Kamloops TSA
Mountain Pine Beetle
Horizontal Initiatives Project
Ecosystem Prediction Model
Developed by:
Oliver Thomae, R.P.F.
ArbourTech Forest Management Services
June 14, 2006
Note this is a default model. It may be replaced in whole or in part with
acceptable alternatives approved by the project officer, if it can be demonstrated
that they would equally or better fulfill project objectives.
Introduction:
Considerable interest has developed over the past few years to model and map ecosystem site units. A
variety of landscape level Terrestrial Ecosystem Mapping and Predictive Ecosystem Mapping projects have
been undertaken to try to predict site series to support wildlife habitat management, timber productivity and
other forest management objectives.
Terrestrial Ecosystem Mapping is labour intensive, expensive and time-consuming. In its place a number of
areas of the Province have employed a modeling approach that approximates site series units based on
topography, climate, and vegetation characteristics. These models generally at best achieve 65-70%
accuracy. This is primarily due to their limitations on input layers, and the sophistication in modelling able
to be deployed.
This model is designed to improve on the approaches in existing models to develop a less subjective, and
more rigorous model which employs the principles of continuous variables, multiple factor analysis, edatopic
grid estimation, and high quality cartographic presentation, at an affordable cost.
Much of this modeling approach is similar to models used by Ecogen1. By using multiple site indicators,
rather than just topographic position, it is hoped that a more reliable estimate of site moisture and nutrients
can be developed. By using an edatopic grid approach the mapping becomes seamless across biogeoclimatic
ecosystem lines. Site series is then interpreted from the grid positions and biogeoclimatic variant. This way
when biogeoclimatic variants are remapped, the underlying site information isn’t lost, it can simply be
adjusted to the new site series distribution.
This model in unique in the way it incorporates variables through a scoring continuum, and through the
incorporation of variables not usually included in these models.
Note: This model has not been tested and field verified and is not intended to be considered as a formal
Predictive Ecosystem Model until these are completed. It is being proposed here to serve an interim need on
a limited budget.
1
Ministry of Forests Research Branch Predictive Ecosystem Modelling System
Modeling Principles:
1) Emulate the site diagnosis and interpretation guides used throughout British Columbia
2) Use as many data sources as possible to ensure that any error or bias in one factor is diluted with
consideration of other indicators.
3) Use continuous or near continuous variables rather than discrete variables. This allows factors to be
applied relative to the magnitude of their effect.
4) Use geographic and topographic information as well as biological information to ensure the most
rigorous possible interpretations.
5) Predict soil moisture and nutrient regime and then translate to site series. This will ensure that any
change in biogeoclimatic variants, or site series designations can be easily reinterpreted. It will allow
for seamless quality assurance across biogeoclimatic lines as the site series numbers are not consistent
between BECs. This avoids the issue of broad site series delineations, and also of overlapping site
series delineations. This approach facilitates mapping of variants that do not yet have site series units
defined.
6) Use consistent scoring out of 10 for each factor so that review and adjustment can be as intuitive as
possible.
7) Assign factor weights according to their reliability and/or significance in determining the overall
score.
8) Assign preliminary edatopic grid scoring based on allocation of the possible outcome range. After
comparing to field data points, these scoring ranges can be adjusted and rerun to ensure the best
possible fit with known data. It is also possible but not essential to use mathematical formulas
(multivariate analysis) to assign the best fit scores.
Modeling Steps:
1) Preparation of Input Layers
The step is similar to Ecogen except that input layers are incremental rather than discrete, and that more
interpretations are extracted from the data sources. All factors are scored on a scale of 1-10 with 5
representing a neutral effect. The relative weight of factors is adjusted when final composite scores are
calculated. As most of the available mapping is 1:20,000 scale with 20m contours, it is suggested that raster
based mapping using 50m x 50m (0.25ha) pixels be used.
a) Slope: Gradient of slope.
Assumptions: The steeper the slope the more snowmelt and rainfall run off. Slope class will also play a
role in defining the aspect effect. Flat areas have a neutral effect so they are scored at 5. As slopes get
steeper the effect reduces soil moisture availability.
Slope Class
0
1
2
3
4
5
6
7
8
9
10
Definition
0%-<10%
10%-<20%
20%-<30%
30%-<40%
40%-<50%
50%-<60%
60%-<70%
70%-<80%
80%-<90%
90%-<100%
>=100%
Moisture Score
5
5
4
4
3
3
2
2
1
1
0
b) Slope Position: (Note contractors may propose alternative slope position models using canned
programs or alternative approaches with approval by the Project Officer)
Assumptions: In accordance with B.C.’s ecological field guides for Site Identification and Interpretation,
slope position affects the relative shedding or accumulation of soil moisture. Nutrients are carried along by
soil moisture influencing nutrient availability. Slope position is shown on the diagram below, and slope
shape plays a role in defining the net shedding or accumulation of moisture. Again, flat areas are scored at 5
as a neutral effect. Water features are scored at 10 and the balance is scored relative to these.
Slope Position
Valley Flat, Terrace or
Plateau
Ridge Crest Barren
Ridge Crest Vegetated
Definition
Slope Class 0-2 with no
slope influence within
100m
Slope Class 0-2 within
100m of steeper slope at
lower elevation on at
least 2 sides, and labelled
rock, NP, or A.
Slope Class 0-2 within
Moisture Score
5
Nutrient Score
5
1
2
2
3
Upper Slope
Middle Slope
Lower Slope
Toe Slope
Valley Floodplain and
Riparian
Water Feature
100m of steeper slope at
lower elevation on at
least 2 sides, and labelled
NP Forest or greater
vegetation.
Slope Class 3 or more,
convex, with slope class
0-2 within 100m above.
Slope Class 3 or more,
straight, with no
significant slope change
within 100m above or
below.
Slope Class 3 or more,
concave, with slope class
0-2 within 100m below.
Slope Class 1-2 with
Slope Class 3 or more
above within 100m
Slope class 0 within 10m
elevation of watercourse,
and 100m of water edge.
Wetland, Lake, Double
line stream.
3
4
5
5
7
7
8
8
9
9
10
N/A
c) Aspect: Slope orientation to the sun.
Assumption: In the mountainous terrain of British Columbia, slope orientation has a significant effect on
snow accumulation and duration, solar radiation, temperature, drought, and to some degree wind effect.
To fully describe the influence of slope orientation, it is adjusted by the steepness of the slope involved.
Where slope is gentle the effect is minimal, but where slope is steep it is significant.
The following table shows how slope and aspect interact to provide more continuous approximations of
the effect.
Aspect
Cold
Definition
0-45 degrees
Cool
>315-360, >45-90
degrees
Neutral
>90-135,>270-315
degrees
Warm
>135-180, >225-270
degrees
Hot
>180-225 degrees
Slope Range
<20%
20-<40%
40-<60%
60-<80%
>=80%
<20%
20-<40%
40-<60%
60-<80%
>=80%
<20%
20-<40%
40-<60%
60-<80%
>=80%
<20%
20-<40%
40-<60%
60-<80%
>=80%
<20%
20-<40%
40-<60%
60-<80%
>=80%
Score
5
6
7
8
9
5
5.5
6
6.5
7
5
5
5
5
5
5
4.5
4
3.5
3
5
4
3
2
1
d) Elevation: Relative height within the biogeoclimatic system.
Assumption: It is well known that precipitation, temperature and humidity all vary with elevation. By
incorporating an elevation continuum, the relative climate effect is characterized. This helps to adjust for
transitional effects between biogeoclimatic units, normally described as discrete changes, when in reality
they are gradual changes.
The lowest elevations experience low snow and precipitation, brisk summer breezes, warm temperatures
and low relative humidity. Often the skies are also less overcast. The factors together cause a significant
drying effect on the landscape causing a shift to the most drought tolerant vegetation, and limiting growth
of trees.
As elevation increases, summer temperature declines, relative humidity increases, snow and precipitation
increase (particularly summer thundershowers), winds are somewhat gentler, and skies are more overcast.
Together these factors gradually make soil moisture conditions wetter as elevation increases.
It is assumed for the model that the mid-elevation range of about 1350m representa a neutral condition.
Below this elevation there is a trend to droughtiness and above this elevation moisture becomes less
limiting. (It is recognized that at high elevations soils become coarser, and shallower which can create a
drought effect, but this is independent of elevations itself. In the model this will be dealt with through
slope position, and oil depth and texture where available).
Although edatopic grids somewhat are intended to represent relative soil moisture and nutrients within
each biogeoclimatic unit, in this case we use a continuum throughout the geographic range. When
combined with other considerations, this will help to locate the relative range of soil moisture on each
biogeoclimatic variant grid. For instance, in the PPdh2, the probability of occurrence of hygric and
subhygric sites is very low. In the ESSFwm the probability of occurrence of xeric sites is very low.
Elevation Range
<900m
900-<1000m
1000-<1100m
1100-<1200m
1200-<1300m
1300-<1400m
1400-<1500m
1500-<1600m
1600-<1700m
1700-<1800m
>=1800m
Score
0
1
2
3
4
5
6
7
8
9
10
e) Tree Species Composition
Assumption: Tree species can be ranked in an approximate sequence of drought tolerance to moisture
requirement. The composition of a stand will somewhat reflect site moisture and nutrient availability
favouring species that are best suited to the site conditions.
Again it is assumed that soil moisture availability has an effect on nutrient availability. Many nutrients are
weathered and carried in the soil moisture profile making them available for vegetation growth. Areas which
are moist tend to have more deciduous growth and litter fall enriching the soil.
The top three species should be used and the score prorated accordingly. For example a stand is composed of
40% Pl, 30% Lw, and 30% Se. The moisture score would be calculated as (0.4x3) + (0.3x8) = (0.3x8) = 4.8
and the nutrient score would be (0.4x3) + (0.3x5) + (0.3x7) = 4.8.
Where no tree species data is available, for non-sufficiently restocked areas for example, use the previous
stand if possible, or use a neutral default score of 5.
Tree Species
NP, Rock
Py, OR
Pa
Lw
Pl
Fd
Ep
NPBr
Hw
Pw
At
Bl
Se
Cw
Act
2
Prov. Rank
Moisture2
N/A
1
5
5
7
8
9
N/A
10
12
13
18
18
18
25
Prov. Rank
Nutrients2
N/A
18
7
14
3
9
13
N/A
3
18
18
9
12
17
18
Moisture Score
Nutrient Score
0
1
2
3
3
4
5
5
6
7
7
8
9
9
10
1
9
4
8
2
5
7
3
2
9
9
5
7
9
9
Guidelines for Tree Species Selection and Stocking Standards for British Columbia, 1993, Silviculture Interpretations Working
Group.
f) Adjusted Site Index: Site growing potential.
Assumption: Site index is a reflection of a site’s moisture and nutrient availability as reflected in the reate of
tree growth (height at breast height age 50). Site index must be corrected for very old or very young stands
due to the old growth site index effect which tends to under-represent site growing potential.
Although site index varies with species on the same site, for the purposes of this model, the generalized site
index will be sufficiently close to site potential to support the interpretation.
Where stand age is over 150 add 2m to site index, and where over 250 add 4m to site index. Use silviculture
site index where available. Although this is imprecise, it will give a realistic enough approximation of site
index to support the model.
The unadjusted mean site index in most areas is roughly 15m BHA 50. It is assumed that if the adjustments
are made as noted the mean would increase to at least 16m. This is assumed to be a mesic site, and site
indices below this have declining moisture and nutrient availability, site indices higher than this have
increasing moisture and nutrient availability. An adjusted site index of below 6 would be very moisture and
nutrient limited, a site above 30 would not be limited by moisture and nutrients.
Site Index
<6
6-<9
9-<12
12-<15
15-<18
18-<21
21-<24
24-<27
27-<30
>=30
Moisture Score
1
2
3
4
5
6
7
8
9
10
Nutrient Score
1
2
3
4
5
6
7
8
9
10
g) Influence of Water Features: Streams, Rivers, Lakes, and Wetlands
Assumption: Proximity to water features indicates the presence of soil moisture and as noted in previous
sections, increasing soil moisture also generally affects soil nutrient availability. As a general rule, moving
water carries nutrients and improves site quality, which stagnant water reduces nutrient availability.
However, in some instances river terraces can be very gravely and well-drained, making them very dry and
nutrient poor. It is important to separate river floodplains where the water table is within 3m of the surface
and thus influences soil moisture in the rooting zone, from elevated terraces where the water table will have
little influence on the rooting zone. A horizontal rooting zone effect is assumed to be 20m.
Water features also have an influence on microclimate, reducing temperature extremes and increasing
relative humidity. This is seen as morning fog near low lying water bodies. This effect is assumed to extend
to about 100m horizontally and 10m vertically.
A neutral effect is given a score of 5 and all other effects are scored relative to that.
Water Feature
Influence
Lakes soil moisture
Lakes microclimate
Wetlands, swamps,
ponds, soil
moisture
Wetlands, swamps,
ponds,
microclimate
Double line
streams, rivers, soil
moisture
Double line
streams, rivers, soil
moisture
Single line streams,
rivers, soil moisture
Single line streams,
rivers, microclimate
Dashed line
intermittent
streams, soil
moisture
Dashed line
intermittent streams
microclimate
No water influence
Vertical Proximity
Horizontal
Proximity
Or <=20m
9
8
And >20m<=100m
7
6
Or <=20m
8
3
>3m – 10m to
water level
And >20m<=100m
6
5
<=3m to water
level
Or <=20m
8
8
>3m – 10m to
water level
And >20m<=100m
7
7
<=3m to water
level
>3m – 10m to
water level
<=3m to water
level
Or <=20m
8
7
And >20m<=100m
7
6
Or <=20m
7
6
>3m – 10m to
water level
And >20m<=100m
6
5
5
5
<=3m to water
level
>3m – 10m to
water level
<=3m to water
level
Moisture Score
Nutrient Score
h) Soil Depth and Texture: Optional Factor Where Available
Assumption: In places where soil mapping is available in digital form, it should be incorporated as an input
layer. Coarse soils and shallow soil both tend to have less moisture and nutrient availability. Fine soil and
deep soil have less moisture and nutrient limitation. Where available these should be factored in as follows:
Coarse soils are defined as sandy with >35% coarse fragments, or loamy with > 70% coarse fragments.
Fine textured soils are defined as silty or clayey with < 20% coarse fragment volume.
Soil depth is the depth from the mineral soil surface to a root restricting layer such as bedrock, strongly
compacted, or strongly cemented materials.
Soil Texture
Coarse
Moderate
Fine
Soil Depth
Very shallow, <0.5m
Shallow 0.5-1m
Normal >1m
Very shallow, <0.5m
Shallow 0.5-1m
Normal >1m
Very shallow, <0.5m
Shallow 0.5-1m
Normal >1m
Moisture Score
2
3
4
3
4
5
5
6
7
Nutrient Score
2
3
4
3
4
5
5
6
7
2) Calculate Composite Scores for Moisture and Nutrients
Once all the input layers have been developed and scored, they should be combined to generate a composite
score for soil moisture and soil nutrients as indicated in the tables below.
a) Composite Moisture Score
For each pixel mapped, calculate the mean weighted score from all seven moisture factor scores. Multiply by
the indicated factor weights (included in formula below), as slope position, aspect and water influence are
more directly and significantly related to soil moisture than the other indicators.
Where soil depth and texture information is not available, a neutral score of 5 should be entered to ensure the
calculation is not skewed.
Composite Moisture Score = (Slope Moisture Score + (2 x Slope Position Score) + (2 x Aspect Score) +
Elevation Score + Tree Species Score + (2 x Water Influence Score) + (2 x Soil depth and Texture
Score))/12
b) Composite Nutrient Score
Similarly for soil nutrients, determine the mean weighted score by following the same procedure. Again if
soil depth and texture information is not available, enter a neutral score of 5 to ensure the calculations are not
skewed.
Composite Nutrient Score = ((2 x Slope Position Score) + Tree Species Score + (2 x Site Index Score) +
(2 x Water Influence Score) + (2 x Soil Depth and Texture Score))/9
3) Determine and Map Edatopic Grid Position
Soil moisture and soil nutrient class should be determined from the edatopic grid position table below. Note
that these ranges may be adjusted through expert review or multivariate analysis of the fit with actual field
data, in situations where this model is intended to serve as a field verified Predictive Ecosystem Model.
Preliminary Edatopic Grid Position: Relative moisture and nutrient regimes.
An approximate suggested map colour scheme is depicted in the table.
Nutrient
Regime Score
Moisture
Regime Score
0
Very Xeric
0-<1
1
Xeric
1-<2
2
Subxeric
2-<3
3
Submesic
3-<4
4
Mesic
4-<6
5
Subhygric
6-<7
6
Hygric
7-<8
7
Subhydric
>=8
Water Features
Roads
Urban
Alpine
A
Very Poor
0-<2
B
Poor
2-<4
C
Medium
4-<6
D
Rich
6-<8
E
Very Rich
8-<10
4) Interpret Site Series from Edatopic Grid
From the edatopic grid values, a site series value should be determined using an approximation cross
reference. An example of a simplified table is shown below.
5) Prepare Summary Statistics and Charts
Report areas for each biogeoclimatic variant by site series and show their relative abundance in charts.