrophenology. Recent studies have highlighted how phenology affects dynamics at broad scales, including species interactions and ranges, carbon fluxes, and climate. At the cusp of this developing field of study, we review the theoretical and practical advances in four primary areas of plant macrophenology: (1) global patterns and shifts in plant phenology, (2) within-species changes in phenology as they mediate species' range limits and invasions at the regional scale, (3) broad-scale variation in phenology among species leading to ecological mismatches, and (4) interactions between phenology and global ecosystem processes. To stimulate future research, we describe opportunities for macrophenology to address grand challenges in each of these research areas, as well as recently available data sources that enhance and enable macrophenology research.

COVID-19 pandemic impacts on conservation research, management, and public engagement in US national parks.

The COVID-19 pandemic has disrupted the timing and substance of conservation research, management, and public engagement in protected areas around the world. This disruption is evident in US national parks, which play a key role in protecting natural and cultural resources and providing outdoor experiences for the public. Collectively, US national parks protect 34 million ha, host more than 300 million visits annually, and serve as one of the world's largest informal education organizations. The pandemic has altered park conditions and operations in a variety of ways. Shifts in operational conditions related to safety issues, reduced staffing, and decreased park revenues have forced managers to make difficult trade-offs among competing priorities. Long-term research and monitoring of the health of ecosystems and wildlife populations have been interrupted. Time-sensitive management practices, such as control of invasive plants and restoration of degraded habitat, have been delayed. And public engagement has largely shifted from in-person experiences to virtual engagement through social media and other online interactions. These changes pose challenges for accomplishing important science, management, and public engagement goals, but they also create opportunities for developing more flexible monitoring programs and inclusive methods of public engagement. The COVID-19 pandemic reinforces the need for strategic science, management planning, flexible operations, and online public engagement to help managers address rapid and unpredictable challenges.

The growing and vital role of botanical gardens in climate change research.

Botanical gardens make unique contributions to climate change research, conservation, and public engagement. They host unique resources, including diverse collections of plant species growing in natural conditions, historical records, and expert staff, and attract large numbers of visitors and volunteers. Networks of botanical gardens spanning biomes and continents can expand the value of these resources. Over the past decade, research at botanical gardens has advanced our understanding of climate change impacts on plant phenology, physiology, anatomy, and conservation. For example, researchers have utilized botanical garden networks to assess anatomical and functional traits associated with phenological responses to climate change. New methods have enhanced the pace and impact of this research, including phylogenetic and comparative methods, and online databases of herbarium specimens and photographs that allow studies to expand geographically, temporally, and taxonomically in scope. Botanical gardens have grown their community and citizen science programs, informing the public about climate change and monitoring plants more intensively than is possible with garden staff alone. Despite these advances, botanical gardens are still underutilized in climate change research. To address this, we review recent progress and describe promising future directions for research and public engagement at botanical gardens.

Machine Learning Using Digitized Herbarium Specimens to Advance Phenological Research.

Machine learning (ML) has great potential to drive scientific discovery by harvesting data from images of herbarium specimens-preserved plant material curated in natural history collections-but ML techniques have only recently been applied to this rich resource. ML has particularly strong prospects for the study of plant phenological events such as growth and reproduction. As a major indicator of climate change, driver of ecological processes, and critical determinant of plant fitness, plant phenology is an important frontier for the application of ML techniques for science and society. In the present article, we describe a generalized, modular ML workflow for extracting phenological data from images of herbarium specimens, and we discuss the advantages, limitations, and potential future improvements of this workflow. Strategic research and investment in specimen-based ML methods, along with the aggregation of herbarium specimen data, may give rise to a better understanding of life on Earth.

Corrigendum: The Extended Specimen Network: A Strategy to Enhance US Biodiversity Collections, Promote Research and Education.

[This corrects the article DOI: 10.1093/biosci/biz140.].

Phenology models using herbarium specimens are only slightly improved by using finer-scale stages of reproduction.

PREMISE OF THE STUDY: Herbarium specimens are increasingly used to study reproductive phenology. Here, we ask whether classifying reproduction into progressively finer-scale stages improves our understanding of the relationship between climate and reproductive phenology. METHODS: We evaluated Acer rubrum herbarium specimens across eastern North America, classifying them into eight reproductive phenophases and four stages of leaf development. We fit models with different reproductive phenology categorization schemes (from detailed to broad) and compared model fits and coefficients describing temperature, elevation, and year effects. We fit similar models to leaf phenology data to compare reproductive to leafing phenology. RESULTS: Finer-scale reproductive phenophases improved model fits and provided more precise estimates of reproductive phenology. However, models with fewer reproductive phenophases led to similar qualitative conclusions, demonstrating that A. rubrum reproduces earlier in warmer locations, lower elevations, and in recent years, as well as that leafing phenology is less strongly influenced by temperature than is reproductive phenology. DISCUSSION: Our study suggests that detailed information on reproductive phenology provides a fuller understanding of potential climate change effects on flowering, fruiting, and leaf-out. However, classification schemes with fewer reproductive phenophases provided many similar insights and may be preferable in cases where resources are limited.

Toward a large-scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras.

PREMISE OF THE STUDY: Phenological annotation models computed on large-scale herbarium data sets were developed and tested in this study. METHODS: Herbarium specimens represent a significant resource with which to study plant phenology. Nevertheless, phenological annotation of herbarium specimens is time-consuming, requires substantial human investment, and is difficult to mobilize at large taxonomic scales. We created and evaluated new methods based on deep learning techniques to automate annotation of phenological stages and tested these methods on four herbarium data sets representing temperate, tropical, and equatorial American floras. RESULTS: Deep learning allowed correct detection of fertile material with an accuracy of 96.3%. Accuracy was slightly decreased for finer-scale information (84.3% for flower and 80.5% for fruit detection). DISCUSSION: The method described has the potential to allow fine-grained phenological annotation of herbarium specimens at large ecological scales. Deeper investigation regarding the taxonomic scalability of this approach is needed.

Digitization protocol for scoring reproductive phenology from herbarium specimens of seed plants.

PREMISE OF THE STUDY: Herbarium specimens provide a robust record of historical plant phenology (the timing of seasonal events such as flowering or fruiting). However, the difficulty of aggregating phenological data from specimens arises from a lack of standardized scoring methods and definitions for phenological states across the collections community. METHODS AND RESULTS: To address this problem, we report on a consensus reached by an iDigBio working group of curators, researchers, and data standards experts regarding an efficient scoring protocol and a data-sharing protocol for reproductive traits available from herbarium specimens of seed plants. The phenological data sets generated can be shared via Darwin Core Archives using the Extended MeasurementOrFact extension. CONCLUSIONS: Our hope is that curators and others interested in collecting phenological trait data from specimens will use the recommendations presented here in current and future scoring efforts. New tools for scoring specimens are reviewed.