My native versus exotic competition experiment is all set up in the greenhouse, so just waiting on germination now. Have planted additive densities of 0,3,6,9,15 and 25 natives with brome at high (10 seeds) and low (5 seeds) densities, 10 reps per treatment at ambient light versus shaded conditions for a total of 200 pots. I hung up a wooden bamboo structure to provide shade and imitate shrub presence to half of the pots, and hung it in a way that it is easy to suspend for pot censusing. Here are photos of what it all looks like.
The enemy release hypothesis (ERH) of plant invasion asserts that translocation to novel communities allows exotic plants to escape population controls imposed by natural enemies in native communities. The ERH predicts that 1) invader densities are greater in non-native communities than native communities, 2) natural enemies impose strong negative effects on invader abundance in the native range but in not the non-native range. These predictions are straightforward, but testing them involves conducting parallel vegetation surveys and enemy exclusion experiments in both the native and non-native ranges of invaders. Due to logistic challenges, very few studies have done this.
As part of an international team of collaborators from the USA, Canada, and Poland, we are explicitly testing the predictions above with respect to the prickly cucumber, Echinocystis lobata (fruit pictured below). This climbing vine is native to North America but invasive in Poland, where it can dominate local communities and extirpate native competitors.
So far, our surveys indicate that E. lobata is much more abundant in Poland than anywhere examined in N. America, and that E. lobata plants are larger and more fecund in Poland than in N. America. It also seems that physical defenses aimed at protecting seeds from generalist granivores are present at much higher frequencies in Poland than in N. America, which is very cool! We look forward to results from enemy exclusion experiments.
We’ll keep you posted!
As we collectively move to platforms that support better reproducibility and open science, a few tiny challenges persist. Reference management. LaTex with BibTex is great, but at times, team members are interested less in reproducibility and more in just sharing the libraries. We recently faced this challenge because we were collaboratively writing a very long white paper and each of us worked in a different management ecosystem in spite of using GitHub to control the versioning and collaboration in the writing.
Here are some resources to support a decision. Anecdotal research similar.
Refworks, Easbib, Endnote, and Mendeley look promising.
Good contrast here including discussion of Zotero.
Gradhacker review here of offerings.
Writing in google docs collaboratively use paperpile.
Writing in RStudio use Zotero.
Great lists of pros and cons out there. Based on the various lists, I vote for key criteria as a. cloud storage, b. can use in RStudio easily, c. allows me to share library with collaborators for a given paper.
The three competitors seem to be Refworks, Mendeley, and Zotero.
Now, need to give them a head-to-head test shortly.
Seven magical steps into a dataframe.
By Nargol Ghazian
This is a summary of the protocol I have been using for that past few months to process all the amazing camera trap photos from the Mojave National Preserve and the Carrizo National Monument. After reading a few papers on cam trap processing and exploring the CamtrapR package, the best approach would be to create your datasets manually as no other program is able to automatically detect animals for you. This method also ensures that you obtain the best dataset for the statistical analysis you wish to perform. This seven step guide should give you a quick rundown on how to get started with processing and maintaining a good workflow.
- Start by naming your columns as below. These heading are best suited for this project but yours can include more columns.
|1.Year||We are working on the 2017 images|
|2.Region||MNP is Mojave and CNM is Carrizo|
|3.Site||Mojave or Carrizo|
|4.Calendar date||The date the picture was taken in dd-mm-yr. I like to do the pictures belonging to the same date for each photo rep in order. If the date is wrong, don’t worry too much, just do it all as one for the last date of the particular week you are working on|
|5.Microsite||Carrizo is shrub or open, 3 weeks for each. Mojave is Buckhorn or Larea, also 3 weeks for each.|
|6.Day||This goes in a 1,2,3..n order|
|7.Rep||This refers to the camera trap station. There are 10 stations per microsite. For example you might have four pictures for the same day in station #2 of open, so you would write 2 four times: 2,2,2,2 each corresponding to an image|
|8.Photo Rep||A continuous number starting at 1 and continuing until you’ve finished processing all your pictures for the particular site|
|9.Animal||The animal in a hit photo. The most common are rat, rabbit, squirrel, fox, lizard and sometimes bird. There are times where you might have to guess. If it’s really hard then write ‘unidentifiable’. If it’s a false hit leave it blank.|
|10.Animal. Capture||binary 0 = false hit, 1 = animal present|
|11.Time block||Look at the timestamp. Is the photo taken at night, noon, afternoon or in the morning. If the timestamp is wrong, guess based on the darkness or lightness.|
|12.Night. Day||Based on whether it’s dark or light.|
|13.Actual time||Actual time written on the photo. Let’s hope it’s the correct timestamp J|
|14.Observations||If you see absolutely anything interesting in the photo, note it! Otherwise leave this column blank. I usually write ‘x2’ or ‘x3’ if there is more than one animal in the photo. Sometimes I write ‘eyes visible’ if it’s dark and you can only tell the presence of the animal from its shining eyes (rats usually)|
|15.Temp of positive||This is noted on the picture in Fahrenheit or Celsius. Whatever unit is shown, note it in your meta-data. If you’ve been working with one unit, and a certain photo rep has a different one, just use a converter to convert to the units you’ve been using for that particular photo rep.|
|16.Week||This is either 1, 2 or 3 since there are only 3 weeks per microsite. This column is super important because sometimes the datestamps are wrong but at least the week of sampling is correct|
*Note: The only time we actually fill in anything for columns 9 and 11-15 is when we have a “hit” and there is an actual animal.
- Since each row corresponds to a particular image, I liked to start with at least 100,000 just so I don’t have to go back and paste more rows. You can delete the extra rows when you’re done processing. For year, region, site and microsite write the correct label in the cell right below your column heading. Select the cell, then using control+C input the number of cells you want by using a colon in the area below the clipboard in Excel. For examples if you are in row B, it would be written as B2:B100000 then click enter.
- You can use the above method for the date as well. I input the date as I go along. For example if the photos start on 2017-05-22 I paste 100 thousand of that date. Obviously this is far too many, so once all the images for that particular date is finished, I control+C from the last cell and enter the new day for up to 100 thousand and so on until you are finished for the week and can delete the extra cells.
- Days work exactly like calendar dates.
- When it comes to rep, select all the image belonging to a particular date in your particular station of cam trap files and click properties. This tells you how many images you have for that date, in that station. Use the above method discussed to paste as many cells needed for that station. Keep a calculator handy because you would need to determine your ending cell by adding the number of images to the current cell you’re on.
- Photo rep is just a continuous number, as already mentioned in the chart. It’s basically the total of all the images processed for either Carrizo or Mojave. Use the ‘fill’ button in excel for numbers in a series to do this step.
- If you come across a cool photo, make sure to copy and paste it in a different folder. This includes two animals together, animals fighting, clear photos of animals etc. (use your judgement). Do not cut and paste!
Connecting most peripherals to a Mac is typically a snap. However, about two years ago, updates to OSX introduced to challenges to connecting Onset Hobo Micro Data Loggers to initialize and then download stored data. I decided to finally work through these challenges instead of switching machines. This may seem trivial, but it was a bit finicky; so, here are the steps quickly summarized.
Configurations: any version of OSX 10.8 and higher likely needs these steps particularly if your Onset product uses a serial port for communication. The steps listed below were developed on a late 2012 iMac running 10.13.1 OSX (High Sierra).
Steps to connect
- Install Java Version 7 or higher (I installed Version 8 Build 151) because this is a dependency in later step.
- You will need a usb to serial adapter for older Onset Stations. The one provided with loggers is manufactured by Tripp Lite and entitled Keyspan High Speed USB to Serial Adapter (USA-19HS). This step was a nightmare. You must install the correct driver and reboot. I tried earlier versions of the adapter and failed. Here is the manufacturer support page with driver downloads. Continue forward with steps, but if you have everything plugged in ready to go and cannot see logger in HoboWare app, return to web and search for driver beta version and install. I had to do this, and I used driver version 4.0b4 (beta version) – not the version 3 from site. Here is a good explanation (need to go to Tripp Lite overview page and search for first three characters of unit, i.e. ‘USA’). If you do any updates to OSX, you also have to reinstall the driver.
- Install HoboWare Free Version (I prefer over the Pro Version that is provided with loggers because it is more updated more frequently and seems to run more smoothly). I used Version 3.7.13 here.
- Connect adapter to station then to serial-to-usb adapter and launch HoboWare. You are now ready to either initialize or download if you have already deployed loggers.
- Initialize via device > launch. This is pretty self-explanatory.
- Download was a bit less obvious to me. Use device > readout. Allow readout to complete (progress bar pops up).
- Once complete, data are read into this instance of HoboWare as a project not saved. I recommend doing plot right away (see below).
- You can now use file > save datafile to locally save a file ending in .dtf. This is not ideal for me. The save project saves data, thumbnail, details, and plots for future reading by app. Again, do not prefer because I work in R. See final step if you want to work outside ecosystem.
- If you prefer to save as .csv, you first select the plot option after readout because this writes workspace to .csv. Then go to file > export data table and you have a nice clean dataframe for additional wrangling. Before you move on to next logger, ensure you close project because it can retain former instance of plots and datable even after you do a readout from new unit.
Now, you are ready to explore some microclimate for your sites!