Final Summary: Modeling Milkweed Population Dynamics

Since the summer research session is coming to an end and the first major phase to my project is almost “finished”, this seems like a good time to write my final post, wrapping up what I’ve been doing all summer, what has come from my work, and how this will transition into the next phases of the project. Just as a friendly reminder, this project is focussed on community ecology and population dynamics of the Common Milkweed, with the specific goal of modeling the size and demographic behavior of the population as a function of factors like herbivory and leaf chemistry. We use field-collected data and computational/statistical models in the R programming language to determine these relationships, that could inform management policies and conservation strategies.

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Update: Model Selection and Fieldwork in Maine

The summer session is almost at an end, and the past few weeks have been absolutely packed! I have fully incorporated the milkweed data that we took in the field last month into a new set of models. We now have 4.5 years of data tracking thousands of individual milkweed stems from sprouting/germination until reproduction and dieback at the end of the season each year. This is enough data to make robust statistical models for the vital rates of the population (survival, growth, reproduction), which is what I have been working on. The process of deciding which type of model is the best fit and most suitable for our purposes is a challenging one that takes a surprising amount of time and thought. This stage in the modeling process is often referred to as “model selection” (and validation), and is very heavy on the statistics. Various techniques are used to quantify and visualize how and where a model is performing well and where it may be lacking. A huge part of this involves keeping track of which models can actually be compared with one another in any sort of a meaningful way. This was one of the issue I needed to solve in my analysis. Specifically, we are working with a class of models called Generalized Linear Mixed Models (GLMMs), which do not perform the same way in tests designed for standard linear models. In order to get a more accurate way of assessing the quality of our models, I needed to find and implement some new statistical tools that would make these types of models comparable for us. The below plot is an example of what I used to visually assess the new models, and was generated by the DHARMa package in R. If it looks even remotely interesting to you, check out it’s vignette here.DHARMa Residual Plot

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Field Work is neither all Work nor all Play

A large chunk of the work for my project in June was spent collecting data and doing field work. Often times this meant crouching in a literal field given our particular pant of study, but sometimes this involved more conventionally outdoorsy activities like crossing rivers, hiking dirt trails, and general bushwhacking. Studying and working in the disciplines of biology, environmental science, or other “macro-scale” natural sciences, you hear the term “field work” thrown around a lot… but what does this vague umbrella term actually mean? Now I’m sure this very well may vary considerably depending on what line of work you’re in and what questions you’re actually trying to answer, but I will give you my impressions from a general /plant ecology perspective.

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Dispatches from the Field: Massachusetts, Maine, and the Merrimack

Hello from Gloucester Point, Virginia!

I am already missing the cooler weather of New England, having just returned to muggy Virginia from two very intense, very rewarding weeks of field work with my project advisors Dr. Christopher J. Hein and Claudia Shuman, a PhD student at the Virginia Institute of Marine Science.

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Piloting an experiment in the field

One of my final activities for the summer was assisting in a pilot research project using crowdsourced data. U-report is a UNICEF tool for gathering citizen feedback on what’s happening at the community level. Using this free SMS-based system, U-reporters send in both unsolicited information and responses to polls asking about service provision and soliciting other community information. Development partners and local leaders can then use this crowdsourced data to take citizen voices into account as they make decisions. The hope is that this will help complete the feedback loop between donors, government officials, and community stakeholders.

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