FIA FSP Project M075009 - for.gov.bc.ca Pine Risk... · FIA FSP FSP Project #M075009: Maclauchlan et al. Integration of GIS analysis and field evaluation to ... PBA attacked and PBA

FIA FSP FSP Project #M075009: Maclauchlan et al. Integration of GIS analysis and field evaluation to identify young pine at risk to mountain pine beetle.

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FIA FSP Project M075009

YEAR END TECHNICAL REPORT

NOT FOR PUBLICATION

April 30, 2007

Janice Hodge, JCH Forest Pest Management, 182 Horner Road, Lumby, B.C., Canada,

V0E 2G7

Lorraine E. Maclauchlan1, Entomologist, BC Ministry of Forests and Range, Southern

Interior Region, 515 Columbia Street, Kamloops, B.C., Canada, V2C 2T7.

Julie Brooks, Consultant, Forest Health Management, 466 Central Ave., Granthams Landing, B.C., Canada, V0N 1X0

1. Corresponding author.


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Integration of GIS analysis and field evaluation to identify young pine at risk to mountain pine beetle.

Phase 1: Analysis of risk factors associated with ‘when’ MPB attacks young stands.

BACKGROUND AND APPROACH

DATA AND METHODS

SPATIAL DATA

Geoprocessing

Predictor Variables

Beetle Populations and Susceptible Pine

Location Factor

Beetle Presence

Climatic Influence

BCMPB variables

Logistic Regression

RESULTS AND DISCUSSION

Small Scale Risk Analysis

Beetle Populations and Susceptible Pine

Potential Models

Large Scale Risk Analysis

ABSTRACT The objective of this project was to 1) quantify the probability of attack in young stands from adjacent mature stands ; and to 2) quantify within stand parameters to estimate the ultimate impact of mountain pine beetle attack in young stands. The first phase involved a spatial and statistical analysis that evaluated MPB populations and adjacent hazard to identify predictor variables, and ultimately a risk model, that will help determine ‘when’ MPB attacks young lodgepole pine stands. Temperature regimes played an important role in MPB occurrence in young stands. In four candidate models, the hot mild and warm biogeoclimatic regimes were significantly different from the cold, cool and very cold in terms of likelihood of MPB attack. Location within the outbreak area played a significant role in the probability of MPB attack and severity.


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BACKGROUND AND APPROACH The objective of the spatial analysis portion of this project was to 1) quantify the risk (probability of attack) from adjacent mature stands (Phase 1); and to 2) quantify within stand parameters to estimate the ultimate impact of mountain pine beetle (MPB) attack (Phase 2). Phase 1 involved a spatial and statistical analysis that evaluated MPB populations and adjacent hazard to identify predictor variables, and ultimately a risk model, which might help determine ‘when’ MPB attacks young lodgepole pine stands. The model inputs included a combination of aerial overview surveys (1999-2005), aerial assessments of young lodgepole pine (2006), Vegetation Resources Inventory (VRI) data and BC Provincial Scale MPB Model (BCMPB). Given the dichotomous nature of aerial assessments i.e. presence versus absence, logistic regression analysis was used to help identify suitable variables for inclusion in a model. Logistic regression is well suited for describing and testing hypotheses about relationships between a categorical outcome (presence or absence) and one or more categorical or continuous predictor variables (Peng et al. 2002). Among others, Wulder et al. (2006), Magnussen et al.(2004), Chojnacky et al. (2000) and Gumpertz et al. (2000), have recently demonstrated the use of logistic regression in the examination of forest insect behaviour. The Phase 1 analysis began at a finer resolution to develop a vector-based model, and then the predictor variables were used to move towards a raster-based model. During its testing and development, consideration was given to the applicability or ease of conversion to a raster-based model. For instance, if 2 models had similar predictive capabilities and similar overall model fit, the predictor variables, which were easily adaptable to a raster-based analysis, were chosen. Similarly, if overall model fits or predictive capabilities were poor, raster-based data were incorporated into the vector-model. While a raster-based approach is the most sensible given the scale of the project, we wanted to be able to compare the predictive capabilities of each candidate model. Ideally a raster-based model is preferred, as risk projections would be easily facilitated with BCMPB. Risk is defined as a function of both beetle pressure and stand hazard. The terms hazard and susceptibility are generally usually used interchangeably. They describe the characteristics of the tree, stand or landscape that measure the suitability to the mountain pine beetle (Shore et al. 2006). A stand hazard rating system developed by Shore and Safranyik (1992) used basal area of pine, density, age and latitude, longitude and elevation to determine the susceptibility index of a stand (SSI). All other factors being equal, a stand with a higher percent pine basal area will have a higher SSI. A retrospective study by Shore et al. (2000) found that under “normal” conditions the loss in basal area was approximately 68% of the SSI. Hence a stand with an SSI of 80 could lose up to 68% of the stand basal area under normal MPB conditions. For the vector-based analysis we chose to use only the stand percent species pine, or pine basal area (PBA) as an indication of hazard. We believe that at this point the other elements of the hazard equation may not be as relevant as they would be under ”normal” conditions, as confirmed by stand-level mortality exceeding 68% of the PBA (Hawkins and Rakocky 2007). The percent species pine is based on the stand basal area; therefore, a stand with 80% pine means that 80% of the basal area is in pine. By using this pine basal area as a


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starting point we were able to reduce the susceptible basal area annually depending upon how much (infestation severity) MPB was detected in the aerial surveys. The initial vector-based analysis was conducted over 24 representative mapsheets (367 openings) to accommodate ongoing developing and testing of a potential model at a manageable scale. The raster-based analysis included all of the 2420 openings aerially surveyed in 2006 in 7 BC Ministry of Forests Districts (MoF). DATA AND METHODS Spatial Data The first step required collating all MPB spatial information for the time period 1999-2005. These data were available in ESRI (Environmental Systems Research Institute Inc.) Arc shapefile format from the Ministry of Forests. For the purposes of analysis we needed to determine how much MPB existed in adjacent stands i.e. beetle pressure. An overlay analysis was completed which unioned the aerial survey data from year to year. This type of analysis creates one non-overlapping spatial theme with unique polygons, which represent the severity or severities, and year(s) that MPB has occurred in a particular location. The overlay theme was used to determine annual basal area losses and total years infested. The second step involved acquiring VRI data from LRDW (Land and Resource Data Warehouse) and creating susceptible forest cover spatial themes for a subset of mapsheets, which had been aerially assessed in 2006. Susceptible adjacent stands were identified as those, which were ≥60 years old and contained lodgepole pine. The third and final step was to acquire BCMPB (version 4) raster files and convert them to a vector (polygon) theme to accommodate possible spatial analysis with other vector files. The following raster files and brief description were obtained for BCMPB V4 (only using data to 2005):

• BCMPB.V4.CumKill – annual themes representing cumulative kill by MPB for the period 1999-2006

• BCMPB.V4.CumKill.NoMgmt – annual themes representing projected cumulative kill from 2007 to 2026 with no forest harvesting

• BCMPB.V4.ForestData – a subset of attributes from VRI • PCPCO.V4. – percent basal area of pine

Geoprocessing An overlay MPB spatial theme was created from the provincial aerial overview data for the period 1999-2005. The overlay theme was converted to raster files of various sizes (100 metres to 10 km) to accommodate various provincial analyses. Openings, which had been aerially assessed in the summer of 2006, were buffered by 1 and 3 kilometers using buffer tools available in ArcMap. The buffer theme, with each buffer being uniquely identified by buffer size (1 or 3), opening mapsheet and polygon number, was


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then intersected with the susceptible forest cover theme which was subsequently intersected with the MPB overlay theme. This sequence of theme intersections ensured that only stands, which contained susceptible pine were included in the analysis. The resultant theme was output to an MS Access geodatabase. Each polygon within each buffer was uniquely identified by buffer, mapsheet, opening polygon number, and mapstand. Predictor Variables Beetle Populations and Susceptible Pine Phase 1 of the spatial analysis focused on determining which factors were influencing ‘when’ MPB attacked young stands, herein referred to as risk analysis. These factors were thought to include one or a combination of beetle pressure and adjacent hazard. Potential predictor variables were defined with basal area used as a basis for the vector analysis and volume for the raster analysis. To assess potential relationships, the following were calculated for each unique buffer:

1. Total years infested Relative to area occupied by susceptible stands (Totyrhaz) Relative to area contained within the buffer (Totyrbuf)

2. Initial PBA or volume available of susceptible stands Relative to area occupied by susceptible stands (WBAhaz) Relative to area contained within the buffer (WBAbuf)

3. Amount of PBA or volume remaining in 2005 Relative to area occupied by susceptible stands RemainBAhaz) Relative to area contained within the buffer (RemainBAbuf)

4. Amount of PBA or volume attacked by MPB from 2000 to 2005 1. Relative to area occupied by susceptible stands (DiffBAhaz) 2. Relative to area contained within the buffer (DiffBAbuf)

The steps involved for each of these analyses were similar in that they weighed the contribution of each forest cover polygon or raster, herein referred to as analysis unit, to either the buffer area or susceptible area of each buffer. Total years infested for each analysis unit were calculated by counting and weighing the number of years for each affected portion of the polygon. Initial PBA was based on PBA available at the onset i.e. percent pine from VRI. PBA attacked and PBA remaining were calculating using the initial PBA and MPB annual severities. To calculate the amount of remaining basal area in adjacent stands in 2005, we determined the annual basal area losses since 1999. To accomplish this we subtracted the median of the annual overview severity categories (Table 1) from the initial PBA. While we recognize that the severity classes represent the percentage of the area of the MPB polygon affected and not the percent of the pine or basal area it does provide a rough estimate and probably the best estimate given available datasets. BCMPB used a similar approach for calculating losses.


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For the raster analysis we also weighed and calculated cumulative kill, pine volume, and pine basal area for each 1 km buffer. A 3 km raster analysis was not performed based on the results of the vector analysis. For each unique buffer in the vector analysis the following were determined: Total Years Infested

1. Sum of (Area of forest cover polygon/total susceptible area * total years infested of the forest cover polygon)

2. Count of year(s) to identify the maximum number of years infested.

Basal area losses 3. Stand per_area affected = [(area infested)/(stand area contained within buffer

area)]

4. Stand basal area affected = Initial PBA–[med_sev99]-[med_sev00]-[med_sev01]- [med_sev02]- [med_sev03]- [med_sev04]- [med_sev05].

Table 1. Aerial overview surveys severity classes and ranges (%) and medians (in

brackets).

Years Trace Low Moderate Severe Very Severe

1999-2003 1-10 (5) 11-30 (20) >30 (65)

2004-2006 <1 (.5) 1-10 (5) 11-30 (20) 30-50 (40) >50 (75) The MPB overlay theme was converted to a 400 metre raster theme using the total years infested to classify rasters. These were then used to calculate maximum total years infested at a buffer level for the raster analysis.

Location Factor Based on both aerial and ground observations we suspect that there may be differences in beetle behaviour, likely a result of beetle pressure, corresponding with various stages of the outbreak. In order to assess this possibility, three location factors were defined. The first location factor (lf_dist_1km) was calculated at an MoF District level based on 2005 cumulative kill within a 1 km buffer of aerially assessed stands. Using ANOVA (p<0.05) three locations categories were determined based on similar 2005 cumulative kill levels (Table 2).


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Table 2. Location categories based on average 2005 cumulative kill levels within 1 km of assessed stands.

Category District 2005 Cumulative Kill ± S.E.

1 Nadina 32.8±2.1

1 Quesnel 33.2±0.8

1 Vanderhoof 30.7±0.8

2 Chilcotin 18.9±0.9

2 100 Mile House 18.8±0.7

2 Prince George 20.1±0.8

3 Central Cariboo 25.8±0.8 The second location factor (lf_dist_raster) was determined by using the zonal statistics function from ‘ArcMap spatial analyst’, which created a range of 2005 cumulative kill levels for all MoF districts in the province. From this, five categories were identified. Only four of the five categories were in the study area, as the lowest levels are found elsewhere in the province. These categories are somewhat different than those identified in Table 3. However the intention is to find the best location factor for the model. Table 3. Summary of outbreak status categories derived from 2005 cumulative kill at a

MoF District level.

Category* MoF Districts % 2005 District Cumulative Kill

2 Chilcotin Nadina 20-30

3 Prince George 100 Mile House 31-50

4 Central Cariboo 51-70

5 Vanderhoof Quesnel >70

*Category 1 (<20%) not found within the study area.

The final location factor (lf_mapsheet) was based on the total number of years that a mapsheet had been infested regardless of severity or size. Beetle Presence Mountain pine beetle presence was coded as 0=absent and 1=present (pres_abs1) based on the total percentage attack assigned during aerial assessments. Very low levels of stand mortality (≤0.5%) may be due to other forest health factors, which are not visible from the air. Another variable (pres_abs2) was added to the dataset to account for this possibility.


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Climatic Influence It has been known for quite some time that climate plays a key role in the success of mountain pine beetle populations. We have chosen to use biogeoclimatic (BEC) zones as an indicator of climatic influence. Using the biogeoclimatic zone classification we defined 5 variables for inclusion into the analysis. Three of these are simply commonly known BEC names and symbols, while the last 2 are based on grouping of the precipitation and temperature regimes into categories (Table 4).

Table 4. Description of BEC climatic influence variables.

Variable BEC equivalent name Group BEC Zones

BEC_Precip Precipitation Regimes Dry (= x, d) Moist (= m) Wet (= w, v)

BEC_Temp Temperature Regimes Warm =(h, m) Mild (=m) Cold (=k, c, v)

BCMPB variables Several of the raster themes from BCMPB were used for the raster analysis. These include: cumulative kill, volume of pine and basal area of pine; all at a 400 metre resolution. Cumulative kill represents the cumulative amount of pine volume losses, while volume and basal area are derived from VRI and are similar to those used in the small-scale risk analysis. For each unique buffer in the raster analysis, the volume losses were calculated by multiplying the initial volume by the percent cumulative kill.

Logistic Regression A forward stepwise selection process using likelihood ratios was used for analysis. This process selects the strongest variables until there are no more significant predictors left in the dataset. A total of nineteen variables were included in the vector analysis and twenty-two in the raster analysis. A classification cut-off of 0.5 was assigned to the data to represent either the present or absent classes. These include the eight buffer variables described in the section “Beetle populations and susceptible pine (p. 5)”, stand opening age, stand opening size (ha), biogeoclimatic zone variables, and the location factor variables. The initial vector analysis was completed for 1 kilometre and 3 kilometres separately, and each of these was tested using both the original presence/absence (pres_abs1) variable and the modified presence/absence variable (pres_abs2). The vector analysis used mostly vector variables but also incorporated some of the location factors derived from the raster themes. The raster analysis included variables similar to those of the vector analysis, with the exception that they were raster-based, and all of the variables derived from the raster themes. Each logistic regression model was assessed based on overall model evaluation (Wald test), tests of individual predictors (Wald Chi-square statistic), goodness of fit statistics (Hosmer and Lemeshow), strength of association (Nagelkerke’s R2), evaluation of the C


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index, and validations of predicted probabilities. The models, which passed all of these tests, were further validated using a random 80/20 split of the data, with 80% belonging to the model group and 20% belonging to the test group. RESULTS AND DISCUSSION Small Scale Risk Analysis Beetle Populations and Susceptible Pine Three-hundred and sixty-seven openings were assessed over 24 mapsheets in the Vanderhoof (DVA), Prince George (DPG), and Central Cariboo (DCC) Forest districts (Figure 1). The adjacent total years infested was significantly (p<0.05) higher in DVA for both infested and uninfested young pine stands, as was total attack of infested stands (Table 5). The average age of the stands selected were similar, however the average age of unattacked stands in DVA was younger than in the other 2 districts (Table 5). Table 5. Summary of average age and total attack (%) of selected stands and total years

infested within a 1 km radius. Values followed by the same letter are not significantly different (ANOVA, p< 0.05).

MPB Forest District Age ± S.E. Total Attack %

± S.E. Total Years

Infested ± S.E.

All (present & absent) Vanderhoof (n=131) 30.7 ± 0.7a 13.9 ± 1.7a 3.3 ± 0.1a

Prince George (n=152) 32.4 ± 0.6a 8.2 ± 1.3b 2.7 ± 0.1b

Central Cariboo (n=84) 30.2 ± 1.0a 8.7 ±1.4ab 2.7 ± 0.1b

MPB Present Vanderhoof (n=91) 33.1 ± 0.9a 20.1 ± 2.1a 3.4 ±0.1a

Prince George (n=119) 32.7 ± 0.7a 10.4 ± 1.6b 2.7 ± 0.1b

Central Cariboo (n=52) 31.8 ± 1.4a 14.1 ± 1.9ab 3.0 ± 0.1b

MPB absent Vanderhoof (n=40) 25.3 ± 0.7a 0.0a 3.0 ± 0.1a

Prince George (n=33) 31.1 ± 1.3b 0.0a 2.5 ± 0.2b

Central Cariboo (n=32) 27.6 ± 1.1ab 0.0a 2.4 ± 0.1b


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Quesnel

Horsefly

Vanderhoof

Burns Lake

Fraser Lake

Alexis CreekWilliams Lake

Prince George

100 Mile House

Fort St. James

1-2

3-4

5-6

Number of Years Infested

Figure 1. Location of young stands selected for analysis, which were aerially surveyed in

the summer of 2006. When looking at the buffer as a whole i.e. including non-forested areas, significant differences existed between the average for all PBA parameters within both the 1 and 3 km radius (Table 6). This trend did not exist when looking solely at the susceptible pine (hazard) within each buffer size. Significant differences existed between total years infested between buffer sizes for both the buffer as a whole and only the susceptible forest types. The mapsheets and districts selected not only represented different stages of the outbreak but also various configurations of forested landscapes. Those near Prince George had a higher proportion of agricultural land and hence a significantly lower level of initial PBA within 1 kilometre (Table 7).


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Table 6. Parameter averages within a 1 or 3 kilometer radius of 367 young pine stands

aerially assessed. Values followed by the same letter not significantly different (Tukey’s p<0.05).

Predictor variable 1 km buffer ± S.E. 3 km buffer ± S.E.

Remaining PBA buffer 14.92±0.71a 16.98±0.68b

Remaining PBA hazard 34.39±1.10a 33.90±1.00a

Difference PBA buffer 12.74±0.63a 15.26±0.59b

Difference PBA hazard 29.13±1.10a 30.2±0.96a

Initial PBA buffer 27.69±0.91a 32.22±0.80b

Initial PBA hazard 63.53±1.10a 64.1±0.95a

Total Years buffer 0.77±0.02a 0.93±0.02b

Total Years hazard 1.79±0.03a 1.88±0.02b

Table 7. Parameter averages (± S.E.) within 1 km radius of 367 young pine stands aerially assessed, by Forest District (). Values followed by the same letter are not significantly different (Tukey’s p<0.05)

Predictor variable Central Cariboo

(n=84) Prince George

(n=152) Vanderhoof

(n=131)

Remaining PBA buffer 25.0 ± 1.9a 9.1 ± 0.5b 15.2 ± 1.1c

Remaining PBA hazard 51.7 ± 3.2a 28.8 ± 1.1b 29.7 ± 1.6b

Difference PBA buffer 7.8 ± 0.6a 7.9 ± 0.7 a 21.5 ± 1.2b

Difference PBA hazard 17.5 ± 1.3a 22.6 ± 1.3a 44.2 ± 1.9b

Initial PBA buffer 32.8 ± 1.9a 17.1 ± 0.9b 36.7 ± 1.4a

Initial PBA hazard 69.1 ± 2.8a 51.4 ± 1.5b 74.0 ± 1.3a

Total Years buffer 0.8 ± 0.04a 0.5 ± 0.03b 1.0 ± 0.04a

Total Years hazard 1.8 ± 0.06a 1.6 ± 0.05b 2.0 ± 0.04c

Total Years count 2.8 ± 0.8a 2.7 ± 0.7a 3.3 ± 0.9b

Potential Models Of the 4 stepwise regression analyses run (Pres_Abs1*buffer1, Pres_Abs1*buffer3, Pres_Abs2*buffer1, PresAbs2*Buffer3) only the 1 kilometre buffer using pre_abs2 (absent≤0.5 percent attack) produced potential models (Table 8). Models 1 and 2 from


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the pre_abs2 (absent ≤ 0.5% attack) analysis had good Nagelkerke R 2 values for logistic regression (range .43-.44). The common variables to both of these models in order of Wald’s statistic were age, BEC temperature regime, and the cumulative kill code (lf_dist_raster). Only 1 of these models incorporated a variable reflecting hazard (WBAbuf), however the hazard is somewhat embedded in the lf_dist_raster variable as it is based on cumulative kill, which is partially a reflection of stand hazard. Predictor variables which were not included in any of the potential models were: DiffBAbuf, DiffBAhaz, RemainBAbuf, RemainBAHaz,WBAHaz, Totyrbuf, BECzone, BECprecip, Precipcode, Tempcode, Lf_mapsheet, Max_tot_years, and Lf_dist_1km. Table 8. Parameter values and model fit statistics for 2 potential models for the small

risk analysis using a 1 kilometre buffer.

Predictor Variables Model 1 Estimate

(±SE) Model 2 Estimate

(±SE)

Constant -4.04 ± 0.89*** -3.57 ± 0.98***

WBAbuf -0.03 ± 0.01*

TotYrHaz 1.04 ± 0.27*** 1.28 ± 0.29***

BECtemp (reference warm) Cool Cold Very Cold

-1.06 ± 0.39** -3.13 ± 0.53*** -6.19 ± 1.20***

-1.08 ± 0.39** -2.94 ± 0.54*** -5.62 ± 1.20***

Age 0.14 ± 0.02*** 0.14 ± 0.02***

Opening Size 0.26 ± 0.01*** 0.03 ± 0.01***

Lf _dist_raster (reference >70%) 31-50% 50-70%

-0.44 ± 0.46*** -2.66 ± 0.42

-0.26 ± 0.50*** -2.75 ± 0.43

Model Fit Nagelkerke R2 0.43 0.44

Hosmer and Lemeshow 0.88 0.11

C Index 0.83 (.79-.87)* 0.83 (.79-.87)*

Correctly Predicted Absent 69.4 67.5

Present 76.6 78.0

Overall Percentage 73.5 73.5

***p ≤ 0.001, **p ≤ 0.01, * p ≤ 0.05 (based on Wald’s X2 test)


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Large Scale Risk Analysis The provincial scale risk analysis was completed for 2420 of the young pine stands aerially surveyed in 2006. This portion of the spatial analysis used the raster themes from BCMPB, rather than the vector-based forest cover polygons used in the small-scale risk analysis. To maintain the similarity between attributes in the vector and raster analysis, the hazard area for each buffer from the vector analysis was used to calculate the hazard attributes. Similar to the vector analysis, the use of pres_abs2 (absent≤0.5) produced models, which had better Nagelkerke’s R2 and Hosmer-Lemeshow values than pres_abs1 (absent=0) (Table 9). The inclusion of the vector-derived variables did not significantly improve any of the potential models. The small differences noted in R2 values combined with the additional geo-and- data processing does not warrant further consideration of these variables. Ideally, the final model will be entirely raster-based so that future projections can be easily facilitated with BCMPB. Table 9. Comparison of potential large-scale models based on dependent variables, type

of analysis and range of Nagelkerke’s R2 and Hosmer and Lemeshow Values.

Dependent Variable Type of variable

Nagelkerke’s R2 Values

Hosmer and Lemeshow Values

Pres_abs1

Raster variables and raster/vector cross variables 0.113-0.374 0.068-0.335

Raster Variables 0.113-0.371 0.170-0.335

Pres_abs2

Raster variables and raster/vector cross variables 0.18-0.39 0.086-0.59

Raster Variables 0.18-0.44 0.086-0.57 Four models were evaluated and cross-validated using the pres_abs2 and raster variables. Cumulative kill, age, BECtemp and the location factor based on cumulative kill at a MoF District level (lf_dist_raster) were the common variables used in each model. R2 values increased as more variables were added to the models, with the final model having 7 variables. Using model 4, we calculated the predicted probabilities and generated a few examples of actual versus predicted values (Figure 2). While the stands near 100 Mile House had a good fit, those just south of Vanderhoof had several false positive stands. As indicated in Table 10, false positives are more likely to occur than false negatives for all four models. Two of the 3 location factors were selected by the model, one that represents cumulative kill at a MoF district level (lf_dist_raster) and the other at a 1:20,000 mapsheet level (lf_mapsheet). This indicates that MPB populations both locally and beyond are influencing when young stands are attacked. At an even more local level is the inclusion of the max_totyr variable, which represents the maximum number of years the buffer was


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infested. Mapsheets, which experienced 5 years of infestation were, for the most part, not significantly different than those which exceeded 5 years. At the 1 km scale however there were significant differences between the number of years the buffer was infested.

100 Mile House100 Mile House

Figure 2. Example of landscape level Model 4, actual (left) versus predicted (right) absence (green) or presence (red) of mountain pine beetle for an area near 100 Mile

House (top) and south of Vanderhoof (bottom). Temperature regimes based on biogeoclimatic zone designations also seem to play an important role in MPB in young stands. In all four candidate models, the hot and mild regimes were not statistically different than the warm regime (Table 10), while the cold, cool and very cold were significantly different (p<0.001). When looking at the average range of predicted probabilities at a mapsheet level those with the highest probability occur in the core of the outbreak. Lower probabilities are associated with mapsheets that are either 1) in areas with lower hazard or 2) areas where the populations have not peaked (Figure 3).


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Table 10. Parameter values and model fit statistics for 4 potential models for the large scale risk analysis using a 1 kilometre buffer.

Predictor Values Model 1 Estimate ±

SE Model 2 Estimate ±



SE

Constant -3.46 ± 0.28*** 12.8 ± 0.32*** -2.01 ± 0.36***

Age 0.13 ± 0.007*** 0.14 ± 0.007*** 0.13 ± 0.007*** 0.13 ± 0.007***

VolPine -0.005 ± 0.001*** -0.005 ± 0.001***

CumKill 0.002 ± 0.000*** 0.001 ± 0.00*** 0.001 ± 0.00*** 0.001 ± 0.00*** BecTemp (ref. Warm) Cold Cool Hot Mild Very Cold

-1.24 ± 0.16*** -0.37 ± 0.13*** 35.6 ± 1.5 38.7 ± 1.6 -1.26 ± 0.25***

-1.27 ± 0.16*** -0.29 ± 0.14** 35.9 ± 1.4 38.4 ± 1.1 -1.4 ± 0.3***

-1.37 ± 0.17*** -0.39 ± 0.14** 35.5 ± 1.2 37.9± 1.2 -1.57 ± 0.26***

-1.47 ± 0.17*** -0.45 ± 0.14*** 35.5 ± 1.3 39.2 ± 1.4 -1.71 ± 0.007***

Lf_dist_raster (ref.>70%) 20-30% 31-50% 51-70%

-1.38 ± 0.20*** -0.97 ± 0.15*** -0.28 ± 0.15

-1.29 ± 0.21*** -0.89 ± 0.15*** -0.32 ± 0.15*

-1.42 ± 0.21*** -1.00 ± 0.15*** -0.49 ± 0.16**

-1.56 ± 0.22*** -1.00 ± 0.15*** -0.53 ± 0.16***

Lf_mapsheet (ref. 7 years) 2 years 3 years 4 years 5 years 6 years

-2.34 ± 0.50*** -0.74 ± 0.17*** -0.78 ± 0.18*** -0.42 ± 0.17* 0.07 ± 0.19

-2.11 ± 0.44*** -0.71 ± 0.17*** -0.69 ± 0.18*** -0.33± 0.17 0.15 ± 0.19

-1.62 ± 0.45*** -0.47± 0.18* -0.44± 0.19* -0.16 ± 0.18 0.24 ± 0.19

Max_toyr (ref. 7 years) 1 year 2 years 3 years 4 years 5 years 6 years ±

-2.76 ± 0.46*** -2.38 ± 0.37*** -1.83 ± 0.36*** -1.51 ± 0.39*** -1.41 ± 0.93*** -1.88 ± 0.50***

Nagelkerke’s R² 0.34 0.36 0.37 0.44

Hosmer and Lemeshow 0.14 0.09 0.42 0.57

C Index (ROC) 0.80 (0.78-0.82)** 0.81 (0.79-0.83)** 0.83 (0.81-.084**) 0.82 (0.80-0.84)

% Absent Correct 58.4 59.0 58.6 60.2

% Present Correct 82.9 83.5 84.8 85.1

Overall Percentage 73.9 74.5 75.2 75.9 ***p≤ 0.001, **p ≤ 0.01, *p ≤ 0.05 (based on Wald’s Χ² test)


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Clinton

Quesnel

McBride

Houston

Horsefly

Lillooet

Logan Lake

Vanderhoof

Burns Lake

Fraser Lake

Alexis CreekWilliams Lake

Prince George

100 Mile House

Fort St. James

2005 Predicted Probabilities

>0 and <=25%

>25% and <=50%

>50% and <=75%

>75%

Figure 3. 2005 probabilities of MPB occurrence on a mapsheet basis based on

average probability of the stands assessed in 2006 (Model 4). REFERENCES Chojnacky, D.C., Bentz, B.J., and Logan, J.A. 2000. Mountain pine beetle attack in

ponderosa pine: Comparing methods for rating susceptibility. USDA Forest Service Rocky Mtn. Res. Sta. Research Paper RMRS-RP-26. 10p.

Gumpertz, M.L., Wu, C, and Pye, J.M. 2000. Logistic regression with southern pine beetle outbreaks with spatial and temporal autocorrelation. Forest Science. 46(1): 95-107.

Hawkins, C. and Rakocky, P. 2007. Stand level effects of the mountain pine beetle outbreak in the central British Columbia interior. Natural Resources Canada, Canadian


17

Forest Service, Pacific Forestry Centre, Mountain Pine Beetle Initiative Working Paper 2007-06. 14p.

Magnussen, S., Boudewyn, P., and R. Alfaro. 2004. Spatial prediction of the onset of spruce budworm defoliation. The Forestry Chronicle. 80(4): 485-494.

Peng, C.Y., Lee, K.L., and Ingersoll, G.M. 2002. An introduction to logistic regression analysis and reporting. Journal of Educational Research. 96(1): 3-14.

Shore, T., and Safranyik, L. 1992. Susceptibility and risk rating systems for the mountain pine beetle in lodgepole pine stands. Forestry Canada. Pacific Forestry Centre, Victoria, British Columbia, Information Report BCX-336. 12 pp.

Shore, T.L., Safranyik, L., and J.P. Lemieux. 2000. Susceptibility of lodgepole pine stands to the mountain pine beetle: testing of a rating system. Can. J. For. Res. 30: 44-49.

Shore, T.L, Riel, B.G., Safranyik, L., Fall, A. 2006. Decision support systems (Chapter 8) in: The mountain pine beetle: a synthesis of biology, management, and impacts on lodgepole pine. Safranyik, L. and Wilson, B. (Eds.). Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre, Victoria, BC. pp. 193-230.

Wulder, M.A., White, J.C., Bentz, B. Alvarez, M.F. and Coops, N.C. 2006. Estimating the probability of mountain pine beetle red-attack damage. Remote Sensing of Environment. 101: 150-166.

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FIA FSP Project M075009 - for.gov.bc.ca Pine Risk... · FIA FSP FSP Project #M075009: Maclauchlan et al. Integration of GIS analysis and field evaluation to ... PBA attacked and PBA