Museum specimens provide novel insights into changing plant-herbivore interactions

Resurrected seeds from herbarium specimens reveal rapid evolution of drought resistance in a selfing annual

PREMISE

Increased aridity and drought associated with climate change are exerting unprecedented selection pressures on plant populations. Whether populations can rapidly adapt, and which life history traits might confer increased fitness under drought, remain outstanding questions.

METHODS

We utilized a resurrection ecology approach, leveraging dormant seeds from herbarium collections to assess whether populations of Plantago patagonica from the semi-arid Colorado Plateau have rapidly evolved in response to approximately ten years of intense drought in the region. We quantified multiple traits associated with drought escape and drought resistance and assessed the survival of ancestors and descendants under simulated drought.

RESULTS

Descendant populations displayed a significant shift in resource allocation, in which they invested less in reproductive tissues and relatively more in both above- and below-ground vegetative tissues. Plants with greater leaf biomass survived longer under terminal drought; moreover, even after accounting for the effect of increased leaf biomass, descendant seedlings survived drought longer than their ancestors.

CONCLUSIONS

Our results document rapid adaptive evolution in response to climate change in a selfing annual and suggest that shifts in tissue allocation strategies may underlie adaptive responses to drought in arid or semi-arid environments. This work also illustrates a novel approach, documenting that under specific circumstances, seeds from herbarium specimens may provide an untapped source of dormant propagules for future resurrection experiments.

Citizen science data reveal regional heterogeneity in phenological response to climate in the large milkweed bug, Oncopeltus fasciatus

Regional populations of geographically widespread species may respond to different environmental factors across the species' range, generating divergent effects of climate change on life-history phenology. Using thousands of citizen science observations extracted from iNaturalist and associated with corresponding temperature, precipitation, elevation, and daylength information, we examined the drivers of adult mating and of nymphal phenology, development, and group size for populations of the large milkweed bug, Oncopeltus fasciatus, in different ecoregions. Research-grade iNaturalist images were correctly identified 98.3% of the time and yielded more than 3000 observations of nymphal groups and 1000 observations of mating adults spanning 18 years. Mating phenology showed distinct regional patterns, ranging from year-round mating in California to temporally restricted mating in the Great Lakes Northeastern Coast ecoregion. Relative temperature increases of 1°C for a given daylength expanded the mating season by more than a week in western ecoregions. While increases in relative temperature delayed mating phenology in all ecoregions, greater winter precipitation advanced mating in the California ecoregion. In the eastern ecoregions, nymphal phenology was delayed by increases in summer rainfall but was advanced by relative temperature increases, whereas in western regions, relative temperature increases delayed nymphal phenology. Furthermore, accumulated growing degree days (AGDD) was a poor predictor of developmental progression, as we found a positive but weak correlation between AGDD and age structure only for the Appalachian Southeast North America and the Great Lakes Northern Coast ecoregions. These complex phenological responses of O. fasciatus are just one example of how populations may be differentially susceptible to a diversity of climatic effects; using data across a species' whole distribution is critical for exposing regional variations, especially for species with large, continental-scale ranges. This study demonstrates the potential of photodocumented biodiversity data to aid in the monitoring of life history, host plant-insect interactions, and climate responsiveness.

Moving past the challenges and misconceptions in urban adaptation research

Although the field of urban evolutionary ecology has recently expanded, much progress has been made in identifying adaptations that arise as a result of selective pressures within these unique environments. However, as studies within urban environments have rapidly increased, researchers have recognized that there are challenges and opportunities in characterizing urban adaptation. Some of these challenges are a consequence of increased direct and indirect human influence, which compounds long-recognized issues with research on adaptive evolution more generally. In this perspective, we discuss several common research challenges to urban adaptation related to (1) methodological approaches, (2) trait-environment relationships and the natural history of organisms, (3) agents and targets of selection, and (4) habitat heterogeneity. Ignoring these challenges may lead to misconceptions and further impede our ability to draw conclusions regarding evolutionary and ecological processes in urban environments. Our goal is to first shed light on the conceptual challenges of conducting urban adaptation research to help avoid the propagation of these misconceptions. We further summarize potential strategies to move forward productively to construct a more comprehensive picture of urban adaptation, and discuss how urban environments also offer unique opportunities and applications for adaptation research.

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.

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.

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.

The complexity of global change and its effects on insects

Global change includes multiple overlapping and interacting drivers: 1) climate change, 2) land use change, 3) novel chemicals, and 4) the increased global transport of organisms. Recent studies have documented the complex and counterintuitive effects of these drivers on the behavior, life histories, distributions, and abundances of insects. This complexity arises from the indeterminacy of indirect, non-additive and combined effects. While there is wide consensus that global change is reorganizing communities, the available data are limited. As the pace of anthropogenic changes outstrips our ability to document its impacts, ongoing change may lead to increasingly unpredictable outcomes. This complexity and uncertainty argue for renewed efforts to address the fundamental drivers of global change.

Changes in plant collection practices from the 16th to 21st centuries: implications for the use of herbarium specimens in global change research

BACKGROUND AND AIMS

Herbaria were recently advertised as reliable sources of information regarding historical changes in plant traits and biotic interactions. To justify the use of herbaria in global change research, we asked whether the characteristics of herbarium specimens have changed during the past centuries and whether these changes were due to shifts in plant collection practices.

METHODS

We measured nine characteristics from 515 herbarium specimens of common European trees and large shrubs collected from 1558 to 2016. We asked botanists to rank these specimens by their scientific quality, and asked artists to rank these specimens by their beauty.

KEY RESULTS

Eight of 11 assessed characteristics of herbarium specimens changed significantly during the study period. The average number of leaves in plant specimens increased 3-fold, whereas the quality of specimen preparation decreased. Leaf size negatively correlated with leaf number in specimens in both among-species and within-species analyses. The proportion of herbarium sheets containing plant reproductive structures peaked in the 1850s. The scientific value of herbarium specimens increased until the 1700s, but then did not change, whereas their aesthetic value showed no systematic trends.

CONCLUSIONS

Our findings strongly support the hypothesis that many characteristics of herbarium specimens have changed systematically and substantially from the 16th to 21st centuries due to changes in plant collection and preservation practices. These changes may both create patterns which could be erroneously attributed to environmental changes and obscure historical trends in plant traits. The utmost care ought to be taken to guard against the possibility of misinterpretation of data obtained from herbarium specimens. We recommend that directional changes in characters of herbarium specimens which occurred during the past 150‒200 years, primarily in specimen size and in the presence of reproductive structures, are accounted for when searching for the effects of past environmental changes on plant traits.

Screening potential insect vectors in a museum biorepository reveals undiscovered diversity of plant pathogens in natural areas

Phytoplasmas (Mollicutes, Acholeplasmataceae), vector-borne obligate bacterial plant parasites, infect nearly 1,000 plant species and unknown numbers of insects, mainly leafhoppers (Hemiptera, Deltocephalinae), which play a key role in transmission and epidemiology. Although the plant-phytoplasma-insect association has been evolving for >300 million years, nearly all known phytoplasmas have been discovered as a result of the damage inflicted by phytoplasma diseases on crops. Few efforts have been made to study phytoplasmas occurring in noneconomically important plants in natural habitats. In this study, a subsample of leafhopper specimens preserved in a large museum biorepository was analyzed to unveil potential new associations. PCR screening for phytoplasmas performed on 227 phloem-feeding leafhoppers collected worldwide from natural habitats revealed the presence of 6 different previously unknown phytoplasma strains. This indicates that museum collections of herbivorous insects represent a rich and largely untapped resource for discovery of new plant pathogens, that natural areas worldwide harbor a diverse but largely undiscovered diversity of phytoplasmas and potential insect vectors, and that independent epidemiological cycles occur in such habitats, posing a potential threat of disease spillover into agricultural systems. Larger-scale future investigations will contribute to a better understanding of phytoplasma genetic diversity, insect host range, and insect-borne phytoplasma transmission and provide an early warning for the emergence of new phytoplasma diseases across global agroecosystems.

Deep learning and computer vision will transform entomology

Most animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is sparse. Insect populations are challenging to study, and most monitoring methods are labor intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors can effectively, continuously, and noninvasively perform entomological observations throughout diurnal and seasonal cycles. The physical appearance of specimens can also be captured by automated imaging in the laboratory. When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity. Further, deep learning models can quantify variation in phenotypic traits, behavior, and interactions. Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates. We present examples of sensor-based monitoring of insects. We show how deep learning tools can be applied to exceptionally large datasets to derive ecological information and discuss the challenges that lie ahead for the implementation of such solutions in entomology. We identify four focal areas, which will facilitate this transformation: 1) validation of image-based taxonomic identification; 2) generation of sufficient training data; 3) development of public, curated reference databases; and 4) solutions to integrate deep learning and molecular tools.

A SMRT approach for targeted amplicon sequencing of museum specimens (Lepidoptera)-patterns of nucleotide misincorporation

Natural history collections are a valuable resource for molecular taxonomic studies and for examining patterns of evolutionary diversification, particularly in the case of rare or extinct species. However, the recovery of sequence information is often complicated by DNA degradation. This article describes use of the Sequel platform (Pacific Biosciences) to recover the 658 bp barcode region of the mitochondrial cytochrome c oxidase I (COI) gene from 380 butterflies with an average age of 50 years. Nested multiplex PCR was employed for library preparation to facilitate sequence recovery from extracts with low concentrations of highly degraded DNA. By employing circular consensus sequencing (CCS) of short amplicons (circa 150 bp), full-length barcodes could be assembled without a reference sequence, an important advance from earlier protocols which required reference sequences to guide contig assembly. The Sequel protocol recovered COI sequences (499 bp on average) from 318 of 380 specimens (84%), much higher than for Sanger sequencing (26%). Because each read derives from a single molecule, it was also possible to quantify the incidence of substitutions arising from DNA damage. In agreement with past work on sequence changes induced by DNA degradation, the transition C/G → T/A was the most prevalent category of change, but its rate of occurrence (4.58E−4) was so low that it did not impede the recovery of reliable sequences. Because the current protocol recovers COI sequence from most museum specimens, and because sequence fidelity is unaffected by nucleotide misincorporations, large-scale sequence characterization of museum specimens is feasible.

Natural history collections are critical resources for contemporary and future studies of urban evolution

Urban environments are among the fastest changing habitats on the planet, and this change has evolutionary implications for the organisms inhabiting them. Herein, we demonstrate that natural history collections are critical resources for urban evolution studies. The specimens housed in these collections provide great potential for diverse types of urban evolution research, and strategic deposition of specimens and other materials from contemporary studies will determine the resources and research questions available to future urban evolutionary biologists. As natural history collections are windows into the past, they provide a crucial historical timescale for urban evolution research. While the importance of museum collections for research is generally appreciated, their utility in the study of urban evolution has not been explicitly evaluated. Here, we: (a) demonstrate that museum collections can greatly enhance urban evolution studies, (b) review patterns of specimen use and deposition in the urban evolution literature, (c) analyze how urban versus rural and native versus nonnative vertebrate species are being deposited in museum collections, and (d) make recommendations to researchers, museum professionals, scientific journal editors, funding agencies, permitting agencies, and professional societies to improve archiving policies. Our analyses of recent urban evolution studies reveal that museum specimens can be used for diverse research questions, but they are used infrequently. Further, although nearly all studies we analyzed generated resources that could be deposited in natural history collections (e.g., collected specimens), a minority (12%) of studies actually did so. Depositing such resources in collections is crucial to allow the scientific community to verify, replicate, and/or re-visit prior research. Therefore, to ensure that adequate museum resources are available for future urban evolutionary biology research, the research community-from practicing biologists to funding agencies and professional societies-must make adjustments that prioritize the collection and deposition of urban specimens.

Biases in estimation of insect herbivory from herbarium specimens

Information regarding plant damage by insects in the past is essential to explore impacts of climate change on herbivory. We asked whether insect herbivory measured from herbarium specimens reflects the levels of herbivory occurring in nature at the time of herbarium sampling. We compared herbivory measurements between herbarium specimens collected by botany students and ecological samples collected simultaneously by the authors by a method that minimized unconscious biases, and asked herbarium curators to select one of two plant specimens, which differed in leaf damage, for their collections. Both collectors and curators generally preferred specimens with lesser leaf damage, but the strength of this preference varied among persons. In addition, the differences in measured leaf damage between ecological samples and herbarium specimens varied among plant species and increased with the increase in field herbivory. Consequently, leaf damage in herbarium specimens did not correlate with the actual level of herbivory. We conclude that studies of herbarium specimens produce biased information on past levels of herbivory, because leaf damage measured from herbarium specimens not only underestimates field herbivory, but it is not proportional to the level of damage occurring in nature due to multiple factors that cannot be controlled in data analysis.

Applying machine learning to investigate long-term insect-plant interactions preserved on digitized herbarium specimens

PREMISE

Despite the economic significance of insect damage to plants (i.e., herbivory), long-term data documenting changes in herbivory are limited. Millions of pressed plant specimens are now available online and can be used to collect big data on plant-insect interactions during the Anthropocene.

METHODS

We initiated development of machine learning methods to automate extraction of herbivory data from herbarium specimens by training an insect damage detector and a damage type classifier on two distantly related plant species (Quercus bicolor and Onoclea sensibilis). We experimented with (1) classifying six types of herbivory and two control categories of undamaged leaf, and (2) detecting two of the damage categories for which several hundred annotations were available.

RESULTS

Damage detection results were mixed, with a mean average precision of 45% in the simultaneous detection and classification of two types of damage. However, damage classification on hand-drawn boxes identified the correct type of herbivory 81.5% of the time in eight categories. The damage classifier was accurate for categories with 100 or more test samples.

DISCUSSION

These tools are a promising first step for the automation of herbivory data collection. We describe ongoing efforts to increase the accuracy of these models, allowing researchers to extract similar data and apply them to biological hypotheses.

LeafMachine: Using machine learning to automate leaf trait extraction from digitized herbarium specimens

PREMISE

Obtaining phenotypic data from herbarium specimens can provide important insights into plant evolution and ecology but requires significant manual effort and time. Here, we present LeafMachine, an application designed to autonomously measure leaves from digitized herbarium specimens or leaf images using an ensemble of machine learning algorithms.

METHODS AND RESULTS

We trained LeafMachine on 2685 randomly sampled specimens from 138 herbaria and evaluated its performance on specimens spanning 20 diverse families and varying widely in resolution, quality, and layout. LeafMachine successfully extracted at least one leaf measurement from 82.0% and 60.8% of high- and low-resolution images, respectively. Of the unmeasured specimens, only 0.9% and 2.1% of high- and low-resolution images, respectively, were visually judged to have measurable leaves.

CONCLUSIONS

This flexible autonomous tool has the potential to vastly increase available trait information from herbarium specimens, and inform a multitude of evolutionary and ecological studies.

Evolution of defense and herbivory in introduced plants-Testing enemy release using a known source population, herbivore trials, and time since introduction

The enemy release hypothesis is often cited as a potential explanation for the success of introduced plants; yet, empirical evidence for enemy release is mixed. We aimed to quantify changes in herbivory and defense in introduced plants while controlling for three factors that might have confounded past studies: using a wide native range for comparison with the introduced range, measuring defense traits without determining whether they affect herbivore preferences, and not considering the effect of time since introduction. The first hypothesis we tested was that introduced plants will have evolved lower levels of plant defense compared to their source population. We grew South African (source) and Australian (introduced) beach daisies (Arctotheca populifolia) in a common-environment glasshouse experiment and measured seven defense traits. Introduced plants had more ash, alkaloids, and leaf hairs than source plants, but were also less tough, with a lower C:N ratio and less phenolics. Overall, we found no difference in defense between source and introduced plants. To determine whether the feeding habits of herbivores align with changes in defense traits, we conducted preference feeding trials using five different herbivore species. Herbivores showed no overall preference for leaves from either group. The second hypothesis we tested was that herbivory on introduced plant species will increase through time after introduction to a new range. We recorded leaf damage on herbarium specimens of seven species introduced to eastern Australia and three native control species. We found no change in the overall level of herbivory experienced by introduced plants since arriving in Australia.

CONCLUSION

In the field of invasion ecology, we need to rethink the paradigm that species introduced to a new range undergo simple decreases in defenses against herbivores. Instead, plants are likely to employ a range of defense traits that evolve in both coordinated and opposing ways in response to a plethora of different biotic and abiotic selective pressures.

Generating segmentation masks of herbarium specimens and a data set for training segmentation models using deep learning

PREMISE

Digitized images of herbarium specimens are highly diverse with many potential sources of visual noise and bias. The systematic removal of noise and minimization of bias must be achieved in order to generate biological insights based on the plants rather than the digitization and mounting practices involved. Here, we develop a workflow and data set of high-resolution image masks to segment plant tissues in herbarium specimen images and remove background pixels using deep learning.

METHODS AND RESULTS

We generated 400 curated, high-resolution masks of ferns using a combination of automatic and manual tools for image manipulation. We used those images to train a U-Net-style deep learning model for image segmentation, achieving a final Sørensen-Dice coefficient of 0.96. The resulting model can automatically, efficiently, and accurately segment massive data sets of digitized herbarium specimens, particularly for ferns.

CONCLUSIONS

The application of deep learning in herbarium sciences requires transparent and systematic protocols for generating training data so that these labor-intensive resources can be generalized to other deep learning applications. Segmentation ground-truth masks are hard-won data, and we share these data and the model openly in the hopes of furthering model training and transfer learning opportunities for broader herbarium applications.

The Global Museum: natural history collections and the future of evolutionary science and public education

Natural history museums are unique spaces for interdisciplinary research and educational innovation. Through extensive exhibits and public programming and by hosting rich communities of amateurs, students, and researchers at all stages of their careers, they can provide a place-based window to focus on integration of science and discovery, as well as a locus for community engagement. At the same time, like a synthesis radio telescope, when joined together through emerging digital resources, the global community of museums (the ‘Global Museum’) is more than the sum of its parts, allowing insights and answers to diverse biological, environmental, and societal questions at the global scale, across eons of time, and spanning vast diversity across the Tree of Life. We argue that, whereas natural history collections and museums began with a focus on describing the diversity and peculiarities of species on Earth, they are now increasingly leveraged in new ways that significantly expand their impact and relevance. These new directions include the possibility to ask new, often interdisciplinary questions in basic and applied science, such as in biomimetic design, and by contributing to solutions to climate change, global health and food security challenges. As institutions, they have long been incubators for cutting-edge research in biology while simultaneously providing core infrastructure for research on present and future societal needs. Here we explore how the intersection between pressing issues in environmental and human health and rapid technological innovation have reinforced the relevance of museum collections. We do this by providing examples as food for thought for both the broader academic community and museum scientists on the evolving role of museums. We also identify challenges to the realization of the full potential of natural history collections and the Global Museum to science and society and discuss the critical need to grow these collections. We then focus on mapping and modelling of museum data (including place-based approaches and discovery), and explore the main projects, platforms and databases enabling this growth. Finally, we aim to improve relevant protocols for the long-term storage of specimens and tissues, ensuring proper connection with tomorrow’s technologies and hence further increasing the relevance of natural history museums.

Museum specimens provide novel insights into changing plant-herbivore interactions

Mounting evidence shows that species interactions may mediate how individual species respond to climate change. However, long-term anthropogenic effects on species interactions are poorly characterized owing to a lack of data. Insect herbivory is a major ecological process that represents the interaction between insect herbivores and their host plants, but historical data on insect damage to plants is particularly sparse. Here, we suggest that museum collections of insects and plants can fill key gaps in our knowledge on changing trophic interactions, including proximate mechanisms and the net outcomes of multiple global change drivers across diverse insect herbivore-plant associations. We outline theory on how global change may affect herbivores and their host plants and highlight the unique data that could be extracted from museum specimens to explore their shifting interactions. We aim to provide a framework for using museum specimens to explore how some of the most diverse co-evolved relationships are responding to climate and land use change.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'.