fvsclim: prognosis re-engineered to incorporate climate variables
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FVSCLIM: Prognosis Re-Engineered to Incorporate Climate Variables. Again. Robert Froese , Ph.D., R.P.F. School of Forest Resources and Environmental Science Michigan Technological University, Houghton MI 49931. This presentation has four parts. - PowerPoint PPT PresentationTRANSCRIPT
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FVSCLIM: Prognosis Re-Engineeredto Incorporate Climate Variables
Robert Froese, Ph.D., R.P.F.School of Forest Resources and Environmental ScienceMichigan Technological University, Houghton MI 49931
Again
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This presentation has four parts
Introduction
Approach
Relevance
Performance
The issue, the question and the model formulations examined
The methods and the data sets
How do revisions affect fit and prediction accuracy?
Does the approach have merit, and what are the next steps?
![Page 3: FVSCLIM: Prognosis Re-Engineered to Incorporate Climate Variables](https://reader035.vdocuments.site/reader035/viewer/2022070403/56813a1d550346895da1f840/html5/thumbnails/3.jpg)
This presentation has four parts
The issue, the question and the model formulations examined
The methods and the data sets
How do revisions affect fit and prediction accuracy?
Does the approach have merit, and what are the next steps?
Introduction
Approach
Relevance
Performance
![Page 4: FVSCLIM: Prognosis Re-Engineered to Incorporate Climate Variables](https://reader035.vdocuments.site/reader035/viewer/2022070403/56813a1d550346895da1f840/html5/thumbnails/4.jpg)
This presentation has four parts
The issue, the question and the model formulations examined
The methods and the data sets
How do revisions affect fit and prediction accuracy?
Does the approach have merit, and what are the next steps?
Introduction
Approach
Relevance
Performance
![Page 5: FVSCLIM: Prognosis Re-Engineered to Incorporate Climate Variables](https://reader035.vdocuments.site/reader035/viewer/2022070403/56813a1d550346895da1f840/html5/thumbnails/5.jpg)
This presentation has four parts
The issue, the question and the model formulations examined
The methods and the data sets
How do revisions affect fit and prediction accuracy?
Does the approach have merit, and what are the next steps?
Introduction
Approach
Relevance
Performance
![Page 6: FVSCLIM: Prognosis Re-Engineered to Incorporate Climate Variables](https://reader035.vdocuments.site/reader035/viewer/2022070403/56813a1d550346895da1f840/html5/thumbnails/6.jpg)
Wykoff’s (1990) Basal Area Increment Model is the subject of this research
DDS = DBH2t+10 - DBH2
t
but actually..
DDS = DBH2t - DBH2
t-10
BAI = π/4 (DBH2t - DBH2
t-10)
DI = (DBH2 + DDS)0.5 - DBH
ln(DDS) = f(SIZE +SITE +COMPETITION)
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Last year I presented results of a validation study of Wykoff’s model
pei yi yi
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“How and Where does Wykoff’s Basal Area Increment Model Fail?”
“I appreciate the opportunity to review your paper. The title certainly grabs your attention, especially if your name is Wykoff and you spent many years developing the subject model.”
I wrote it up as a manuscript…
Bill replied:
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The Prognosis BAI model is a multiple linear regression on the logarithmic scale
Wykoff 1990
ln DDS HAB LOC b1 ln DBH b2 LOC :DBH 2
b3 cos ASP SL b4 sin ASP SL b5 SL b6 SL2 b7 EL b8 EL
2
b9 CR b10 CR2 b11 HAB :
CCF
100 b12
(1 PCT )SBAln(DBH 1)
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Wykoff (1997) proposed a number of revisions to the model formulation
ln DDS HAB LOC b1 ln DBH b2 LOC :DBH 2
b3 cos ASP SL b4 sin ASP SL b5 SL b6 SL2 b7 EL b8 EL
2
b9 CR b10 CR2 b11 HAB :
CCF
100 b12
(1 PCT )SBAln(DBH 1)
Wykoff 1990
Wykoff 1997
ln DDS HAB LOC b1 ln DBH b2 LOC :DBHb3 cos ASP SL b4 sin ASP SL b5 SL b6 SL
2 b7 EL b8 EL2
b9 CR b10 CR
ln DBH 1 b11 HAB :SBA b12 (1 P90)PBAln(DBH 1)
b13 SBA
DBH
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Froese (2003) proposed replacing climate proxies with climate variables
Wykoff 1997
Froese 2003
ln DDS HAB b1 ln DBH b2 DBH b3 ANP b4 GSP b5 GST
b6 cos ASP SL b7 sin ASP SL b8 SL b9 SL2
b10 CR b11 CR
ln DBH 1 b12 HAB :SBA b13 (1 P90)PBAln(DBH 1)
b14 SBA
DBH
ln DDS HAB LOC b1 ln DBH b2 LOC :DBHb3 cos ASP SL b4 sin ASP SL b5 SL b6 SL
2 b7 EL b8 EL2
b9 CR b10 CR
ln DBH 1 b11 HAB :SBA b12 (1 P90)PBAln(DBH 1)
b13 SBA
DBH
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The approach involves two parts
• evaluating model revisions– Fit Wykoff (1990), Wykoff (1997) and Froese
(2003) to the new FIA data– Compare fit and lack-of-fit statistics of different
model formulations
• testing on independent data– generate predictions for independent testing data– compare bias of prediction residuals across
model formulations– Compare results using equivalence tests
Introduction
Approach
Relevance
Performance
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Froese (2003) pretended to be a physiologist
• ANP: total annual precipitation
• GSL: growing season length(days with nighttime minimum temperature greater than 0°C)
• GSP: total precipitation during the growing season
• GST: mean daily temperature during the growing season
• GSV: mean daily water vapour pressure deficit during the growing season
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Froese (2003) also pretended to be a climatologist
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Changing model formulation had small effect on fit statistics
Introduction
Approach
Relevance
Performance
Fit to the FIA data:
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The Froese (2003) model provided biologically-rational behaviour
• Biologically reasonable sign and magnitude of model coefficients
• Extrapolation issues remain to be resolved
Douglas-fir on median site
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Testing revealed that every formulation over-predicts on the validation data
Tested on the Region 1 data:
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The 1990 formulation failed to be validated for the monitoring data
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The 1997 model performed better, but was still not validated in this situation
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The 2003 model performed similarly to the 1997 model but was also not validated
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The substitution of climate variables for proxies is validated using equivalence tests
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The model is not appropriately responsive to small and suppressed trees
Results for Pseudotsuga menziesii
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Some results are encouraging, some suggest that more work is needed
• Are we (am I) splitting hairs?– Is an RMSE reduction of 2% useful?
• Does it really matter if RMSE reductions are small?
• Can we come up with better DDS model formulations?
• What’s wrong with predictions for small trees?
• Have I modelled climate effects on growth or climate effects on genes?
Introduction
Approach
Relevance
Performance