biogeography

Julia framework for spatial ecology - data types and utilities

Faster, better, smarter ecological niche modeling and species distribution modeling

Functions and types to access GBIF data from Julia

virtualNicheR: creating virtual fundamental and realised niches

Calculating temperature and precipitation predictability at the global scale

BioModelos web application v 2

Processing NEON soil microbe marker gene sequence data into ASV tables.

The Center for Conservation Biology's imaging-spectroscopy-based species classification approach.

R is nowadays probably the most powerful tool for calculations of all kinds. There are plenty of modules available for work with molecular data. Those will be introduced during the course.

Arctic Community Similarity using DNA Barcoding Data

Analyzing the biogeographical accuracy of 🐠Finding Nemo🐡

Test the benefits and costs of proactively preparing for climate change-driven species shifts.

Updated R code for Nunes & Pearson, 2017 in Journal of Biogeography. Nunes, L. A. and Pearson, R. G. (2017), A null biogeographical test for assessing ecological niche evolution. Journal of Biogeography. doi: 10.1111/jbi.12910

Spatial patterns in biodiversity change

Niche-based vulnerability of species and communities.

Data and scripts for "Dating the Hawaiian silversword radiation" project

Data used in: "Estudo da distribuição do gênero chamaeza no estado do Rio de Janeiro"

Develop repo for TreeToM

BioModelos is a collaborative online system to map species distributions. This repository stores functions to automate the construction of Species Distribution Models (SDM) from databases gathered, managed and curated by The Alexander Von Humboldt Institute and a network of experts in order to be evaluated and refined. The functions follow an automatized and flexible SDM general routine. First, it cleans occurrence data when is necessary and construct geographical areas in which those models will be trained. Second, they crops and masks current and future environmental variables. Third, the functions train SDM's using one or several algorithms, then evaluate them quantitatively and ensemble the best of each one. Fourth, they project to different scenarios at user discretion.

My critical essay, for my BSc Hons, on Gloger’s eco-geographic rule (Gloger 1833)

Workshop on Biogeographic Regionalization of the QCBS Biodiversity Intensive Course for the 2019

Biogeography and species delimitation of the rheophilic suckermouth-catfish genus _Pseudolithoxus_ (Siluriformes: Loricariidae), with the description of a new species from the Brazilian Amazon

Kelsey Brock's Personal Website; visit kcbrockresearch.com

These data were created by grafting several existing shapefiles, and creating new ones in QGIS to fill in the voids. These data have an estimated accuracy of +/- 20 km. Given the fuzziness of a region I consider this acceptable for all pedagolical purposes, and most academic. Clearly points near the boundaries of these should be evaluated per user discretion. The author is very open to collaboration and updates of this product!!!

A guide to using fungarium for comprehensive analyses of ecological traits in fungi.

Species-level phylogeny and biogeography of the POS-clade (Arecaceae) with special focus on the disjunct distribution of Orania, using the target capture loci of Heyduk et al. 2016 (doi: 10.1111/bij.12551) • Second part: testing the 'time-for-speciation' hypothesis in Madagascan palms