Understanding and managing forests is crucial to understanding and potentially mitigating the effects of climate change, invasive species, and shifting land use on natural systems and human society. However, collecting data on individual trees in the field is expensive and time consuming, which limits the scales at which this crucial data is collected. Remotely sensed imagery from satellites, airplanes, and drones provide the potential to observe ecosystems at much larger scales than is possible using field data collection methods alone.
We running the second in a series of data science competition where multiple groups attempt to use the same remote sensing data from low flying airplanes to infer the locations, sizes and species identities of millions of trees. This kind of collaborative data analysis challenge has proven highly effective in other fields for quickly improving methods for converting image data to useful information. This round of the competition focuses on exploring how methods generalize beyond a single forest.
There are two tasks in the current competition: 1) identifying individual trees in remote sensing images; and 2) classifying trees into species.
Teams (or individuals) can participate in either or both tasks. Task 1 requires working with remote sensing data (RGB, LIDAR, and Hyperspectral). Task 2 can either leverage this raw remote sensing data or use simplified tabular data provided by the organizers. Details of the different tasks will be available starting March 1st. To read more and sign up check out the competition website:
You can learn about the results of the first round of this competition in the summary paper and full PeerJ paper collection. We plan to write up the results of this round of the competition in a similar way, with a synthetic paper covering the competition, data, and comparison of different methods, and with each team given the opportunity to write up and publish associated short papers on the methods they used and results they produced.
This challenge is supported by the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative through grant GBMF4563 and the National Science Foundation through grant DEB-1926542. It uses data from the National Ecological Observatory Network in addition to data collected by the organizers. It is being organized by the Weecology lab, Machine Learning and Sensing lab, Data Science Research lab, and Stephanie Bohlman and Aditya Singh‘s labs all at the University of Florida and the Environmental Observation & Informatics program at the Nelson Institute for Environmental Studies at the University of Wisconsin.
PH.D STUDENT POSITION IN COMMUNITY ECOLOGY
The Ernest Lab at the University of Florida has an opening for a Ph.D student interested in research in the area of community ecology, forecasting, and/or temporal dynamics to start fall 2020. The student will participate in the collection of small mammal and plant data at our long-term site in southeastern Arizona which will be used as part of our recently funded NSF grant to tackle challenges associated with ecological forecasting under novel conditions. While participation in the field data collection at the long-term site is expected, students in the Ernest Lab are free to develop their own research projects depending on their interests. The Ernest lab is interested in general questions about the processes that structure communities, with a particular focus on understanding when and how ecological communities change through time and how we can forecast those changes. Examples of research that students in the Ernest lab have pursued as part of their dissertation include: Does long-term change in communities occur through gradual species replacements or rapid reorganization events?, Are biodiversity patterns sensitive to changes in biotic interactions?, Do disturbances impact species populations and community-level properties similarly?, and How does the colonization of new species impact habitat patch preferences?
The Ernest Lab is part of the Weecology research group, Weecology is a partnership between the Ernest Lab, which tends to be more field and community ecology oriented and the White Lab, which tends to be more quantitatively and computationally oriented. The Weecology group supports and encourages students interested in a variety of career paths. Former weecologists are currently employed in the tech industry, with the National Ecological Observatory Network, at teaching-focused colleges, and as postdocs in major research groups. We are also committed to supporting and training a diverse scientific workforce. Current and former group members encompass a variety of racial and ethnic backgrounds from the U.S. and other countries, members of the LGBTQIA+ community, military veterans, people with disabilities, and students who are the first generation in their family to go to college. We work hard to create a supportive and inclusive lab environment and expect all members of Weecology to abide by the lab code of conduct.
More information about the Ernest lab and Weecology research group is available at: (https://weecology.org). You can also check out the blog for our long-term study (https://portalproject.wordpress.com) and our lab blog (https://jabberwocky.weecology.org; if you’re reading the ad here, you’ve found it already!).
Interested students should contact Dr. Morgan Ernest (firstname.lastname@example.org) by Oct 15th, 2019 to start a dialogue about the position and receive further information about the next steps in the application process.
It’s not uncommon to hear stories of mistakes resulting in graduates students missing paychecks. This is a major problem because most students live month-to-month and can’t wait for a missed check to be fixed in the next pay cycle. Despite the commonness and dramatic impact of missed pay in graduate school*, it’s common to see these issues written off as isolated incidents and not part of a more systematic problem. As a counterpoint to this idea, here are just some of the responses to a single tweet about this problem.
If you’d like to add your own experiences either post them to Twitter (responding to or quote tweeting the original tweet or tagging me @ethanwhite; I’ll keep updating this post as new experiences come in) or post a comment on this blog post.
*As a number of folks have noted this issue is also a real challenge for lots of folks both at universities (adjuncts, postdocs, and even professors) and beyond. I couldn’t agree more. That said I think there are some unique things related to graduate students that make the occurrence of these mistakes more common (like the fact that at many universities they are “hired” at least once and often 2-3 times/year; more on this soon) and I also think it’s OK to highlight and attempt to fix general issues within specific populations.
The weecology group is coming in force to the annual meeting of the Ecological Society of America which is being held in New Orleans next week. We’ve been up to a quite diversified list of things over the past year ranging from temporal dynamics of communities to forecasting and remote sensing. We also have people involved in a number of outreach or training events this year. We’re super proud of all the work everyone has been doing – both on the science end of things and the culture of science end of things. The Weecologists involved with each presentation/event are highlighted in electric blue with links to either their web page or google scholar profile so you can learn more about them. Talk or event titles link to the relevant listing in the ESA program.
10:15-11:30 Special Session: Path to being a strong ally in science. Location: Convention Center 238-239. (Juniper Simonis is involved in this session)
1:50-2:10 Weak impacts of climatic factors on intraspecific body size variation in endothermic species. Kristina Riemer, Narayani Barve, Brian Stucky, Stephen Mayor, Robert Guralnick, Ethan White. Location: Convention Center Rm 356.
3:20-3:40 The influence of data type, functional traits, and ecoregion on native bee phenology. Joan Meiners, Michael Orr, Kristina Riemer, Shawn Taylor, Terry Griswold. Location: Convention Center – R05
3:30-5:00 INSPIRE Session on Rapid Ecological Transitions: Synthesizing Concepts about Abrupt State Changes in Nature. Organized by SK Morgan Ernest, Christie Bahlai, Kendi Davies, and Sarah Elmendorf. Location: Convention Center – Rm 243
3:30 Rapid Ecological Transitions: A state space of concepts and ideas. SK Morgan Ernest. Location: Convention Center – Rm 243
4:40-5:00 Prediction and forecasting of Portal fauna via particle filtration. Juniper Simonis, Glenda Yenni, Shawn Taylor, Erica Christensen, Ellen Bledsoe, Ethan White, SK Morgan Ernest. Location: Convention Center Rm 355.
2:00-2:30 Evidence of rapid transitions in long-term community data. Erica Christensen, David Harris, Renata Diaz, SK Morgan Ernest. Location: Marriott – River Bend 1
5:00-6:00 during the poster session Juniper Simonis will hold an informal chat for those interested in knowing more about starting your own consulting business (likely in the poster hall, assuming there’s room). For more information, contact Juniper on Twitter @DapperStats
6:30-8:00 Long-term Studies and Paleoecology Mixer. Location: Rusty Nail Bar (SK Morgan Ernest is the chair of the Long-term Studies Section. If you like talking about temporal dynamics or want to learn more about paleoecology, come join us!).
11:30-12:30 during lunch Juniper Simonis will hold another informal chat for those interested in knowing more about starting their own consulting business (location not yet determined). For more information, contact Juniper on Twitter @DapperStats
3:30-5:00 Software skills for reproducible data-intensive research. Ethan White. Location: Convention Center Rm 243.
4:20-4:40 Scaling up remote sensing fundamental unit: from pixels to crowns. Sergio Marconi, Sarah Graves, Stephanie Bohlman, Jeremy Lichstein, Aditya Singh, Ethan White. Location: Convention Center 338
1:30-2:30 Getting a handle on your data with the dplyr R Package. Shawn Taylor . At the Data Help Desk in the Poster/Exhibit Hall.
4:30-6:30 Evaluating a near-term ecological forecast of plant phenology. Shawn Taylor, Ethan White. Location: Poster 110, Exhibit Hall
We are excited to announce the first release of a new Julia package that let’s you run our Data Retriever software with a native Julia interface.
For those of you not familiar with Julia it is a new programming language that is similar to R and Python, has a central focus on data analysis, and is designed from the ground up to be fast. It is an emerging scientific programming and data analysis language. Tim Poisot and his lab have been leaders in introducing Julia to the ecology community (thanks to them you can access GBIF data, analyze ecological networks, and more) and we’re pleased to start following his lead.
After installing the Python package getting your favorite dataset into Julia involves opening Julia and running:
julia> Pkg.add("Retriever") julia> using Retriever julia> iris_data = Retriever.install_csv("iris") julia> iris_data = readcsv("iris_Iris.csv")
Like the Python and R versions of the retriever the Julia version also lets you install into a number of different database management systems and formats to meet your needs including PostgreSQL, MySQL, SQLite, JSON, and XML. So if you need to install a large dataset and access if from the database you can do that:
julia> Pkg.add("SQLite") julia> using SQLite julia> Retriever.install_sqlite("breed-bird-survey", file="bbs.sqlite") julia> db = SQLite.DB("bbs.sqlite") julia> SQLite.query(db, "SELECT * FROM breed_bird_survey_counts LIMIT 10")
We use the PyCall package to directly run the Python code from the main retriever package. Cross-language support like this is really useful for letting difficult to develop core code be easily used in different languages and it’s great that this is a core feature of Julia.
This is our first Julia package and so there are sure to be lots of things to improve (starting with the documentation). If you use Julia, or are interested in experimenting with it, we’d love feedback, issues, and pull requests. We’re always enthusiastic to have new contributors and help everyone get started, especially if they’re just learning. For more information see:
Scaling-up ecological patterns and processes is crucial to understanding the effects of environmental change on natural systems and human society. We are piloting a Data Science Challenge where multiple groups attempt to use the same remote sensing data from low flying airplanes to infer the location and type of trees in forests. This will allow forests to be studied in detail at much larger scales than is currently possible. This kind of collaborative data analysis challenge has proven highly effective in other fields for quickly improving methods for converting image data to useful information.
There are three sets of tasks: 1) identifying individual trees in remote sensing images; 2) aligning ground data with remote sensing data; and 3) classifying trees into species.
Teams (or individuals) can participate in all of them or just pick the tasks they are most interested in. Tasks 2 and 3 can be accomplished using just tabular data. Task 1 requires working directly with spatial data. Details of the different tasks and links to the data are available at the challenge website:
We plan to write a general paper about the competition, the data, and the performance of the different methods used. Individual participants will be invited to write and publish associated short papers on the methods they used and results they produced. We already have a journal that has agreed to publishing all of these related contributions together into a collection (pending review of course).
The challenge is already open and the deadline for submissions is December 15th. Once you sign up on the website you will receive an email with some additional details. If you have any questions feel free to respond to that email or checkout the FAQ to see if they have already been answered.
This challenge is sponsored by the National Institute of Standards Technology as part of it’s Data Science Evaluation series and is also partially supported by he Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative through grant GBMF4563.It uses data from the National Ecological Observatory Network in addition to data collected by the organizers. It is being organized by the Data Science Research lab, the Weecology lab, and Stephanie Bohlman’s lab all at the University of Florida.
We are exited to announce a new release of the Data Retriever, our software for making it quick and easy to get clean, ready to analyze, data.
The Data Retriever, automates the downloading, cleaning, and installing of data into your choice of databases and flat file formats. Instead of hours tracking down the data on the web, downloading it, trying to import it, running into issues, fixing one problem, and then encountering the next, all you need to do is run a single command from the command line, R, or (now!!) Python:
$ retriever install csv iris
> portal_data <- rdataretriever::fetch('portal')
In : import retriever as rt In : rt.install_postgres('breed-bird-survey')
- Python interface: While the retriever is written in Python the package previously only had a command line interface. Now you can access the full power of the retriever from directly inside Python. See the full tutorial for more details.
- Conda packaging: The conda package manager has become one of the two main ways to install Python packages. You can now install the retriever using
conda install retriever -c conda-forge
- Command line autocomplete: As the number of datasets and backends supported by the retriever goes it can be difficult to remember specific names. Using Tab will now autocomplete retriever commands, backends, and dataset names. (Currently only available of OSX and Linux)
- We also made some changes to the metadata script system so if you’ve previously installed the retriever you should update your scripts using:
retriever reset scripts retriever update
Find out more
To find out more about the Data Retriever checkout the:
Ongoing work on the Data Retriever lead by Henry Senyondo is made possible by the generous support of the Gordon and Betty Moore Foundation’s Data Driven Discovery Initiative. This kind of active support for the development and maintenance of research oriented software makes sustainable software development at universities possible. Shivam Negi developed the Python interface as part of his Google Summer of Code project. You can read more about his time in GSOC at his blog.
Twelve different folks contributed code to this release. A big thanks to Henry Senyondo, Ethan White, Shivam Negi, Andrew Zhang, Kapil Kumar, Kunal Pal, Amritanshu Jain, Kevin Amipara, David LeBauer, Amritanshu Jain, Goel Akash, and
Parth-25m for making the retriever better.
Someone (**cough** **cough** Morgan) fell down on their job rebloging the Portal Project 40th anniversary posts to Jabberwocky. But that means this week is the week o’ Portal as we reblog the various posts from the past few weeks. First up, what happens when we go out in the summer to count plants?
Twice a year the Portal crew gets a little larger, and spends a few extra days, and we count plants on all 384 quadrats. Despite some of us being in our second decade of visiting the site, and everyone on the plant crew being intimately familiar with most of the species at the site, and that the rodent RA has been watching the plants grow and giving us monthly updates, we still never really know what we’re going to find once we get out there. The desert does what it wants.
The uncertainty seems especially high for the summer plant community. Some years we arrive to an ocean of grass, waving in the breeze. Those are the years we spend a lot of ‘quality time’ with each quadrat. Other years we arrive to a dustbowl. We walk around the site laying our PVC quadrat down and picking it back up…
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