#Carrizo National Monument research update 2018_2

We had a more ambitious set of goals this season.

Goals

  1. Habitat use frequency estimates. Tools: a. telemetry of blunt-nosed leopard lizard with a total at least 1200 relocations split between AM/PM with an estimate of shrub-open and behavior. b. cam traps at shrub-open on still mode.
  2. Behavior estimates. Tools: a. cam traps on video mode at a total of 100 hours recording time. b. direct observation (with recording too) by humans of lizards and grasshoppers at a total of 100hrs.
  3. Shrub-plant-animal interaction estimates. Tools: exclosures at two sites to exclude different taxa in shrub-open mesohabitats. a. cages. b. cams c. vacuums. d. sweeps.
  4. Temperature profile estimates. Tools: a. pulse of collars on lizards b. loggers at microhabitat scales.
  5. Census grasshoppers. Tools: stick, sweep, and vacuum. Also do direct observation to assess whether they are significant consumers.

Teams

1a. Mario and Steph. Goals 1,2,3,5.

1b. Malory and Nargol. Goals 1,2,5.

2. Emily and Kat. Goals 1 & 4.

Extensions

Deploy one set of cam traps on still mode at a total no-shrub zone.

Get a solid handle on behavior by verts and inverts in the context of paired interactions with plants (at micro-scale) and shrubs at mesoscale.

Need an assay of insect diversity.

Animal sampling contrast protocol

Telemetry-scat design

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.

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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).

DATA OUTCOME

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.

 

 

 

 

 

 

 

 

 


Congratulations to Dr. Filazzola

Completing facilitation research and being a facilitator through collaborative mentoring and supporting team science, Alex defended his PhD yesterday. Congratulations!

 

The subes and all of us will miss your open spirit!
So tempting to post a different pic :)!

 

 

 

Posted in fun

Collaborative and open science writing

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.

List with satisfaction scores

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.

Summary

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 steps for turning camera trap photos into useful datasets: manual processing workflow

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.

 

  1. 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.

  1. 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.
  2. 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.
  3. Days work exactly like calendar dates.
  4. 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.
  5. 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.
  6. 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!