- Make status assessments for reasons such as evaluating the influences of management actions or potential changes in the environment.
- Monitor species or habitat changes over space or time.
Summary and Objectives:
This course emphasizes developing skills in the design of monitoring studies and analysis of species/habitat statuses or trends, as well as identifying factors influencing statuses or trends. A course goal is to build a working knowledge of uncomplicated but useful sampling designs, based on the sampling concepts of what, why, when, where, and how many. Participants will analyze data collected in such a framework for status or trend assessment. During field and lab exercises, participants will develop and apply sampling designs, collect data, and make estimates of a population characteristic (e.g., density or abundance) with confidence intervals.
Upon completion of this course, participants will be able to:
- Develop critical monitoring and design skills based on reliable analytical techniques that are integrated with statistical sampling theory and field implementation.
- Practice a variety of sampling designs and subsequent data analysis during a field exercise.
- Understand Generalized Random Tessellation Stratified sampling and balanced acceptance sampling strategies for spatial coverage.
- Examine ways to address imperfect detection, such as double observer sampling, adaptive sampling, distance sampling, and occupancy modeling.
- Evaluate power of sampling designs to detect trends or make point estimates with a desired level of precision.
- Generate point estimates of population characteristics and develop confidence intervals by classic normal data techniques and bootstrapping.
- Apply occupancy modeling to determine proportion of area occupied by a species and change in POA over time.
- Use before-after-control-impact designs for impact assessment.
- Model detectability and adjust estimates to account for imperfect detectability.
Making Sense of Biological Data with R (CSP4200) and Statistical Modeling for Conservation (CSP4210) or equivalent experience with R and statistical background are required for this course. Consult the course leader regarding requests to bypass course prerequisites.