In the last 0.50 seconds, a leopard lizard runs in the bottom right corner.
Have no clue what this guy could be? Can anyone lend me a hand? (Who’s that Pokemon?)
A video showing a Jack Rabbit Pooping at night, in the Carrizo Plain. One of the videos I have found while going through my camera trap data.
Similar to the previous post showing the Shrike take-off. This one shows it from a side profile.
A video of a Kitfox interacting with the camera trap at night. It seems that it has taken an interest in it and is a little curious.
Now that the Kitfox is gone, we have 2 Kangaroo Rats in view. Seems that they are foraging for some food to eat. This video was a few hours after the Kitfox was around.
The final video shows a Jack Rabbit early in the morning taking an interest in the Camera Trap. Similar to the Kitfox, it seems that the animals are a little curious.
I want my final paper to be useful for and applicable to restoration ecology. This led me to inquire what data I should collect for my second census. My germination rates are up, and all four species are present, so would relying on number of individuals and biomass of each species per pot be enough data? I decided that since I am using light as a limiting factor I must include height in my data; the plants may have somewhat similar biomass, but if it is due to leggy growth in the shaded pots then it will be important to note that although biomass was similar resource allocation was not equal. Are great amounts of leggy, weak, and nutrient deficient plants with few leaves better for ecosystems then having fewer shorter but thicker, more leafy plants? I measured the number of individuals per species per pot, alongside with the height and number of leaves the tallest member of each species had per pot. I have yet to analyze these numbers, but did notice trends when doing the census!
Side note: I conducted a germination experiment in the greenhouse prior to using these seeds, and have let them grow out. My Phacelia tanacetifolia is growing a beautiful flower!
Writing can be scary. Writing can be scary for everyone, not just us scientists. But whether or not we enjoy it, or think we’re good at it, it’s probably the best tool for communicating our findings. So removing as much pain from the process is key.
That’s why I’ve started using R Markdown for writing.
If you’re like me, the worst part about writing scientific papers is formatting. I hate it. I hate getting bogged down in font size, citation style, line numbers–all that stuff. Not only does it take me forever to get just right, but it gives me so much room to mess up stuff that isn’t based in content. If I’m spending time fighting with format, that’s time away from thinking about stuff that really matters. And the idea of switching between different journals’ format style makes me want to cry. R Markdown made worrying about that a thing of the past.
But perhaps even better than the formatting convenience R Markdown provides, it makes collaboration so much easier. This is especially true when you pair R Studio with your Github account. All changes and additional files referenced are all neatly connected, and any code printout included in your paper is already sitting in your paper.
So, I’ve switched to writing in R Markdown. I’ve always worked in either Word or Google Docs, and I still will if I’m writing something that isn’t going to require a lot of coordinating; but for big projects, I’m moving on up. I’m ready to get productive.
When I first tried this new step in my workflow, I felt less than skilled. I have experience in R Studio and Markdown, but when learning anything new I feel like a cat trying to type. So here’s some important tips I’ve collected from my first time through the process to hopefully make it easier.
For me, it was a steep learning curve to make the transition from rich text programs to R markdown. In this post, I included some introductory tips for switching to R Markdown. There are lots of more advanced options with R Markdown, but for this post I wanted to focus on the challenges that I struggled with while writing my first paper in an .rmd file. This doesn’t include steps that I found intuitive, or questions that are associated with learning to code in R, or tricks that are so advanced that I didn’t run into them. But I found the answers to most of my questions by scouring the web, so even if I didn’t answer something here, the answer is probably out there. Hopefully, the tips I devised can help an intermediate R coder get the most out of their work with R Markdown.
Salvia columbariae, Phacelia tanacetifolia, and Plantago insularis are key phytometers (plants that indicate ecosystem conditions) in the San Joaquin Desert of California. As the highly invasive exotic Bromus madritensis colonizes in this non-native environment it lacks the environmental suppressors and competitors it faces in its native habitat. This leads to native Californian desert ecosystems to shift to a new model where native plants are excluded due to competitive disadvantages. decreases in native biodiversity are directly correlated to the health of an ecosystem, ecosystem services, resiliency to climate change, as well as the resources and for these reasons, identifying methods of restoration ecology is crucial.
Using my 3 factor (ambient light vs shaded conditions, low vs high B.madritensis density, native seeds at 6 levels of density (0,3,6,9,15, or 30 natives)) greenhouse competition trials I aim to identify what density of native species must present in a pot with a surface area of 153cm2 to outcompete an exotic one.I have previously run an experiment to identify optimal density in pots of the same surface area using each of the native species in monoculture, implementing the same light versus shade conditions with a total of 365 replicates. I will assess if I am able to compare these differences in optimal monoculture mix density to a polyculture mix with invader presence. If my data finds an optimal density using these methods, I hope to further my research and apply my findings to population ecology by estimating necessary population metrics required to apply this to ecosystem for large scale restoration and contribute it towards field work.
My experiment currently contains 200 pots, 100 of which are shaded by a bamboo structure I suspended. Germination has begun, yet it is still difficult to differentiate among species this early on. As predicted, the shaded individuals have demonstrated leggy growth as they reach towards the light source, yet there seems to be leaf production in possibly higher concentrations in the shaded pots than the ones experiencing ambient light. It appears that the shaded pots have a higher germination and growth rate (measured by number of individuals and number of leaves per pot). Is it possible that the shade-preferring B.madritensis is facilitating growth through positive density dependence? Am I witnessing an Allee effect in the form of environmental conditioning? Or is the answer as simple as light levels in the shaded conditions being sufficient for the natives as well as B.madritensis? Using the metrics of germination of species per pot as well as leaves per species and finally above ground biomass at the end of my experiment I will continually assess success through the different factors and levels I have designed and implemented in my experiment and hope to achieve a successful conclusion.
I am currently running an experiment to observe how an invasion of red brome impacts the growth and success of 3 native Californian plants (Plantago insularis, Phacelia tancetifolia, Salvia columbariae) across 5 different watering regimes. These watering regimes simulate conditions from extreme drought to very wet years. The experiment utilizes a total of 300 pots with 10 replicates per treatment; 100 pots are being used for each species, with 50 of those pots containing brome and 50 lacking brome.
As the experiment progresses 4 measurements will be obtained:
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!