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Rüppell's vultures are critically endangered, primarily due to anthropogenic activities such as habitat degradation, climate change, and intentional and unintentional poisoning, which have led to the loss of nesting and breeding sites. To aid in the conservation and protection of these species, habitat evaluation and niche mapping are crucial. Species distribution modeling (SDM) is a valuable tool in conservation planning, providing insights into the ecological requirements of species under conservation concerns. This study employed an ensembling modeling approach to assess the habitat suitability and distribution of Rüppell's vultures across Kenya. We utilized four algorithms; Gradient Boosting Machine, Generalized Linear Model, Generalized Additive Model, and Random Forest. Data on Rüppell's vultures were sourced from the Global Biodiversity Information Facility, while key environmental variables influencing the species' distribution were obtained from WorldClim. The resultant species distribution map was overlaid with a conservation area map to evaluate the overlap between suitable habitats and existing protected areas. Our analysis identified suitable habitats in regions such as the Masai Mara Game Reserve, Mount Kenya National Park, Nairobi National Park, Tsavo East National Park, and Hell's Gate National Park, with the majority of these habitats located outside protected areas, except those within Hell's Gate National Park. Precipitation and elevation emerged as the primary environmental predictors of the distribution of Rüppell's vultures. Based on these findings, we recommend establishing vulture sanctuaries in suitable habitats and hotspots to enhance the conservation of Rüppell's vultures outside the protected areas. 

Participatory science and amateur participation in scientific data collection and work has been common for hundreds of years, but has become a more formalised field of practice in recent decades. The inclusion and reliance on informally trained members of the public in scientific endeavours has especially helped connect natural history collections to the general public. In recent decades, the term used to describe these participants — citizen scientists — was intended to unite formal and informal scientists as global citizens working towards a common goal. However, the term 'citizen' today has negative connotations for many members of the public and can have a polarising effect on certain individuals. Given that the nature of participatory science is to be inclusive and inviting, it is time to change this terminology. The term 'community' science has been suggested as an alternative by some practitioners and programmes. This self-awareness within the scientific community is important, but lacks impact without input from the community members potentially participating in these programmes. We addressed this knowledge gap by posing the question of term preference to groups of volunteers who have attended participatory science activities from the Field Museum of Natural History (Chicago, Illinois, USA) and the Natural History Museum of Los Angeles County (Los Angeles, California, USA) from 2019 to 2023. A majority of respondents showed a clear preference for the term 'community' over 'citizen' science. This was especially true for younger individuals and those who belong to ethnic groups other than White. This information can impact which terms are used for specific programme populations and supports community involvement in selecting terminology and in project design. We advise stopping use of the term 'citizen' in all participatory science programmes and adopting terminology that is most appropriate depending on region, research, audience and activity. Moreover, participant populations should be solicited to hear their voices. 

In the first decades of the 21^stcentury, there has been a global trend towards digitisation and the mobilisation of data from natural history museums and research institutions. The development of national and international aggregator systems, which focused on data standards, made it possible to access millions of museum specimen records. These records serve as an empirical foundation for research across various fields. In addition, community efforts have expanded the concept of natural history collection specimens to include physical preparations and digital resources, resulting in the Digital Extended Specimen (DES), which also includes derived and related data. Within this context, the paper proposes using the FAIR Digital Object (FDO) framework to accelerate the global vision of the DES, arguing that FDO-enabled infrastructures can reduce barriers to the discovery and access of specimens, help ensure credit back to contributors and increase the amount of research that incorporates biodiversity data. 

Abstract PremiseAmong the slowest steps in the digitization of natural history collections is converting imaged labels into digital text. We present here a working solution to overcome this long‐recognized efficiency bottleneck that leverages synergies between community science efforts and machine learning approaches. MethodsWe present two new semi‐automated services. The first detects and classifies typewritten, handwritten, or mixed labels from herbarium sheets. The second uses a workflow tuned for specimen labels to label text using optical character recognition (OCR). The label finder and classifier was built via humans‐in‐the‐loop processes that utilize the community science Notes from Nature platform to develop training and validation data sets to feed into a machine learning pipeline. ResultsOur results showcase a >93% success rate for finding and classifying main labels. The OCR pipeline optimizes pre‐processing, multiple OCR engines, and post‐processing steps, including an alignment approach borrowed from molecular systematics. This pipeline yields >4‐fold reductions in errors compared to off‐the‐shelf open‐source solutions. The OCR workflow also allows human validation using a custom Notes from Nature tool. DiscussionOur work showcases a usable set of tools for herbarium digitization including a custom‐built web application that is freely accessible. Further work to better integrate these services into existing toolkits can support broad community use. 

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. 

Over the past five decades, a large number of wild animals have been individually identified by various observation systems and/or temporary tracking methods, providing unparalleled insights into their lives over both time and space. However, so far there is no comprehensive record of uniquely individually identified animals nor where their data and metadata are stored, for example photos, physiological and genetic samples, disease screens, information on social relationships.Databases currently do not offer unique identifiers for living, individual wild animals, similar to the permanent ID labelling for deceased museum specimens.To address this problem, we introduce two new concepts: (1) a globally unique animal ID (UAID) available to define uniquely and individually identified animals archived in any database, including metadata archived at the time of publication; and (2) the digital ‘home’ for UAIDs, the Movebank Life History Museum (MoMu), storing and linking metadata, media, communications and other files associated with animals individually identified in the wild. MoMu will ensure that metadata are available for future generations, allowing permanent linkages to information in other databases.MoMu allows researchers to collect and store photos, behavioural records, genome data and/or resightings of UAIDed animals, encompassing information not easily included in structured datasets supported by existing databases. Metadata is uploaded through the Animal Tracker app, the MoMu website, by email from registered users or through an Application Programming Interface (API) from any database. Initially, records can be stored in a temporary folder similar to a field drawer, as naturalists routinely do. Later, researchers and specialists can curate these materials for individual animals, manage the secure sharing of sensitive information and, where appropriate, publish individual life histories with DOIs. The storage of such synthesized lifetime stories of wild animals under a UAID (unique identifier or ‘animal passport’) will support basic science, conservation efforts and public participation. 

A comprehensive overview of volunteer-driven public programs focused on activities to enhance natural history collections (NHCs) is provided. The initiative revolves around the WeDigBio events and the Collections Club at the Field Museum, aiming to deepen the public’s connection with scientific collections, enhance participatory science, and improve data associated with natural history specimens. The implementation and journey of these programs are outlined, including surveys conducted from 2015 through 2021 to gauge participant motivation, satisfaction, and the impact of these events on public engagement with NHCs. Results show trends in on-site and virtual volunteer participation over the years, especially during the peak period of the COVID-19 pandemic. The majority of participants expressed high satisfaction, indicating a willingness to continue participating in similar activities. The surveys revealed a shift towards more altruistic motivations for participation over time, with increased emphasis on supporting the Field Museum and contributing to the scientific community. The success of participatory science events demonstrates the potential of volunteer-driven programs to contribute meaningfully to the preservation, digitisation, and understanding of biodiversity collections, ultimately transforming spectators into stewards of natural history. From 2015 to present participants celebrate a significant milestone, with over a thousand community scientists contributing to the inventorying, collection care, curation, databasing, or transcription of 286,071 specimens, objects or records. We also discuss accuracy and quality control as well as a checklist and recommendations for similar activities. 

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.