bbarker505 Goto Github PK

followers: 19.0 following: 5.0 repos: 24.0 gists: 1.0

Name: Brittany Barker

Type: User

Company: Oregon State University

Bio: I am an Assistant Research Professor at the Oregon IPM Center @ Oregon State Univ. I'm interested in modeling, mapping, biodiversity, ecology, and invasions.

Twitter: brittanysbarker

Location: Portland, OR

Blog: brittanysbarker.org

👋 Hi, I’m @bbarker505. I am a Senior Research Associate in the Oregon IPM Center at Oregon State University.
👀 I’m interested in using R to investigate population dynamics, ecological change, drivers of species distributions, and genomics of biological invasions.
🌱 I’m currently learning how to use GitHub better, and how to work with R projects to increase reproducibility in my work.
💞️ I’m looking to collaborate on my work with DDRP, a platform to conduct real-time phenology and climate suitability modeling of pests.
📫 You can contact me at [email protected].

Brittany Barker's Projects

1990_daily_30yr

Daily downscaled gridded climate averages for 1961-1990. Input data are monthly tmin and tmax data from the PRISM database. Output data were generated using the "dailynorms.pl" script in bbarker505/dailynorms.

2021-04-02-r-ladies-seattle-online

ashs_r_presentation_2023

Presentation given to ASHS on 1/30/23

bbarker505

Config files for my GitHub profile.

boxb

boxb-ddrp

cascadia_r_intro_to_gis_2024

Materials for 2024 Cascadia R Conference workshop, "Intro to GIS and Mapping"

cps-climsuit-modeling

Project and R scripts used in climate suitability modeling study of Calonectria pseudonaviculata

dailynorms

Perl and Octave code used to temporally downscale gridded monthly climate averages to daily averages.

ddrp-cohorts-v1

The objective of the Degree-Day, establishment Risk, and Pest event mapping system (DDRP) is to predict phenology and climate suitability of invasive, biocontrol, and IPM species for the conterminous United States. DDRP is written entirely in the R statistical programming language (R Development Core Team 2019), making it flexible and extensible, and has a simple command-line interface that has already been adapted for online use. The platform can use a variety of gridded weather and climate data types for any historical (post-hoc), real-time, or future (downscaled GCM) time period. Model products include gridded (raster) and graphical outputs of number of completed generations, phenological/pest events, and climate suitability (i.e., establishment risk maps).

ddrp_v2

A final production version of the DDRP platform that includes cohorts, parallel processing, and improving mapping routines. The objective of the Degree-Day, establishment Risk, and Pest event mapping system (DDRP) is to predict phenology and climate suitability of invasive, biocontrol, and IPM species for the conterminous United States. DDRP is written entirely in the R statistical programming language (R Development Core Team 2019), making it flexible and extensible, and has a simple command-line interface that has already been adapted for online use. The platform can use a variety of gridded weather and climate data types for any historical (post-hoc), real-time, or future (downscaled GCM) time period. Model products include gridded (raster) and graphical outputs of number of completed generations, phenological/pest events, and climate suitability (i.e., establishment risk maps). The platform is described in a peer-reviewed paper in PLoS ONE (https://doi.org/10.1371/journal.pone.0244005).