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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Data Wrangling

Near the conception of this project, I was part of several discussions on “data workflow” and other such monikers that allude to the crazy, messy world of what we call data. In a time when information and “big data” are valuable and only relatively recently tapped sources of knowledge, extracting insights from this messy world is a skill that seems like it just makes everything easier. So naturally, I was excited at the idea of being able to learn some of the techniques used to make sense of everything in the process of my summer research project. After all, when practicing science and statistics, there is a lot information to keep track of. Unfortunately that means there are that many more ways that all that information can get mixed up and jumbled around… I learned several important things about cleaning and managing data in the course of my project so far. [Read more…]

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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Abstract: Modelling the Effects of Density Dependence and Leaf Chemistry on Population Dynamics of Common Milkweed

The monarch butterfly has seen dramatic population declines in the past two decades (81% since 1999). A major contributing factor of this is the simultaneous decline in populations of common milkweed, on which the monarch is entirely dependent for much of its life cycle. As part of a plan to combat this decline, major replanting programs have begun in order to rejuvenate both species. However, while we know a great deal about the biology of monarchs, we know much less about the ecology of milkweed. This project aims to fill that gap by examining the role that leaf chemistry and plant density play in common milkweed population dynamics. To do this, I will collect demographic, spatial, and chemical data on milkweed in the field, and use that to expand a mathematical population model for milkweed.

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