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PNS0225

Getting more out of community data

Unit 4: Getting more out of community data

Tomas Roslin (tomas.roslin@slu.se) and Giovanni Strona (giovanni.strona@helsinki.fi). May 3 and May 8, 2021



Matching the focus of the course on interpretation rather than methods, this unit will depart not from a specific method but from a series of ecological questions. Thus, we will start by defining fundamental questions on communities, then point to methods for approaching them:



1. Fundamental question: What can co-occurrence data reveal about ecological interactions?

Methodological application: How do we quantify co-occurrence? How do we separate a "real" pattern from one caused by chance alone (null model analysis)?



2. Fundamental question: More generally: if there is structure, then what fundamental forces are behind it? What signals of such forces are hidden in the data? Methodological application: How can the signals of community assembly be translated into a parameterized statistical model? (HMSC)



3. Fundamental question: Moving from patterns in the abundance and distribution of species to direct observations of who interacts with whom: can we identify the forces structuring interaction networks?

Methodological application: Analyses of trait-matching using recent techniques





The course activites includes initial readings, classroom discussions, hands-on activities, followed by home exercises. This unit corresponds to one theme/workshop and is planned for two days, equaling one credit (ca 27 hours of work for the PhD student). Example datasets will be provided but students could use their own datasets as well.

Kursplan

PNS0225 Getting more out of community data, 1,0 Hp

Ämnen

Matematisk statistik

Utbildningens nivå

Forskarnivå

Förkunskapskrav

Admitted to PhD-studies. Basic knowledge of statistics and R

Mål

This course is a separate unit of the course theme Advanced Statistics in Practice that aims to fill potential knowledge gaps by preparing students to analyze, interpret and report their data using the most up-to-date methods in R. Special attention will be given to discussion of questions springing from the students’ own work and the biological interpretation of data. The course is intended to deepen the students’ understanding of all aspects of ecological inferences, not as a cookbook of "how to".



Learning outcomes

1- Demonstrate the ability to identify relevant functions and packages in R for analyzing their own datasets.

2- Analyze data using R, including statistical modelling and reporting advanced statistics and generation of graphs.

3- Interpret, think critically and draw conclusions on data analysis results.

Innehåll

Unit 4: Getting more out of community data

Tomas Roslin (tomas.roslin@slu.se) and Giovanni Strona (giovanni.strona@helsinki.fi). May 3 and May 8, 2021



Matching the focus of the course on interpretation rather than methods, this unit will depart not from a specific method but from a series of ecological questions. Thus, we will start by defining fundamental questions on communities, then point to methods for approaching them:



1. Fundamental question: What can co-occurrence data reveal about ecological interactions?

Methodological application: How do we quantify co-occurrence? How do we separate a "real" pattern from one caused by chance alone (null model analysis)?



2. Fundamental question: More generally: if there is structure, then what fundamental forces are behind it? What signals of such forces are hidden in the data? Methodological application: How can the signals of community assembly be translated into a parameterized statistical model? (HMSC)



3. Fundamental question: Moving from patterns in the abundance and distribution of species to direct observations of who interacts with whom: can we identify the forces structuring interaction networks?

Methodological application: Analyses of trait-matching using recent techniques





The course activites includes initial readings, classroom discussions, hands-on activities, followed by home exercises. This unit corresponds to one theme/workshop and is planned for two days, equaling one credit (ca 27 hours of work for the PhD student). Example datasets will be provided but students could use their own datasets as well.

Examinationsformer och fordringar för godkänd kurs

Participating PhD students will be evaluated as passed/not passed based on their overall engagement in classroom activities and fulfilling the assignments.

Ytterligare information

This course is the fourth independent course unit of five in the course theme Advanced Statistics in Practice organized by Mohammad Bahram in collaboration with the NJ-faculty research schools ’Ecology-basics and applications’ and ’Focus on Soils and Water’. The course plan for the theme and this course unit was accepted 20200922 by the steering group of the research school Ecology-basics and application.



Course units of the theme Advanced Statistics in Practice 2021

•Time series analysis: Jonas Knape and Örjan Östman, February 1

•GIS and spatial analysis: Alistair Auffret and Mohammad Bahram, Feb. 15 and 18

•Meta-analysis: Julia Koricheva, April 12-13

•Getting more out of community data, Tomas Roslin and Giovanni Strona, May 3 & May 6

•Dealing with complexities of GLMs: Matt Low, May 31 & June 1

Ansvarig institution/motsvarande

Institutionen för Ekologi