TY - JOUR

T1 - Reorganisation following disturbance: multi trait-based methods in R

AU - Richardson, Laura

AU - Magneville, Camille

AU - Grange, Laura

AU - Shepperson, Jennifer

AU - Skov, Martin

AU - Hoey, Andrew

AU - Heenan, Adel

PY - 2024/6

Y1 - 2024/6

N2 - Trait-based approaches in ecology are now commonplace. Originating in terrestrial plant ecology, multi trait-based methods are increasingly applied across ecological disciplines to quantify community structure beyond taxonomic descriptions, to understand mechanistic rules for community assembly, and predict changes following disturbance (Zakharova et al. 2019 Ecol Model). Using morphological and ecological traits as a proxy for the ecological roles of species, these methods can translate multivariate species data into synthetic, complementary, and responsive indicators of ecosystem state (Mouillot et al. 2013 TREE; Richardson et al. 2018 Glob Chang Biol). The analytical tools to do so are increasingly refined and mathematically demanding and so are typically applied via accessible packages in R (e.g. Magneville et al. 2021 Ecography). Nonetheless, the use of these packages requires a degree of computational literacy. Computational literacy (informatics competencies including data and coding literacy) is deemed a critical skill STEM students must acquire for effective science education or careers to meet the demands of the 21st century, but its integrated teaching alongside natural science subjects is lacking (Braun and Huwe 2022 Front Educ). Our Data Set teaching module addresses this gap by providing teaching materials aimed at third-year undergraduate students (junior level bachelor’s degree in the United States) in the form of a 16-hour practical (total class time) divided into six separate sessions: 1 x introductory lecture; 1 x 6-hr computer practical; 3 x 2-hr computer practicals; 1 x 3-hr in-person poster presentation conference. The Data Set is designed to teach students to use the statistical programming tool R to examine how coral reef fish communities were impacted by a severe marine heatwave which resulted in mass coral bleaching on the Great Barrier Reef, Australia (Richardson et al. 2018 Glob Chang Biol). Through student-active approaches including guided enquiry, problem-based learning, critical thinking, and ‘authentic’ assessment (poster presentation), students are offered knowledge of trait-based ecology, and taught skills in data manipulation, analysis, and visualization in R; hypothesis testing; and communicating science.

AB - Trait-based approaches in ecology are now commonplace. Originating in terrestrial plant ecology, multi trait-based methods are increasingly applied across ecological disciplines to quantify community structure beyond taxonomic descriptions, to understand mechanistic rules for community assembly, and predict changes following disturbance (Zakharova et al. 2019 Ecol Model). Using morphological and ecological traits as a proxy for the ecological roles of species, these methods can translate multivariate species data into synthetic, complementary, and responsive indicators of ecosystem state (Mouillot et al. 2013 TREE; Richardson et al. 2018 Glob Chang Biol). The analytical tools to do so are increasingly refined and mathematically demanding and so are typically applied via accessible packages in R (e.g. Magneville et al. 2021 Ecography). Nonetheless, the use of these packages requires a degree of computational literacy. Computational literacy (informatics competencies including data and coding literacy) is deemed a critical skill STEM students must acquire for effective science education or careers to meet the demands of the 21st century, but its integrated teaching alongside natural science subjects is lacking (Braun and Huwe 2022 Front Educ). Our Data Set teaching module addresses this gap by providing teaching materials aimed at third-year undergraduate students (junior level bachelor’s degree in the United States) in the form of a 16-hour practical (total class time) divided into six separate sessions: 1 x introductory lecture; 1 x 6-hr computer practical; 3 x 2-hr computer practicals; 1 x 3-hr in-person poster presentation conference. The Data Set is designed to teach students to use the statistical programming tool R to examine how coral reef fish communities were impacted by a severe marine heatwave which resulted in mass coral bleaching on the Great Barrier Reef, Australia (Richardson et al. 2018 Glob Chang Biol). Through student-active approaches including guided enquiry, problem-based learning, critical thinking, and ‘authentic’ assessment (poster presentation), students are offered knowledge of trait-based ecology, and taught skills in data manipulation, analysis, and visualization in R; hypothesis testing; and communicating science.

KW - R

KW - Ecology

KW - Traits

KW - Trait-based diversity

KW - Coral reefs

KW - Reef fish

KW - Data exploration

KW - Statistical programming

KW - Computer literacy

KW - STEM

KW - Pedagogy

KW - Undergraduate students

KW - Marine Science

KW - Climate change

KW - Guided enquiry

KW - Problem-based learning

KW - Critical thinking

KW - ‘Authentic’ assessment

KW - Disturbance dynamics

KW - Coral bleaching

KW - Community ecology

KW - Functional diversity

M3 - Article

VL - 20

JO - Teaching Issues and Experiments in Ecology

JF - Teaching Issues and Experiments in Ecology

ER -