Evidence map›Paper›PMID 33976765›Full record

ArticleEcology and evolution2021

Developing a flexible learning activity on biodiversity and spatial scale concepts using open-access vegetation datasets from the National Ecological Observatory Network.

Diane M Styers, Jennifer L Schafer, Mary Beth Kolozsvary, Kristen M Brubaker, Sara E Scanga, Laurel J Anderson, Jessica J Mitchell, David Barnett

Abstract read
In one paragraph

Article in Ecology and evolution, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Diane M StyersWestern Carolina University Cullowhee NC USA.ORCID https://orcid.org/0000-0002-2658-9357
Jennifer L SchaferWinthrop University Rock Hill SC USA.
Mary Beth KolozsvarySiena College Loudonville NY USA.ORCID https://orcid.org/0000-0002-7549-5277
Kristen M BrubakerHobart and William Smith Colleges Geneva NY USA.
Sara E ScangaUtica College Utica NY USA.
Laurel J AndersonOhio Wesleyan University Delaware OH USA.
Jessica J MitchellSpatial Analysis Lab University of Montana Missoula MT USA.
David BarnettNational Ecological Observatory Network Battelle Memorial Institute Boulder CO USA.ORCID https://orcid.org/0000-0002-0485-3567

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biodiversity is a complex, yet essential, concept for undergraduate students in ecology and other natural sciences to grasp. As beginner scientists, students must learn to recognize, describe, and interpret patterns of biodiversity across various spatial scales and understand their relationships with ecological processes and human influences. It is also increasingly important for undergraduate programs in ecology and related disciplines to provide students with experiences working with large ecological datasets to develop students' data science skills and their ability to consider how ecological processes that operate at broader spatial scales (macroscale) affect local ecosystems. To support the goals of improving student understanding of macroscale ecology and biodiversity at multiple spatial scales, we formed an interdisciplinary team that included grant personnel, scientists, and faculty from ecology and spatial sciences to design a flexible learning activity to teach macroscale biodiversity concepts using large datasets from the National Ecological Observatory Network (NEON). We piloted this learning activity in six courses enrolling a total of 109 students, ranging from midlevel ecology and GIS/remote sensing courses, to upper-level conservation biology. Using our classroom experiences and a pre/postassessment framework, we evaluated whether our learning activity resulted in increased student understanding of macroscale ecology and biodiversity concepts and increased familiarity with analysis techniques, software programs, and large spatio-ecological datasets. Overall, results suggest that our learning activity improved student understanding of biological diversity, biodiversity metrics, and patterns of biodiversity across several spatial scales. Participating faculty reflected on what went well and what would benefit from changes, and we offer suggestions for implementation of the learning activity based on this feedback. This learning activity introduced students to macroscale ecology and built student skills in working with big data (i.e., large datasets) and performing basic quantitative analyses, skills that are essential for the next generation of ecologists.

Indexed as

big databiodiversity metricsquantitative skillsscalingteachingundergraduate education

Identifiers

PMID33976765
PMCPMC8093704

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.