linking fia data and satellite imagery to build a habitat model for the marbled murrelet
DESCRIPTION
Linking FIA Data and Satellite Imagery to Build a Habitat model for the Marbled Murrelet. Martin G. Raphael Pacific Northwest Research Station Funding contributed by: Region 6, USFS, PNW Research Station USDI Fish and Wildlife Service. Northwest Forest Plan of 1994. - PowerPoint PPT PresentationTRANSCRIPT
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Linking FIA Data and Satellite Imagery to Build a Habitat model for the Marbled
MurreletMartin G. Raphael
Pacific Northwest Research StationFunding contributed by:
Region 6, USFS, PNW Research StationUSDI Fish and Wildlife Service
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Northwest Forest Plan of 1994• Conservation plan for older forests and
species on 57 mill. ac. of federal land• Effectiveness Monitoring modules for
older forest, n. spotted owl, marbled murrelet, watershed condition
• Key questions for monitoring older forest: – How much, how is it changing, how
might it change in the future?– Is the Plan providing for its
conservation and management?
Physiographic
provinces(57 mill. ac.,46 mill. ac
forest)
USA
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A primary objective of the Northwest Forest Plan was to achieve:
“maintenance and/or restoration of habitat conditions for the Northern Spotted Owl and the Marbled Murrelet that will provide for viability of each species -- for the owl, well distributed along its current range on federal lands, and for the murrelet so far as nesting habitat is concerned”
--FEMAT 1993:iv
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Objectives• Estimate amount and distribution of
murrelet nesting habitat in WA, OR, CA• Estimate change over time – from start
of plan to now• Make estimates over all lands within
the murrelet range in WA, OR, CA• Use existing sources for environmental
variables (e.g., IMAP, PRISM)
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Needs for regional vegetation information• Methods that integrate plot and remotely sensed data to provide
info.:– Consistent over large, multi-ownership regions (“all lands”)– Spatially explicit (mapped)– Detailed attributes of forest composition and structure– Support integrated landscape analyses of multiple forest
values• Latest challenge: provide trend information that is spatial
– Monitoring older forest for Northwest Forest Plan
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Effectiveness Monitoring for Late-Successional and Old-Growth Forest (LSOG)• Objective: develop tools and data to assess change in older
forest– Gradient nearest neighbor (GNN) imputation (maps of
detailed forest attributes)– Change detection from Landsat time series (LandTrendr)
(trends)• Approach: minimize sources of error in models, map real change
– Corroborate with sample-based estimates• Monitoring report every 5 years
– 10-year report (Moeur et al. 2005)– In progress: 15-year report– 1996 to 2006 (Wash. and Oreg.), 1994 to 2007 (Calif.)
* Moeur, M., et al. 2005. Northwest Forest Plan–The first 10 years (1994-2003): status and trend of late-successional and old-growth forest. Gen. Tech. Rep. PNW-GTR-646.
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Gradient Nearest Neighbor Imputation (GNN)
k=1
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Accuracy assessment (‘obsessive transparency’)• Local- (plot-) scale accuracy via cross-
validation:– Confusion matrices, kappa statistics,
root mean square errors, scatterplots, etc.
• Landscape- to regional-scale accuracy: – Area distributions in map vs. plot sample– Range of variation in map vs. plot
sample– Riemann et al. (2010) diagnostics– Bootstrap variance estimators for kNN
(Magnussen et al. 2010)• Spatial depictions of uncertainty:
– Variation among k nearest neighbors– Distance to nearest neighbor(s)
(sampling sufficiency)• ‘Look-and-feel’ issues
1
2 3 4
5 6 7* 8 9
10 11 12
13
local(1-ha
plot) scale
regionalscale
landscape- or watershed-
scale
Oregon
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LSOG change from GNN ‘bookend’ maps, 1994/6 to 2006/7
• GNN models and change at 30-m pixel scale
• Recommend summarizing to coarser scales
• Example: 10-km hexagonsLSOGchange(% of forest)
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Change in habitat suitabilityNWFP Effectiveness
Monitoring
• Maxent (machine learning) models based on forest structure and composition attributes from GNN, trained with nest location data
• Subtract models to get change
Marbled murrelet
Northern spotted owl
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Modeling Marbled Murrelet Nesting Habitat: Estimating Nesting Habitat Suitability
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Natural History Fish-eating
seabirdDistributed
along West coast S to Monterey
Bay Nests on limbs
of big conifers Nests within 20
to 50 km from shore
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Model Form
• Presence/available• Presence = set of murrelet nests plus
equal number of “occupied” sites• Available = entire landscape within
study region that is “capable” of being habitat– We masked out barren lands, non-
forested areas
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Variable selection• Team developed initial list from
available data based on experience, literature
• Variables must cover range– Used GNN for forest attributes– PRISM for climate variables– DEM for slope, aspect
• Ran correlations, dropped one if r > 0.9– Kept variable with better support in
literature
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Model area
Nests Occupied
WA 54 54OR 65 65CA 52 52Total 171 171
Murrelet Sites
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Examples of GNN data
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Platforms per Tree (all species)We used these data to derive a new variable from the GNN data
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Washington Oregon California0
200
400
600
800
1000
1200
1400
1600
Fed. Reserved
Fed. Nonreserved
Nonfederal
High
er-s
uita
bilit
y ha
bita
t (th
ousa
nds o
f acr
es) Baseline
estimate
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Washington Oregon California0.0
0.5
1.0
1.5
2.0
2.5 Baseline (1994/96)
Maxent bookend
LandTrendr
Hig
her
suit
abili
ty h
abit
at (m
il-lio
ns o
f acr
es)
Change in habitat from 1994/96 to 2006/07
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Baseline Fire Harvest Other Total Percent
- Thousands of acres -
Fed.reserved
2,163.1 51.6 8.8 3.7 64.2 3.0
Fed.Non-reserved
262.7 5.3 6.5 0.8 12.6 4.8
Non-fed
1,386.6 0.9 394.3 18.7 413.9 29.8
Loss of suitable habitat from baseline to 2006/07
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Murrelet population sizein relation to amount of
nesting habitat
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Murrelet numbers are declining
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Is amount and trend of nesting habitat a primary driver of population trend?
• Spatial distribution of murrelets is well-predicted by spatial distribution of habitat• Habitat trend is not as clear, but
suggest a possible correlation• If marine conditions are the driver,
we’d expect similar trends among other related birds and we don’t see such trends
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Sources of uncertainty in overall monitoring results
• Multiple estimates, lots of moving parts with different limitations – Map- and plot-based estimates can’t be compared
statistically– Look for corroboration– Complexity and uncertainty pose challenges for users
• Error in model-based estimates– Error in plots, spatial predictors; model specification; etc.– Limitation of Landsat for mapping LSOG recruitment– Time period is short (10-13 years), and data will improve
• Uncertainty associated with murrelet habitat definition: – Habitat attributes can be affected by one or a few trees– Disturbance can create habitat gain, habitat loss, or no
change
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Products from NWFP monitoring study • GNN models and diagnostics available for download
– 2006/7 and 1994/96 vegetation maps and accuracy assessments
• 15-year reports (PNW GTRs) published or in press:– LSOG, northern spotted owl, marbled murrelet, watershed
condition