MONITORING and ASSESSMENT:

Fish 7380. Dr. e. irwin(many slides provided by Dr. Jim Nichols)

Rivers are inherently difficult to assess

Diverse fauna (hard to enumerate) Populations change through time Abundance estimates (measures?)

Habitat specialists (or not) Unidirectional flow

Pseudoreplication Upstream affects downstream

Standardization Efficiency Detectability

Why Monitor?

Science Understand ecological systems Learn stuff

Management/Conservation Apply decision-theoretic approaches Make smart decisions

Key Component of Science: Confront Predictions with DataDeduce predictions from hypothesesObserve system dynamics via monitoringConfrontation: Predictions vs.

Observations Ask whether observations correspond to

predictions (single-hypothesis) Use correspondence between observations and

predictions to help discriminate among hypotheses (multiple-hypothesis)

Use of Monitoring in ScienceStrength of inference:

Manipulative experiment > Impact study > Observational study

Strength of inference for observational studies: Prospective (a priori hypotheses) > Retrospective

(a posteriori stories)Claim: monitoring is most useful to science

when coupled with manipulations of system

““Monitoring of populations is Monitoring of populations is politically attractive but politically attractive but ecologically banal unless it is ecologically banal unless it is coupled with experimental work to coupled with experimental work to understand the mechanisms behind understand the mechanisms behind system changes.” (Krebs 1991)system changes.” (Krebs 1991)

Management/ConservationKey ElementsObjective(s): what do you want to achieveManagement alternatives: stuff you can doModel(s) of system response to

management actions (for prediction)Measures of model credibility Monitoring program to estimate system

state and other relevant variables

Role of Monitoring in Management

Determine system state for state-dependent decisions

Determine system state to assess degree to which management objectives are achieved

Determine system state for comparison with model-based predictions to learn about system dynamics (i.e., do science)

How to Monitor?Basic Sampling Issues Detectability

Counts represent some unknown fraction of animals in sampled area

Proper inference requires information on detection probability

Geographic variation Frequently counts/observations cannot be conducted

over entire area of interest Proper inference requires a spatial sampling design that

permits inference about entire area, based on a sample

Detectability: Monitoring Based on Some Sort of Count Ungulates seen while walking a line transect Tigers detected with camera-traps Birds heard at point count Small mammals captured on trapping grid Bobwhite quail harvested during hunting season Kangaroos observed while flying aerial transect

Detectability: Conceptual Basis

N = abundance C = count statisticp = detection probability; P(member of N

appears in C)

pNCE )(

Detectability: Inference

Inferences about N require inferences about p

pCNˆ

ˆ

Indices Assume Equal Detectability

Ni = abundance for time/place ipi = detection probability for iCi = count statistic for i

jjij NN / ijij CC /ˆ

ii

jj

i

jij Np

NpCC

EE )()ˆ(

How Do We Generate System Dynamics? Study Designs Use design that imposes, or takes advantage of,

a manipulation of some sort Manipulative experimentation (randomization,

replication, controls) Impact study (lacks randomization and perhaps

replication, but includes time-space controls) No manipulation - observational study

Prospective (confrontation with predictions from a priori hypotheses)

Retrospective (a posteriori story-telling)

Spatial Sampling Designs

Simple random samplingStratified random samplingSystematic samplingCluster samplingDouble samplingAdaptive samplingDual-frame sampling

Measurement Error

RecognizeAccount for itScale of study

Match to critterDetectability

Efficiency P of capture

Patchy organisms (and/or habitat)

Nested designs

Quantify spatial patchinessIdentify scale

Spatial and Temporal Variation

BACI design

Before-After/Control-Impact

Disturbances

Biological response to disturbances Anthropomorphic Pulse Press Catastrophes

Time scale of recovery

Rapid Techniques

Categorical and regression treesOther Multimetric techniques

How much to sample

LogisticsTime$$$$

What State Variable to Monitor:3 Levels of Inference Community – multiple species

State variable: Species richness Vital rates: rates of extinction and colonization

Patch – single species State variable: Proportion patches occupied Vital rates: P(patch extinction/colonization)

Population – single species State variable: abundance Vital rates: P(survival, reproduction, movement)

What State Variable to Monitor?Choice Depends On: Monitoring objectives

Science: what hypotheses are to be addressed?

Management/conservation: what are the objectives?

Geographic and temporal scaleEffort available for monitoring

Required effort: species richness, patch occupancy < abundance

Indices: Dealing with Variation in Detectability Standardization (variation sources that we

can identify and control)Covariates (variation sources that we can

identify and measure)Prayer (variation sources that we cannot

identify, control or measure)CONCLUSION:

ESTIMATE DETECTABILITY!

Patch Occupancy Estimation and Modeling: Applications Amphibian monitoring

Wetlands: anurans, aquatic salamanders Terrestrial plots: salamanders

Spotted owl monitoring and patch-dynamic modeling

Waterbird colony dynamics Tiger distribution surveys Landbird monitoring Fish monitoring

Animal Abundance: Estimation and Modeling Traditional monitoring foci:

Variation over time: trend Variation over space or species: relative abundance

Many estimation methods (e.g., Seber 1982, Williams et al. 2002)

Each estimation method is simply a way to estimate detection probability for the specific count statistic of interest

Final step is always:

pCNˆ

ˆ

Observation-based Count Statistics: Detectability Distance samplingDouble samplingMarked subsetsMultiple observers (dependent,

independent)Sighting probability modelingTemporal removal modeling

Capture-based Count Statistics: Detectability Closed-population capture-recapture

modelsOpen-population capture-recapture

modelsRemoval models (constant and variable

effort)Trapping webs with distance samplingChange-in-ratio models

Rate Parameters Relevant to Changes in Abundance

Population growth rate Survival rate, harvest rate Reproductive rate (young per breeding adult) Breeding probability Movement rate Process variance Slope parameters for functional relationships

Recommendations: Why Monitor? Monitoring is most useful when integrated into

efforts to do science or management Role of monitoring in science

Comparison of data with model predictions is used to discriminate among competing models

Role of monitoring in management - determine system state for: State-specific decisions Assessing success of management relative to

objectives Discrimination among competing models

Recommendations:What to Monitor?Decision should be based on objectives Decision should consider required scale

and effortReasonable state variables

Species richness Patch occupancy Abundance

Recommendations:How to Monitor? Detectability

Counts represent some unknown fraction of animals in sampled area

Proper inference requires information on detection probability

Geographic variation Frequently counts/observations cannot be conducted

over entire area of interest Proper inference requires a spatial sampling design that

permits inference about entire area, based on a sample

Adaptive Management

Seeks to optimize management decisions in the face of uncertainty,

using learning at one stage to influence decisions at subsequent stages,

while considering the anticipated learning in the optimization.

Final considerations

Other disciplines are kicking our buttsStandardization can be harmful at timesRemember scale…New (?) techniques for analysis are

emerging Bayesian methods Heirarchical method (account for spatial

dependancy)