Ellen Bledsoe

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• Byrnes, J. E., Brown, A., Sheridan, K., Peller, T., Lawlor, J., Beaulieu, J., Muñoz, J., Hesketh, A., Pereira, A., Knight, N. S., Super, L., Bledsoe, E. K., Burant, J. B., Dijkstra, J. A., & Benes, K. (2024). Notes from the past show how local variability can stymie urchins and the rise of the reds in the Gulf of Maine. Ecopshere. doi:https://doi.org/10.32942/osf.io/u6exy
• Byrnes, J. E., Brown, A., Sheridan, K., Peller, T., Lawlor, J., Beaulieu, J., Muñoz, J., Hesketh, A., Pereira, A., Knight, N. S., Super, L., Bledsoe, E. K., Burant, J. B., Dijkstra, J. A., & Benes, K. (2024). Notes from the past show how local variability can stymie urchins and the rise of the reds in the Gulf of Maine. Ecosphere, 15(4). doi:10.1002/ecs2.4800
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  The impacts of global change—from shifts in climate to overfishing to land use change—can depend heavily on local abiotic context. Building an understanding of how to downscale global change scenarios to local impacts is often difficult, however, and requires historical data across large gradients of variability. Such data are often not available—particularly in peer reviewed or gray literature. However, these data can sometimes be gleaned from casual records of natural history—field notebooks, data sheet marginalia, course notes, and more. Here, we provide an example of one such approach for the Gulf of Maine, as we seek to understand how environmental context can influence local outcomes of region-wide shifts in subtidal community structure. We explore a decade of hand-drawn algal cover maps around Appledore Island made by Dr. Art Borror while teaching at the Shoals Marine Lab. Appledore's steep wave exposure gradient—from exposed to the open ocean to fully protected—provides a living laboratory to test interactions between global change and local conditions. We then recreate Borror's methods two and a half decades later. We show that overfishing-driven urchin outbreaks in the 1980s were slowed or stopped by wave exposure and benthic topography. Similarly, local variation appears to have curtailed current invasions by filamentous red algae. Last, some formerly dominant kelps have disappeared over the past 40 years—an observation verified by subtidal surveys. Global change is altering life in the seas around us. While underutilized, solid natural history observations stand as a key resource for us to begin to understand how global change will translate to the heterogeneous mosaic of life in a future Gulf of Maine and other ecosystems around the world.
• Bledsoe, E. K. (2023). Postdoctoral scientists are mentors, and it is time to recognize their work. PLoS Biology, 21(Issue 11). doi:10.1371/journal.pbio.3002349
• Higino, G., Barros, C., Bledsoe, E. K., Roche, D. G., Binning, S. A., & Poisot, T. (2023). Postdoctoral scientistis are mentors, and it is time to recognize their work. PLoS Biology, 21(11), e3002349. doi:https://doi.org/10.1371/journal.pbio.3002349
• Verdolin, J. L., & Bledsoe, E. K. (2023). Snake Herders: Novel anti-predator behavior by black-tailed prairie dogs in response to prairie rattlesnakes. . Journal of Ethology, 6.
• Verdolin, J. L., & Bledsoe, E. K. (2023). Snake herders: novel anti-predator behavior by black-tailed prairie dogs in response to prairie rattlesnakes. Journal of Ethology, 42(1). doi:10.1007/s10164-023-00797-y
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  We describe a case of a unique antipredator behavior sequence in response to rattlesnakes in a population of black-tailed prairie dogs in Fort Collins, Colorado. Our analysis revealed individuals across multiple social groups within the population engaged in novel behavioral responses to prairie rattlesnakes, including ‘escorting’ behavior, where a prairie dog would walk alongside the snake. Using Markov chain analysis, we also found that prairie dogs engaged in non-random behavioral transitions and that specific pairs of behaviors were contributing to this pattern. Digital video images related to the article are available at http://www.momo-p.com/showdetail-e.php?movieid=momo230929cl01a).
• Bledsoe, E. K. (2022). Data rescue: saving environmental data from extinction. Proceedings of the Royal Society B: Biological Sciences.
• Bledsoe, E. K. (2022). Seasonal and annual dynamics of western Canadian boreal forest plant communities: A legacy data set spanning four decades. Ecology.
• Bledsoe, E. K. (2022). portalcasting: Supporting automated forecasting of rodent populations. Journal of Open Source Software.
• Bledsoe, E. K., Burant, J. B., Higino, G. T., Roche, D. G., Binning, S. A., Finlay, K., Pither, J., Pollock, L. S., Sunday, J. M., & Srivastava, D. S. (2022). Data rescue: Saving environmental data from extinction. Proceedings of the Royal Society B: Biological Sciences, 289(Issue 1979). doi:10.1098/rspb.2022.0938
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  Historical and long-term environmental datasets are imperative to understanding how natural systems respond to our changing world. Although immensely valuable, these data are at risk of being lost unless actively curated and archived in data repositories. The practice of data rescue, which we define as identifying, preserving, and sharing valuable data and associated metadata at risk of loss, is an important means of ensuring the long-term viability and accessibility of such datasets. Improvements in policies and best practices around data management will hopefully limit future need for data rescue; these changes, however, do not apply retroactively. While rescuing data is not new, the term lacks formal definition, is often conflated with other terms (i.e. data reuse), and lacks general recommendations. Here, we outline seven key guidelines for effective rescue of historically collected and unmanaged datasets. We discuss prioritization of datasets to rescue, forming effective data rescue teams, preparing the data and associated metadata, and archiving and sharing the rescued materials. In an era of rapid environmental change, the best policy solutions will require evidence from both contemporary and historical sources. It is, therefore, imperative that we identify and preserve valuable, at-risk environmental data before they are lost to science.
• Hesketh, A. V., Loesberg, J. A., Bledsoe, E. K., Karst, J., & Macdonald, S. E. (2022). Seasonal and annual dynamics of western Canadian boreal forest plant communities: A legacy data set spanning four decades. Ecology, 103(Issue 11). doi:10.1002/ecy.3805
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  As boreal forests rapidly warm due to anthropogenic climate change, long-term baseline community data are needed to effectively characterize the corresponding ecological changes that are occurring in these forests. The combined seasonal dynamics (SEADYN) and annual dynamics (ANNDYN) data set, which documents the vegetative changes in boreal forests during the snow-free period, is one such source of baseline community data. These data were collected by George H. La Roi and colleagues in Alberta, Canada from 1980 to 2015 within permanent sampling plots established in the Hondo-Slave Lake area (eight stands; 1980–2015) in central Alberta and the Athabasca Oil Sands (AOS) region (17 stands; 1981–1984) near Fort McMurray in northeastern Alberta. Various data were collected, with temporal and spatial coverage differing by data set. These data sets include, but are not limited to, cover of each identified vascular plant and bryoid (moss, liverwort, and lichen) species; forest mensuration; forest litter production; and soil temperature and moisture. Notably, permanent sampling plots were set up as a grid, which will facilitate analyses of spatial relations. These data can be used to analyze long-term changes in seasonal dynamics and succession within boreal forest communities and serve as a baseline for comparison with future forest conditions in unmanaged, managed, and reclaimed forests. Data are released under a CC-BY license; please cite this data paper when using the data for analyses.
• Aiello-Lammens, M. E., Bledsoe, E. K., Crispo, E., Emery, N., Farrell, K. J., Kerkhoff, A. J., McCall, A. C., O'Donnell, K. L., & Supp, S. R. (2021). Data Science in Undergraduate Life Science Education: A Need for Instructor Skills Training. BioScience, 71(12). doi:10.1093/biosci/biab107
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  There is a clear demand for quantitative literacy in the life sciences, necessitating competent instructors in higher education. However, not all instructors are versed in data science skills or research-based teaching practices. We surveyed biological and environmental science instructors (n = 106) about the teaching of data science in higher education, identifying instructor needs and illuminating barriers to instruction. Our results indicate that instructors use, teach, and view data management, analysis, and visualization as important data science skills. Coding, modeling, and reproducibility were less valued by the instructors, although this differed according to institution type and career stage. The greatest barriers were instructor and student background and space in the curriculum. The instructors were most interested in training on how to teach coding and data analysis. Our study provides an important window into how data science is taught in higher education biology programs and how we can best move forward to empower instructors across disciplines.
• Bledsoe, E. K. (2021). Cultivating inclusive instructional and research environments in ecology and evolutionary science. Ecology and Evolution.
• Bledsoe, E. K. (2019). portalr: an R package for summarizing and using the Portal Project Data. Journal of Open Source Software.
• Bledsoe, E. K., & Ernest, S. K. (2019). Temporal changes in species composition affect a ubiquitous species’ use of habitat patches. Ecology, 100(Issue 11). doi:10.1002/ecy.2869
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  Across landscapes, shifts in species composition often co-occur with shifts in structural or abiotic habitat features, making it difficult to disentangle the role of competitors and environment on assessments of patch quality. Using over two decades of rodent community data from a long-term experiment, we show that a small, ubiquitous granivore (Chaetodipus penicillatus) shifted its use of different experimental treatments with the establishment of a novel competitor, C. baileyi. Shifts in residency, probability of movement between patches, and the arrival of new individuals in patches altered which treatment supported the highest abundances of C. penicillatus. Our results suggest that the establishment of a new species worsened the quality of the originally preferred treatment, likely by impacting resource availability. Paradoxically, the presence of the new species also increased C. penicillatus’ use of the less preferred treatment, potentially through shifts in the competitive network on those plots.
• Bledsoe, E. K., & Ernest, S. M. (2019). Temporal changes in species composition affect a ubiquitous species’ views of patch quality. Ecology. doi:https://doi.org/10.1002/ecy.2869
• Bledsoe, E. K., Christensen, E. M., Diaz, R. M., Ernest, S. K., Supp, S. R., White, E. P., & Yenni, G. M. (2019). Developing a modern data workflow for regularly updated data. PLOS Biology, 17(1). doi:10.1371/journal.pbio.3000125
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  Over the past decade, biology has undergone a data revolution in how researchers collect data and the amount of data being collected. An emerging challenge that has received limited attention in biology is managing, working with, and providing access to data under continual active collection. Regularly updated data present unique challenges in quality assurance and control, data publication, archiving, and reproducibility. We developed a workflow for a long-term ecological study that addresses many of the challenges associated with managing this type of data. We do this by leveraging existing tools to 1) perform quality assurance and control; 2) import, restructure, version, and archive data; 3) rapidly publish new data in ways that ensure appropriate credit to all contributors; and 4) automate most steps in the data pipeline to reduce the time and effort required by researchers. The workflow leverages tools from software development, including version control and continuous integration, to create a modern data management system that automates the pipeline.
• Bledsoe, E. K., Christensen, E. M., Ernest, S. K., Simonis, J. L., Taylor, S. D., White, E. P., & Yenni, G. M. (2019). Developing an automated iterative near‐term forecasting system for an ecological study. Methods in Ecology and Evolution, 10(3). doi:10.1111/2041-210x.13104
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  Most forecasts for the future state of ecological systems are conducted once and never updated or assessed. As a result, many available ecological forecasts are not based on the most up-to-date data, and the scientific progress of ecological forecasting models is slowed by a lack of feedback on how well the forecasts perform. Iterative near-term ecological forecasting involves repeated daily to annual scale forecasts of an ecological system as new data becomes available and regular assessment of the resulting forecasts. We demonstrate how automated iterative near-term forecasting systems for ecology can be constructed by building one to conduct monthly forecasts of rodent abundances at the Portal Project, a long-term study with over 40 years of monthly data. This system automates most aspects of the six stages of converting raw data into new forecasts: data collection, data sharing, data manipulation, modeling and forecasting, archiving, and presentation of the forecasts. The forecasting system uses R code for working with data, fitting models, making forecasts, and archiving and presenting these forecasts. The resulting pipeline is automated using continuous integration (a software development tool) to run the entire pipeline once a week. The cyberinfrastructure is designed for long-term maintainability and to allow the easy addition of new models. Constructing this forecasting system required a team with expertise ranging from field site experience to software development. Automated near-term iterative forecasting systems will allow the science of ecological forecasting to advance more rapidly and provide the most up-to-date forecasts possible for conservation and management. These forecasting systems will also accelerate basic science by allowing new models of natural systems to be quickly implemented and compared to existing models. Using existing technology, and teams with diverse skill sets, it is possible for ecologists to build these systems and use them to advance our understanding of natural systems.
• Bledsoe, E. K. (2018). Developing a modern data workflow for evolving data. bioRxiv.
• Bledsoe, E. K. (2014). What is the sound of fear? Behavioral responses of white-crowned sparrows Zonotrichia leucophrys to synthesized nonlinear acoustic phenomena. Current Zoology.

Others

• Bledsoe, E. K. (2021, August). An Introduction to Ally Skills for Natural History Collections Professionals. https://doi.org/10.31219/osf.io/e2yrv
• Bledsoe, E. K. (2018, February). Developing an automated iterative near-term forecasting system for an ecological study. https://doi.org/10.1101/268623
• Bledsoe, E. K. (2018, May). The Portal Project: a long-term study of a Chihuahuan desert ecosystem. https://doi.org/10.1101/332783