Postdoctoral scientists are mentors, and it is time to recognize their work.

Academia often fails to recognize the important work that supports its functioning, such as mentoring and teaching performed by postdoctoral researchers. This is a particular problem for early-career researchers, but opportunities exist to improve the status quo.

Seasonal and annual dynamics of western Canadian boreal forest plant communities: A legacy data set spanning four decades.

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.

Data rescue: saving environmental data from extinction.

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.

Data Science in Undergraduate Life Science Education: A Need for Instructor Skills Training.

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.

Cultivating inclusive instructional and research environments in ecology and evolutionary science.

As we strive to lift up a diversity of voices in science, it is important for ecologists, evolutionary scientists, and educators to foster inclusive environments in their research and teaching. Academics in science often lack exposure to research on best practices in diversity, equity, and inclusion and may not know where to start to make scientific environments more welcoming and inclusive. We propose that by approaching research and teaching with empathy, flexibility, and a growth mind-set, scientists can be more supportive and inclusive of their colleagues and students. This paper provides guidance, explores strategies, and directs scientists to resources to better cultivate an inclusive environment in three common settings: the classroom, the research laboratory, and the field. As ecologists and evolutionary scientists, we have an opportunity to adapt our teaching and research practices in order to foster an inclusive educational ecosystem for students and colleagues alike.

Temporal changes in species composition affect a ubiquitous species' use of habitat patches.

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.

Developing a modern data workflow for regularly updated data.

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.