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!

 

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!

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.

 

Waiting for the rain

Rainfall updates

The growing season of 2015-2016 has come and gone with disappointing results from the supposed El Niño year. The 2016-2017 season is approaching and a few had feared that it would just continue the current pattern of drought. I was especially fearful having battling drought four years in a row in my study of plant interactions. It would be nice to have a chance with at least “average” precipitation amounts. Half way through the rain season and this year looks promising. Areas of California have been seeing some pretty significant precipitation including some potential floods. While this is great news in terms of drought relief for coastal cities and the Sierra Nevada snow pack, I wonder what the consequences will be for the deserts? In particular, the Mojave always seems to be in the unluckiest of rain shadows, missing most of the precipitation that the rest of the state experiences. I took a snap shot of the rainfall and average temperatures since seeding at the end of October. Here are the results:

Interpretation

The right combination of rain, temperature, and timing are absolutely crucial in desert ecosystems in regards to how the plant composition will respond. In an older paper by Beatley (1974) is a description of how these three variables determine plant composition. From this and my own experience, the absolute minimum rain to see any annual vegetation on the ground is 2.5 cm. However, these plants usually die within a month if there is no subsequent rain. I have seen this occur in multiple years where Halloween rain is not followed by any other precipitation until mid-January. The result? Many dead plants, and a new representation for plant communities. The Mojave has seen enough rain to begin germination and this rain has all occurred within the last 3 weeks. This, plus continued cold temperatures, should encourage the persistence of annuals for at least another month. If at least one other major rain storm passes through in that time I would expect to see these plants make it to flowering. On the more westerly side of the state, my sites have been seeing fairly consistent rain. This is great news for my Panoche Hills site that likely has passed its precipitation threshold that guarantees emerged plants to flowering.

Fingers crossed as always!

How to use colour in manuscripts

untitled

I thought this was a very helpful guide on using colour in figures. There are a few rules, but one comment from the whole document stands out. “If colour serves a purpose, but something other than colour would do the job better, avoid using it”.

http://www.perceptualedge.com/articles/visual_business_intelligence/rules_for_using_color.pdf

Here are the simple rules:

1. If you want different objects of the same color in a table or graph to look
the same, make sure that the background—the color that surrounds
them—is consistent.

2. If you want objects in a table or graph to be easily seen, use a background
color that contrasts sufficiently with the object.

3. Use color only when needed to serve a particular communication goal.

4. Use different colors only when they correspond to differences of meaning
in the data.

5.  Use soft, natural colors to display most information and bright and/or dark
colors to highlight information that requires greater attention.

6.  When using color to encode a sequential range of quantitative values,
stick with a single hue (or a small set of closely related hues) and vary
intensity from pale colors for low values to increasingly darker and brighter
colors for high values.

7.  Non-data components of tables and graphs should be displayed just
visibly enough to perform their role, but no more so, for excessive salience
could cause them to distract attention from the data.

8.  To guarantee that most people who are colorblind can distinguish groups
of data that are color coded, avoid using a combination of red and green in
the same display.

9. Avoid using visual effects in graphs.

 temp

ESA 2016 – my highlights

Functional traits

I have known for a while that functional traits are at the core of ecology. Unfortunately, for most of my experiments I focus solely on biomass and abundance. However, at ESA this year I noticed many researchers who measured community abundance and used a secondary database such as TRY for extracting functional traits per plots. This is a great re-purposing of already collected data to be reused to answer different questions. I am definitely going to consider analyzing data sets I currently have the species composition for to answer questions about functional diversity rather than typical species richness.

functional traits

Statistics

There was a considerable amount of NMDS usage this year, furthering that ecological analyses are becoming more complicated and requiring ordinations. Although ecologists may love NMDS, there is a preference in the statistical world to avoid it. I won’t get into the pros and cons here, but I believe ordinations such as PCoA, CA and RDA can accomplish the same as NMDS with less limitations. I will need to explore this further but I found this document a good starting point. Permuted ANOVAs were also another particularly popular statistical test and one I need to find a good R package for.

Another great talk from the conference discussed the advantages of the Negative Binomial distributions particularly for species abundance distributions. The talk discussed the tendency to fit discrete count data into log-normal distributions or Poisson. Often these distributions do not fit the data, while negative binomial does. The author A. Rominger promised me that he would be publishing an article detailing this commonality later in the year. I will hold him to that! In the mean time, here is an R package that he developed for species abundance distributions and the negative binomial distribution called pika.negative binomial distribution

Species distribution modeling

I had the opportunity to sit in on a talk by C. Merow that discussed used “expert maps” to further refined predictions of species distributions. It was a great talk and introduced a new concept to refine species predictions. Using the Map of Life (MOL), ecologists create species boundary maps that are would go into an SDM. The maps are compatible with MaxEnt and were shown to be effective at better predicting species occurrence. While a much better technique, I think the major limitation is that specific species will not have these maps delineated yet. However, until MOL catches up, big picture questions can be addressed with a greater degree of accuracy. The talk also reminded me I need to learn more Point Process Poisson modeling

The other instance SDMs that I thought was extremely informative was by Leung discussing co-occurrence models for invasion. This models have strong similarities with our work except instead of testing how an invasive species co-occurs with native species, we are trying to determine how a facilitating benefactor species interacts with neighbouring species. The way this is done is either using a proxy measurement or by using multi-species models. The example Leung used for a proxy measurement was boat movement in Ontario as a function of invasibility for a particular non-native species. I though this was a great idea and something I will consider for my next SDM experiment. The multi-species models is something that was a bit more complicated and that I will need to explore further.

Species distributions

General

Overall ESA went well! The attendance appeared a bit lower than last year, but I felt I was still super busy. It was a great experience and I learned a lot more that the highlights I am listing here. We also received some great feedback on micronet on how to improve it and I go some ideas to develop an R package that hopefully satisfies everyone’s micro-environmental challenges. A full list of my participation at ESA below:

A test of the stress-gradient hypothesis including both abiotic stress and consumer pressure during an extreme drought year

How to Set Up Automated Sensors Arrays for Measuring Micro-Environmental Characteristics and Synthesize to Larger Scales

Microenvironmental change as a mechanism to study global change

The use of shrubs as a tool for re-establishing native annuals to an invaded arid shrub land

 

esa2016

Precipitation mediates the mechanism of facilitation in a Californian Desert

ESA 101 at Fort Lauderdale is coming up! I will be presenting on recent findings from an experiment we conducted over two years. I am extremely excited for both the presentation and the results! Here is the slide deck, statistical analyses and program outline.

https://afilazzola.github.io/water.consumer/

Background/Question/Methods
The stress gradient hypothesis original purposed the frequency of plant interactions along countervailing gradients of abiotic stress and consumer pressure. However, research to date has studied these two stressors in isolation rather than together, thereby potentially neglecting the interaction of these factors on plant composition. In the arid central valley of California, we artificially manipulated a soil moisture gradient and erected animal exclosures to examine the interactions between dominant shrubs and the subordinate annual community. We conducted this experiment in an extreme drought year (2014) and a year of above-average rainfall (2016).

Results/Conclusions

Shrubs positive affected the abundance and biomass of the annual community at all levels of soil moisture and consumer pressure. In the drought year, shrub facilitation and water addition produced similar positive effect sizes on plant communities; however, the shrub facilitation effect was significantly greater in watered plots. During the year with higher rainfall, there was no observed water or exclosure effect, but shrubs still significantly increased biomass of the subordinate plants. Shrubs and positive interactions maintain productivity of annual plant communities at environmental extremes despite reductions in droughts stress or consumer pressure and these positive effects are even more pronounced with water addition. The relationship between consumer pressure and abiotic stress on plant interactions is non-linear particularly since shrubs can facilitate understorey plants through a series of different mechanisms.

Ephedra can escape me, but I can’t escape Ephedra

Taking a short trip to the American southwest, I have discovered my study shrub occurs quite commonly. Although I normally examine Ephedra californica in the Mojave and San Joaquin deserts in my travels, I have found it i the Chihuahuan Desert, Sonoran Desert, Great Basin Desert, and Colorado Plateau.

Petrified Forest National Park

Petrified Forest National Park

Canyonlands National Park

Canyonlands National Park

White Sands National Monument

White Sands National Monument

Seeds different than the California species

Seeds different than the California species

Monument Valley

Monument Valley

UCSB workshop – programming for ecologists

A two day work session at UCSB was extremely informative covering a wide range of topics for programming and ecology. The course was divided into four components: bash-cmd, intro to R-studio, Github, and data manipulation in R-studio. I especially liked how the course took a more abstract approach without going through statistics. Rather it was focused on data manipulation for the day to day ecologist. One of the more unexpected things I learned from the process was R-markdown and developing websites using it with Github. All of these tools can significantly help with collaboration. Although most of what I had been doing in R is not wrong, it may be difficult for a collaborator to pick up my code and start using it. I think this course really helps me bridge that gap and it is something I am going to push forward on. Gone are the days of sharing Word Documents with 6 versions of the same figure.

All the course materials are found on the website here! I would recommend anyone even slightly interested in the above topics to go through it. Below are some highlighted parts that I believe deserve a little extra attention.

Bash shell

One thing that was lightly talked about within the short 3 hour time frame we had, is the power of Bash Shell.  Bash Shell (cmd) is Neo from the matrix. All the rules are off and boundaries are endless. It has happened to me before on simple tasks that files or hard drives will be written off as corrupt, yet all the files are still there. Bash Shell has allowed me to see what my OS restricts. This unrestricted access and combination for programming can allow tasks to be committed that otherwise are not possible in real-time or at all. To bring in another movie reference, “with great power comes great responsibility”. Despite the overwhelming power of Bash Shell, it is easy to do things wrong… very wrong. It that way it may be intimidated to users because there is no undo or recycling bin. Still, a very powerful tool for the ecologist who wants to do something on their computer, but can’t.

temp

Base vs Dplyr

Why is Dplyr better than Base? I haven’t quite found out if Dplyr better, but I have noticed that it is easier to understand when sharing with collaborators. Nesting functions within funtions may make sense to you, but to others it can look like a disaster. Will I switch over to dplyr? Maybe. It does mean learning a bunch more commands and most are the same character length as base. However, collaboration is everything and seeing subset(subset(subset… may scare a few people off.

R Markdown

Such an unexpected surprised! I really like Rmarkdown and how easy it is to generate a quality website with little code. The best part is that it still has easy functionality to link to CSS or HTML files. Nothing is perfect, and it unfortunately means learning another series of commands and codes for something that already exist. However, it does tie in better with R scripting. This allows for the development of half-websites, half-experiment results that can be used as a blog post, shared with others, etc. The course taught us a lot about it and I’m already forgetting much of what I heard, but I will begin to incorporate as much as I can.

temp