Determining Regional Gradient

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!

 

How to download data from @HOBODataLoggers using a Mac #openscience #opendata @apple

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

  1. Install Java Version 7 or higher (I installed Version 8 Build 151) because this is a dependency in later step.
  2. Reboot.
  3. 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.
  4. 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.
  5. 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.
  6. Initialize via device > launch. This is pretty self-explanatory.
  7. Download was a bit less obvious to me. Use device > readout. Allow readout to complete (progress bar pops up).
  8. Once complete, data are read into this instance of HoboWare as a project not saved. I recommend doing plot right away (see below).
  9. 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.
  10. 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!

Field sample processing

This fall I have been processing the insects and pollen samples that I collected this spring from my fieldwork in the Mojave Desert. The insects were primarily caught using pantraps, and were transferred into 90% isopropyl alcohol for preservation. With the help of our lab’s two undergraduate practicum students, Shobika and Shima, we are gradually getting them nicely organized into collection boxes.

I pinned many, many bees and wasps when I worked on a pollinator census during my undergrad in West Hamilton. These are the steps I use for processing insect samples:

  • Remove insects from alcohol.
  • Give the bees a rinse in water to fluff out their body hairs (this step works variably well, we may need to give some of the larger specimens a spa day in the future)
  • Gently dry with a paper towel, this causes the wings to uncurl. Wing venation is very important for identification.
  • Under a dissecting microscope, pin from top to bottom through the upper right-hand side of the insect’s thorax into a stryofoam block. You want the insect to be completely horizontal.
  • Gently uncurl the legs from the body and unfurl 1 antenna.
  • Affix an insect identification label underneath the insect with the text readable from the left side of the insect. These labels should have date and location of collection, unique identifier and the name of the collector.
  • Place into foam lined box.
  • Repeat!
  • Very small insects get pointed rather than pinned. The right side of the thorax is glued to a triangle cut out of cardstock, and the triangle based is pinned instead.

I have also been mounting pollen samples whenever I can squeeze the time in. I collected stigmas from the field and have been storing them in ethanol-filled small tubes.

Process:

  • Let slide warmer heat up
  • Using a transfer pipette, remove the pollen-ethanol suspension and transfer drop by drop onto warm slide, letting the alcohol evaporate and ensuring it does not run over the edges.
  • Place stigma onto slide.
  • Rub the inside of the centrifuge tube that was storing the sample with a bit of fushcin jelly, place  onto slide as well. Cut out 2 more small cubes of jelly, place over drop locations. Cover with slide cover and leave on warmer to melt jelly. Label slide.

For a different experiment that I have not yet processed, I will put the tubes into a centrifuge, spin down and pipette out the pellet to save time and labour. Quite a few tubes from the current experiment are extremely small and I am concerned about their ability to hold up under the force of a centrifuge. I need a less labour intensive process to make slides for my upcoming field season. I can think of two main options right now – use sturdy tubes that I can centrifuge, or collect into small tubes without adding ethanol, and mount each evening while at the research station. This will cut down the need to let the alcohol evaporate.

Data logger discoveries

The first micronet study is now complete. Cuyama Valley was instrumented using Onset micro-stations with temperature and soil moisture sensors for two full seasons. I made some very important discoveries in wrapping it up this autumn.

 

  1. Cables are heavy.
  2. Animals are much more active in the summer.
  3. Cables are yummy to animals.
  4. Deploying loggers with sensors is a magnet for new burrows.
  5. Heat cooks batteries.

Here is hoping the data recovered are just as fascinating. Honestly, I am tempted to do a wire-addition experiment. Observation suggests that there is a very real magnet effect of wires.

 

Posted in fun

Cam trapping: ten simple steps to process all those amazing photos

Workflow

  1. Read some cam trap papers.
  2. Check camtrapR package and see what it does to decide if it suits your specific needs.
  3. Open folder for each site, each day, each rep.  Do a folder ‘get info’ to count # of total pics.  These are your ‘reps’ within reps, i.e. literally total number of snapshots (or use command line to get dir info for all your photo data).
  4. I would honestly just paste 0’s all the way down because many will be ‘false hits’.
  5. Then, open them all up and scroll through.
  6. Every time a positive hit, overwrite 0 in ‘animal.capture’ vector and in ‘animal’ vector record what it is.
  7. Also, copy all positive hits photos into a separate folder for additional analyses.  Use a folder structure or ID system that keeps track of the place and time that photo was from.  For instance, have a folder entitle positive-hits for each site, day, location, rep or aggregate into a single positive hit folder but use a mechanism to ensure we know where/when photo was taken. Do not cut and paste, copy. This is a backup mechanism for additional analyses and sharing data.
  8. We also want to know when animals are most active, or not; and hence, check timestamps and paste down in that column too. Ideal is actual time but morning, afternoon, night is absolutely adequate and more rapid if we cannot automate the scraping using R-package.
  9. If timestamp is incorrect, do a light-dark assessment to code as night or day – this is a very rapid process.
  10. Record observations if more than one animal or if the same animal was recaptured from previous instance. Record anything of note ecologically to calibrate the quantitatives and link photo-capture processing to data mapping/translation. The goal is to accurately map photos onto numbers that represent the dynamics of the system in study.

Outcomes

The goal is have to have both an evidence folder of positive hits and a dataframe that can then be wrangled to estimate relative efficacy of sampling, frequencies of different animals, spatiotemporal dynamics, and differences between structured treatments in the implementation of trapping.

Meta-data for manual processing spreadsheet workflow

Attribute is the column headers.

attribute description
year we have many years for Carrizo (evil laugh) so good to list here in a vector
region MNP for Mojave, CNM for Carrizo
site if you have more than one site, put name of site
calendar date dd-m-year
microsite larrea, buchhorn, ephedra or open depending on region
day this is census day, 1,2,3, to however many days sampled
rep if more than one rep per day
photo rep just cut and paste to total number of photos each cam took on one day, could be 10 to 10000
animal.capture binary 0 = false hit, 1 = animal present
animal list animal as ‘none’ if false hit, then animal name if one was there
timeblock with animal telemetry work, morning, afternoon, night is usually sufficient 6am to noon, noon to 6pm, them night time
night.day back up if timestamps are incorrect – just do by night and day using light and darkness in photos. very quick
filename.ID.positive.hits
optional depending on your filing system, copy all positive hits to a separate folder. somehow, keep track of positive locations and times for subsequent analyses
observations
record observations of anything ecological that pops such as if there was another animal in the photo OR if it was the same animal repeatedly recaptured

CSEE 2017 Highlights

This year the ecoblender lab attended CSEE 2017. The conference was great and covered four days of talks, workshops, and networking events. I attended a free workshop that taught some basics in mapping spatial data and different packages to use in R. There was also a wide range of talks that mostly seemed interdisciplinary. This included discussions of uncertainty in ecology, estimate the value of natural resources, and developing models of habitat selection. Here are some of the highlights I took away from the conference:

Modelling:

There was discussion over the usage and power of mechanistic vs. phenomenological models. This is a topic discussed often in ecology (see of that discourse here), but can be defined here as:

mechanistic: includes a process (physical, biological, chemical, etc) that can be predicted and described.

phenomenological: Is a correlative model that describes trends in associated data but not the mechanism linking them.

The discussion mostly described the relationship between phenomenological and Mechanistic models as not binary and rather a gradient of different models that describe varying amounts of a particular system. However, it did touch upon models such as GARP and MaxEnt that are often used for habitat selection or SDM but neglect the mechanism that is driving species occurrence. Two techniques I would like to learn more about are Line Search MCMC and HMSC which is a newly developed method for conducting joint species distribution models.

Camera traps:

There was also a morning session that described benefits and tools for using camera traps. These sessions are always great as they give a chance to see some wildlife without disturbance. Topics focus around deer over abundance harming caribou populations, how wildlife bridges do not increase predation through the Prey-Trap Hypothesis and techniques for using wildlife cameras or drones. One talk that was particularly interested used call back messages when triggered to see how animals respond to noises such as human’s talking or a mating call.

One of the more useful things I believe to have taken out of the session is how to estimate animal abundance and movement when the animals in your camera traps are unmarked. One modelling technique using Bayesian modelling and was found to be equivalent to genetic surveys of animal fur for estimating animal abundance. This is in contrast to the more frequent spatial capture-recapture (SCR) methods that either mark individuals or supplement camera trap data with other surveys. I also discovered there the eMammal project at the Smithsonian that is an Open Access project for the management and storage of camera trap data.

Ecology and climate change:

Climate change as always is a big topic at these conferences. There was a good meta-analysis out of the Vellend lab that show artificial warming of plant communities does not result in significant species loss. However, there was evidence that changes in precipitation does significant impact plant communities. The results are very preliminary, but I look forward to seeing more about it in the future. I also liked a talk that is now a paper in Nature that models networks in the context of climate change. The punchline of the results being that species composition in communities is dependent on dispersal, and high dispersal rates can maintain network structure although members of the community may change.

I presented results from our upcoming paper modelling positive interactions in desert ecosystems:

Overall I learned a lot from the CSEE 2017 conference and thought it was a health balance of size and events. Victoria was also a great city and made hosting the conference very easy. Next year it will in the GTA and I plan on connecting with the organization committee to potentially host an R workshop at the beginning of the conference. Until then!

MSc or PhD for Canadians to do research in desert ecology or open science in California

Great news, we have had some funding come through for some research in California.

Two options, MSc or PhD.

Desert ecology research

The primary focus of the research is exploring how we might better use positive interactions between plants for restoration and management of arid systems. In particular, we want to examine influences on other taxa such as insects (including pollinators), endangered animal species (such as leopard lizards and kangaroo rats – cute), and on community biodiversity dynamics.

Details

Graduate-level research with The Nature Conservancy in Carrizo National Monument of positive-plant animal interactions.

GPA for YorkU Biology is A-, A.

Need to be able to drive.

Competent in R.

Admission Requirements

Open science research

Graduate-level research on open scientific synthesis. The goal is to explore existing data in high-stress ecosystems such as deserts and do synthesis. Data aggregation, systematic reviews, and meta-analyses to explore the importance of foundation species and biodiversity. This is a unique opportunity because this person can also collaborate with NCEAS to explore teaching open scientific synthesis, develop materials, and do research with the process of doing open science for synthesis.

Details

Excited about open and team science.

Competent in R.

Excited to work with big data, access data repositories, and do synthesis.

Excited to become an educator and contribute to positive change by developing materials (code, packages, guidelines, etc) that use these approaches.

Same admission requirements as first position.

Start date is Sept 1, 2017 for both/either opportunity.

http://futurestudents.yorku.ca/graduate/apply-now

I recommend you pop me an email too if you are interested: lortieATyorku.ca

Ecoblender hosting a workshop: An Introduction to R and Generalized Linear Models

Full details are provided here.
https://afilazzola.github.io//YorkU.GLM.2017-04-28/

General Information

The purpose of this workshop is to provide tools for a new/novice analyst to more effectively and efficiently analyse their data in R. This hands-on workshop will introduce the basic concepts of R and use of generalized linear models in R to describe patterns. Participants will be encouraged to help one another and to apply what they have learned to their own problems.

Who: The course is aimed at R beginners and novice to intermediate analysts. You do not need to have any previous knowledge of the tools that will be presented at the workshop.

Where: 88 Pond Road, York University. Room 2114 DB (TEL). Google maps

Requirements: Participants should bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) with administrative privileges. If you want to work along during tutorial, you must have R studio installed on your own computer. However, you are still welcome to attend because all examples will be presented via a projector in the classroom. Coffees and cookies provided for free.

 

How we use a handheld soil moisture probe to supplement in situ plant ecology sampling

It is best to deploy loggers with appropriate sensors to capture an environmental signal within a set of study sites. Nonetheless, when actively sampling for plant-plant interactions dynamics,  an estimate of soil moisture at that particular point in time and space precisely is useful (at least as a covariate). We use the Delta-T SM-150 handheld unit to complement our long-term logging arrays.

Here is a brief summary of the settings/methodology we use.

Method

  1. Push right button to activate unit.
  2. Repeatedly depress right button to cycle through modes until you reach ‘organic’.
  3. Insert probe into ground and ensure that metal conductors are fully embedded in ground with ceramic/plastic unit flush with ground surface.
  4. Left button to measure. Typically, it should take only 1-2 seconds.
  5. Avoid rocks and voids in the ground when inserting probe.

Comments: Ranges you can expect at least in arid and semi-arid systems we have tested within California are between 1-40% but most frequently < 10%.  The unit is durable, and the control unit is ‘water resistant’. However, when the controller gets wet in the rain, it stops working until it drys out again (typically at least a day later). The cable is not that robust, and to be safe, we insert/push the sensors into the ground using the ceramic casing.

 

Microenvironmental change in Cuyama Valley 2017.2 goals

In 2016, we deployed micro-environmental data logger arrays to monitor global change dynamics at very fine scales. We also structured measurements to ensure we can infer and link to a biotic interaction signal between common plants within this region.

This is very important region to study for at least two reasons ecologically.

(1) Water issues with people, plants, and agricultural are critical here.

(2) Cuyama Valley is an excellent set of sites or mesocosm for the San Joaquin Valley at large. The San Joaquin Valley is still sinking (NASA report). We need to understand temperature, precipitation, and soil moisture availability patterns at many scales within the region.

This season, 2017, is a relative boom year in terms of precipitation. Here are the immediate sampling goals for this season.

1. April (mid). At peak flowering, count burrows, re-measure shrub sizes, sample annual vegetation, and collect biomass.
2. May (mid). Retrieve all logger units, download data, and check functionality. These data capture two growing seasons – one drought, one wet.
3. May (mid). Re-deploy and re-initialize loggers. Rationale – need data on shrub effects when it matters for animals like lizards and hoppers etc and when it is really hot.
4. Sept (end). Retrieve loggers and sensors, download data, end experiment.
5. Oct. Design and test a missing-data strategy to address missing sensor and logger failures. I will likely implement a within-site, resampling data strategy associated with central tendency measures to fill gaps.