SALMONID (BROOK TROUT) POPULATION PERSISTENCE
Development of a DSS
Ben LetcherUSGS, Conte Anadromous Fish Research Center, Turners Falls, MA
Keith NislowUSFS, Northern Research Station, Amherst, MA
Why care about brook trout?
Widespread Found in most northeastern
streams with decent habitat Small isolated streams, rivers,
lakes, bogs, sea-run… Indicator of water quality
Temperature, acidity Sensitive to land use change
Mobile Habitat connectivity important
– what’s the key scale? Important component of
aquatic community Abundant Predation, food source,
nutrient dynamics
Invaders in the west Important to understand
population dynamics Important fishery
Native and stocked Indicator of functioning
habitat Sensitive species, harbinger
Good data available Distribution, local
abundance Individual-based studies
Who cares about brook trout?
Eastern Brook Trout Joint Venture Coalition of state and federal managers
The Nature Conservancy Connecticut River program
USFWS LCC project
USFS Long-term funding
Trout unlimited Sea-run brook trout coalition
Threats to population persistence
Habitat fragmentation Isolated populations
Water withdrawals Seasonal effects of stream flow
Land use/land change Riparian buffer, impervious
surfaces
Climate change Air temperature and
precipitation affecting: Stream flow and temperature
Interactions with climate change
Overall goal
Understand how populations work
What affects local population persistence?
Endpoint – probability of persistence after x years Body size distributions
Develop DSS tool for managers Probability of population
persistence under varying management scenarios
Eastern brook trout joint venture, 2007
LCC project tasks Task 1: Hierarchical modeling framework to account for multiple
scales and sources of uncertainty in climate change predictions
Task 2: Statistical models to predict stream flow and temperature based on air temperature and precipitation.
Task 3: Incorporate climate change forecasts into population persistence models
Task 4: Develop a decision support system for evaluating effects of alternate management strategies in the face of climate change.
Task 5. Develop curriculum and run training workshops for users of the decision support system.
Approach
Data
Analysis
Model
Simulation
Management tool
Synthetic data collection and analysis to:
Account for multiple sources of
uncertainty Allow error propagation Provide answers in form of
statistical distribution How certain are we of result?
UncertaintiesMeasurement,Observation
Process[survival…]
Inputs[environment,GCC]
Run-to-run
Outcome[Persistence]
Approach
Data
Analysis
Model
Simulation
Management tool
Fine-scale data collection at multiple sites ~ 1 km, 20-m units Seasonal Tagged individuals, >35,000 since 1997
Model dynamics and uncertainty using Bayesian estimation Growth Survival Reproduction Movement
Combine statistical models into simulations Link components- interactions
Develop management tool - DSS Web-based Evaluate alternate management strategies
What questions can we address?
Habitat fragmentation Which barriers do we prioritize for
removal/repair? Water withdrawal
How much water can be extracted? Importance of water source
How does extent of groundwater input affect persistence?
Climate change forecasts What are the effects of variation in
stream flow, temperature? Interactions
How much will effects of isolation and water supply be magnified under GCC?
Approach
ReproductionBody growth
SurvivalMovement
Age structureBody size
distributions
Population processes
AbundanceNe, Nb
Environment
Outcome
Stream TemperatureStream flow
HabitatFish community
Catchment scale model (< 1 Km)
Density dependence
Probability of
persistence
Fish modelFish
model
Links to Terrestrial project
Hydrologic model
DriversClimate change
Fish model
Seasonal settingPrecip, air TStream flow,
water T
Resulting DSS: evaluate alternate management strategies
DriversUrban
growth, etc
Decadal settingImpervious…Succession
Scen
ario
s
Habitat Caps
Probability of
persistence
Probability of
persistence
SeasonalDecadal
Near-term linkages between projects
Working with terrestrial group Develop models for catchments in three large
watersheds South, James River, VA Middle, ~Westfield River, MA North, Kennebec River, ME
Expand models to entire watersheds Collaborate with Eastern Brook Trout Joint Venture to
estimate occupancy in specific catchments Collaborate with Dept C+E Engineering and terrestrial
group to generate downscaled predictions of P and T and to develop hydrologic models
Project components
USFWS LCC Tasks 1-5
1 Post-doc, Paul Schueller (Feb 2012 - 2013)
1 PhD student, Krzysztof Sakrejda (current – 2013)
1 Programmer (2012-2013) USFWS LCC holdback
Flow modeling 1 post-doc, TBD (2011 – 2013)
USGS LCC Assist with tasks 1-5
1 post-doc, Doug Sigourney (current – 2013)
Add in evolutionary dynamics 1 post-doc, Michael Morrisey (Jan
2011 - 2013)
TNC fragmentation project Barrier removal/repair
prioritization 1 post-doc, Cailin Xu (2008 - 2010) 1 PhD student, Paul Schueller
(2008 – 2012) 1 Technician
USFS Air temperature/stream
temperature relationship Several technicians
UMass Hydrologic model
Dept of Civil and Environmental Engineering
1 post-doc, ~Austin Polebitski
Decision support
Good understanding of catchment and sub-watershed population persistence models in MA
USFWS LCC and TNC funding to Scale up to watershed models Identify minimum data needs to scale up to among-
watershed models Evaluate GCC effects on the landscape Develop tools for managers to use
Not limited to well-studied systems Apply to specific sites to address management needs Can we apply models range-wide? Need test sites Better local data = more realistic simulations
Decision support
How will the DSS work? Identify management question Identify space and time scales Pick stream segments on web-based map Load local data
Environmental conditions, size distributions, community, genetics, movement data, etc
Simulation will automatically fine-tune model to local conditions
Run simulations Evaluate alternatives
Approach – working across scales
Hierarchical models Scale up Propagate error
Watershed Sub-watershed
Catchment
Among-watershed Multiple study sites
Fine scale (10 Km) Westfield River, western MA 100-m long sample sites 12 microsatellites Pairwise Fst 0.11 – 0.24 Assignment tests using Structure
Similar results in NH, VT, VA
Catchment and sub-watershed scales
Need detailed data, ~ 1 km
Spatial population genetics – what’s the right minimal scale?
Approach
Sub-watershed scale model (1-5 km)
OutcomesConnected catchment
scale modelsSub-
watershed abundance
and body size
Movement patterns and catchment-
specific production
Movement
Movement
Movement
Movement is observed with repeat sampling and PIT tag antennas
Approach
Connected sub-watershed scale models
Watershed scale model (5-50 Km)
Watershed-scale
abundance and body size
Meta-population and genetic population structure
Outcomes
Movement
Movement is observed with radio-tagged fish and is inferred with genetic data
Approach – broad questions Do we need a detailed tagging
study for each catchment? Define catchment types
Size, connectivity Apply type to each unstudied
catchment Use existing data to tune catchment
type model to local conditions (Hierarchical Bayesian modeling)
Can we apply models across watersheds? Minimum local data needs?
Existing studies in MA, ME, NH Planned for VA, PA/NJ (DEWA) Workshop in Feb
Defining these relationships is key
Progress to date
Development of linear models for Growth, survival,
movement Population dynamics
simulation incorporating existing estimates Climate change
scenarios Not hierarchical
High Q
Low Q
Control T x Control F = 174 yrs
Stronger climate change effect
Task Year 1 Year 2 Year 31. Hierarchical model development
1. Determine statistical model structure
2. Estimate statistical model parameters3. Develop simulation model based on #24. Combine all statistical models into simulation model
5. Incorporate simulation model into user interface
2. Air temperature/ stream temperature model
1 Deploy paired temperature recorders
2. Develop statistical model for paired temperature recorder data
3. Apply statistical model to selected watersheds
3. Climate change modeling 1. Obtain downscaled stream flow and temperature predictions for the West brook
2. Develop model to apply downscaled estimates to selected watersheds
4. Decision support system 1. Develop web-based user interface2. Incorporate simulation model into web-based user interface
5. Model use/application workshops
1. Develop training tools
2. Conduct training class at USFWS Region 5 office
Probability of
persistence
Fish modelFish
model
Links to Terrestrial project
Hydrologic model
DriversClimate change
Fish model
Seasonal settingPrecip, air TStream flow,
water T
Resulting DSS: evaluate alternate management strategies
DriversUrban
growth, etc
Decadal settingImpervious…Succession
Scen
ario
s
Habitat Caps
Probability of
persistence
Probability of
persistence
SeasonalDecadal
Big questions Which barriers should be prioritized for repair/removal? How much water can be extracted from a stream?
Minimum flows
How do populations with very low effective population size persist? Adaptation to isolation? What is the minimum patch size for persistence?
Strongholds or hopeless?
How will brook trout populations respond to climate change? Range contraction Effects of stream flow and temperature Interactions between fragmentation and GCC
What are the best strategies to mitigate future challenges?
Challenges Scale
How to scale up? Space
Define a population – how big? Where are the fish? Importance of local adaptation? Can we apply models to unstudied or poorly studied systems?
Time Can we apply short-term studies (1-15 years) to long-range forecasts
(>50 years)? Timing of local adaptation?
At what organizational level do we collect data? Population Individual Genotype
Uncertainty How propagate across scales?
For example, downscaled predictions of temperature and precipitation are uncertain in space and time
Need an approach to propagate this (and other) uncertainty all the way to projections of population persistence
Eastern brook trout joint venture, 2007
