Search for: All records

Abstract 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. 

As we look to the future of natural history collections and a global integration of biodiversity data, we are reliant on a diverse workforce with the skills necessary to build, grow, and support the data, tools, and resources of the Digital Extended Specimen (DES; Webster 2019, Lendemer et al. 2020, Hardisty 2020). Future “DES Data Curators” – those who will be charged with maintaining resources created through the DES – will require skills and resources beyond what is currently available to most natural history collections staff. In training the workforce to support the DES we have an opportunity to broaden our community and ensure that, through the expansion of biodiversity data, the workforce landscape itself is diverse, equitable, inclusive, and accessible. A fully-implemented DES will provide training that encapsulates capacity building, skills development, unifying protocols and best practices guidance, and cutting-edge technology that also creates inclusive, equitable, and accessible systems, workflows, and communities. As members of the biodiversity community and the current workforce, we can leverage our knowledge and skills to develop innovative training models that: include a range of educational settings and modalities; address the needs of new communities not currently engaged with digital data; from their onset, provide attribution for past and future work and do not perpetuate the legacy of colonial practices and historic inequalities found in many physical natural history collections. Recent reports from the Biodiversity Collections Network (BCoN 2019) and the National Academies of Science, Engineering and Medicine (National Academies of Sciences, Engineering, and Medicine 2020) specifically address workforce needs in support of the DES. To address workforce training and inclusivity within the context of global data integration, the Alliance for Biodiversity Knowledge included a topic on Workforce capacity development and inclusivity in Phase 2 of the consultation on Converging Digital Specimens and Extended Specimens - Towards a global specification for data integration. Across these efforts, several common themes have emerged relative to workforce training and the DES. A call for a community needs assessment: As a community, we have several unknowns related to the current collections workforce and training needs. We would benefit from a baseline assessment of collections professionals to define current job responsibilities, demographics, education and training, incentives, compensation, and benefits. This includes an evaluation of current employment prospects and opportunities. Defined skills and training for the 21st century collections professional: We need to be proactive and define the 21st century workforce skills necessary to support the development and implementation of the DES. When we define the skills and content needs we can create appropriate training opportunities that include scalable materials for capacity building, educational materials that develop relevant skills, unifying protocols across the DES network, and best practices guidance for professionals. Training for data end-users: We need to train data end-users in biodiversity and data science at all levels of formal and informal education from primary and secondary education through the existing workforce. This includes developing training and educational materials, creating data portals, and building analyses that are inclusive, accessible, and engage the appropriate community of science educators, data scientists, and biodiversity researchers. Foster a diverse, equitable, inclusive, and accessible and professional workforce: As the DES develops and new tools and resources emerge, we need to be intentional in our commitment to building tools that are accessible and in assuring that access is equitable. This includes establishing best practices to ensure the community providing and accessing data is inclusive and representative of the diverse global community of potential data providers and users. Upfront, we must acknowledge and address issues of historic inequalities and colonial practices and provide appropriate attribution for past and future work while ensuring legal and regulatory compliance. Efforts must include creating transparent linkages among data and the humans that create the data that drives the DES. In this presentation, we will highlight recommendations for building workforce capacity within the DES that are diverse, inclusive, equitable and accessible, take into account the requirements of the biodiversity science community, and that are flexible to meet the needs of an evolving field. 

Abstract 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. 

International collaboration between collections, aggregators, and researchers within the biodiversity community and beyond is becoming increasingly important in our efforts to support biodiversity, conservation and the life of the planet. The social, technical, logistical and financial aspects of an equitable biodiversity data landscape – from workforce training and mobilization of linked specimen data, to data integration, use and publication – must be considered globally and within the context of a growing biodiversity crisis. In recent years, several initiatives have outlined paths forward that describe how digital versions of natural history specimens can be extended and linked with associated data. In the United States, Webster (2017) presented the “extended specimen”, which was expanded upon by Lendemer et al. (2019) through the work of the Biodiversity Collections Network (BCoN). At the same time, a “digital specimen” concept was developed by DiSSCo in Europe (Hardisty 2020). Both the extended and digital specimen concepts depict a digital proxy of an analog natural history specimen, whose digital nature provides greater capabilities such as being machine-processable, linkages with associated data, globally accessible information-rich biodiversity data, improved tracking, attribution and annotation, additional opportunities for data use and cross-disciplinary collaborations forming the basis for FAIR (Findable, Accessible, Interoperable, Reproducible) and equitable sharing of benefits worldwide, and innumerable other advantages, with slight variation in how an extended or digital specimen model would be executed. Recognizing the need to align the two closely-related concepts, and to provide a place for open discussion around various topics of the Digital Extended Specimen (DES; the current working name for the joined concepts), we initiated a virtual consultation on the discourse platform hosted by the Alliance for Biodiversity Knowledge through GBIF. This platform provided a forum for threaded discussions around topics related and relevant to the DES. The goals of the consultation align with the goals of the Alliance for Biodiversity Knowledge: expand participation in the process, build support for further collaboration, identify use cases, identify significant challenges and obstacles, and develop a comprehensive roadmap towards achieving the vision for a global specification for data integration. In early 2021, Phase 1 launched with five topics: Making FAIR data for specimens accessible; Extending, enriching and integrating data; Annotating specimens and other data; Data attribution; and Analyzing/mining specimen data for novel applications. This round of full discussion was productive and engaged dozens of contributors, with hundreds of posts and thousands of views. During Phase 1, several deeper, more technical, or additional topics of relevance were identified and formed the foundation for Phase 2 which began in May 2021 with the following topics: Robust access points and data infrastructure alignment; Persistent identifier (PID) scheme(s); Meeting legal/regulatory, ethical and sensitive data obligations; Workforce capacity development and inclusivity; Transactional mechanisms and provenance; and Partnerships to collaborate more effectively. In Phase 2 fruitful progress was made towards solutions to some of these complex functional and technical long-term goals. Simultaneously, our commitment to open participation was reinforced, through increased efforts to involve new voices from allied and complementary fields. Among a wealth of ideas expressed, the community highlighted the need for unambiguous persistent identifiers and a dedicated agent to assign them, support for a fully linked system that includes robust publishing mechanisms, strong support for social structures that build trustworthiness of the system, appropriate attribution of legacy and new work, a system that is inclusive, removed from colonial practices, and supportive of creative use of biodiversity data, building a truly global data infrastructure, balancing open access with legal obligations and ethical responsibilities, and the partnerships necessary for success. These two consultation periods, and the myriad activities surrounding the online discussion, produced a wide variety of perspectives, strategies, and approaches to converging the digital and extended specimen concepts, and progressing plans for the DES -- steps necessary to improve access to research-ready data to advance our understanding of the diversity and distribution of life. Discussions continue and we hope to include your contributions to the DES in future implementation plans. 

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.

 

Abstract 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.