Predictions to test
Do relocations map onto where scat is found too?
Is there scat fidelity from day to day?
Does telemetry relocation or conversely scat presence within a likelihood MCP (polygon estimating 95% percent change animals present within area) correlate with one another?
ie – imagine a telemetry polygon and a scat one too – never been done!! and then we statistically overlay them.
How to test
1. do relocations this saturday (day #1), enter into usual relocation datasheet attached. Do morning and afternoon.
Enter data that evening and do a baton handoff to the scat team.
2. On sunday (day #2), scat people ‘check’ all locations where there was certainly a lizard. In each day 1 relocation, is there scat the next day!! So, imagine day 1 there were 100 spots where lizards where spotted. On day 2, what proportion of these have scat deposited.
3. On sunday (day #2), team telemetry repeats process and finds another 100 spots or whatever they can where they see lizards. Same process – enter and hand off to team scat.
4. On Monday (day 3#), same process – team scat check day 2 telemetry relocations for scat – so we are holding as best we can scat scent cones etc to 1-day old and team telemetry repeats and finds new spots for next sampling.
5. On tuesday (day #4), team scat checks team telemetry relocations from the day before (day #3).
A total of 4 days sampling with 3 statistical days to test for scat detections with a 1-day lag where lizards were spotted. SO POWERFUL.
NOW — -as you can imagine – there are also a few bonus opportunities here…. 🙂
A. If dogs have time, check back more than 1-day lag – ie on day #4, dogs can check ALL previous days days 3,2,1 – this gets the second main prediction – ie site fidelity. BE amazing to know this.
B. If team telemetry has the people and the receivers, it can also go the other way – team telemetry looks for lizards where there was scat detected – anywhere – the following day – so we pass the baton back and forth.
C. Team scat if they have time daily, checks other sites to fill in the region more – ie the polygon idea – to see how well regional NOT just point sampling works.
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!
Ephedra regional gradient
My biggest project examines positive interactions along a regional gradient of continentality. The immediate question though is what is continentality? What abiotic and biotic variables change along this gradient in addition to plant-plant interactions. When we initially constructed this gradient the two main considerations were aridity and cold stress. For plants in the Deserts of California these are two very important considerations. After two years of conducting this experiment, I had very different climate profiles during the seasons. The most striking was the differences in my plant phytometers between the two seasons. In 2015-2016 growing season, the majority of my plants were present in the San Joaquin Desert. This desert is generally colder and wetter than the more continental Mojave Desert to the east. However, in the 2016-2017 the San Joaquin Desert sites had few plants of my chosen phytometer relative to the abundant Mojave Desert sites. All my plants were present at all my sites at some point, suggesting that this gradient shifts with inter-annual variability. Let’s take a look at what some of that looks like:
San Joaquin Desert year
The 2015-2016 shown in black had similar temperatures on average relative to the 2016-2017 growing season (in grey). The precipitation patterns though were different between years. These sites form a parabola with distances from the ocean. Sites closest to the ocean and most inland have the highest precipitation, while sites in the middle are the least. Overall the 2016-2017 season saw significantly more rainfall. Sites in the 2015-2016 season were extremely arid. For instance, Barstow and my site along Hwy40 saw as little as 30 mm of rainfall. The low abundance of my phytometer in the Mojave sites for that season is therefore likely because of low rainfall amounts. However, the San Joaquin sites has similar rainfall between years so then why so few plants in the 2016-2017. I believe this has to do with the cold stress factor:
Precipitation in mm (black) and temperature in C° (red) during the 2015-2016 growing season for the San Joaquin desert (top) and Mojave Desert (bottom).
Precipitation in mm (black) and temperature in C° (red) during the 2016-2017 growing season for the San Joaquin desert (top) and Mojave Desert (bottom).
Mojave Desert year
Both of these seasons had similar precipitation and temperature patterns. The patterns were also similar between the two deserts, but the noticeable difference that I believe contributed to low plant abundance in the San Joaquin in 2016-2017 is temperature. The year before had warmer temperatures from January onward, which is a key period for plant development. In January 2017 following the majority of rainfall there was a long freeze period of approximately 5 days, followed by another cold period with freezing temperatures end of February. This pattern was much warmer in 2016 and is why I believe cold stress negatively affected plants in San Joaquin Desert for 2017. On the other hand, the Mojave saw significantly ore precipitation and cooler temperatures that all contributed to greater plant abundance.
Slicing through this climate data was interesting and challenging because of all the different ways to summarize variables. Using season means collapses a significant amount of the information and can make conclusions more difficult to derive. I am primed and excited now to dig into the plant responses!