Population state (size and distribution) is driven by four vital rates:

- Births
- Deaths
- Immigration
- Emigration

The analysis of animal populations concerns estimating the state(s) or the vital rates of a population and the factors that influence those states and rate. **Estimating these states and rates and modeling what influences them will be the focus of this course.**

- Contribution to science = learn stuff
- Understanding life history & ecology
- Population theory

- Conservation & management = use what we know to achieve goals = make smart decisions
- Increase or sustain populations deemed to be too small
- Reduce or Control populations deemed to be too large
- Maintain numbers at current levels for populations deemed to be at acceptable levels
- Manage for a harvestable surplus in harvested populations

*For either goal, we must employ good scientific process to progress*

I know that most of you have had (or will soon receive) training on the scientific method in other courses. Thus, we will not spend much time on those topics in this course. We will focus on careful estimation of key population features such as vital rates and population state (size or presence/absence). While doing so, we will also work on how to evaluate a set of complementary hypotheses about sources of variation in those rates or states. We will typically do so as follows:

- Build a set of competing models, where models represent hypotheses
- Evaluate support for each model from empirical data
- Base conclusions on set of models

- Models are approximations of reality and don’t capture full reality
- There are many types of models and uses for models
- Models can vary from the conceptual to the physical to the mathematical
- A statistical model is a mathematical expression that helps us predict a response (dependent) variable from a function of explanatory (independent) variables based on a set of assumptions that allow the model not to fit the data exactly
- In applied work, we often search for the best approximating model of a process of interest by
- Developing a set of complementary hypotheses that can be expressed as statistical models
- Collecting data that will help us discern amongst our competing models
- Evaluating how strongly the data support the various models
- Considering the trade-off between precision and bias when choosing how complex to make our models. If the model is too simple, we will obtain highly precise wrong answers. If the model is overly complex, inferences might be made about spurious results, and results might be less precise than those from a simpler, more-appropriate model. We will use information-theoretic methods for making the tradeoff.

In many field studies of wild animals, we have imperfect ability to observe the animals. This ability has important consequences for how we must model the data we collect on vital rates, abundance, and/or presence/absence. Specifically, we’ll often need to simultaneously model:

- The observation process, i.e., our ability to detect animals
- The biological process of interest

We will mostly concern ourselves with mark-recapture models this semester that were developed to handle such situations.