YKL REA Northern Pike Model
description
Transcript of YKL REA Northern Pike Model
YKL REA Northern Pike Model
Photo: ADF&G
Fish Distribution Models
Photo: USFWS
Evaluate model performance
Classification tree and random forest
models
ADF&G AFFID species
occurrence data
GIS source data
Predict species habitat across REA
study area
Fish distributions
Create stream network and
landscape predictor variables in GIS
Process AFFID data for use in models
Stream Network Used TauDEM to process DEM1. Add in additional HUCs on boundary of study area that
flow into the study area2. Fill pits3. Calculate flow direction (D8 method)4. Calculate contributing area 5. Create stream network based on curvature method and
drop analysis
Predictor Variables
Photo: USFWS
Predictors of Fish HabitatElevationPermafrostGradientFloodplainSlope over area ratioStream orderWatershed areaAverage watershed annual precipitationAverage watershed annual temperatureAverage watershed elevationAverage watershed slope over area ratioAverage watershed slopePercent permafrost cover in watershedPercent lake cover in watershed
Process AFFID data- Presences from AFFID
and ADF&G/BLM telemetry project in Kuskokwim
- Absences from projects in AFFID that listed fish community sampling as an objective
- Resampled data in areas of high intensity (Pebble area and telemetry)
- Shifted points along flow direction grid until they reached the stream network
- Extracted all predictor variables to each data point
Classification Trees
Photo: USFWS
Classification Tree Analysis Steps:– Identify the groups– Choose the variables– Identify the split that
maximizes the homogeneity of the resulting groups
– Determine a stopping point for the tree
– Prune the tree using cross-validation
Absent0.97(263)
Asterospicularia laurae
Shelf: Inner, Mid Shelf: Outer
Absent0.78(64)
Location: Back, Flank Location: Front
Depth < 3m Depth ≥ 3m
(De'Ath and Fabricious 2000)
Absent0.56(9)
Present0.81(37)
Misclassification rates: Null = 15%, Model = 9%
Random Forests
Creates many classification trees and combines predictions from all of them:- Start with bootstrapped samples of data- Observations not included are called out-of-bag (OOB)- Fit a classification tree to each bootstrap sample, for each
node, use a subset of the predictor variables- Determine the predicted class for each observation based
on majority vote of OOB predictions- To determine variable importance, compare
misclassification rates for OOB observations using true and randomly permuted data for each predictor
Run models in Rct1<-mvpart(pres.f~.,data=fish.pred1[s1,],xv="1se")rf1<-randomForest(pres.f~.,data=fish.pred1[s1,],ntree=999)
Photo: USFWS
CT training CT validation RF training RF validation1 0.096 0.161 0.108 0.1132 0.108 0.194 0.092 0.1613 0.12 0.161 0.096 0.0974 0.12 0.145 0.116 0.1295 0.108 0.194 0.108 0.1456 0.072 0.097 0.112 0.0487 0.124 0.177 0.108 0.0978 0.112 0.097 0.104 0.0819 0.137 0.081 0.124 0.065
10 0.12 0.145 0.141 0.097summary 0.1117 0.1452 0.1109 0.1033
Model Performance
Photo: USFWS
Confusion Matrix0 1 Error
0 184 13 6.6%1 21 93 18.4%
Top five variables are watershed area, stream order, stream elevation, percent of watershed covered by lakes, and stream floodplain.
Northern Pike
Results:~ 10,900 km of predicted summer habitat (restricted to stream reaches > 1 km in length)
Predictor Presence AbsenceWatershed area 13,000 km2 60 km2
Stream elevation 60 m 200 mStream floodplain Yes NoWatershed lake cover 2.8% 2.1%
Stream order 4th 1st
Invasive MacrophytesClimate Change
Precipitation
Permafrost
FireHuman Uses
Mining
Infrastructure
Harvest
Contaminants
Temperature
Perm
afro
st
thaw
Reduction in age at maturity and shift in spawning season
Bio
accu
mul
atio
n of
m
ercu
ry in
adu
lts
Expa
nded
ice
-fre
e se
ason
Tem
pora
ry in
crea
ses i
n nu
trien
t inp
uts
Elodea
ssp
coul
d re
duce
qua
lity
of sp
awni
ng
habi
tat
In creased toxicity
Increased potential for establishment of invasive macrophytes and changing fire dynamics
Incr
ease
d co
ntam
inan
t so
urce
s
Cha
nge
in
depo
sitio
n ra
tes
Northern PikeEsox lucius
Habitat
Increase depth of active layer will increase lake drainage area
Subs
iste
nce
harv
est p
ress
ures
on
over
win
terin
g po
pula
tions
Dire
ct d
estru
ctio
n of
hab
itat,
hind
ranc
e of
mig
ratio
n ro
utes
, inc
reas
ed d
owns
tream
turb
idity
and
sedi
men
tatio
n
Change AgentsDriversCEGeneral EffectCE-Specific Effect
Incr
ease
d w
inte
r pr
ecip
itatio
n m
ay i
ncre
ase
over
win
terin
g ha
bita
t
Review
Please review and provide comments:- Distribution models for fish and habitats- Conceptual models and text descriptions for fish
Contact: Rebecca [email protected], 907-786-4965
Photo: USFWS