Global Patterns of Genetic Diversity - Mangrove Forests
The aim of this Professional Practice (PP) was to compile existing genetic data of mangrove forests to detect patterns of intraspecific diversity variation across species and to identify relevant hotspots for future conservation.
Meta-Analysis
"A meta-analysis is a statistical analysis that combines the results of multiple scientific studies. "(4)(5)
Pros:
low cost
requires minimal facilities (computer)
Cons:
pooling of data causes bias, which requires correction
rigorous & time-consuming
How to do your own Meta-Analysis?
1.
Literature Screening
Use a search engine to retrieve all papers related to your topic of research
use keywords designed to optimize your output
e.g. (genetic diversity + mangrove) OR (genetic differentiation + mangrove)
using search engines that only contain peer reviewed papers insures the credibility of your meta-analysis
e.g. Web of Science
Export the search result into an excel file
Tab limited UTF-8 format
Skim the titles and the abstracts of each paper and label those that potentially have relevant data for your meta-analysis
2.
Data Extraction and Database Compilation
Read papers that where previously labeled relevant
Extract the data on genetic diversity
GPS coordinates must be presented in the same format, convert coordinates into the decimal degree format if necessary
Extract data on markers
Extract the Differentiation matrix (DM) as a separate excel file
be aware that the populations/locations in the DM must be listed in the same order as in the diversity database
take a look at the supplementary data if available
3.
Data Handling
Part 1: Maps per Study
Run an R-script on each study separately to get a feeling of the data that you have collected
Several maps are produced per study:
Genetic diversity maps per species and per index
Genetic differentiation maps per species
Part 2: Summary Maps
Run an R-script to create summary maps containing the data of several studies
combining data of several studies requires a certain level of comparability
e.g. by limiting the number of studies to those whose sampling sites cover at least two marine ecoregions
Outcomes
Skills Learned
general knowledge on mangroves
ability to skim papers for relevant information
skill to conduct the data compilation of a meta-analysis
competence in using the R-studio interface
confident use of several online tools (Zoom, Dropbox, etc.)
video editing and animating using several programs (windows video editor, blender, opentoonz)
Future Aspirations
to familiarize my self further with the program R
the chance to work with my supervisors in other research projects (Jorge Assis, Eliza Fragkopoulou, Ester Serrao).
a PhD position in which I would be able to collaborate with several IMBRSea/EMBRC parter institutes.
Acknowledgements
I would like to thank Ester A. Serrao for presenting me with an interesting research topic despite the short notice which was provided to her. I would like to thank Eliza Fragkopoulou and Jorge Assis for guiding me through each step of the meta-analysis.
Furthermore, I want to thank the IMBRSea coordination office for their swift response to the increased regulations imposed during the COVID-19 pandemic, without their efforts the continuation of the master program would have been impossible.
Supplementary Videos
CCMAR: Interview with a PhD student
Eliza Fragkopoulou is studying how climate change is going to affect the gene pools of marine forest species by developing models for different climatic change scenarios.
Traveling during times of a global Pandemic
16th-17th of May, 2020 Banyuls-sur-Mer, France -> Faro, Portugal
Created by E.H. Taraneh Westergerling for the IMBRSea Online Symposium 2020
contact: taraneh.westergerling@imbrsea.eu