Download - MONITORING and ASSESSMENT:
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MONITORING and ASSESSMENT:
Fish 7380. Dr. e. irwin(many slides provided by Dr. Jim Nichols)
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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
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Why Monitor?
Science Understand ecological systems Learn stuff
Management/Conservation Apply decision-theoretic approaches Make smart decisions
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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)
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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
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““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)
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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
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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)
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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
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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
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Detectability: Conceptual Basis
N = abundance C = count statisticp = detection probability; P(member of N
appears in C)
pNCE )(
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Detectability: Inference
Inferences about N require inferences about p
pCNˆ
ˆ
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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(
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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)
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Spatial Sampling Designs
Simple random samplingStratified random samplingSystematic samplingCluster samplingDouble samplingAdaptive samplingDual-frame sampling
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Measurement Error
RecognizeAccount for itScale of study
Match to critterDetectability
Efficiency P of capture
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Patchy organisms (and/or habitat)
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Nested designs
Quantify spatial patchinessIdentify scale
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Spatial and Temporal Variation
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BACI design
Before-After/Control-Impact
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Disturbances
Biological response to disturbances Anthropomorphic Pulse Press Catastrophes
Time scale of recovery
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Rapid Techniques
Categorical and regression treesOther Multimetric techniques
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How much to sample
LogisticsTime$$$$
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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)
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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
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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!
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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
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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
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Observation-based Count Statistics: Detectability Distance samplingDouble samplingMarked subsetsMultiple observers (dependent,
independent)Sighting probability modelingTemporal removal modeling
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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
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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
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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
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Recommendations:What to Monitor?Decision should be based on objectives Decision should consider required scale
and effortReasonable state variables
Species richness Patch occupancy Abundance
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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
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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.
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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)