Publications

Plant and fungal specimens in herbaria are becoming primary resources for investigat-

ing how plant phenology and geographic distributions shift with climate change, greatly expanding

inferences across spatial, temporal, and phylogenetic dimensions. However, these specimens contain a

wealth of additional data, including nutrients, defensive compounds, herbivore damage, disease

lesions, and signatures of physiological processes, that capture ecological and evolutionary responses

to the Anthropocene but which are less frequently utilized. Here, we outline the diversity of herbarium

data, global change topics to which they have been applied, and new hypotheses they could inform.

We find that herbarium data have been used extensively to study impacts of climate change and inva-

sive species, but that such data are less commonly used to address other drivers of biodiversity loss,

including habitat conversion, pollution, and overexploitation. In addition, we note that fungal speci-

mens are under-explored relative to vascular plants. To facilitate broader application of plant and fun-

gal specimens in global change research, we consider the limitations of these data and modern

sampling and statistical tools that may be applied to surmount challenges they present. Using a case

study of insect herbivory, we illustrate how novel herbarium data may be employed to test hypotheses

for which few data exist. With the goal of positioning herbaria as hubs for global change research, we

suggest future research directions and curation priorities.

Key words: climate change; extinction; global change; habitat conversion; herbarium; historical data; invasive

species; museum specimens.

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.

Climate change affects not just where species are found, but also when species’ key life-history events occur—their phenol-

ogy. 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 poten-

tially 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.

PREMISE OF THE STUDY: Partitioning of population genetic variation in plants may be affected by numerous factors including life history and dispersal characteristics. In parasitic plants, interactions with host populations may be an additional factor influencing partitioning. To test for hierarchical population genetic patterns related to obligate endoparasitism, we studied three species of Rafflesiaceae, which grow as extremely reduced endophytes infecting Tetrastigma vines in Southeast Asia.

METHODS: Microsatellite markers were developed and multilocus genotypes were determined for Rafflesia cantleyi, Rafflesia tuan-mudae, and Sapria himalayana and each of their Tetrastigma hosts. Relatedness among parasite individuals was estimated, and AMOVAs were used to determine levels of population genetic subdivision.

KEY RESULTS: Microsatellite genotypes for 340 paired parasite and host samples revealed that host vines were infected by numerous Rafflesiaceae individuals that may spread for up to 14 m within stem tissues. Surprisingly, Rafflesiaceae parasites within a given host are significantly more closely related to each other than individuals of the same species in other host individuals. The pattern of hierarchical population genetic subdivision we detected across species is likely due to limited seed dispersal with reinfection of natal host vines.

CONCLUSIONS: These findings demonstrate common population genetic patterns between animal and plant parasites, potentially indicating advantages of close relatives infecting hosts. This study also has important conservation implications for Rafflesiaceae since our data suggest that destruction of a single infected host vine could result in large genetic losses.

• Phenology is a key aspect of plant success. Recent research has demonstrated that herbarium specimens can provide important information on plant phenology. Massive digitization efforts have the potential to greatly expand herbarium-based phenological research, but also pose a serious challenge regarding efficient data collection.
• Here, we introduce CrowdCurio, a crowdsourcing tool for the collection of phenological data from herbarium specimens. We test its utility by having workers collect phenological data (number of flower buds, open flowers and fruits) from specimens of two common New England (USA) species: Chelidonium majus and Vaccinium angustifolium. We assess the reliability of using nonexpert workers (i.e. Amazon Mechanical Turk) against expert workers. We also use these data to estimate the phenological sensitivity to temperature for both species across multiple phenophases.
• We found no difference in the data quality of nonexperts and experts. Nonexperts, however, were a more efficient way of collecting more data at lower cost. We also found that phenological sensitivity varied across both species and phenophases.
• Our study demonstrates the utility of CrowdCurio as a crowdsourcing tool for the collection of phenological data from herbarium specimens. Furthermore, our results highlight the insight gained from collecting large amounts of phenological data to estimate multiple phenophases.

Aim When precise coordinate data for training species distribution models (SDMs) are lacking, climatic variables are often assigned to centroids of geopolitically defined regions, frequently counties. This is problematic because approximations using centroids may not be representative of the regional climate or the locality from where species actually occur, thus leading to spurious conclusions. We evaluated county centroid climate versus simple alternatives for assigning climate to species observations in the absence of precise occurrence data.

Location United States of America.

Methods We assessed the disparity between the actual climate of all points within a county and metrics estimating county climate using the climate of geographical centroid, mean county climate and median county climate. To further evaluate the performance of these metrics, we generated SDMs of four common species using these estimates and compared the results with observed

species distributions (red trillium, Pacific trillium, tall thistle and annual fleabane). Finally, we projected future ranges for annual fleabane to examine the difference in predicted range change between models.

Results Mean and median climate metrics were significantly better fits for approximating the climate of specimen records than climate of the geographical centroid. Moreover, county mean climate SDMs were the most similar to SDMs using actual coordinate data. In contrast, models applying climate to county centroid significantly overpredicted species range. This had implications for future projections of annual fleabane SDMs: the county centroid model predicted a decrease in suitable habitats for this species while other models predicted an increase.

Main conclusions County centroid climate, although commonly applied, is not suitable for SDMs as a means to approximate species climate when locality data are less precise. When only county level data are available, and more computationally intensive methods of accounting for spatial uncertainty cannot be readily implemented, we suggest considering mean county climate as an alternative.