As my first year at ASU comes to a close, I am told to submit some materials for review. Here's some of the information I'm submitting.
Early Draft: Explaining Success and Assessing Optimality of Species Distribution Modeling in Conservation Biology (email me)
One of the tasks of conservation biology is to prioritize places in a region for their incorporation into a conservation area network such that species of interest are represented. Current practice assumes that prioritizing places to meet representation goals corresponds to solving the inductive problem of modeling actual species distributions in the region. In addition, current practice assumes that standard methods such as maximum likelihood estimation are up for the job. In other words, conservation biologists assume that models of actual species distributions inferred from statistical and machine learning methods are the inputs to place prioritization algorithms. Recently, Sarkar and colleagues have challenged this assumption and claim to offer a better solution. However, the challenge is left largely underdeveloped. This paper tries to make the skeptical sentiment of Sarkar and colleagues more explicit in order to (i) explain under what conditions species distribution modeling is successful and (ii) assess its optimality within conservation biology.
Research Proposal
I am mainly interested in how methodological choices in conservation biology can possibly be rational given the cognitive aims and background assumptions of conservation biologists. Currently, I am interested in exploring this broad topic with respect to species distribution modeling and population viability analysis. However, for dissertation purposes, I will likely choose one or the other. So, I see two possible tracks for a dissertation.
Other than the introductory chapter, the first two chapters will probably include mapping out the current paradigm in conservation biology---i.e., adaptive management---and my approach to rational choice in either species distribution modeling or population viability analysis. This approach will likely concern how to apply decision theoretic criteria such as admissibility and multi-objective decision making frameworks to methodological choices. So, the emphasis is on how to make choices regarding inductive methods rather than, say, how to make direct management or policy decisions. Of course, however, the choices with respect to inductive methods can help or hinder management or policy choices. As such, much of the discussion will be based around what cognitive aims are reflections of values to conserve biodiversity. The next four chapters would differ depending on whether I choose species distribution modeling or population viability analysis as my main topic of interest.
If I choose species distribution modeling, then the focus of chapters four and five will be the graphical/interventionist theory of causation and causal interpretations of statistical/machine learning models, respectively. The problem species distribution modelers set out to solve is a system structure problem. They are attempting to understand the causes of species distributions. As such, I see chapters four and five as laying out the theoretical framework behind such modeling. Chapters six and seven would then go on to try and explain the rational choices species distributions modelers should make given the background assumptions and cognitive aims associated with solving system structure problems in conservation biology qua adaptive management. Chapter six would focus on actual methods used to estimate causal parameters (MLE, Bayesian estimators, etc.). Chapter seven would focus on actual methods used to select causal models of species distributions (AIC, BIC, etc.).
On the other hand, if I choose population viability analysis, then the focus of chapters four and five will be the graphical/interventionist theory of causation and theories of actual causation, which have been somewhat of an after thought in the graphical/interventionist theory of causation. The problem population viability analysts set out to solve is an actual cause problem. They are attempting to understand not only the causes of species extinction but also the actual course of the fate of the species through time (i.e., dynamics). That is, population viability analysts are interested in understanding the actual causes of species extinction. As such, I see chapters four and five, again, as laying out the theoretical framework behind such analysis. Chapters six and seven would then go on to try and explain the rational methodological choices population viability analysts should make given the background assumptions and cognitive aims associated with solving actual cause problems in conservation biology qua adaptive management. Chapter six would focus on actual methods used to estimate parameters. Chapter seven would focus on actual methods used to select models of population viability (AIC, BIC, etc.).
Plan of Study
Fall 2012
Research Prospectus Writing
Mathematical Population Biology
Stochastic Modeling in Biology
Choice and Belief
Spring 2013
Biotic Distributions
Bayesian Modeling for Life Sciences
Biology and Society Lab
Fall 2013
History of Science I
Statistical Learning
Spring 2014
History of Science II
Computability
That's a wrap! Good luck with finals everyone!
