Ecology

The USDA maintains a database of plant information, some of it trait data, some of it life history. Check it out at https://plants.usda.gov/java/ They’ve been talking about releasing an API for a long time, but have not done so. Thus, since at least some version of their data is in the public web, I’ve created a RESTful API for the data: source code: https://github.com/sckott/usdaplantsapi/ base URL: https://plantsdb.xyz Check out the API, and open issues for bugs/feature requests in the github repo. ...

scrubr is an R library for cleaning species occurrence records. It’s general purpose, and has the following approach: We think using a piping workflow (%>%) makes code easier to build up, and easier to understand. However, you don’t have to use pipes in this package. All inputs and outputs are data.frame’s - which makes the above point easier Records trimmed off due to various filters are retained as attributes, so can still be accessed for later inspection, but don’t get in the way of the data.frame that gets modified for downstream use User interface vs. speed: This is the kind of package that surely can get faster. However, we’re focusing on the UI first, then make speed improvements down the road. Since occurrence record datasets should all have columns with lat/long information, we automatically look for those columns for you. If identified, we use them, but you can supply lat/long column names manually as well. We have many packages that fetch species occurrence records from GBIF, iNaturalist, VertNet, iDigBio, Ecoengine, and more. scrubr fills a crucial missing niche as likely all uses of occurrence data requires cleaning of some kind. When using GBIF data via rgbif, that package has some utilities for cleaning data based on the issues returned with GBIF data - scrubr is a companion to do the rest of the cleaning. ...

After my presentation yesterday to a group of grad students on R resources, I did a presentation today on intro to R data manipulation, visualizations, and analyses/visualizations of biparite networks and community level analyses (diversity, rarefaction, ordination, etc.). As I said yesterday I’ve been playing with two ways to make reproducible presentations in R: RStudio’s presentations built in to RStudio IDE, and Slidify. Yesterday I went with RStudio’s product - today I used Slidify. See the Markdown file for the presentation here. ...

As ecologists, we often start graduate school worshiping the ivory tower of academia with its freedom to pursue important ecological questions. However, studies have shown that most of us do not end up in academia. Greater numbers of ecology graduates are leaving the ivory tower for non-academic career paths. But for many graduates, moving from an academic environment to a non-academic job may be difficult. In graduate school we are trained to work in a particular way, often with loose deadlines and unlimited intellectual freedom (within reason of course). The culture and expectations of the non-academic world may be quite different. What are the skills that you need in a government job, or in science journalism? How do you market yourself for a non-academic position? This is a timely topic because funding to academic ecologists is being cut, leaving fewer opportunities in the academic arena. In fact, an ESA Student Section survey found that an ESA 2013 session on non-academic career paths in ecology was the topic of greatest interest. ...

Have you ever wanted to easily visualize your ecology data in Google Earth? R2G2 is a new package for R, available via R CRAN and formally described in this Molecular Ecology Resources article, which provides a user-friendly bridge between R and the Google Earth interface. Here, we will provide a brief introduction to the package, including a short tutorial, and then encourage you to try it out with your own data! Nils Arrigo, with some help from Loren Albert, Mike Barker, and Pascal Mickelson (one of the contributors to Recology), has created a set of R tools to generate KML files to view data with geographic components. Instead of just telling you what the tools can do, though, we will show you a couple of examples using publically available data. Note: a number of individual files are linked to throughout the tutorial below, but just in case you would rather download all the tutorial files in one go, have at it (tutorial zip file). ...

So I want to mine some #altmetrics data for some research I’m thinking about doing. The steps would be: Get journal titles for ecology and evolution journals. Get DOI’s for all papers in all the above journal titles. Get altmetrics data on each DOI. Do some fancy analyses. Make som pretty figs. Write up results. It’s early days, so jus working on the first step. However, getting a list of journals in ecology and evolution is frustratingly hard. This turns out to not be that easy if you are (1) trying to avoid Thomson Reuters, and (2) want a machine interface way to do it (read: API). ...

So I heard many people say after or during the recent ESA conference in Portland that they really enjoyed the converstations more than listening to talks or looking at posters. There was some chatter about doing an unconference associated with next year’s ESA conference in Minneapolis. And Sandra Chung (@sandramchung) got on it and started a wiki that we can all conribute ideas to. The wiki is here What is an unconference? The idea of an unconference is to have a community organized meetup in which interactions among people are emphasized over the traditional lecture and poster format. For example, many sessions may just be organized a single idea, and people attending have a discussion around the topic. The format can be decided by the community. ...

Many R packages/tools have come out recently for doing ecology and evolution. All of the below were described in Methods in Ecology and Evolution, except for spider, which came out in Molecular Ecology Resources. Here are some highlights. mvabund paper - get R pkg Model-based analysis of multivariate abundance data. Visualising data, fitting predictive models, checking assumptions, hypothesis testing. popdemo paper - get R pkg Population demography using projection matrix analysis. motmot paper - get R pkg Models of trait macroevolution on trees spider paper - get R pkg Analysis of species identity and evolution, with particular reference to DNA barcoding BaSTA paper - get R pkg Bayesian estimation of age-specific survival from incomplete mark–recapture/recovery data with covariates abc paper - get R pkg Approximate Bayesian Computation (ABC) RNetLogo paper - get R pkg Running and exploring individual-based models implemented in NetLogo phytools paper - get R pkg Tools for phylogenetic comparative biology smatr paper - get R pkg Estimation and inference about allometric lines RBrownie paper - get R pkg ? Testing hypotheses about rates of evolutionary change polytomy resolver paper - get R pkg Resolve polytomies on dated phylogenies with their R scripts [here][]. And a cool tool came out for the Python programming language. ...

Many ecologists are R users, but we vary in our understanding of the math and statistical theory behind models we use. There is no clear consensus on what should be the basic mathematical training of ecologists. To learn what the community thinks, we invite you to fill out a short and anonymous questionnaire on this topic here. The questionnaire was designed by Frédéric Barraquand, a graduate student at Université Pierre et Marie Curie, in collaboration with the International Network of Next-Generation Ecologists (INNGE). ...

This blog has lasted a whole year already. Thanks for reading and commenting. There are a couple of announcements: Less blogging: I hope to put in many more years blogging here, but in full disclosure I am blogging for Journal of Ecology now, so I am going to be (and already have been) blogging less here. More blogging: If anyone wants to write guest posts at Recology on the topics of using R for ecology and evolution, or open science, please contact me. Different blogging: I was going to roll out the new dynamic views for this blog, but Google doesn’t allow javascript, which is how I include code using GitHub gists. Oh well… Anywho, here is the breakdown of visits to this blog, visualized using #ggplot2, of course. There were a total of about 23,000 pageviews in the first year of this blog. ...