This result matches incredibly well with the predictions outlined in Figure 3.2. It also demonstrates that any spatial externalities associated with increased corn cultivation due to ethanol refinery location occur entirely within 30 miles of ethanol refineries. This suggests highly localized effects. What do the acreage increases highlighted in Figure 3.11 mean for nitrogen application? A 2007 Iowa State University Extension publication suggests that optimal nitrogen application for corn-after-soy is 125 lb N/acre, and optimal application for corn-after-corn is 175 lb N/acre . Taking a middle value of 140 lb N/acre, , the 298,718 acres of increased corn acreage estimated in Figure 3.11 represent 41,820,520 lbs, or almost 21,000 tons of extra nitrogen. These 21,000 tons of additional nitrogen that are attributable to the distance effect of ethanol refinery placement are essentially all applied to areas within 30 miles of an ethanol refinery. While this number is relatively small relative to the total application of nitrogen in the US Corn Belt, there is cause for concern about localized geographic effects. Nitrate runoff into local water sources is harmful to water ecosystems, animals, and humans, and has been a growing problem in the US Corn Belt . Local water quality data from the USGS could be used in future research to look for an effect of ethanol refineries on nitrate levels directly.The digitization of herbarium specimens and their associated data have advanced our ability to understand complex and changing biological systems . Digitizing herbarium records has advanced our ability to track changes in the distributions of organisms , stacking flower pot tower but herbarium specimens are rich with additional information regarding plant health, reproductive condition, and morphology that is generally not captured in current digitization workflows .
Because the utility of specimens for research is accelerating, it is essential that we structure digital data collection in ways that best facilitate longevity and integration across data sources. Of particular interest is the enormous potential of herbarium specimens as a resource for information on plant phenology . Plant phenology has complex, cascading effects on multiple levels of biological organization from individuals to ecosystems . Temporal mismatches between plants and pollinators can quickly drive populations extinct, cause rapid evolutionary shifts, and result in billions of dollars of agricultural losses . Phenology has also been used to study the impact of climate change in a range of organisms and vegetation types . Consequently, maximizing the use of herbarium specimens for phenological research is not only important for improving our understanding of evolutionary change, it is also a matter of great practical concern for addressing environmental problems. Recent studies have demonstrated the potential of herbarium specimens to be used in evaluating temporal and spatial variation in plant phenology despite known biases of herbarium records . These studies have provided three valuable outcomes. First, for several species, we now have a quantitative historical understanding of their phenological change over time . Second, for some species, relationships between temporal or spatial variation in phenology and climate have been detected; these relationships, in turn, provide a basis for forecasting the effects of ongoing climate change on the seasonal cycles of these taxa . Third, we have an improved understanding of the specific advantages of herbarium specimens for phenological research, such as filling gaps in long-term or observational data sets, either for a period of time or for underrepresented regions .
Given the ecological importance of phenology, the demonstrated value of herbarium specimens for phenological research, and the potential for digitization efforts to maximize herbarium records as a resource, it is necessary to develop robust standards for how phenological data are captured during or after the digitization process. There are currently two principal limitations to accessing and using phenological data from herbarium specimens: the paucity of high-quality images accompanying digitized specimen records and the lack of standardized methodology for capturing specimens’ reproductive traits and sharing the resulting data. If any phenological information is present on a label or visible on a specimen, it is parsed in numerous—and often arbitrary—ways during digitization. For example, phenological data embedded in a label might be “on a south facing slope in full flower,” but this information might be digitally captured in the ‘habitat,’ ‘notes,’ ‘plant description,’ or other field of a local database. Even if a local database does contain a field explicitly for phenological characters, each institution independently decides how to record the states present on the sheet. For example, in the SEINet collaborative , which consists of 251 U.S. collections and 11.8 million records, there are 2.6 million records with text present in a database field called ‘reproductive Condition.’ The majority of terms found within this field specify flowering, fruiting, sterile, spores, and/or cones; however, these terms are expressed in over 4000 unique text strings . Some collections specify “flowering,” while other records state “flws.” Some are ambiguous . The lack of a controlled vocabulary for this field makes aggregating these data for research purposes onerous.
Local databases often share their data with data aggregators such as iDigBio or directly with users as a suite of Darwin Core Archive files, an exchange standard described more fully below . However, the relevant Darwin Core fields are equally diverse, with most phenological traits being placed into the fields ‘occurrence Remarks,’ ‘organism Remarks,’ ‘dynamic Properties,’ ‘reproductive Condition,’ or ‘field Notes.’ It is clear that there is a huge potential for using phenological data from herbarium specimens . We propose a method here to broaden the scope and longevity of digitization efforts through a standardized methodology for scoring reproductive characters from herbarium specimens and provide a means of sharing the resulting data in a Darwin Core format. The protocol we describe here will unlock the potential of herbaria for phenological research by facilitating comparability among herbaria, research groups, and other methodologies used to collect phenological data .Prior to the workshop, we developed a survey to assess needs of the phenological community and herbarium data users and to review the current ways phenological data were being captured. We received 76 responses to the survey, and the respondents identified themselves as being from collections, monitoring, or research areas . With this survey and input from participants at the workshop, we reviewed the ways in which herbaria currently capture phenological traits. The two most-scored traits from specimens are the presence of open flowers and the presence of fruit . Most respondents also felt that of all possible traits, open flowers and fruits were the most important traits to score on a specimen. Participants of the workshop echoed this sentiment. We reviewed previous phenological research that was based on data derived from herbarium specimens in order to identify the types of raw data necessary and sufficient to achieve a variety of research goals. These findings are summarized in Willis et al. . When developing a scoring protocol, we considered the challenges and limitations to scoring specimens and the potential solutions to these limitations. We considered hard-to-see floral parts, trained vs. untrained scorers, the limited resources of most herbaria, and the likelihood of community-wide adoption. We also considered the costs and benefits of recording qualitative data vs. quantitative data . One of our primary concerns is that any resulting data from attempts to score phenological traits should be shareable in Darwin Core–formatted files to help ensure the usefulness and longevity of these data. Representatives from the data standards community, Biodiversity Information Standards , including Darwin Core and Apple Core, provided input for representing phenological stages using current biodiversity standards. Finally, to ensure that phenological traits from specimens can be integrated with other sources, participants included members of the USA National Phenology Network, the California Phenology Network, the National Ecological Observatory Network, the Royal Botanic Garden Edinburgh, the Pan-European Phenology Network, and the Plant Phenology Ontology. We propose that reproductive traits for specimens of seed plants be scored according to the following hierarchical categories/questions . Our protocol uses terminology from the Plant Ontology to represent plant parts and traits that correspond to plant phenological traits in the Plant Phenology Ontology. By using a vocabulary that directly maps to ontologies, danish trolley data collected with this method can be easily ingested into data stores using those ontologies and thereby integrated with other sources of phenological data such as direct observations in situ or remote sensing .
The question “Are ‘reproductive structures’ present? ,” while the broadest question, was still determined to have value for scoring specimen records. Having this information allows researchers to filter millions of records quickly to find those that contribute to phenological research. It is also relatively easy for users with different levels of botanical training to score. A “yes” means that some reproductive structures of some kind are present. A “no” means that the specimen is sterile and strictly vegetative. It is important to note that this first-order scoring can apply to all taxonomic groups, even beyond seed plants. Some taxonomic groups may exhibit specialized structures that make it more difficult for non-experts to complete this process , but we anticipate that this challenge will be limited. Minimally, first-order scoring will allow for records to be filtered and then subsequently scored in more detail.For specimens that are scored as having reproductive structures present, it is valuable to characterize which reproductive structures are present. Most research thus far has used specimens with open flowers. For flowering plants, we propose the following second order, non-mutually exclusive questions: “Are ‘unopened flowers’ present?,” “Are ‘open flowers’ present?,” “Are ‘fruits’ presents?” For gymnosperms the questions are: “Are ‘pollen cones’ present?” and “Are ‘seed cones’ present?” . The term “bud/s” can confuse floral buds with leaf buds; therefore, the PPO and this protocol refer to unopened flowers only. The second-order questions are not mutually exclusive. If unopened flowers, open flowers, and fruits are all present on a sheet, all questions can be answered in the affirmative. Having these data allows researchers to quickly identify the records that pertain to their individual research questions. The second-order questions require greater training for personnel to accurately discriminate unopened flowers, open flowers, and fruits. For many taxa , floral structures are small and distinguishing between unopened flowers, open flowers, and fruits can be challenging. Additionally, it is important that scorers are trained to distinguish between leaf buds and flower buds . As training materials are developed for various plant groups they should be shared widely across the community. While second-order scorings will determine which specimens should be included in phenological research, it is often valuable to know the specimen’s specific phenophase. Analyses can be more precise if we can distinguish between specimens in full flower from those specimens at the beginning or end of the flowering cycle. The third-order scorings are intended to place individual specimens at a specific point in phenological development. As such, these subcategories and the units used to report them may vary depending on the institutional or research priorities that generate them. We do not specify exactly what the third-order categories should be, as these will be determined by research priorities and staff time, but rather we explain how these questions are most commonly expressed or could be expressed within our proposed framework. Although we do not specify third-order categories, we do strongly recommend that researchers clearly define their categories and make their definitions broadly accessible, along with pertinent metadata. For example, the Simple Knowledge Organization System provides a framework for representing controlled vocabularies that easily lends itself to being shared online. The New England Vascular Plant project has devised a vocabulary following these guidelines and has published their vocabulary using SKOS . Furthermore, regardless of the nature of third-order categories, we strongly recommend that researchers share these data via the Darwin Core extensions explained below. For quantitative phenological data, some research groups require count data from a specimen, such as the numbers of unopened flowers, open flowers, and fruits on each sheet. Counts would be considered third-order scoring. For some analyses, raw count data may be transformed to express the proportions of reproductive organs represented by unopened flowers, open flowers, and fruits, thereby distinguishing specimens that represent early-, peak-, or late-flowering individuals. Even if count data are not precisely recorded from a sheet, the degree of flowering can be binned into categories representing early, peak, and late flowering . Note that in the example shown in Table 4, the third-order scorings, if categorical, are mutually exclusive. A sheet cannot simultaneously be ‘mostly unopened flowers’ and ‘mostly open flowers.’ Finally, in order to integrate third-order scorings with other sources of phenological data, we recommend use of the PPO.