CrowdCurio: an online crowdsourcing platform to facilitate climate change studies using herbarium specimens

Sex-dependent phenological responses to climate vary across species' ranges

Anthropogenic climate change has significantly altered the flowering times (i.e., phenology) of plants worldwide, affecting their reproduction, survival, and interactions. Recent studies utilizing herbarium specimens have uncovered significant intra- and inter-specific variation in flowering phenology and its response to changes in climate but have mostly been limited to animal-pollinated species. Thus, despite their economic and ecological importance, variation in phenological responses to climate remain largely unexplored among and within wind-pollinated dioecious species and across their sexes. Using both herbarium specimens and volunteer observations of cottonwood (Populus) species, we examined how phenological sensitivity to climate varies across species, their ranges, sexes, and phenophases. The timing of flowering varied significantly across and within species, as did their sensitivity to spring temperature. In particular, male flowering generally happened earlier in the season and was more sensitive to warming than female flowering. Further, the onset of flowering was more sensitive to changes in temperature than leaf out. Increased temporal gaps between male and female flowering time and between the first open flower date and leaf out date were predicted for the future under two climate change scenarios. These shifts will impact the efficacy of sexual reproduction and gene flow among species. Our study demonstrates significant inter- and intra-specific variation in phenology and its responses to environmental cues, across species' ranges, phenophases, and sex, in wind-pollinated species. These variations need to be considered to predict accurately the effects of climate change and assess their ecological and evolutionary consequences.

FieldPrism: A system for creating snapshot vouchers from field images using photogrammetric markers and QR codes

Premise

Field images are important sources of information for research in the natural sciences. However, images that lack photogrammetric scale bars, including most iNaturalist observations, cannot yield accurate trait measurements. We introduce FieldPrism, a novel system of photogrammetric markers, QR codes, and software to automate the curation of snapshot vouchers.

Methods and Results

Our photogrammetric background templates (FieldSheets) increase the utility of field images by providing machine-readable scale bars and photogrammetric reference points to automatically correct image distortion and calculate a pixel-to-metric conversion ratio. Users can generate a QR code flipbook derived from a specimen identifier naming hierarchy, enabling machine-readable specimen identification for automatic file renaming. We also developed FieldStation, a Raspberry Pi-based mobile imaging apparatus that records images, GPS location, and metadata redundantly on up to four USB storage devices and can be monitored and controlled from any Wi-Fi connected device.

Conclusions

FieldPrism is a flexible software tool designed to standardize and improve the utility of images captured in the field. When paired with the optional FieldStation, researchers can create a self-contained mobile imaging apparatus for quantitative trait data collection.

Complex climate-mediated effects of urbanization on plant reproductive phenology and frost risk

Urbanization can affect the timing of plant reproduction (i.e. flowering and fruiting) and associated ecosystem processes. However, our knowledge of how plant phenology responds to urbanization and its associated environmental changes is limited. Herbaria represent an important, but underutilized source of data for investigating this question. We harnessed phenological data from herbarium specimens representing 200 plant species collected across 120 yr from the eastern US to investigate the spatiotemporal effects of urbanization on flowering and fruiting phenology and frost risk (i.e. time between the last frost date and flowering). Effects of urbanization on plant reproductive phenology varied significantly in direction and magnitude across species ranges. Increased urbanization led to earlier flowering in colder and wetter regions and delayed fruiting in regions with wetter spring conditions. Frost risk was elevated with increased urbanization in regions with colder and wetter spring conditions. Our study demonstrates that predictions of phenological change and its associated impacts must account for both climatic and human effects, which are context dependent and do not necessarily coincide. We must move beyond phenological models that only incorporate temperature variables and consider multiple environmental factors and their interactions when estimating plant phenology, especially at larger spatial and taxonomic scales.

Environmental citizen science in Greece: perceptions and attitudes of key actors

Citizen Science (CS), the voluntary participation of lay people in scientific work, is well-established in the fields of nature conservation and biodiversity monitoring due to its potential to create large environmental datasets. This study aims to understand the familiarity, perceptions and attitudes towards CS of the key environmental actors in Greece. The target group consisted of employees and/or representatives of Environmental Non-Governmental Organisations (ENGOs), scientists and civil servants related to nature conservation. Quantitative data were collected using an electronic questionnaire, 178 fully completed questionnaires and subsequently eight semi-structured interviews with experts were conducted. Descriptive statistics were used to measure the familiarity and attitude of the actors, as well as the obstacles to the development of CS in Greece. We used Cronbach’s test to measure the reliability of the used Likert scale and Kruskal-Wallis non-parametric test to identify significant differences amongst the three groups of actors. Qualitative data were analysed following a Thematic Analysis methodology. The results show that ca. 40% of the key actors are familiar with the terms and CS practice while over 65% with the concept. The general attitude of the actors towards CS is positive although concerns about data quality collected were highlighted. “Lack of cooperation culture”, “Ignorance of the existence of the phenomenon” and “Lack of know-how” emerged as the most important obstacles to CS development in Greece. Although CS is present in Greece, it is not visible enough. The main reasons are that relevant projects employ different terms, are under-represented in the formal literature and include limited, if at all, project dissemination. There are significant differences regarding familiarity and the attitude towards CS between actors, but also similarities concerning the main obstacles. The study sets a baseline which can be employed to improve and further expand Environmental Citizen Science (ECS) in Greece.

Can Artificial Intelligence Help in the Study of Vegetative Growth Patterns from Herbarium Collections? An Evaluation of the Tropical Flora of the French Guiana Forest

A better knowledge of tree vegetative growth phenology and its relationship to environmental variables is crucial to understanding forest growth dynamics and how climate change may affect it. Less studied than reproductive structures, vegetative growth phenology focuses primarily on the analysis of growing shoots, from buds to leaf fall. In temperate regions, low winter temperatures impose a cessation of vegetative growth shoots and lead to a well-known annual growth cycle pattern for most species. The humid tropics, on the other hand, have less seasonality and contain many more tree species, leading to a diversity of patterns that is still poorly known and understood. The work in this study aims to advance knowledge in this area, focusing specifically on herbarium scans, as herbariums offer the promise of tracking phenology over long periods of time. However, such a study requires a large number of shoots to be able to draw statistically relevant conclusions. We propose to investigate the extent to which the use of deep learning can help detect and type-classify these relatively rare vegetative structures in herbarium collections. Our results demonstrate the relevance of using herbarium data in vegetative phenology research as well as the potential of deep learning approaches for growing shoot detection.

Phenological displacement is uncommon among sympatric angiosperms

Interactions between species can influence successful reproduction, resulting in reproductive character displacement, where the similarity of reproductive traits - such as flowering time - among close relatives growing together differ from when growing apart. Evidence for the overall prevalence and direction of this phenomenon, and its stability under environmental change, remains untested across large scales. Using the power of crowdsourcing, we gathered phenological information from over 40 000 herbarium specimens, and investigated displacement in flowering time across 110 animal-pollinated species in the eastern USA. Overall, flowering time displacement is not common across large scales. However, displacement is generally greater among species pairs that flower close in time, regardless of direction. Furthermore, with climate change, the flowering times of closely related species are predicted, on average, to shift further apart by the mid-21^st century. We demonstrate that the degree and direction of phenological displacement among co-occurring closely related species pairs varies tremendously. However, future climate change may alter the differences in reproductive timing among many of these species pairs, which may have significant consequences for species interactions and gene flow. Our study provides one promising path towards understanding how the phenological landscape is structured and may respond to future environmental change.

Spatiotemporal Variation of Osmanthus fragrans Phenology in China in Response to Climate Change From 1973 to 1996

Climate change greatly affects spring and autumn plant phenology around the world consequently, and significantly impacts ecosystem function and the social economy. However, autumn plant phenology, especially autumn flowering phenology, has not been studied so far. In this study, we examined the spatiotemporal pattern of Osmanthus fragrans phenology, including both leaf phenology (the date of bud-bust, BBD; first leaf unfolding, FLD; and 50% of leaf unfolding, 50 LD) and flowering phenology (the date of first flowering, FFD; peak of flowering, PFD; and end of flowering, EFD). Stepwise multiple linear regressions were employed to analyze the relationships between phenophases and climatic factors in the long term phenological data collected by the Chinese Phenological Observation Network from 1973 to 1996. The results showed that spring leaf phenophases and autumn flowering phenophases were strongly affected by latitude. BBD, FLD, and 50LD of O. fragrans were delayed by 3.98, 3.93, and 4.40 days as per degree of latitude increased, while FFD, PFD and EFD in O. fragrans advanced 3.11, 3.26, and 2.99 days, respectively. During the entire study period, BBD was significantly delayed across the region, whereas no significant trends were observed either in FLD or 50LD. Notably, all flowering phenophases of O. fragrans were delayed. Both leaf and flowering phenophases negatively correlated with growing degree-days (GDD) and cold degree-days (CDD), respectively. BBD and FLD were negatively correlated with total annual precipitation. In addition to the effects of climate on autumn flowering phenology, we found that earlier spring leaf phenophases led to delayed autumn flowering phenophases. Our results suggest that future climate change and global warming might delay the phenological sequence of O. fragrans. Our findings also advanced the flowering mechanism study of autumn flowering plants, and facilitated the accurate prediction of future phenology and climate change.

Using Convolutional Neural Networks to Efficiently Extract Immense Phenological Data From Community Science Images

Community science image libraries offer a massive, but largely untapped, source of observational data for phenological research. The iNaturalist platform offers a particularly rich archive, containing more than 49 million verifiable, georeferenced, open access images, encompassing seven continents and over 278,000 species. A critical limitation preventing scientists from taking full advantage of this rich data source is labor. Each image must be manually inspected and categorized by phenophase, which is both time-intensive and costly. Consequently, researchers may only be able to use a subset of the total number of images available in the database. While iNaturalist has the potential to yield enough data for high-resolution and spatially extensive studies, it requires more efficient tools for phenological data extraction. A promising solution is automation of the image annotation process using deep learning. Recent innovations in deep learning have made these open-source tools accessible to a general research audience. However, it is unknown whether deep learning tools can accurately and efficiently annotate phenophases in community science images. Here, we train a convolutional neural network (CNN) to annotate images of Alliaria petiolata into distinct phenophases from iNaturalist and compare the performance of the model with non-expert human annotators. We demonstrate that researchers can successfully employ deep learning techniques to extract phenological information from community science images. A CNN classified two-stage phenology (flowering and non-flowering) with 95.9% accuracy and classified four-stage phenology (vegetative, budding, flowering, and fruiting) with 86.4% accuracy. The overall accuracy of the CNN did not differ from humans (p = 0.383), although performance varied across phenophases. We found that a primary challenge of using deep learning for image annotation was not related to the model itself, but instead in the quality of the community science images. Up to 4% of A. petiolata images in iNaturalist were taken from an improper distance, were physically manipulated, or were digitally altered, which limited both human and machine annotators in accurately classifying phenology. Thus, we provide a list of photography guidelines that could be included in community science platforms to inform community scientists in the best practices for creating images that facilitate phenological analysis.

Machine Learning Undercounts Reproductive Organs on Herbarium Specimens but Accurately Derives Their Quantitative Phenological Status: A Case Study of Streptanthus tortuosus

Machine learning (ML) can accelerate the extraction of phenological data from herbarium specimens; however, no studies have assessed whether ML-derived phenological data can be used reliably to evaluate ecological patterns. In this study, 709 herbarium specimens representing a widespread annual herb, Streptanthus tortuosus, were scored both manually by human observers and by a mask R-CNN object detection model to (1) evaluate the concordance between ML and manually-derived phenological data and (2) determine whether ML-derived data can be used to reliably assess phenological patterns. The ML model generally underestimated the number of reproductive structures present on each specimen; however, when these counts were used to provide a quantitative estimate of the phenological stage of plants on a given sheet (i.e., the phenological index or PI), the ML and manually-derived PI's were highly concordant. Moreover, herbarium specimen age had no effect on the estimated PI of a given sheet. Finally, including ML-derived PIs as predictor variables in phenological models produced estimates of the phenological sensitivity of this species to climate, temporal shifts in flowering time, and the rate of phenological progression that are indistinguishable from those produced by models based on data provided by human observers. This study demonstrates that phenological data extracted using machine learning can be used reliably to estimate the phenological stage of herbarium specimens and to detect phenological patterns.

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.

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.

Scale gaps in landscape phenology: challenges and opportunities

Phenology, or the timing of life history events, can be heterogeneous across biological communities and landscapes and can vary across a wide variety of spatiotemporal scales. Here, we synthesize information from landscape phenology studies across different scales of measurement around a set of core concepts. We highlight why phenology is scale dependent and identify gaps in the spatiotemporal scales of phenological observations and inferences. We discuss the consequences of these gaps and describe opportunities to address the inherent sensitivities of phenological metrics to measurement scale. Although most studies we review and discuss are focused on plants, our work provides a broadly relevant overview of the role of observation scale in landscape phenology and a general approach for measuring and reporting scale dependence.

Reversing extinction trends: new uses of (old) herbarium specimens to accelerate conservation action on threatened species

Although often not collected specifically for the purposes of conservation, herbarium specimens offer sufficient information to reconstruct parameters that are needed to designate a species as 'at-risk' of extinction. While such designations should prompt quick and efficient legal action towards species recovery, such action often lags far behind and is mired in bureaucratic procedure. The increase in online digitization of natural history collections has now led to a surge in the number new studies on the uses of machine learning. These repositories of species occurrences are now equipped with advances that allow for the identification of rare species. The increase in attention devoted to estimating the scope and severity of the threats that lead to the decline of such species will increase our ability to mitigate these threats and reverse the declines, overcoming a current barrier to the recovery of many threatened plant species. Thus far, collected specimens have been used to fill gaps in systematics, range extent, and past genetic diversity. We find that they also offer material with which it is possible to foster species recovery, ecosystem restoration, and de-extinction, and these elements should be used in conjunction with machine learning and citizen science initiatives to mobilize as large a force as possible to counter current extinction trends.

A new fine-grained method for automated visual analysis of herbarium specimens: A case study for phenological data extraction

PREMISE

Herbarium specimens represent an outstanding source of material with which to study plant phenological changes in response to climate change. The fine-scale phenological annotation of such specimens is nevertheless highly time consuming and requires substantial human investment and expertise, which are difficult to rapidly mobilize.

METHODS

We trained and evaluated new deep learning models to automate the detection, segmentation, and classification of four reproductive structures of Streptanthus tortuosus (flower buds, flowers, immature fruits, and mature fruits). We used a training data set of 21 digitized herbarium sheets for which the position and outlines of 1036 reproductive structures were annotated manually. We adjusted the hyperparameters of a mask R-CNN (regional convolutional neural network) to this specific task and evaluated the resulting trained models for their ability to count reproductive structures and estimate their size.

RESULTS

The main outcome of our study is that the performance of detection and segmentation can vary significantly with: (i) the type of annotations used for training, (ii) the type of reproductive structures, and (iii) the size of the reproductive structures. In the case of Streptanthus tortuosus, the method can provide quite accurate estimates (77.9% of cases) of the number of reproductive structures, which is better estimated for flowers than for immature fruits and buds. The size estimation results are also encouraging, showing a difference of only a few millimeters between the predicted and actual sizes of buds and flowers.

DISCUSSION

This method has great potential for automating the analysis of reproductive structures in high-resolution images of herbarium sheets. Deeper investigations regarding the taxonomic scalability of this approach and its potential improvement will be conducted in future work.

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.

Digitization and the Future of Natural History Collections

Natural history collections (NHCs) are the foundation of historical baselines for assessing anthropogenic impacts on biodiversity. Along these lines, the online mobilization of specimens via digitization—the conversion of specimen data into accessible digital content—has greatly expanded the use of NHC collections across a diversity of disciplines. We broaden the current vision of digitization (Digitization 1.0)—whereby specimens are digitized within NHCs—to include new approaches that rely on digitized products rather than the physical specimen (Digitization 2.0). Digitization 2.0 builds on the data, workflows, and infrastructure produced by Digitization 1.0 to create digital-only workflows that facilitate digitization, curation, and data links, thus returning value to physical specimens by creating new layers of annotation, empowering a global community, and developing automated approaches to advance biodiversity discovery and conservation. These efforts will transform large-scale biodiversity assessments to address fundamental questions including those pertaining to critical issues of global change.

Collective hybrid intelligence: towards a conceptual framework

Purpose

Human or machine, which one is more intelligent and powerful for performing computing and processing tasks? Over the years, researchers and scientists have spent significant amounts of money and effort to answer this question. Nonetheless, despite some outstanding achievements, replacing humans in the intellectual tasks is not yet a reality. Instead, to compensate for the weakness of machines in some (mostly cognitive) tasks, the idea of putting human in the loop has been introduced and widely accepted. In this paper, the notion of collective hybrid intelligence as a new computing framework and comprehensive.

Design/methodology/approach

According to the extensive acceptance and efficiency of crowdsourcing, hybrid intelligence and distributed computing concepts, the authors have come up with the (complementary) idea of collective hybrid intelligence. In this regard, besides providing a brief review of the efforts made in the related contexts, conceptual foundations and building blocks of the proposed framework are delineated. Moreover, some discussion on architectural and realization issues are presented.

Findings

The paper describes the conceptual architecture, workflow and schematic representation of a new hybrid computing concept. Moreover, by introducing three sample scenarios, its benefits, requirements, practical roadmap and architectural notes are explained.

Originality/value

The major contribution of this work is introducing the conceptual foundations to combine and integrate collective intelligence of humans and machines to achieve higher efficiency and (computing) performance. To the best of the authors’ knowledge, this the first study in which such a blessing integration is considered. Therefore, it is believed that the proposed computing concept could inspire researchers toward realizing such unprecedented possibilities in practical and theoretical contexts.

A new method and insights for estimating phenological events from herbarium specimens

PREMISE OF THE STUDY

A novel method of estimating phenology of herbarium specimens was developed to facilitate more precise determination of plant phenological responses to explanatory variables (e.g., climate).

METHODS AND RESULTS

Simulated specimen data sets were used to compare the precision of phenological models using the new method and two common, alternative methods (flower presence/absence and ≥50% flowers present). The new "estimated phenophase" method was more precise and extracted a greater number of significant species-level relationships; however, this method only slightly outperformed the simple "binary" (e.g., flowers present/absent) method.

CONCLUSIONS

The new method enables estimation of phenological trends with greater precision. However, when time and resources are limited, a presence/absence method may offer comparable results at lower cost. Using a more restrictive approach, such as only including specimens in a certain phenophase, is not advised given the detrimental effect of decreased sample size on resulting models.

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.

Integrating herbarium specimen observations into global phenology data systems

PREMISE OF THE STUDY

The Plant Phenology Ontology (PPO) was originally developed to integrate phenology observations of whole plants across different global observation networks. Here we describe a new release of the PPO and associated data pipelines that supports integration of phenology observations from herbarium specimens, which provide historical and modern phenology data.

METHODS AND RESULTS

Critical changes to the PPO include key terms that describe how measurements from parts of plants, which are captured in most imaged herbarium specimens, relate to whole plants. We provide proof of concept for ingesting annotations from imaged herbarium sheets of Prunus serotina, the common black cherry. We then provide an example analysis of changes in flowering timing over the past 125 years, demonstrating the value of integrating herbarium and observational phenology data sets.

CONCLUSIONS

These conceptual and technical advances will support the addition of phenology data from herbaria, but also could be expanded upon to facilitate the inclusion of data from photograph-based citizen science platforms. With the incorporation of herbarium phenology data, new historical baseline data will strengthen the capability to monitor, model, and forecast plant phenology changes.

A novel proof of concept for capturing the diversity of endophytic fungi preserved in herbarium specimens

Herbarium specimens represent important records of morphological and genetic diversity of plants that inform questions relevant to global change, including species distributions, phenology and functional traits. It is increasingly appreciated that plant microbiomes can influence these aspects of plant biology, but little is known regarding the historic distribution of microbes associated with plants collected in the pre-molecular age. If microbiomes can be observed reliably in herbarium specimens, researchers will gain a new lens with which to examine microbial ecology, evolution, species interactions. Here, we describe a method for accessing historical plant microbiomes from preserved herbarium specimens, providing a proof of concept using two plant taxa from the imperiled boreal biome (Andromeda polifolia and Ledum palustre subsp. groenlandicum, Ericaceae). We focus on fungal endophytes, which occur within symptomless plant tissues such as leaves. Through a three-part approach (i.e. culturing, cloning and next-generation amplicon sequencing via the Illumina MiSeq platform, with extensive controls), we examined endophyte communities in dried, pressed leaves that had been processed as regular herbarium specimens and stored at room temperature in a herbarium for four years. We retrieved only one endophyte in culture, but cloning and especially the MiSeq analysis revealed a rich community of foliar endophytes. The phylogenetic distribution and diversity of endophyte assemblages, especially among the Ascomycota, resemble endophyte communities from fresh plants collected in the boreal biome. We could distinguish communities of endophytes in each plant species and differentiate likely endophytes from fungi that could be surface contaminants. Taxa found by cloning were observed in the larger MiSeq dataset, but species richness was greater when subsets of the same tissues were evaluated with the MiSeq approach. Our findings provide a proof of concept for capturing endophyte DNA from herbarium specimens, supporting the importance of herbarium records as roadmaps for understanding the dynamics of plant-associated microbial biodiversity in the Anthropocene.This article is part of the theme issue 'Biological collections for understanding biodiversity in the Anthropocene'.

Biological collections for understanding biodiversity in the Anthropocene

Global change has become a central focus of modern biology. Yet, our knowledge of how anthropogenic drivers affect biodiversity and natural resources is limited by a lack of biological data spanning the Anthropocene. We propose that the hundreds of millions of plant, fungal and animal specimens deposited in natural history museums have the potential to transform the field of global change biology. We suggest that museum specimens are underused, particularly in ecological studies, given their capacity to reveal patterns that are not observable from other data sources. Increasingly, museum specimens are becoming mobilized online, providing unparalleled access to physiological, ecological and evolutionary data spanning decades and sometimes centuries. Here, we describe the diversity of collections data archived in museums and provide an overview of the diverse uses and applications of these data as discussed in the accompanying collection of papers within this theme issue. As these unparalleled resources are under threat owing to budget cuts and other institutional pressures, we aim to shed light on the unique discoveries that are possible in museums and, thus, the singular value of natural history collections in a period of rapid change.This article is part of the theme issue 'Biological collections for understanding biodiversity in the Anthropocene'.

Herbarium specimens reveal substantial and unexpected variation in phenological sensitivity across the eastern United States

Phenology is a key biological trait that can determine an organism's survival and provides one of the clearest indicators of the effects of recent climatic change. Long time-series observations of plant phenology collected at continental scales could clarify latitudinal and regional patterns of plant responses and illuminate drivers of that variation, but few such datasets exist. Here, we use the web tool CrowdCurio to crowdsource phenological data from over 7000 herbarium specimens representing 30 diverse flowering plant species distributed across the eastern United States. Our results, spanning 120 years and generated from over 2000 crowdsourcers, illustrate numerous aspects of continental-scale plant reproductive phenology. First, they support prior studies that found plant reproductive phenology significantly advances in response to warming, especially for early-flowering species. Second, they reveal that fruiting in populations from warmer, lower latitudes is significantly more phenologically sensitive to temperature than that for populations from colder, higher-latitude regions. Last, we found that variation in phenological sensitivities to climate within species between regions was of similar magnitude to variation between species. Overall, our results suggest that phenological responses to anthropogenic climate change will be heterogeneous within communities and across regions, with large amounts of regional variability driven by local adaptation, phenotypic plasticity and differences in species assemblages. As millions of imaged herbarium specimens become available online, they will play an increasingly critical role in revealing large-scale patterns within assemblages and across continents that ultimately can improve forecasts of the impacts of climatic change on the structure and function of ecosystems.This article is part of the theme issue 'Biological collections for understanding biodiversity in the Anthropocene'.

The history and impact of digitization and digital data mobilization on biodiversity research

The first two decades of the twenty-first century have seen a rapid rise in the mobilization of digital biodiversity data. This has thrust natural history museums into the forefront of biodiversity research, underscoring their central role in the modern scientific enterprise. The advent of mobilization initiatives such as the United States National Science Foundation's Advancing Digitization of Biodiversity Collections (ADBC), Australia's Atlas of Living Australia (ALA), Mexico's National Commission for the Knowledge and Use of Biodiversity (CONABIO), Brazil's Centro de Referência em Informação (CRIA) and China's National Specimen Information Infrastructure (NSII) has led to a rapid rise in data aggregators and an exponential increase in digital data for scientific research and arguably provide the best evidence of where species live. The international Global Biodiversity Information Facility (GBIF) now serves about 131 million museum specimen records, and Integrated Digitized Biocollections (iDigBio) in the USA has amassed more than 115 million. These resources expose collections to a wider audience of researchers, provide the best biodiversity data in the modern era outside of nature itself and ensure the primacy of specimen-based research. Here, we provide a brief history of worldwide data mobilization, their impact on biodiversity research, challenges for ensuring data quality, their contribution to scientific publications and evidence of the rising profiles of natural history collections.This article is part of the theme issue 'Biological collections for understanding biodiversity in the Anthropocene'.

Using citizen science to bridge taxonomic discovery with education and outreach

PREMISE OF THE STUDY

Biological collections are uniquely poised to inform the stewardship of life on Earth in a time of cataclysmic biodiversity loss. Efforts to fully leverage collections are impeded by a lack of trained taxonomists and a lack of interest and engagement by the public. We provide a model of a crowd-sourced data collection project that produces quality taxonomic data sets and empowers citizen scientists through real contributions to science. Entitled MicroPlants, the project is a collaboration between taxonomists, citizen science experts, and teachers and students from universities and K-12.

METHODS

We developed an online tool that allows citizen scientists to measure photographs of specimens of a hyper-diverse group of liverworts from a biodiversity hotspot.

RESULTS

Using the MicroPlants online tool, citizen scientists are generating high-quality data, with preliminary analysis indicating non-expert data can be comparable to expert data.

DISCUSSION

More than 11,000 users from both the website and kiosk versions have contributed to the data set, which is demonstrably aiding taxonomists working toward establishing conservation priorities within this group. MicroPlants provides opportunities for public participation in authentic science research. The project's educational component helps move youth toward engaging in scientific thinking and has been adopted by several universities into curriculum for both biology and non-biology majors.

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.

Widespread sampling biases in herbaria revealed from large-scale digitization

Nonrandom collecting practices may bias conclusions drawn from analyses of herbarium records. Recent efforts to fully digitize and mobilize regional floras online offer a timely opportunity to assess commonalities and differences in herbarium sampling biases. We determined spatial, temporal, trait, phylogenetic, and collector biases in c. 5 million herbarium records, representing three of the most complete digitized floras of the world: Australia (AU), South Africa (SA), and New England, USA (NE). We identified numerous shared and unique biases among these regions. Shared biases included specimens collected close to roads and herbaria; specimens collected more frequently during biological spring and summer; specimens of threatened species collected less frequently; and specimens of close relatives collected in similar numbers. Regional differences included overrepresentation of graminoids in SA and AU and of annuals in AU; and peak collection during the 1910s in NE, 1980s in SA, and 1990s in AU. Finally, in all regions, a disproportionately large percentage of specimens were collected by very few individuals. We hypothesize that these mega-collectors, with their associated preferences and idiosyncrasies, shaped patterns of collection bias via 'founder effects'. Studies using herbarium collections should account for sampling biases, and future collecting efforts should avoid compounding these biases to the extent possible.

A statistical estimator for determining the limits of contemporary and historic phenology

Climate change affects not just where species are found, but also when species' key life-history events occur-their phenology. Measuring such changes in timing is often hampered by a reliance on biased survey data: surveys identify that an event has taken place (for example, the flower is in bloom), but not when that event happened (for example, the flower bloomed yesterday). Here, we show that this problem can be circumvented using statistical estimators, which can provide accurate and unbiased estimates from sparsely sampled observations. We demonstrate that such methods can resolve an ongoing debate about the relative timings of the onset and cessation of flowering, and allow us to place modern observations reliably within the context of the vast wealth of historical data that reside in herbaria, museum collections, and written records. We also analyse large-scale citizen science data from the United States National Phenology Network and reveal not just earlier but also potentially more variable flowering in recent years. Evidence for greater variability through time is important because increases in variation are characteristic of systems approaching a state change.