bceenet

[jdeck88]

Landscape Genetics CURE, part of BCEENET

The code is available to the public

ABSTRACT:

Fill in Abstract Here

HOW CODE WORKS AND HANDLE DATA:

Fill in details here

• List of R dependencies:
• library(shiny)
  library(shinydashboard)
  library(base)
  library(datasets)
  library(graphics)
  library(grDevices)
  library(methods)
  library(stats)
  library(seqinr)
  library(tidyverse)
  library(dplyr)
  library(magrittr)
  library(factoextra)
  library(stringr)
  library(stringi)
  library(utils)
  library(ggrepel)
  library(fuzzyjoin)
  library(RColorBrewer)
  library(scales)
  library(sjmisc)
  library(ggpubr)
  library(DT)
  library(BceenetPCAPackage)

[mkoo]

@libbybeckman please fill in the abstract to get this shiny started!
Thanks!

[libbybeckman]

Abstract:

This educational module introduces the concepts and tools used in Landscape genetics, a suite of methods used to examine genetic variation in wild organisms distributed across complex, real-world landscapes. Landscape genetics is used to understand how migration between habitat patches, the distribution of suitable habitat, and barriers like mountains, deserts or cities impact critical species characteristics like population structure and genetic diversity. These data help design effective conservation management strategies.

To learn these concepts, students will choose a focal species from 12 datasets of California vertebrates, form a hypothesis on landscape connectivity in CA and test their hypothesis using genetic and spatial data from vouchered museum specimens. Vouchered specimens are essential in landscape genetics since an individual specimen includes detailed locality information and potentially a tissue sample available for genetic analysis. Briefly, students (1) download and process data from the public databases VertNet and GenBank, (2) create a sampling map using QGIS, (3) align and analyze their genetic data using principal component analyses (PCA) through a web interface, and (4) evaluate their hypothesis by interpreting their genetic PCA. The students present their results and conclusions to their classmates; last, the class results are summarized together to identify if there are any shared barriers to migration impacting California vertebrates.