22
Brook trout population dynamics: Integrated modeling across scales and data types Keith H. Nislow, Jason Coombs Northern Research Station, USDA Forest Service, Amherst, MA, USA Ben Letcher, Yoichiro Kanno, Ron Bassar, Ana Rosner, Paul Schueller, Kyle O’Neil, Krzysztof Sakrejda, Matt O'Donnell, Todd Dubreuil Conte Anadromous Fish Research Center, U.S. Geological Survey, Turners Falls, MA, USA Andrew Whiteley Department of Natural Resources Conservation UMass, Amherst, MA, USA Steve Hurley

Brook trout population dynamics: Integrated modeling across scales and data types

  • Upload
    hada

  • View
    41

  • Download
    0

Embed Size (px)

DESCRIPTION

Ben Letcher, Yoichiro Kanno , Ron Bassar , Ana Rosner, Paul Schueller , Kyle O’Neil, Krzysztof Sakrejda , Matt O'Donnell, Todd Dubreuil Conte Anadromous Fish Research Center, U.S. Geological Survey, Turners Falls, MA, USA. Keith H. Nislow, Jason Coombs - PowerPoint PPT Presentation

Citation preview

Page 1: Brook trout population dynamics: Integrated modeling across scales and data types

Brook trout population dynamics: Integrated modeling across scales and data types

Keith H. Nislow, Jason Coombs Northern Research Station, USDA Forest Service, Amherst, MA, USA

Ben Letcher, Yoichiro Kanno, Ron Bassar, Ana Rosner, Paul Schueller, Kyle O’Neil, Krzysztof Sakrejda, Matt O'Donnell, Todd DubreuilConte Anadromous Fish Research Center, U.S. Geological Survey, Turners Falls, MA, USA

Andrew Whiteley Department of Natural Resources Conservation UMass, Amherst, MA, USA

Steve Hurley

Page 2: Brook trout population dynamics: Integrated modeling across scales and data types

Overview

Goal: understand population dynamics and provide broad spatial scale forecasts in response to environmental change

Problem: specificity/generality tradeoff Can’t do detailed, mechanistic studies

everywhere Lots of good survey data

Approach/solution: combined approach Response ~ f( env change,… )

How does/will environmental change affect stream salmonids?

Page 3: Brook trout population dynamics: Integrated modeling across scales and data types

Data types

PIT tag Single-site demographic models

Seasonal sensitivity of lambda (population growth)

Abundance Multiple-site demographic models

Sensitivity + basin characteristics

Presence/absence Occupancy models

Effects of long term means + basin characteristics

Page 4: Brook trout population dynamics: Integrated modeling across scales and data types

Data types

West Brook Isolated PIT tag Single-site demographic

model Body growth, survival,

movement, reproduction Integral projection model

Abundance Abundance models

Presence/absence Occupancy models

Page 5: Brook trout population dynamics: Integrated modeling across scales and data types

Lambda sensitivities

Spring ↔Winter ↔Autumn ↓Summer ↓

Summer↑

Autumn ↑ Spring ↔

Winter↓

Page 6: Brook trout population dynamics: Integrated modeling across scales and data types

Lambda response surfaces

Page 7: Brook trout population dynamics: Integrated modeling across scales and data types

Forecast

Page 8: Brook trout population dynamics: Integrated modeling across scales and data types

Data types

Yearly data, many sites

Age-0+ > age-0+ All

PIT tag Single-site demographic model

Abundance Abundance models

Autumn, Winter, Spring Flow Spring Temperature Elevation

State space Population projection

Presence/absence Occupancy models

Page 9: Brook trout population dynamics: Integrated modeling across scales and data types

Estimated abundances

PIT tag Single-site demographic model

Abundance Abundance models

Autumn, Winter, Spring Flow Spring Temperature Elevation

State space Population projection

Presence/absence Occupancy models

Page 10: Brook trout population dynamics: Integrated modeling across scales and data types

Forecast

PIT tag Single-site demographic model

Abundance Abundance models

Autumn, Winter, Spring Flow Spring Temperature Elevation

State space Population projection

Presence/absence Occupancy models

Page 11: Brook trout population dynamics: Integrated modeling across scales and data types

Forecasts

Presence/absence Occupancy models

Abundance Abundance models Simple population

projection - state space

PIT tag Mechanistic models

Page 12: Brook trout population dynamics: Integrated modeling across scales and data types

Extreme events forecast

PIT tag Single-site demographic model

Abundance Abundance models

Autumn, Winter, Spring Flow Spring Temperature Elevation

State space Population projection

Presence/absence Occupancy models

Page 13: Brook trout population dynamics: Integrated modeling across scales and data types

Data types

Single or multiple year data, many sites

PIT tag Single-site

demographic model

Abundance Abundance models

Presence/absence Occupancy models

Page 14: Brook trout population dynamics: Integrated modeling across scales and data types

Model estimates

Precip

Air T

% forest

PIT tag Single-site demographic

model

Abundance Abundance models

Presence/absence Occupancy models

Annual precipitation Minimum temperature Soil drainage class Drainage area Forest cover Stream slope

Page 15: Brook trout population dynamics: Integrated modeling across scales and data types

Probability of Occupancy for Current Conditions

Drainage areaForest coverStream slope

Annual precipitationMinimum temperatureSoil drainage class

Model drivers

Brook TroutProbability of Occupancy

< 10%11% - 20%21% - 30%31% - 40%41% - 50%51% - 60%61% - 70%71% - 80%81% - 90%> 90%

Page 16: Brook trout population dynamics: Integrated modeling across scales and data types

Drainage areaForest coverStream slope

Probability of Occupancy for Current Conditions

Annual precipitationMinimum temperatureSoil drainage class

Model drivers

Brook TroutProbability of Occupancy

< 10%11% - 20%21% - 30%31% - 40%41% - 50%51% - 60%61% - 70%71% - 80%81% - 90%> 90%

Probability of Occupancy 2 C increase

Probability of Occupancy 4 C increase

Page 17: Brook trout population dynamics: Integrated modeling across scales and data types

Probability of Occupancy for Current Conditions

Brook Trout ResilienceIncrease tolerated (°C)

0°0.1° - 0.5°0.6° - 1°1.1° - 1.5°1.6° - 2°2.1° - 2.5°2.6° - 3°3.1° - 8.5°

Currently below threshold

Resilience of occupancy to temperature increase

Drainage areaForest coverStream slope

Annual precipitationMinimum temperatureSoil drainage class

Model drivers

Brook TroutProbability of Occupancy

< 10%11% - 20%21% - 30%31% - 40%41% - 50%51% - 60%61% - 70%71% - 80%81% - 90%> 90%

Page 18: Brook trout population dynamics: Integrated modeling across scales and data types

Bringing it together

Variable Season Model

Single-site demographic

Multiple-site demographic

Occupancy

Flow Fall ↑ ** ↑ ***

Precip ↑Winter ↓ ** ↓ **

Spring ↔ ↔

Summer ↑ *** NA

Temperature Fall ↓ ** NA

Temperature ↓Winter ↔ NA

Spring ↔ ↔

Summer ↓ *** NA

Page 19: Brook trout population dynamics: Integrated modeling across scales and data types

Summary

Congruent environmental effects on population growth across scales Increases confidence in generality of

results Negative effects of temperature Positive effects of flow in fall and

summer, negative effects in winter

Many brook trout populations at risk in future Flow and temperature Extreme events

Can identify resilient populations

Steve Hurley

Page 20: Brook trout population dynamics: Integrated modeling across scales and data types
Page 21: Brook trout population dynamics: Integrated modeling across scales and data types

Web app

Map viewer Standard layers Data Model results Select a basin for scenario tester

Scenario tester Climate -> Landuse -> Environment -> Population response Evaluate management actions under alternate futures

http://felek.cns.umass.edu:8080/geoserver/www/data.html

Page 22: Brook trout population dynamics: Integrated modeling across scales and data types

Data types

Sensitivity of annual survival