I am an applied evolutionary/theoretical ecologist. My goals are to use the methods and ideas from evolutionary ecologist, in an applied context. My primary applied research is focused on a series of themes; all focused on the general question of making optimal resource management decisions under uncertainty. Each of the themes addresses one particular area of uncertainty: (i) structural (research on modeling population dynamics), (ii) observation (research on estimation of model parameters), (iii) controllability (estimation on harvest management of structured populations, from both technical and human dimensions perspective), and (iv) optimal decision theory for state-dependent management strategies. His conceptual research is directed at 2 primary questions: (i) assessment of methods for direct and indirect assessment of spatial structuring and coupling in populations, and (ii) analysis of evidence for phenotypic life-history trade-offs, using advanced statistical methodologies.
Outreach and Extension Focus
Although I do not have a formal extension outreach appointment, I firmly believe it is incumbent upon all academics - especially those employed at a land-grant institution - to engage in outreach activities whenever possible. To this end, I actively contribute to several 'online' textbooks which are freely disseminated. In addition, I have also developed, manage and moderate a very large (~1000 member) online discussion forum on the use of data from marked individuals for purposes of resource conservation and management.
I regularly teach NR3100 (Applied Population Ecology), and NR4100 (Advanced Conservation Biology). In alternate years, I teach NTRES4120-6120 (Wildlife Population Analysis).
My basic approach to teaching can be summarized as follows: the study of ‘natural resources’ is, arguably, the study of applications of fundamental principles in ecology, economics, and social science to particular problems, typically those involving resources of some importance to people. In an environment increasingly modified
by human influence, there is a growing need to make informed, scientifically defensible decisions concerning strategies adopted to achieve conservation or management objectives.
Current conservation and management approaches are built upon advances in the monitoring, assessment, and science of population and ecosystem biology, conditioned on the social, economic and legal context of human interactions with those environments. While
biological understanding is clearly a cornerstone of this endeavor, substantial uncertainty remains about the impacts of management decisions, and how to optimize those decisions in the presence of such uncertainties.
Applied resource conservation/management is at the heart of what we do, both now, and traditionally. Implicit in conservation/management is the concept of taking actions to achieve objectives. It is the process of formulating the objectives, understanding the uncertainties in the system we attempt to manage, and the technical and ‘human’ challenges of implementing a plan aimed to optimally meet those objectives, that is the unifying structure under which natural resource departments operate.
Motivated by this view, I have and continue to be very involved in technical training at student, faculty and staff levels. However, I also strive to make sure that students understand that techniques are simply tools, albeit often essential, and that science cannot progress without ideas. My general approach to teaching quantitative courses (my primary teaching focus at Cornell) is to emphasize both the theoretical and mathematical underpinnings of the approaches, as well as modern implementation of various analyses
through the computer. In all cases, the mechanics of “how an analysis works” are stressed before the students work with the computer. It is my goal to ensure that the students understand “what is happening”, rather than have them simply view the computer as a statistical “black box”, and master only the mechanics of getting the software to run.
However, computers are an essential component of data analysis and theoretical modeling, and students receive a considerable amount of practical exposure to computer-based analysis, once basic concepts have been mastered. All of my classes make extensive use of analysis of problem data sets, uniquely generated for each student, supplemental reading and classroom instruction.