Course (University of Florida, Department of Wildlife Ecology and Conservation): Analysis and Management of Vertebrate Populations and Communities

 

Venue: Architecture Computer Lab (Arch 116), University of Florida main campus, Gainesville, FL

 

Dates: March 10, 12 -17, Timing: 8.00 am – 5.00 pm with a one hour lunch break and 15 minute morning and afternoon breaks

 

Instructors: 

James E. Hines (Patuxent Wildlife Research Center, U.S. Geological Survey, Laurel, MD)

 

William L. Kendall (Patuxent Wildlife Research Center, U.S. Geological Survey, Laurel, MD)

 

James D. Nichols (Patuxent Wildlife Research Center, U.S. Geological Survey, Laurel, MD)

 

John R. Sauer (Patuxent Wildlife Research Center, U.S. Geological Survey, Laurel, MD)

 

 

Course Coordination:

Madan Oli and H. Franklin Percival (University of Florida, Department of Wildlife Ecology and Conservation, Gainesville, FL)

 

Course Objective:  

To present a unified and science-based approach to the conservation and management of natural animal populations, and to provide participants with information and resources for implementation of this approach. This approach involves three major methodological components: modeling, estimation and decision making.

 

Specific Objectives:

(1)   To provide a conceptual framework for the use of models in the conduct of science

and management.

(2)   To briefly review frequently-used populations models, with emphasis on tailoring models to their intended use in conservation and/or science. 

(3)   To present a general conceptual framework for animal population/community

estimation methods.

(4)   To show how this framework can be used to develop estimation methods applicable to various sampling and logistic situations.

(5)   To present the specific rationale and logic underlying the more commonly used

approaches to estimating population and community-level attributes, with emphasis on tailoring these methods to meet objectives under logistical constraints.

(6)   To present a general rationale and approach for the development of an animal

monitoring program, with emphasis on the use of resulting inferences for conservation and management.

(7)   To present a logical framework for making management decisions and to identify the

 major components of uncertainty typically encountered in the management process.

(8)   To outline the implementation of a formal adaptive management process for making

 informed management decisions in the face of uncertainty.

 

 

Outline:

 

Day 1:

1. Introductions

1.1. Introduction to workshop (Nichols) (0.25 hr)

1.2. Introduction of instructors/participants/students and their backgrounds and objectives

(Group) (0.5 hr)          

2. Overview Material

            2.1. Conceptual framework for population ecology & management (Nichols)

(0.25 hr)

            Include roles of modeling, estimation and decision theory

            BIDE model

3. Statistical Inference

            3.1. Statistical distributions (e.g., normal, multinomial) (Kendall) (0.5 hr)

            3.2. Parameter estimation (Kendall) (0.5 hr)

                        Estimator properties (bias, precision, accuracy)

                        Estimation methods

                        Confidence intervals

BREAK

            3.3. Hypothesis testing (Kendall) (0.5 hr)

                        Type I and II errors

                        Power

                        Likelihood ratio tests

                        Goodness-of-fit tests

            3.4. Model selection (information theoretic approaches) (Kendall) (0.5 hr)

            3.5. Bayesian model updating (Kendall) (0.25 hr)

LUNCH

3.6. Hierarchical modeling: Bayesian approach (Sauer) (0.5 hr)

3. Statistical Inference (Continued) (Sauer) (1.00 hr)

            3.7. Survey sampling (sources of variation)

            3.8. Sampling design features

                        Replication

                        Randomization

                        Control of variation

            3.9. Some designs

                        Simple random sampling

                        Stratified random sampling

                        Other (cluster, systematic, double, dual frame, adaptive)

4. Models

            4.1. Role of models in science and management (Nichols) (0.5 hr)

BREAK

            4.2. Population modeling review: basic principles (Sauer) (1.5 hr)

Discrete time matrix modeling (age/stage)

Projection matrix asymptotics (l, sensitivity, reproductive value, stable stage distribution)

Stochasticity (demographic, environmental), PVAs

Models for management

4.3. Population modeling exercise (Sauer, Hines) (0.75 hr)

 

Day 2:

4.3. Population modeling exercise cont. (Sauer, Hines) (0.75 hr)

5. Estimation of Animal Abundance and Density

            5.1. Overview (Nichols) (0.5 hr)

                        Why estimate abundance? Role of monitoring in science and management.

How to estimate abundance: a canonical estimator

                        Indices

            5.2. Observation-based methods: miscellaneous (Nichols) (0.5 hr)

                        Marked subpopulation

                        Temporal removal modeling

BREAK

5.2. Observation-based methods: miscellaneous cont. (Nichols) (0.75 hr)

                        Sighting probability modeling

Multiple independent observers

                        Multiple dependent observers

            5.3. Implementing observation-based methods

Introduction to MARK (Hines) (0.5 hr)

                        Computer exercises with DOBSERV and/or MARK (Hines) (0.5 hr)

LUNCH

            5.4. Observation-based methods: distance sampling

            Introduction to Distance Sampling (Sauer) (0.5 hr)

                                    Introductory Concepts

Assumptions Underlying the Sampling Technique

                        Estimating the proportion of animals detected & counted (Sauer)(0.5 hr)

Line Transects

Point transect

Contrasting Line Transect & Point Transect Sampling

Survey Design & Field Protocol (Sauer) (0.5 hr)

Precision

Bias

BREAK

                        DISTANCE 4 Software (Sauer-Hines) (1.0 hr)

                                    Brief overview

Automated Survey Design (Distance 4 exercises)

Distance Sampling Analysis (Sauer) (1.5 hr)

                                    Basic Analysis

                                    Analysis for Clustered Populations

Introducing Covariates into the Analysis

                                    Distance 4 CDS/MCDS analysis exercise

Day 3:

            5.5. Capture-based methods: closed CR models 

                        2-sample model (Nichols) (0.50 hr)

                                    Data structure

                                    Models and estimators

                                    Study design

                        2-sample model exercises (SURVIV, MARK) (Hines) (1.0 hr)

                        K-sample closed models (Kendall) (0.5 hr)

                                    Data structure

                                    Models

BREAK

K-sample closed models cont. (Kendall) (0.75 hr)

                                    Models

                                    Model testing and selection

                                    Confidence interval estimation

                                    Study design

                        K-sample closed model exercises, CAPTURE, MARK (Hines) (1.0 hr)

LUNCH

                        K-sample closed model exercises cont. (Hines) (0.5 hr)

5.6. Density estimation with closed CR models (Nichols) (0.5 hr)

Ad hoc boundary strip approach

                        Nested grids

                        Gradient designs (e.g., trapping webs)

5.7. Other capture-based methods        (Kendall 0.5 hr)

                        Removal methods

                        Change-in-ratio methods

6. Estimation of Animal Vital Rates (survival, reproduction, movement)

            6.1. Introduction, relevance of detection probability (Nichols) (0.25 hr)

BREAK

            6.2. All marked animals detected (Sauer) (1.0 hr)

                        Binomial survival model

                        Nest success

                        Radiotelemetry data

                        Study design

                        Computer exercises (SURVIV, MARK) (Hines) (1.0 hr)

Day 4:

6.3. Tag recovery models (Sauer) (1.0 hr)

            6.4. Open population CR models

                        Single-age models (Nichols) (0.5 hr)

                                    Data structure

                                    Modeling

                        Single-models continued (Nichols) (0.5 hr)

                                    Time-specific covariates

                                    Multiple groups

                                    Capture history effects

                                    Individual covariates

                                    Model selection

                                    Model assumptions

                                    Estimator robustness

BREAK

MARK: PIMs and design matrices (Hines) (0.75 hr)

MARK exercises: Single-age models, band recovery models (Hines) (1.0 hr)

LUNCH

                        Single-age models (Nichols) (1.0 hr)

                                    Estimation of abundance

                                    Estimation of 8 and components of 8

Multiple-age models (Nichols) (0.5 hr)

            Data structure

            Modeling         

                        Multiple-age model exercise (Hines (0.5 hr)

BREAK

Multiple-age model exercise cont. (Hines (0.5 hr)

Multistate models (Kendall) (0.75 hr)

                                    Data structure

                                    Modeling

Multistate model exercise (Hines) (0.5 hr)

 

Day 5:

Multistate model exercise cont. (Hines) (0.5 hr)

Multistate models: special uses (Kendall) (1.0 hr)

                                    Unobservable states

                                    Band loss

Multistate models: state misclassification (Kendall 0.5 hr)

BREAK

Multiple-age multisdtate models: variable age at recruitment (Nichols)    (0.5 hr)

6.5.    Open models with extra information (Nichols 0.5 hr)

Capture-recapture + band recoveries

Capture-recapture + radio telemetry

Capture-recapture + auxiliary sightings (Barker models)

6.6   Pollock’s robust design

                        Introduction (Kendall) (1.0 hr)

Data structure              

Ad hoc approach

Recruitment components

                                    Model-based approach

LUNCH          

            Model extensions (Kendall) (1.0 hr)

                                    Temporary emigration

                                    Open robust design

                                    Robust design with band recoveries

                                    Multistate robust design

                                    “Mother of all Models”

                        Robust design computer exercises (Hines) (1.0 hr)

BREAK

7. Estimation  of species richness and community dynamics

            7.1. Population-community analogy (Sauer) (0.25 hr)

7.2. Species richness estimation (Sauer) (0.5 hr)

            Data structure and designs

            Modeling and estimation

7.3. Multiple-season community dynamics (Sauer) (0.5 hr)

            Data structure

            Modeling and estimation

7.4. Community dynamics exercises with SPECRICH, COMDYN (Hines)(0.5 hr)

 

DAY 6:

            7.4. Community dynamics exercises cont. (Hines) (0.5 hr)

8. Estimation of site occupancy and occupancy dynamics

            8.1. Single-season, single species occupancy (Nichols) (0.5 hr) 

                        Data structure and designs

                        Modeling

                        Assumptions and their relaxation

                        Computer exercise (PRESENCE) (Hines) (0.75)

BREAK

            8.2. Multiple-season occupancy dynamics (Nichols) (0.75 hr)

                        Data structure

                        Modeling

                        Example(s)

                        Computer exercise with PRESENCE (Hines) (0.5 hr)

8.3    Occupancy extensions (Nichols) (0.5 hr)

            2-species occupancy

            Multi-state occupancy

            Joint occupancy-habitat modeling

            Community level occupancy                 

LUNCH

9. Conservation/Management in the face of uncertainty

9.1. Elements of an informed decision (Kendall) (0.5 hr)

                        Objectives

                        Management alternatives

                        Model(s) of system response to management

                        Model weights (for multiple models)

                        Monitoring program

            9.2. Sources of uncertainty (Kendall) (0.25 hr)

                        Environmental variation

                        Partial controllability

                        Structural uncertainty

                        Partial observability

            9.3. Decision analysis under uncertainty (Nichols 0.5 hr)

                        General approach

                        Example

            9.4. Adaptive management (Kendall)               

The process (0.5 hr)

BREAK

                        Examples (0.5 hr)

10. Exam review/questions (Hines-Kendall-Nichols-Sauer) (1.5 hr +)

 

Day 7:

11. Exam (4.0 hr)

LUNCH

12. Discussion/Evaluation/Consultation (Hines-Kendall-Nichols-Sauer) (4 hr)