Biology subjects

Bledsoe, E. K.

Publications and source records attributed to Bledsoe, E. K..

5 recordsLinked to original sources

bioRxiv2022

Snake Herders: novel cultural adaptation in response to prairie rattlesnakes in a localpopulation of Black-tailed prairie dogs.

We describe a case of a unique antipredator behavior sequence in response to rattlesnakes observed in a population of black-tailed prairie dogs in Fort Collins, Colorado. An analysis of 5 independent opportunistic video recordings of 6 identifiable individuals revealed individuals across multiple social groups within the population engaged in novel behavioral responses to prairie rattlesnakes. Using Markov-chain analysis we found that prairie dogs engaged in non-random behavioral transitions and that specific pairs of behaviors were contributing to this pattern. We also observed that prairie dogs in this population engaged in novel responses to prairie rattlesnakes, including escorting behavior, where a prairie dog would walk alongside the snake.

animal behavior and cognition↗

bioRxiv2021

Training Data: How can we best prepare instructors to teach data science in undergraduate biology and environmental science courses?

There is a clear and concrete need for greater quantitative literacy in the biological and environmental sciences. Data science training for students in higher education necessitates well-equipped and confident instructors across curricula. However, not all instructors are versed in data science skills or research-based teaching practices. Our study sought to survey the state of data science education across institutions of higher learning, identify instructor needs, and illuminate barriers to teaching data science in the classroom. We distributed a survey to instructors around the world, focused on the United States, and received 106 complete responses. Our results indicate that instructors across institutions use, teach, and view data management, analysis, and visualization as important for students to learn. Code, modeling, and reproducibility were less valued by instructors, although there were differences by institution type (doctoral, masters, or baccalaureate), and career stage (time since terminal degree). While there were a variety of barriers highlighted by respondents, instructor background, student background, and space in the curriculum were the greatest barriers of note. Interestingly, instructors were most interested in receiving training for how to teach code and data analysis in the undergraduate classroom. Our study provides an important window into how data science is taught in higher education as well as suggestions for how we can best move forward with empowering instructors across disciplines.

scientific communication and education↗

bioRxiv2019

Temporal changes in species composition affect a ubiquitous species views of patch quality

Metacommunity theory, particularly the patch dynamics archetype, suggests that an organisms perspective of patch quality can depend solely on the local competitive environment. Across landscapes, however, shifts in species composition often co-occur with shifts in habitat, making it difficult to disentangle the role of competitors and environment on assessments of patch quality. Using 26 years of rodent community time-series data, we show that perception of patch quality by a small, ubiquitous granivore (Chaetodipus penicillatus) shifted with both spatial and temporal changes in species composition. In the mid-1990s, C. baileyi, a novel competitor, colonized and the study site. C. penicillatus patch preference shifted with increasing abundance of C. baileyi, including corresponding changes in estimated survival, probability of movement between patches, and the arrival of new individuals in patches. Changes in energy use on patches due to the establishment of C. baileyi point to a potential mechanism for the differences in patch quality perceived by C. penicillatus. These results demonstrate that experimental time-series data can be used to examine how changes in species composition and, specifically, changes in the competitive landscape, can affect perception of patch quality and patch preference.

ecology↗

bioRxiv2018

Developing a modern data workflow for living data

Data management and publication are core components of the research process. An emerging challenge that has received limited attention in biology is managing, working with, and providing access to data under continual active collection. \"Evolving data\" present unique challenges in quality assurance and control, data publication, archiving, and reproducibility. We developed a evolving data 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 uses two tools from software development, version control and continuous integration, to create a modern data management system that automates the pipeline.

ecology↗

bioRxiv2018

Developing an automated iterative near-term forecasting system for an ecological study

O_LIMost 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.\nC_LIO_LIIterative 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.\nC_LIO_LIThe 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.\nC_LIO_LIAutomated 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 automated forecasting systems and use them to advance our understanding of natural systems.\nC_LI

ecology↗