Timber Supply Analysis - Alberta · Timber Supply Analysis . 2017-2027 DFMP Prepared by FORCORP March 2017 . Binder Type ID Name ONE Executive Summary Chapter 1 Corporate Overview

ANNEX VI

Timber Supply Analysis

.

2017-2027 DFMP

Prepared by FORCORP

March 2017

Binder Type ID Name

ONE Executive Summary

Chapter 1 Corporate Overview and Forest Management Approach

Chapter 2 DFMP Development

Chapter 3 Forest Landscape Assessment

Chapter 4 Summary of Previous DFMP

Chapter 5 Values, Objectives, Indicators, and Targets (VOITs)

Chapter 6 Preferred Forest Management Scenario

Chapter 7 DFMP Implementation

Chapter 8 Research

Glossary

TWO Annex I Forest Management Agreement (FMA)

Annex II Communication and Consultation Plans

Annex III Stewardship Report 2007-2011

Annex IV Growth and Yield Program

Annex V Growth and Yield

Annex VI Timber Supply Analysis

Annex VII Spatial Harvest Sequence

THREE Annex VIII Landbase Development Document

Table of Contents i

Millar Western Forest Products Ltd. 2017-2027 DFMP – Annex VI TSA

Table of Contents Table of Contents ........................................................................................................................................... i

List of Tables ................................................................................................................................................ iii

List of Figures ............................................................................................................................................... iv

1. Overview ........................................................................................................................................... 1

1.1 Indicators ...................................................................................................................................... 1

2. Forecasting Methods ........................................................................................................................ 3

2.1 Objective ....................................................................................................................................... 3

2.2 Process .......................................................................................................................................... 3

2.2.1 Development of the Model Dynamics .................................................................................. 4

2.2.2 Scenario Development .......................................................................................................... 5

2.2.3 PFMS Development ............................................................................................................... 5

2.3 Limitations ..................................................................................................................................... 5

2.3.1 Landbase ............................................................................................................................... 5

2.3.2 Yield curves ........................................................................................................................... 5

2.3.3 Stochastic events .................................................................................................................. 6

2.4 Modeling Tools .............................................................................................................................. 6

2.4.1 Woodstock ............................................................................................................................ 6

2.4.2 Patchworks ............................................................................................................................ 7

3. ECA .................................................................................................................................................... 9

3.1 Description .................................................................................................................................... 9

3.2 Development of Stand Equivalent Clearcut Area (ECA) curves .................................................. 10

3.3 GoA ECA Assessment .................................................................................................................. 12

3.4 Patchworks ECA Assessment ...................................................................................................... 13

3.5 Outputs ....................................................................................................................................... 14

3.6 Differences between GOA WHA Patchworks Assessment.......................................................... 14

4. Timber Supply Analysis ................................................................................................................... 15

4.1 Harvest Volume Analysis ............................................................................................................. 15

4.1.1 Methodology ....................................................................................................................... 15

4.1.2 Harvest Volume Summary .................................................................................................. 16

4.1.3 Discussion ............................................................................................................................ 17

4.1.4 Assumptions ........................................................................................................................ 17

4.2 Seral Stage Sensitivity Analysis ................................................................................................... 19

4.2.1 Methodology ....................................................................................................................... 19

4.2.2 Harvest Volume Summary .................................................................................................. 21

4.2.3 Old Forest Summary ........................................................................................................... 22

ii Table of Contents


4.2.4 Discussion ............................................................................................................................ 24

4.3 Barred Owl .................................................................................................................................. 24

4.3.1 Compartments of concern .................................................................................................. 24

4.3.2 Barred Owl Model Results .................................................................................................. 25

4.3.3 Summary ............................................................................................................................. 37

4.4 ECA Sensitivity Analysis ............................................................................................................... 38

4.4.1 Analysis ............................................................................................................................... 38

4.4.2 Results ................................................................................................................................. 38

4.4.3 Discussion ............................................................................................................................ 41

4.5 Regeneration Curve Sensitivity Analysis ..................................................................................... 41

4.5.1 RSA volume curves .............................................................................................................. 41

4.5.2 TSA implications .................................................................................................................. 41

4.6 Carry Forward Sensitivity Analysis .............................................................................................. 44

4.7 Minimum Harvest Age Sensitivity Analysis ................................................................................. 44

4.7.1 Background ......................................................................................................................... 44

4.7.2 Scenarios Used for Sensitivity Analysis ............................................................................... 46

4.7.3 Scenario 60008 MHA........................................................................................................... 47

4.7.4 Scenario 63005 MHA........................................................................................................... 47

4.7.5 Scenario 63004 MHA........................................................................................................... 47

4.7.6 Assumptions for All Scenarios ............................................................................................. 47

4.7.7 MHA Used in Sensitivity Analysis ........................................................................................ 47

4.7.8 Results ................................................................................................................................. 49

4.8 RSA and Minimum Harvest Age Sensitivity Analysis ................................................................... 52

4.9 Black-throated Green Warbler .................................................................................................... 56

5. PFMS Datasets ................................................................................................................................ 57

5.1 Woodstock .................................................................................................................................. 57

5.2 Patchworks .................................................................................................................................. 57

5.2.1 Pin File ................................................................................................................................. 57

5.2.2 Tracks .................................................................................................................................. 58

5.2.3 Landbase ............................................................................................................................. 58

5.3 Patchworks PFMS Outputs .......................................................................................................... 59

5.3.1 Standard Patchworks Outputs ............................................................................................ 59

5.3.2 Target Files .......................................................................................................................... 59

5.3.3 Future Forest Condition ...................................................................................................... 59

5.3.4 Harvest Schedule ................................................................................................................ 60

5.3.5 SHS Shapefile....................................................................................................................... 60

Appendix I – Q6 Response .......................................................................................................................... 61

List of Tables iii


List of Tables Table 1. Baseline Scenario Matrix ............................................................................................................... 16 Table 2. Harvest volumes by scenario ........................................................................................................ 18 Table 3. Interpretation of seral stage by organization to create a comparable dataset structure ............ 19 Table 4. Scenario matrix for analysis of seral stage definitions .................................................................. 20 Table 5. Old forest targets effect on harvest volume by seral stage definition ......................................... 22 Table 6. Barred Owl model results for back to natural scenario – 54002 .................................................. 26 Table 7. Barred Owl model results for pre-PFMS scenario - 64001 ............................................................ 26 Table 8. Barred Owl model results for PFMS scenario - 64006 ................................................................. 27 Table 9. Barred Owl model results for the PFMS scenario – 64006 in identified primary habitat. ............ 29 Table 10. Barred Owl model results for the PFMS scenario – 64006 in identified secondary habitat. ...... 29 Table 11. Harvest volumes for the three scenarios ................................................................................... 44 Table 12. Carryover volume sensitivity analysis ......................................................................................... 44 Table 13. MHA scenarios ............................................................................................................................ 46 Table 14. FMU W11 minimum harvest ages ............................................................................................... 48 Table 15. FMU W13 minimum harvest ages – Scenario 60008 .................................................................. 48 Table 16. FMU W13 minimum harvest ages – Scenarios 63005 and 63004............................................... 49 Table 17. Harvest level impacts of changing MHA in W13 ......................................................................... 52

iv List of Figures


List of Figures Figure 1. Forecasting planning process ......................................................................................................... 4 Figure 2. Example of ECA curve using PL natural curve for FMU W13 ....................................................... 11 Figure 3. All ECA curves for all strata in both FMU’s. ................................................................................. 11 Figure 4. GOA Watershed Assessment Data Preparation ........................................................................... 13 Figure 5. MWFP Watershed Assessment Data Preparation ....................................................................... 14 Figure 6. Seral stage definitions used in analysis ........................................................................................ 21 Figure 7. Percent area of old seral stage in the active landbase for the no-harvest scenario across the 5 seral stage definitions ................................................................................................................................. 23 Figure 8. Percent area of old seral stage in the active landbase for the maximize harvest scenario with no seral stage targets across the 5 seral stage definitions .............................................................................. 23 Figure 9. Percent area of old seral stage in the active landbase for the maximize harvest scenario while maintaining at least 5% old seral stage across the 5 seral stage definitions .............................................. 23 Figure 10. Percent area of old seral stage in the active landbase for the maximize harvest scenario while maintaining at least 10% of old seral stage across the 5 seral stage definitions........................................ 24 Figure 11. Map of primary and secondary compartments of concern. ..................................................... 25 Figure 12. Comparison of barred owl RSF values for all three scenarios .................................................. 27 Figure 13. Comparison of breeding pair estimates for all three scenarios ................................................ 28 Figure 14. Comparison of barred owl patch targets for Pre-PFMS and PFMS scenarios ........................... 28 Figure 15. RSF for primary and secondary compartments compared to FMA total. ................................. 30 Figure 16. Breeding pairs for primary and secondary compartments compared to FMA total. ............... 30 Figure 17. Area 80 years and older for strata within primary compartments of concern ......................... 31 Figure 18. Area 80 years and older for strata within secondary compartments of concern ..................... 31 Figure 19. BTN Breeding pair results of time zero Barred Owl model ........................................................ 32 Figure 20. Pre-PFMS and PFMS scenarios Breeding pair results of time 0 Barred Owl model .................. 32 Figure 21. BTN Breeding pair results of time 10 Barred Owl model ........................................................... 33 Figure 22. Pre-PFMS and PFMS scenarios Breeding pair results of time 10 Barred Owl model ................ 33 Figure 23. BTN Breeding pair results of time 20 Barred Owl model ........................................................... 34 Figure 24. Pre-PFMS and PFMS scenarios Breeding pair results of time 20 Barred Owl model ................ 34 Figure 25. BTN Breeding pair results of time 50 Barred Owl model ........................................................... 35 Figure 26. Pre-PFMS and PFMS scenarios Breeding pair results of time 50 Barred Owl model ................ 35 Figure 27. BTN Breeding pair results of time 100 Barred Owl model ......................................................... 36 Figure 28. Pre-PFMS and PFMS scenarios Breeding pair results of time 100 Barred Owl model .............. 36 Figure 29. BTN Breeding pair results of time 200 Barred Owl model ......................................................... 37 Figure 30. Pre-PFMS and PFMS scenarios Breeding pair results of time 200 Barred Owl model .............. 37 Figure 31. Comparison of year 0 and 10 for the two scenarios. PFMS is on left, removed harvest from Virginia Hills on right. .................................................................................................................................. 39 Figure 32. Comparison of year 20 and 30 for the two scenarios. PFMS is on left, removed harvest from Virginia Hills on right. .................................................................................................................................. 40 Figure 33. W13 conifer harvest volume for the three scenarios. .............................................................. 42 Figure 34. W13 deciduous harvest volume for the three scenarios. ......................................................... 42 Figure 35. W11 conifer harvest volume for the three scenarios. .............................................................. 43 Figure 36. W11 deciduous harvest volume for the three scenarios. ......................................................... 43 Figure 37. Area available for harvest by BCG and decade, demonstrating growing stock low point ......... 45

List of Figures v


Figure 38. W13 conifer growing stock for Scenario 63005, demonstrating growing stock low point ....... 46 Figure 39. Scenario 60008 - W13 harvest area by decade when most strata have an MHA of 65 years old .................................................................................................................................................................... 50 Figure 40. Scenario 63005 - W13 harvest area, maximum 5,000 ha of RSA stands per decade ................ 51 Figure 41. Scenario 63005 - Comparison of conifer area available for harvest at the start of the decade and the actual harvested area .................................................................................................................... 51 Figure 42. Baseline and PFMS conifer harvest level for FMU W11 ........................................................... 53 Figure 43. Baseline and PFMS conifer harvest level for FMU W13 ........................................................... 53 Figure 44. RSA conifer harvest level for FMU W11 .................................................................................... 54 Figure 45. RSA conifer harvest level for FMU W13 .................................................................................... 54 Figure 46. MHA and RSA conifer harvest level for FMU W11 .................................................................... 55 Figure 47. MHA and RSA conifer harvest level for FMU W13 .................................................................... 55

Overview 1


1. Overview

As part of the DFMP development, forecasting was conducted to aid in development of the Preferred Forest Management Scenario (PFMS). The forecasting process involved evaluation of management alternatives and selection of a PFMS, with an associated AAC. This section describes the process used to derive the PFMS and determine the associated AAC.

The Plan Development Team (PDT) created the PFMS with the support of computer based forecasting. Forecasting models the management actions to be undertaken in detail for the next 20 years and with a lower level of detail for the following 180 years. Forecasting also predicts, under the proposed management actions, what the condition of the forest will be over the same 200-year planning horizon. Computer based modeling is part of the adaptive forest management process that is required for sustainable forest management and was undertaken so that the proposed forest management actions did not compromise forest sustainability.

This annex describes the forecasting process and sensitivity analysis undertaken for the development of the 2017-2027 Detailed Forest Management Plan (DFMP). It details the forecasting assumptions, methods and results, the knowledge gained, and the application of the results leading up to the development of the PFMS. A description of the data files supporting the TSA and the PFMS is included here. The PFMS is described in Chapter 6 – Preferred Forest Management Scenario.

1.1 Indicators The Canadian Standards Association defines a forecast as: “an explicit statement of the expected future condition of an indicator”. Forecasting in the context of the 2017-2027 DFMP, is the process that creates the predicted future condition of DFMP indicators. Indicators describe the condition of the forest, the products derived from the forest and the values present in the forest.

Examples of indicators are patches of old growth forest and the amount of timber harvested. These example indicators are non-complementary in that increasing levels of old growth will decrease the amount of timber that can be harvested. This highlights the essence of forecasting within the forest

2 Overview


management planning context; it is necessary to make tradeoffs between the desired amounts of each indicator in order to achieve a preferred scenario. Usually it is not possible to obtain everything that is desired and often undesirable outcomes are predicted for some of the indicators no matter what actions are proposed. Forecasting is a complex process and was used by the forest managers and the PDT to predict the outcomes of specific forest management activities and to assist the managers in deciding what activities and their levels should be proposed in a PFMS that best meets forest management objectives.

Forecasting Methods 3


2. Forecasting Methods

Forecasting is a complex process requiring numerous inputs and assumptions. This section describes the 2017-2027 DFMP forecasting process including a description of the modeling tools, inputs, assumptions, outcomes, and tradeoffs required to develop the Preferred Forest Management Scenario (PFMS).

2.1 Objective The objective of forecasting is to create a reasonable prediction of the forest attributes and non timber values using timber harvesting as the main agent of change, which leads to the creation of the PFMS that best achieves the forest management objectives.

2.2 Process

Developing a forecast involves combining data, in the form of spatial landbases and yield curves, with management assumptions into a coherent spatial model that is capable of both fine and coarse scale analysis. Following a structured progressive approach, scenarios were developed to explore the impacts of the options available, guided by the existing operability limitations and the 2006 Alberta Forest Management Planning Standard, Version 4.1 (Planning Standard) specifications that balance social, economic and ecological forest management objectives.

The development of landbases and yield curves, the refinement of indicators and goals, and the process of evaluating scenario output to derive new scenarios are all iterative processes and are interdependent. Figure 1 outlines the process involved in developing the PFMS. Any one of the cycles shown can be repeated as many times as necessary to ensure the best possible solution is achieved.

4 Forecasting Methods


Figure 1. Forecasting planning process

2.2.1 Development of the Model Dynamics The forecasting process begins with the development of the model inputs; the landbase, yield curves, and initial indicators and goals. These inputs were then used to construct the model within the forecasting tools framework. Model results were analyzed to ensure the indicators correctly represent

PFMS Development

Development of Model Dynamics

Scenario

Forecast

Alter Goals or

Indicators

Indicators

Acceptable?

Forest

Assessments

No

Yes

Assessments

Acceptable?No

Yes

PFMS

Landbase &

Yield curve

Datasets

Indicators &

Goals

Forecast Tool

(Woodstock or

Patchworks)

Scenario Development

Change Target

Values or Weights,

or refine Preblocks

Change Model

Parameters



the metrics to be evaluated and that the model dynamics are realistic. If any metric or assumption was deemed to be inaccurate or insufficient, it was re-worked and the model was rebuilt.

2.2.2 Scenario Development Scenarios were developed to test the implications of specific management strategies. Each scenario’s impact on the forest and its associated values was examined, as well as differences between scenarios. By altering the types, locations and levels of management actions in a scenario, or by altering the desired future forest condition, the PDT was able to determine the long term forest dynamics, desirable activities and assess the forest management tradeoffs.

Scenarios were developed within a structured process. The PDT identified forest management issues that could be addressed through forecasting. Scenarios were created to address identified issues and results were summarized in issue documents for the PDT to review and action. Through this process, the primary trade-off decisions such as old growth level and timber yield assumptions were resolved.

2.2.3 PFMS Development After the management issues were resolved, a series of scenarios were generated to work towards the PFMS. These scenarios were primarily focused on changes to the Spatial Harvest Sequence (SHS) to ensure operability and that the proposed harvest blocks met the social and ecological objectives.

2.3 Limitations

There are limitations in any forecasting process. The primary limitations related to the development of the PFMS are the generalization of inputs and the inability to directly address stochastic events, such as wildfire, in the timber supply models.

2.3.1 Landbase

The landbase is built with the best information available, but it is a snapshot of the current status of many attributes such as forest fires, roads, towns, and oil and gas activity. Future changes to the landbase for fires, landuse or other industrial infrastructure development were not incorporated into the modeling.

2.3.2 Yield curves

Timber yield curves were created by projecting the growth of individual plot-level data using the GYPSY model and averaging the projections for each yield strata. Adjustments were applied to the average projections to address bias and growth elements such as stand decay and breakup which are not accounted for in GYPSY. The resulting yield curves represent averages across the landscape. While this approach produces reasonable results for strategic planning, the variation between individual polygons of the same strata can be large. This is especially true of the incidental volume predictions. Large variations will be observed in recovered individual block level volumes compared to volumes predicted from the yield curves. However, over large areas the harvested volumes will be close to the predicted volumes.



2.3.3 Stochastic events

Stochastic, or random, events such as fire or insect outbreaks are not explicitly modeled in this TSA process. Stochastic events by their very nature are unpredictable and less predictable when spatial location is required as it is for the development of the SHS. For these reasons, stochastic events were excluded from the forecasting. The DFMP process addresses stochastic events through re-planning when unplanned events cumulatively impact 2.5% or more of the net landbase.

2.4 Modeling Tools

Two forecasting modeling tools were used for this analysis: Woodstock for non-spatial analysis and Patchworks for spatial analysis. The Patchworks interface allows the conversion of Woodstock models into Patchworks format, permitting common datasets to be used between scenarios and to ensure continuity and meaningful comparison of results.

Woodstock was used for strategic analysis to test and compare non-spatial management assumptions. Patchworks was used to address spatial issues and to develop the PFMS. Where possible, sensitivity analyses were completed using Woodstock because Woodstock optimization provides the maximum solution possible, so there is no difference attributable to a sub optimal solution and secondly, Woodstock is much faster compared with Patchworks.

The recommended harvest level, associated SHS and the treatment regime were derived from the PFMS created with a Patchworks scenario.

2.4.1 Woodstock

Woodstock, version 2016.06.1, is a strategic forest estate-modeling tool developed and serviced by Remsoft (Remsoft, 2006). It was used for strategic analysis of timber supply and comparisons of alternative strategies and formulations. This strategic analysis provided insight for the resolution of specific issues including growing stock, minimum harvest age and harvest flow.

Woodstock is completely non-spatial; every unique type is rolled up into forest classes (TSA themes by age class). The model applies treatments to all or a portion of that unique forest class. Post-treatment transitions can be one-to-many relationships defined as percentages. The optimizer selects the optimal combination of treatments throughout the entire planning horizon to solve the objective function.

Woodstock can be formulated as either basic optimization, where there is one modeling objective with rigid constraints or goal programming, where the modeling objective is to minimize deviations from a goal.

Goal programming requires the identification of a weighting, which is the penalty for deviating from the goal, to allow the model to rank the goals. Typically, a high weighting results in a small deviation from the goal.



For this analysis, basic optimization was the only Woodstock formulation used. The modeling objective was to maximize primary coniferous and deciduous harvest volume subject to constraints such as even flow harvest volume and minimum ending growing stock.

A structured, progressive approach was used in the development and analysis of Woodstock scenarios. Increasing levels of constraints were applied in successive scenarios to meet forest management objectives and to answer specific management questions and issues. The end result of the Woodstock stage of modeling were scenarios that met all of the non-spatial key objectives. For this analysis, Woodstock runs and reports in 5-year periods.

2.4.1.1 Linear Programming

Woodstock uses a mathematical technique called linear programming to quickly determine the optimum answer to the management assumptions. Linear programming is a commonly used mathematical tool for forest management because of its speed and accuracy in finding the ‘optimal’ solution with regards to a single objective and several constraints. Davis et al. (2001) describes linear programming as: “Problems that are linear with respect to the relationships between the decision variables can be solved by a technique called linear programming. By linear, we mean the operators are restricted to plus or minus.”

The linear programming solver used in this analysis is Mosek version 4.0.

2.4.2 Patchworks

Patchworks, version 1.3, is a spatially-explicit forest estate modeling tool developed and serviced by Spatial Planning Systems. It is designed to provide the user with operational-scale decision-making capacity within a strategic analytical environment. Trade-off analysis of alternative operational decisions are quickly determined and visually displayed.

Patchworks operates at the polygon level. In Patchworks terminology, polygons are the smallest element, which in this case, are the subdivided Alberta Vegetation Inventory (AVI) stands in the modeling landbase. The treatments applied to each polygon are an all or nothing decision for the model. There is only one post-treatment transition for each polygon. When Patchworks operates, one or more polygons adjacent to each other that meet specific criteria can be combined to form “patches”. The modeling landbase is comprised of small polygons to allow for more options in creating patches.

The tool is fully spatial through time and the impact on an adjacent polygon 200 years into the future is considered in the first year of the simulation. Patchworks decision space can be thought of as a matrix consisting of each polygon and each potential outcome for every time slice in the planning horizon.

Patchworks is a heuristic model that provides close to an optimal solutions for the defined goals or targets (similar to the goal-programming in Woodstock) by applying simulated annealing and generic algorithms. In this analysis, a variety of goals were included such as harvest levels, minimum growing stock levels, minimum seral stage areas, maximum block size and range of regeneration patch sizes by period.



Goals were represented by different features (elements present on the landscape) or products (something produced from the landscape, e.g. cubic meters of timber or hectares of habitat) and multiplied by weighting factors, which ranked the importance and contribution of each feature or product towards the modeling objective. The weighting does not represent the relative importance of each goal but rather represents the weighting required to achieve an acceptable solution.

Patchworks solves in annual periods, however, it was set up to model and report in 40 five-year periods. There was a two-year period at the start of the simulation to advance the landbase to the beginning of the planning horizon, May 1, 2017. The model covers the entire 200 year planning horizon, beginning in 2017 and ending in 2217. Patchworks scenarios were developed from Woodstock, to ensure identical assumptions, including landbase, yield curves, treatments and responses. This combination of Woodstock and Patchworks has been successfully applied in previous DFMPs approved in Alberta for over a decade, including Millar Western’s 2007-2016 DFMP.

ECA 9


3. ECA

As part of the DFMP process, GoA commonly conducts watershed assessments on the SHS using the Equivalent Clearcut Area model (ECA). The ECA model predicts the change in water runoff due to changes in the vegetation condition within the watershed. For DFMPs, the GoA provides an option of incorporating ECA into the timber supply models to provide faster feedback on ECA results. For the 2017-2027 DFMP, Millar Western chose to build ECA into Patchworks. The GoA did not complete an ECA assessment on the SHS but provided guidance and was involved in the process to calibrate and test the ECA model implementation in Patchworks. This section describes the steps that were used to conduct a watershed assessment with Patchworks.

3.1 Description GoA has developed a watershed assessment application that uses the net landbase, the SHS and a watershed layer as spatial input along with non-recovered percent curves to calculate watershed impact hazard over time as described in the Watershed Hazard Assessment (WHA) Application with Data Preparation documentation. The WHA application assesses the level of impact each watershed undergoes over time with the current SHS plan. When a watershed is disturbed, it is possible to calculate the percent of the watershed that is not yet recovered. The non-recovered percent of the watershed is equated to Equivalent Clearcut Area (ECA). This calculation is based on yield curves. (GOA Document: how to calculate Equivalent Clearcut Area).

The Hazard Levels are assigned as:

Low - < 30% Non-recovered percent

Medium – 30-50% Non-recovered percent

High - > 50% Non-recovered percent

10 ECA


The Patchworks modeling environment uses “yield” curves to assign values to indicators that change over time. In this case, ECA or non-recovered percent of watershed is the indicator of watershed hazard. It is assigned to each watershed based on the cumulative impact of each stand that is within a watershed boundary for each time period of interest.

The GOA WHA application uses the net landbase with the SHS as the principle disturbance layer to assess hazard over time. Disturbances are classified into 2 classes: permanent and recoverable. Non-recovered percents (or ECA) are assigned a value from 0 to 1 where 0 is fully recovered and 1 is recently disturbed. A permanent disturbance is given a non-recovered percent value of 1 from its date of disturbance onward.

For recoverable disturbances, the non-recovered percents are assigned through the ECA value as described in the GoA documentation: How to Calculate Equivalent Clearcut Area.

3.2 Development of Stand Equivalent Clearcut Area (ECA) curves

1. Calculate the Periodic Annual Increment (PAI) from the Yield Curves

2. Full recovery (ECAs = 0) when age of stand is greater than or equal to maximum PAI (PAImax)

3. Calculate the ECAs for stand age (i ) less than PAImax:

max

max

PAI

PAIPAIECA ii

s

All polygons within the landbase are assigned an ECA value. Permanent disturbances such as pipelines, roads and other anthropogenic non-vegetated areas are assigned a static value of 1; non forested stands that have not been disturbed for a long time such as natural grass and scrubland are given a static value of 0, while forested areas are assigned a non-recovered percent based on time since disturbance.

The non-recovered percent or ECA values are based on stand age and are yield curves. An ECA value is calculated for each yield strata in the landbase for each stand age. The non-recovered percent yield curve is used to assign indicator values over time in Patchworks in the same fashion as volume yield curves. An example curve showing the volume and resulting ECA curve for the PL natural strata in FMU W13 is shown in Figure 2. In this example, the ECA curve reaches zero at age 60. For most volume curve types, PAI is reached between the ages of 50 and 70 (Figure 3).

ECA 11


Figure 2. Example of ECA curve using PL natural curve for FMU W13

Figure 3. All ECA curves for all strata in both FMU’s.

Reporting for watershed ECA values is done by watershed and for all watersheds in total. The total ECA value (∑(curve value * stand area)) for each watershed is divided by the total area of each watershed.

12 ECA


The result is a percentage, where lower percent represent watersheds with older forest, and larger percentages represent watersheds with young forests. These percentages are then classified into three classes:

1. Less than 30%; 2. Equal or greater than 30% and less than 50%; or 3. Equal or greater than 50%.

These categories are used to compare between scenarios and evaluate watershed condition over time.

3.3 GoA ECA Assessment The GoA WHA required inputs are:

1. Watershed layer with watershed ID; 2. Disturbance spatial layer – originating with net Landbase and SHS; and 3. Non-recovered percent lookup table (i.e. yield curve) – with Lookup key based on yield strata

and region where required with values by stand age. A large part of the assessment for the GoA application is compiling the disturbance dataset. An overview for the GoA ECA Assessment is shown in Figure 4.

ECA 13


Figure 4. GOA Watershed Assessment Data Preparation

The watershed layer is incorporated into the net landbase. Watershed ID number is carried in the net landbase so all data can be summarized by watershed ID.

The disturbance layer is the net landbase with the required fields assigned. Within the WHA application, the non-recovered percent lookup table is used during the iterative process to assign values over time. The disturbance layer includes the disturbance date which is used with the lookup table.

3.4 Patchworks ECA Assessment The data preparation for the ECA assessment built into Patchworks is shown in Figure 5. The non-recovered percents are yield curves that are incorporated into Patchworks. The watershed boundaries are incorporated into the TSA landbase and used in Patchworks with an ECA value output created for each polygon which is exported for each period of interest.

Watershed

Polygons

Disturbance

Polygons

Watershed

Compilation

Disturbance

Compilation

Watershed Risk

Assessment

Application

Assessment

Tables

Non-recoverable

Percent Look-up

Table

Net-landbase

14 ECA


Figure 5. MWFP Watershed Assessment Data Preparation

3.5 Outputs The GOA WHA application produces two main output tables:

1. Watershed Hazard Assessment table with a non-recovered percent value for each watershed for each assessment period and

2. Watershed Maximum Assessment table with the maximum non-recovered percent for each watershed with its associated assessment period.

These tables can then be used to link to the watershed spatial layer and be symbolized based on non-recovered percent. The tables are used to assess the critical period for each watershed.

3.6 Differences between GOA WHA Patchworks Assessment The main difference in watershed hazard assessment is that the GoA application requires and uses fields assigned to the disturbance layer and the recovery lookup table whereas the Patchworks model uses a set of yield curves. The GoA application does the calculations and outputs 2 main summary tables for non-recovered percents by watershed. The Patchworks model uses the yield curve to assign non-recovered percents to each polygon and the outputs are reported by polygon for each period of interest. The results are then summarized by watershed.

Disturbance Compilation

Net Landbase includes

Watershed ID

ECA Yield Curve

Patchworks Shapefile

Oracle Assessment

Tables

Patchworks Model

Timber Supply Analysis 15


4. Timber Supply Analysis

This section outlines the scenarios and sensitivity analysis examined in the lead up to the PFMS. It includes scenarios completed in both Woodstock and in Patchworks. Sensitivity analyses were completed using different inputs assumptions such as landbase versions. For this reason, these results are not intended for direction comparison with the PFMS, but rather should be reviewed with the appropriate context. Appendix I contains more details about the sensitivity analyses and provides clarification to questions the GoA asked after initial DFMP submission.

4.1 Harvest Volume Analysis The harvest volume forecasting objectives in the 2017-2027 DFMP were built for a combined coniferous and deciduous landbase and were based upon maximizing the total conifer and deciduous harvest volumes with even flow and non-declining yield constraints on both the conifer and deciduous total volumes. The W13 and W11 landbases were to be run as separate entities, each with their own distinct AAC. To aid in determining the harvest volume objectives for the new DFMP, twenty four baseline TSA scenarios were completed which looked at six different objective functions, two FMUS, and two sets of model constraints.

4.1.1 Methodology Six main objective functions, two sets of yield curves and two sets of constraints were combined to produce 24 baseline Woodstock scenarios, as aspatial scenarios (Table 1). All scenarios were based on landbase LB4_v1_20160810 and by FMU yield curves (MWFP_TSA_curves_20160608).

The six objective functions considered were:

1. Maximize total conifer harvest level;

2. Maximize primary conifer harvest level;

3. Maximize total deciduous harvest level;

16 Timber Supply Analysis


4. Maximize primary deciduous harvest level;

5. Maximize total conifer and deciduous harvest level; and

6. Maximize primary conifer and deciduous harvest level.

FMUs W13 and W11 were run as separate Sustained Yield Units (SYU). The two sets of constraints applied were:

1. Even flow harvest constraint on the objective function for the 200 year planning horizon; and

2. Even flow harvest with non-declining operable growing stock yield (NDY) from 150 years onward.

Table 1. Baseline Scenario Matrix

4.1.2 Harvest Volume Summary Comparison between scenarios relies upon which summary values are of interest. When the objective function changes and different harvest volumes are maximized and constrained then the comparison between total harvest volumes and primary and secondary volumes becomes more complex.

It is difficult to make a straight comparison across the board between the 24 scenarios as the objective function changes between considering a combined conifer and deciduous landbase to a divided landbase as well as changing between maximizing conifer versus deciduous volume. When scenarios based on total harvest volumes (combining primary and secondary volumes to simulate a combined landbase) are run, then comparisons are best made between combined primary and secondary totals. On the other hand, when scenarios based on divided landbase that maximize a primary volume are run, then comparisons are best made between primary and secondary volumes.

Traditional harvest volume summaries are presented in which the primary conifer and deciduous harvest volumes display the 200 year average and the secondary volumes represent the 20 year average. Those summaries were based on an objective function maximizing both conifer and deciduous primary volume with conifer and deciduous primary volume even flow constraints. In contrast, some of the baseline scenarios maximize only conifer or deciduous primary volume as well as the scenarios that maximize both conifer and deciduous primary and / or total harvest volumes.

EF EF, NDY EF EF, NDY

Maximize Total Conifer Volume W1001 W1013 W1007 W1019

Maximize Primary Conifer Volume W1002 W1014 W1008 W1020

Maximize Total Deciduous Volume W1003 W1015 W1009 W1021

Maximize Primary Deciduous Volume W1004 W1016 W1010 W1022

Maximize Total Harvest Volume W1005 W1017 W1011 W1023

Maximize Primary Harvest Volume W1006 W1018 W1012 W1024

W13 W11Objective Function



For this reason, four main sets of harvest volume summaries are presented in the summary table (Table 2). The first set represents the 200 year average total volumes (combined primary and secondary); the second set displays the 200 year average primary volumes; thirdly the 200 year average secondary volumes are shown.

4.1.3 Discussion The baseline scenarios are very basic in nature in the sense that they include an objective function and only 1 or more constraints, i.e. an even flow constraint and for runs W1013 to W1024 a non-declining growing stock (NDY) constraint. They provide an overview of harvest volume differences when the objective function or constraints are changed without being clouded by other model constraints that may play out differently for different main objective functions. It is important to note that the numbers produced in these baseline scenarios are not realistic harvest volumes in the sense that they are not spatial nor do they consider any other forest value or VOIT other than an even flow and NDY constraint.

There is only a modest change moving from even flow constraints to an Even Flow with non-declining growing stock constraint set between most scenarios. Scenarios W1005, W1011, W1017, and W1023 represent the objective function that is used in the 2017 DFMP (maximize total harvest volume). The difference between the EF and the EF+NDY constraints is 8,000 m³/yr conifer, -4,000 m³/yr deciduous in W13 and less than 500 m³/yr for both conifer and deciduous in W11.

4.1.4 Assumptions The primary assumptions made were:

Non-spatial modeling environment;

Same landbase process as DFMP based on landbase version 8 and corresponding yield curves; and

Basic model objectives and constraints meant for initial volume comparisons and not inclusive of DFMP VOIT constraints.


Millar Western Forest Products Ltd.

2017-2027 DFMP – Annex VI TSA

Table 2. Harvest volumes by scenario

Conifer Deciduous Conifer Deciduous Conifer Deciduous

W1001 Total Conifer Volume EF 2,646,194 792,743 1,961,682 207,682 684,511 585,061

W1002 Primary Conifer Volume EF 1,812,161 501,844 1,812,161 0 0 501,844

W1003 Total Deciduous Volume EF 2,675,916 824,028 1,960,169 226,195 715,746 597,833

W1004 Primary Deciduous Volume EF 703,254 229,645 0 229,645 703,254 0

W1005 Total Harvest Volume EF 2,493,273 822,675 1,830,788 219,734 662,485 602,941

W1006 Primary Harvest Volume EF 2,515,415 733,757 1,812,161 229,645 703,254 504,113

W1007 Total Conifer Volume EF 227,072 93,857 86,751 45,786 140,321 48,071

W1008 Primary Conifer Volume EF 88,551 48,991 88,551 0 0 48,991

W1009 Total Deciduous Volume EF 231,034 95,520 91,517 47,142 139,517 48,378

W1010 Primary Deciduous Volume EF 137,616 47,416 0 47,416 137,616 0

W1011 Total Harvest Volume EF 222,521 93,753 85,623 45,585 136,897 48,169

W1012 Primary Harvest Volume EF 226,167 96,407 88,551 47,416 137,616 48,991

W1013 Total Conifer Volume EF, NDY 2,640,327 782,606 1,968,242 201,122 672,085 581,483

W1014 Primary Conifer Volume EF, NDY 1,812,161 511,482 1,812,161 0 0 511,482

W1015 Total Deciduous Volume EF, NDY 2,653,823 802,152 1,975,215 215,966 678,607 586,186

W1016 Primary Deciduous Volume EF, NDY 686,003 226,923 0 226,923 686,003 0

W1017 Total Harvest Volume EF, NDY 2,485,262 827,051 1,831,790 217,762 653,472 609,290

W1018 Primary Harvest Volume EF, NDY 2,496,410 736,579 1,812,161 226,695 684,249 509,884

W1019 Total Conifer Volume EF, NDY 225,365 93,712 86,418 45,960 138,947 47,752

W1020 Primary Conifer Volume EF, NDY 88,391 48,517 88,391 0 0 48,517

W1021 Total Deciduous Volume EF, NDY 225,034 92,170 89,243 45,209 135,791 46,961

W1022 Primary Deciduous Volume EF, NDY 133,299 45,001 0 45,001 133,299 0

W1023 Total Harvest Volume EF, NDY 222,013 93,340 84,840 46,147 137,173 47,194

W1024 Primary Harvest Volume EF, NDY 221,687 93,518 88,388 45,001 133,299 48,516

Total Harvest Level 200 Year

Average (m³/yr)

Primary Harvest Level 200 Year

Average (m³/yr)

Secondary Harvest Level 200 Year

Average (m³/yr)ConstraintObjective MaximizeName

W13

W11

W13

W11

FMU



4.2 Seral Stage Sensitivity Analysis Under the Alberta Forest Management Planning Standard maintaining a certain level of old and old plus mature forest in the managed landbase is a required coarse indicator for the value of conservation biodiversity. This analysis was prepared to aid in determining a desirable target level of old growth forest on the managed landbase. Only W13 results are presented here, but the same seral stage targets were applied in FMU W11.

4.2.1 Methodology The definition of seral stage for a given landscape is determined through biology of the stratum and management objectives. In MWFP’s case, there are many definitions which could be used. An analysis to test the effects of identified seral stage (SS) definitions on their ability to meet old age targets at different thresholds was performed.

A total of 5 definitions have been examined (Table 3). Each of the 5 SS definitions were tested on the W13 landbase. Each definition was tested with a no harvest scenario, a maximize harvest scenario with no old SS target, and 2 other maximize harvest scenarios with a 5 and 10% old SS target. The outcome of the analysis was to report on the percent area in old SS across definitions and the net effect of old age targets on AAC.

To support the analysis, seral stages had to be collapsed so that a direct comparison could be made. Figure 6 displays how the seral stage thresholds from the different organizations was collapsed for analysis.

The Woodstock objective function was set to maximize total volume harvested while maintaining even flow conifer and deciduous volumes and ensuring a non-declining growing stock in the last 50 years of the model for harvest scenarios.

BW and PB strata were removed from the AW strata where necessary. DU is considered as AS in the analysis where there is no DU definition.

Table 4 identifies the scenario matrix.

Table 3. Interpretation of seral stage by organization to create a comparable dataset structure

Al-Pac HWP GoA MWFP 2007 MWFP 2017

Clearing

Regenerated

Young Young

Immature Immature

Early Mature

Late Mature

Early Old Growth

Late Old Growth

Comparison

Seral Stage

Seral Stage Roll-up by Organization

Young Young Regenerated

Old Old

Young

Immature

Mature

Old

Young

Old

Regenerated

PoleImmature

Mature Mature Mature Mature

Overmature




Table 4. Scenario matrix for analysis of seral stage definitions

Seral Stage

DefinitionScenario Objective Function

W1051 No Harvest

W1052 Maximize Harvest with no old forest target

W1053 Maximize harvest with > 5% old active forest


W1055 No Harvest




W1059 No Harvest




W1063 No Harvest




W1067 No Harvest




Alpac

HWP

2007 MWFP

FMP

2017 MWFP

FMP

GoA



Figure 6. Seral stage definitions used in analysis

4.2.2 Harvest Volume Summary As observed in Table 5, the net effect of implementing the various seral stage definitions is correlated to the definition and the level of old forest maintenance. The effect is different for the coniferous and the deciduous components of the cut.

Al-Pac definitions have the least amount of effect on AAC while the GoA definition is the most limiting. The 2007 and 2017 MWFP seral stage definitions have, relatively, the same net effect on AAC, while the HWP definition offers a little less volume than the 2007 and 2017 MWFP definitions.

0 50 100 150 200 250 300

Pl

Sx

MW

Aw

Age

Young Immature Mature Overmature

0 50 100 150 200 250 300

Pl

Sw

Sb

MW

Aw

Age

Young Pole Early Mature Late Mature Old

0 50 100 150 200 250 300

Pl

Sw

Sb_Low

SwSb…

Pl_Dec

Pb_Con

Aw_S…

Aw_Pl

Bw

Pb

Aw

Age

Clearing Regenerated Young Immature Mature Old

0 50 100 150 200 250 300

C_Pl

C_Sw

C_Sb

CD_Pl

CD_Sw

DC_Pl

DC_Sw

D_Pb

D_Aw

Age

Regenerated Young Mature Early Old Growth Late Old Growth

Al-Pac

HWP

MWFP 2007 FMP

0 50 100 150 200 250 300

SW

SB

PL

SA

PA

AS

AP

DU

AW

Age

Regenerated Young Immature Mature Old

GoA Lower Foothills

MWFP 2017 FMP




Table 5. Old forest targets effect on harvest volume by seral stage definition

4.2.3 Old Forest Summary Al-Pac’s SS definitions result in the most active area being designated as old. In the no harvest scenarios (Figure 7), all but the Al-Pac definition start at around the same point (0-2%). From there, scenarios have a range of response to no harvesting with a general difference between scenarios of approximately 10%.

In the maximize harvest scenarios (Figure 8) where no old SS target is defined, the old SS has an initial gain in area in all MWFP and GoA scenarios. This is short lived as we see the area in old SS drop to zero 40-50 years out. HWP has a very restrictive definition as for approximately 140 years there is no area in old SS. It is hypothesized that the initial increase in old SS is due to the even flow constraint on AAC and the impending growing stock gap within that 40-50 year window.

In both the maximize harvest maintaining 5 and 10% old SS, it takes a number of years to attain these targets. In Figure 9 it requires 10 years for the MWFP 2017 proposed SS definition to reach 5% while it takes 20 years for the others. In Figure 10, the scenarios require up to 25 years to reach the 10% target. Generally, the Al-Pac SS definitions do not follow the trends of the other scenarios.

Confier Deciduous

W1051 No Harvest 0 0

W1052 Maximize Harvest with no old forest target 394,513 240,097

W1053 Maximize harvest with > 5% old active forest 394,020 (<1%) 239,920 (<1%)

W1054 Maximize harvest with > 10% old active forest 382,539 (3%) 236,099 (2%)

















Harvest Volumes (m³/yr) with

Change From No Old Target

Alpac

HWP

2007 MWFP

FMP

2017 MWFP

FMP

GoA

Objective FunctionScenarioSeral Stage

Definition



Figure 7. Percent area of old seral stage in the active landbase for the no-harvest scenario across the 5 seral stage definitions

Figure 8. Percent area of old seral stage in the active landbase for the maximize harvest scenario with no seral stage targets across the 5 seral stage definitions

Figure 9. Percent area of old seral stage in the active landbase for the maximize harvest scenario while maintaining at least 5% old seral stage across the 5 seral stage definitions

0%

20%

40%

60%

80%

100%

2016 2036 2056 2076 2096 2116 2136 2156 2176 2196 2216

Al-Pac HWP MWFP 2007 MWFP 2017 GoA

0%

5%

10%

15%

20%

25%

30%

2016 2036 2056 2076 2096 2116 2136 2156 2176 2196 2216


0%

5%

10%

15%

20%

25%

30%

2016 2036 2056 2076 2096 2116 2136 2156 2176 2196 2216





Figure 10. Percent area of old seral stage in the active landbase for the maximize harvest scenario while maintaining at least 10% of old seral stage across the 5 seral stage definitions

4.2.4 Discussion The net effect of applying the proposed 2017-2027 DFMP seral stage definitions is marginally different when compared to that of the past MWFP DFMP or the GoA definition. When comparing the effect of the various levels of old SS targets, at 5% there is a cost of approximately 3% of the AAC. When the target moves to 10% is the AAC cost increases to approximately 9%.

4.3 Barred Owl The barred owl model, specifically in regards to the breeding pairs output, has yielded lower than desired values when compared to current state modeled conditions. Three scenarios were completed in a sensitivity analysis to test the barred owl habitat assumptions under different situations to determine the impacts of each scenario.

1. Scenario 54002 – “back to natural” (BTN) scenario; 2. Scenario 64001 – Pre-PFMS - Similar to the PFMS without barred owl patch targets; and 3. Scenario 64006 – PFMS scenario, using patch targets to modify the spatial arrangement of

habitat features.

The barred owl model was constructed by the GoA based on Mike Russell’s thesis. The model used for this analysis was modified from the original GoA model using the following three agreed upon updates:

The thesis used 30 as the age cut-off for the DISTOPEN metric. Model updated to use the same age as the cut-off for the forested vs. non-forested polygons for the ATOP calculation;

The landbase was dissolved into forested and open polygons, this implements the intent of the thesis ensuring that ATOP values will be within the designed range of the thesis statistical analysis; and

Removed two lines of code which identify MOD1 records with “CC” attributes. In the future, time periods, the stands are identified by the age field.

4.3.1 Compartments of concern Several compartments were identified as being primary habitat for Barred Owl. These are primary and secondary compartments of concern. They are:

0%

5%

10%

15%

20%

25%

30%

2016 2036 2056 2076 2096 2116 2136 2156 2176 2196 2216




Primary Compartments o Whitecourt Mountain o Hardluck Creek o Paddle River o Groat Creek o Goodwin Lake o Bessie Creek

Secondary Compartments: o Sand Hills o Robison o Long End Lake o Klondike o South Freeman o North Freeman o Alexis Reserve

These compartments are the focus for the Barred owl analysis, and are reported separately for the majority of the metrics.

Figure 11. Map of primary and secondary compartments of concern.

4.3.2 Barred Owl Model Results Table 6, Table 7, and Table 8 summarize the model results from the three difference scenarios: the back to natural scenario – 54002, the pre-PFMS scenario – 64001, and the PFMS scenario, respectively.




Table 6. Barred Owl model results for back to natural scenario – 54002

Time Period

Breeding Pair Model Raster

Values

Potential Breeding

Pairs

Percent Change from

time 0 of potential

breeding pairs

RSF Values

Percent Change

from time 0 of RSF values

0 4,687,585 187 - 0.1113 -

10 5,819,681 232 24.2% 0.1182 6.1%

20 3,926,958 157 -16.2% 0.1099 -1.3%

50 3,095,575 123 -34.0% 0.1068 -4.1%

100 2,936,374 117 -37.4% 0.1071 -3.8%

200 2,034,235 81 -56.6% 0.1020 -8.4%

Table 7. Barred Owl model results for pre-PFMS scenario - 64001

Time Period


Values

Potential Breeding

Pairs

Percent Change from

time 0 of potential

breeding pairs

RSF Values

Percent Change


0 5,094,432 203 - 0.1138 -

10 3,679,312 147 -27.8% 0.1041 -8.6%

20 4,298,427 172 -15.6% 0.1080 -5.1%

50 2,617,972 104 -48.6% 0.1063 -6.6%

100 2,794,862 111 -45.1% 0.1086 -4.6%

200 2,281,881 91 -55.2% 0.1070 -6.0%



Table 8. Barred Owl model results for PFMS scenario - 64006

Time Period


Values

Potential Breeding

Pairs

Percent Change from

time 0 of potential

breeding pairs

RSF Values

Percent Change


0 4,581,451 183

0.1105 -

10 3,767,757 150 -17.8% 0.1045 -5.4%

20 4,333,805 173 -5.4% 0.1081 -2.1%

50 2,777,173 111 -39.4% 0.1064 -3.7%

100 3,007,130 120 -34.4% 0.1082 -2.1%

200 2,352,637 94 -48.6% 0.1071 -3.0%

The Pre-PFMS and PFMS scenarios have similar trends in both RSF (Figure 12) and breeding pairs (Figure 13). Both of these scenarios have different trends than the BTN scenario, but all of the scenarios have a decreasing trend in breeding pairs even as the RSF is relatively stable. The patch targets used in the PFMS scenario result in a slight increase in the breeding pairs over the Pre-PFMS scenario where the patch targets are not used, even though the amount of areas in patches older than 90 years old is about 25% higher than the Pre-PFMS scenario (Figure 14)

Figure 12. Comparison of barred owl RSF values for all three scenarios

0

0.05

0.1

0.15

0.2

0 10 20 50 100 200

RSF

Years

BTN Pre-PFMS PFMS




Figure 13. Comparison of breeding pair estimates for all three scenarios

Figure 14. Comparison of barred owl patch targets for Pre-PFMS and PFMS scenarios

4.3.2.1 Primary and secondary compartments

For the PFMS scenario, the changes in breeding pairs and RSF over time is presented in Table 9 for primary compartments and in Table 10 for the secondary compartments. These levels decrease more in the primary compartments, while they are fairly stable in the secondary compartments.

0

50

100

150

200

250

0 10 20 50 100 200

Bre

ed

ing

Pai

rs

Years

BTN Pre-PFMS PFMS

0

10

20

30

40

50

60

70

80

2017 2037 2057 2077 2097 2117 2137 2157 2177 2197 2217Pe

rce

nt

are

a in

pat

che

s >

20

0 h

a (%

)

Years

PFMS ATOP Patches Pre-PFMS ATOP Patches

PFMS Old Patches Pre-PFMS Old Patches



Table 9. Barred Owl model results for the PFMS scenario – 64006 in identified primary habitat.

Time Period

Breeding Pair Model

Raster Values

Potential Breeding

Pairs

Percent Change from

time 0 of potential

breeding pairs

RSF Values

Percent Change

from time 0 of

RSF values

0 1,181,155 47 - 0.165383 -

10 1,022,702 41 -13.4% 0.1564 -5.4%

20 1,044,321 42 -11.6% 0.152467 -7.8%

50 684,596 27 -42.0% 0.14572 -11.9%

100 594,918 24 -49.6% 0.139887 -15.4%

200 409,383 16 -65.3% 0.131261 -20.6%

Table 10. Barred Owl model results for the PFMS scenario – 64006 in identified secondary habitat.

Time Period

Breeding Pair Model

Raster Values

Potential Breeding

Pairs

Percent Change from time 0 of

potential breeding pairs

RSF Values

Percent Change


0 805,524 32 - 0.126827 -

10 635,404 25 -21.1% 0.119761 -5.6%

20 875,105 35 8.6% 0.134696 6.2%

50 608,080 24 -24.5% 0.122102 -3.7%

100 731,162 29 -9.2% 0.130767 3.1%

200 641,101 26 -20.4% 0.128731 1.5%

The primary and secondary compartments have a higher RSF value than the FMA total initially and over time (Figure 15). The breeding pairs within the compartments maintains a more consistent level than the rest of the FMA (Figure 16).




Figure 15. RSF for primary and secondary compartments compared to FMA total.

Figure 16. Breeding pairs for primary and secondary compartments compared to FMA total.

4.3.2.2 Area greater than 80 years old

Barred owl relies on older aspen, mixedwood and white spruce forests. For the primary compartments, the majority of strata are consistent over time, with the exception of aspen strata (Figure 17). For the secondary compartments, most strata are consistent over time and are slightly higher than current conditions (Figure 18).

0

0.05

0.1

0.15

0.2

0 10 20 50 100 200

RSF

Years

PFMS Primary Secondary

0

50

100

150

200

250

0 10 20 50 100 200

Bre

ed

ing

Pai

rs

Years

Primary Secondary Other FMA



Figure 17. Area 80 years and older for strata within primary compartments of concern

Figure 18. Area 80 years and older for strata within secondary compartments of concern

4.3.2.3 Barred Owl breeding pair maps

Figure 19 through Figure 30 illustrate the spatial distribution of the potential breeding pair model results for each time period for both scenarios. For this exercise, the same maps are used for both the Pre-PFMS and the PFMS scenarios as the results are so similar. Primary and secondary compartments of concern are mapped and the green grids represent barred owl breeding pair habitat.

0

2,000

4,000

6,000

8,000

10,000

12,000

2017 2057 2097 2157

Are

a >

= 8

0 y

ear

s o

ld (h

a)

Years

PFMS ScenarioAW DU AS SA SW

0

2,000

4,000

6,000

8,000

10,000

12,000

2017 2057 2097 2157

Are

a >

= 8

0 y

ear

s o

ld (h

a)

Years

Pre-PFMS Scenario

AW DU AS SA SW

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

2017 2057 2097 2157

Are

a >

= 8

0 y

ear

s o

ld (h

a)

Years

PFMS ScenarioAW DU AS SA SW

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

2017 2057 2097 2157

Are

a >

= 8

0 y

ear

s o

ld (h

a)

Years

Pre-PFMS Scenario

AW DU AS SA SW




Figure 19. BTN Breeding pair results of time zero Barred Owl model

Figure 20. Pre-PFMS and PFMS scenarios Breeding pair results of time 0 Barred Owl model



Figure 21. BTN Breeding pair results of time 10 Barred Owl model




















4.3.3 Summary The results show that in general all three scenarios follow a similar declining trend in the number of potential Barred Owl breeding pairs. The exception being in year 10, where in the back to natural




scenario there is a large increase in potential breeding pairs, before a sharp decline in numbers in year 20.

4.4 ECA Sensitivity Analysis

The GoA requested a sensitivity analysis highlighting the impacts of harvesting in the Virginia Hills portion of FMU W13. This area is considered the best habitat for Arctic Grayling fish species, and harvesting in the area may be an issue. The ECA values for this area are already impacted by a historical fire and subsequent salvage harvesting. There is also a large amount of mature timber in these compartments that MWFP would like to harvest within the 20 year SHS.

4.4.1 Analysis

Two scenarios are compared for this sensitivity analysis. 1. PFMS (Scenario 63006); and 2. Deleted harvest in Virginia hills for 20 years. Scenario is not re-optimized. (Scenario 60006)

The ECA maps and harvest levels are then compared between the two scenarios.

4.4.2 Results

For the first 20 years of the PFMS, the harvest volumes coming from the Virginia Hills area is approximately 70,000 m3/yr, a total of 1,400,000 m3 over 20 years. This represents approximately 19% of the total conifer harvest for W13 FMU. If this harvest is removed from the sequence, harvest volume will increase in other portions of the FMA, resulting in higher ECA values in other watersheds.

The change in ECA values after 20 years of harvest (scenario 63006) was compared to a 20 year deletion of harvest (scenario 60006).

Figure 31 shows the initial conditions and the status after 10 years for both scenarios. Figure 32 shows the status in year 20 and year 30 for both scenarios.



Figure 31. Comparison of year 0 and 10 for the two scenarios. PFMS is on left, removed harvest from Virginia Hills on right.




Figure 32. Comparison of year 20 and 30 for the two scenarios. PFMS is on left, removed harvest from Virginia Hills on right.



4.4.3 Discussion

The difference between the PFMS harvest and the no harvest in Virginia Hills represents a similar trend in ECA, both are trending to better values, simply at different rates. The initial high ECA values are largely created by the historical fire, and the proposed harvesting activity still allows for a recovery in the ECA values.

4.5 Regeneration Curve Sensitivity Analysis

Consistent regeneration standards since 1991 are producing stands that are faster growing than fire origin stands. The sampling programs and resulting volume curves indicate a much faster growth rate in the regenerating stands. When these yields are applied to timber supply, the AAC is larger than using only fire origin yield curves. This document describes the difference in AAC volumes when the regenerating (RSA) curves are applied and when they are not.

4.5.1 RSA volume curves

RSA volume curves are generated the same way as fire origin curves. Sample plots are distributed among the appropriate strata. The plot information is entered into the GYPSY stand growth model and a timber volume curve is generated for each strata for both the coniferous trees and deciduous trees.

4.5.2 TSA implications

To determine the implications of the RSA curves, three scenarios were completed.

1. All applicable strata transition to RSA curves after harvest (scenario 50003);

2. RSA and Juvenile curves are omitted, for both existing and future stands (scenario 52001); and

3. Same as #2, with the first 20 years of scenario #1 used. This scenario mimics the risk of using the RSA curves and later proving that the volume gains are not realistic (scenario 52002).




Figure 33. W13 conifer harvest volume for the three scenarios.

Figure 34. W13 deciduous harvest volume for the three scenarios.



Figure 35. W11 conifer harvest volume for the three scenarios.

Figure 36. W11 deciduous harvest volume for the three scenarios.




Table 11. Harvest volumes for the three scenarios

Forecasted Harvest volumes derived using RSA curves for the first 20 years of the planning horizon do not drop below 10% from the evenflow harvest level that uses only natural yield curves.

4.6 Carry Forward Sensitivity Analysis Carry forward volumes (also known as carry over) have been included in the PFMS but were capped at 125% of the dropdown level post carryover. In W13, combined MWFP and Weyerhaeuser deciduous carry forward volume exceeded 125% of the drop down level, so the carry forward volume was reduced and shared between the companies. Table 12 below summarizes the results of a sensitivity analysis completed to test the impact of including carryover volumes in PFMS. This information was presented to the PDT on October 20, 2016.

Table 12. Carryover volume sensitivity analysis

4.7 Minimum Harvest Age Sensitivity Analysis

4.7.1 Background MWFP has known, since the development of its first DFMP, that there is a coniferous growing stock low point that constrains the conifer AAC in W13. In the 2017-2027 DFMP, this low point is approximately 40 years into the future. Millar Western has long believed that its silviculture practices would lead to AAC benefits, with increased volume available at earlier ages which would help fill this growing stock

50003 52001 52002

m3/yr m3/yr m3/yr

W13

Conifer 377,936 285,846 271,795 -5%

Deciduous 103,823 189,284 196,346 4%

W11

Conifer 103,823 91,299 89,076 -2%

Deciduous 121,167 120,516 120,887 0%

% change

from 52001

Harvest volumes year 20-100

Scenario # Conif Decid Conif Decid

m3/yr % drop m3/yr % drop m3/yr % drop m3/yr % drop

Normal 50003 376,000 204,000 105,000 122,000 376,000 204,000 105,000 122,000

Carryover to

25% max 50006 454,000 256,614 132,629 149,637 368,000 2% 203,000 0% 102,000 3% 121,000 1%

Full carryover

request 50007 463,160 284,231 158,599 149,637 368,000 2% 203,000 0% 99,000 6% 121,000 1%

Five year harvest Harvest volume, years 21-200

W11W13

Conif Decid Conif Decid

W13 W11



void (Figure 37 and Figure 38). With an abundance of older cutblocks (pre 91) on the DFMP area, managed stands growing rates are important to Millar Western. Through a juvenile sampling program conducted for this DFMP (for blocks harvested as long ago as 40 years) it has been substantiated that the older cut blocks are growing well.

Figure 37. Area available for harvest by BCG and decade, demonstrating growing stock low point




Figure 38. W13 conifer growing stock for Scenario 63005, demonstrating growing stock low point

Applying the results of the RSA performance surveys and juvenile regeneration surveys with GoA’s stand growth model, GYPSY, to produce regenerated yield trajectories, the company developed timber supply scenarios to explore options to mitigate impacts of the growing stock low point on AAC. Analysis indicated that a minimum harvest age (MHA) of 65 for a portion of the managed stands harvested would be beneficial for AAC instead of the normal 80 year MHA. In an effort to understand the situation, three scenarios were completed to explore the impacts of moving the MHA to either 65 or 80, or a compromise in between, understanding that there is some concern from GOA associated with the risk of harvesting at younger ages.

With the pressures on the landbase impacting timber supplies (e.g. caribou range plans, mountain pine beetle, protected areas, other industry, etc) it is of Millar Western’s opinion that younger MHAs are an opportunity to begin to offset some of the losses.

4.7.2 Scenarios Used for Sensitivity Analysis

Three scenarios are used for this analysis (Table 13). The majority of assumptions are the same among the scenarios, with the MHA rules being the only difference.

Table 13. MHA scenarios

Scenario Number Conifer MHA in W13

60008 Mostly 65

63005 Some 65

63004 No 65 in RSA



4.7.3 Scenario 60008 MHA Scenario 60008 uses the MHA as described in Table 15. Most conifer blocks are harvested at either a MHA of 65 or 70 years.

4.7.4 Scenario 63005 MHA

Scenario 63005 uses the MHA as described in Table 16. The harvest of W13 RSA at 65 years old is constrained to a maximum of 5,000 ha per decade from PL and SW stands. Juvenile stands are allowed to be harvested at age 65.

4.7.5 Scenario 63004 MHA

Scenario 63004 also uses the MHA as described in Table 16. However, no harvest is allowed in W13 RSA at below 80 years old. Juvenile stands are allowed to be harvested at age 65.

4.7.6 Assumptions for All Scenarios

• Existing reviewed stands in the 20 year SHS can be below MHA of 80 • Planned Blocks – Most blocks in the first 20 years are locked down. However, for

these scenarios some planned blocks are allowed to move from years 11-20 to 1-10 to even out the volume flows.

• Species – Black Throated Green Warbler is constrained to maintain no more than a 15% decrease from current conditions.

• PL and SW growing stock are constrained to 1,000,000 m3 and 500,000 m3 respectively at their lowest point during the modeling timeframe to ensure adequate supplies of these strata are available.

• Patch targets Block Sizes- desire to remove small blocks (<5 ha) Harvest Patches – desire to group blocks Old Interior patches – create old patches greater than 120 ha in size

• Tree Improvement – Maximum of 200 ha per year • Carryover volumes – All existing requests implemented • Old seral stages – Maintain 4% of landbase in old seral stage. Is distributed among

all strata

4.7.7 MHA Used in Sensitivity Analysis

While the majority of this issue document revolves around FMU W13, the MHA for W11 is also presented. Section 3.1 describes the minimum harvest areas that have been chosen for W11 and sections 3.2, 3.3 and 3.4 outline the scenarios.




4.7.7.1 FMU W11 minimum harvest ages

As W11 does not have the same growing stock constraints that W13 does, it was not necessary to look for a similar solution as that proposed for W13. Also, the silviculture effort for conifer blocks has not been at the same level as W13 so the opportunity would be significantly less. The minimum harvest ages (MHA) for W11 are set to 65 for most AW strata and to 80 for most conifer strata (Table 14). In the caribou zone, the MHA is 80 for all strata. Natural stands are fire origin stands that have not been previously harvested. RSA stands are second rotation harvest of RSA stands. Basic stands are second rotation stands that are not RSA.

Table 14. FMU W11 minimum harvest ages

4.7.7.2 FMU W13 minimum harvest ages – scenario 60008

This scenario uses minimum harvest ages for W13 that are mostly 65 years old (Table 15), with the exception for natural and basic PL, SB and SW strata. Juvenile stands are pre-91 blocks that were included in the Juvenile sampling program. The RSA stands are split into two groups. These strata use a MHA of 65, and are planted with regular stock. Some SW strata are planted with tree improvement stock, which are then also eligible for re-harvest at 65 years of age.

Table 15. FMU W13 minimum harvest ages – Scenario 60008

Strata

Natural Basic RSA Caribou zone

AW 65 65 - 80

AP 80 75 - 80

AS 80 75 - 80

PA 80 80 - 80

SA 80 80 - 80

PL 80 80 80 80

SW 80 80 80 80

Curve Type

FMU Strata

Natural Basic Juvenile

Majority Tree Improvement

W13 AW 65 65 - - -

DU 65 65 - - -

AP 65 65 - 65 -

AS 65 65 - 65 -

PA 65 65 65 65 -

SA 65 65 65 65 -

PL 70 70 65 65 -

SB 110 110 - - -

SW 70 70 65 65 65

RSA

Curve Type



4.7.7.3 FMU W13 minimum harvest ages – scenario 60005 and 63004

These scenarios primarily use the ages as noted below (Table 16). Juvenile stands are second rotation harvest of pre-91 blocks that are included in the Juvenile sampling program. The RSA stands are split into three groups. The majority use a MHA of 80, and are planted with regular stock. Some SW strata are planted with tree improvement stock, which are then eligible for re-harvest at 80 years of age.

A limited area of RSA stands will be allowed to be harvested at 65 years of age in the PL and SW strata in Scenario 63005

Table 16. FMU W13 minimum harvest ages – Scenarios 63005 and 63004

4.7.8 Results

To demonstrate the harvest ages used by the model, two scenarios are presented.

Scenario 60008 (Figure 39) is fairly wide open, allowing the model to sequence stands as young as 65 years old. In the first 40 years, the model uses a small amount of area less than 80 years old, while the majority of pure conifer stands after 40 years come from stands less than 80 years old, as it mitigates the AAC impacts of the growing stock low point.

In contrast, scenario 63005 (Figure 40) primarily constrains the minimum harvest age to 80 years old with the exception of allowing for a small amount of less than 80 year old stands in the first two decades and then allowing the model to sequence the juvenile strata at a minimum of 65 years old and a portion of the RSA at a minimum of 65 years old as well. This scenario shows a lower amount of overall harvest area, reflecting a lower harvest volume without the ability to harvest the younger RSA stands (as in scenario 60008). It also relies on the existing natural forest and juvenile stands further into the future and shows a decreasing amount of less than 80 year old stands harvested, once the growing stock low point has been passed.

In addition, Figure 41 summarizes the amount of available conifer growing stock by yield curve strata versus the amount that is harvested in each decade. This clearly shows that at no point is any strata

FMU Strata

Natural Basic Juvenile

Majority Tree Improvement Limited ha

W13 AW 65 65 - - - -

DU 80 80 - - - -

AP 80 80 - 80 - -

AS 80 80 - 80 - -

PA 80 80 65 80 - -

SA 80 80 65 80 - -

PL 80 80 65 80 - 65

SB 110 110 - - - -

SW 80 80 65 80 80 65

Curve Type

RSA




being maximized and that considerable flexibility will remain, especially so once the growing stock low point has been passed.

Figure 39. Scenario 60008 - W13 harvest area by decade when most strata have an MHA of 65 years old



Figure 40. Scenario 63005 - W13 harvest area, maximum 5,000 ha of RSA stands per decade

Figure 41. Scenario 63005 - Comparison of conifer area available for harvest at the start of the decade and the actual harvested area




The change in MHA has a large impact on the even flow harvest level in FMU W13 (Table 17). As the MHA of RSA stands increases, the existing natural stands and juvenile stands are required to last longer, thus reducing the harvest levels over the planning horizon.

Table 17. Harvest level impacts of changing MHA in W13

MWFP recommended and implemented the MHA rules from scenario 63005 in the PFMS. This strategy will harvest a portion of the conifer landbase at ages between 65 and 80 years old. This provides a range of MHA harvested at all periods in time. This range of MHA should reflect the range of variability between stands and their growth potential, which still provides for sustaining ecological values across the landscape.

4.8 RSA and Minimum Harvest Age Sensitivity Analysis The RSA curves used in the PFMS provide an increase in conifer harvest levels, as do the lower minimum harvest ages. The previous sensitivity analyses in section 4.5 and section 4.7 summarized the impacts each alone, and this sensitivity analysis reviews the two combined together. The baseline scenario (50003) for this analysis is the same as used in the RSA analysis, which is a higher conifer harvest level than the PFMS (64006) (W11 in Figure 42 and W13 in Figure 43). The difference between the baseline and the PFMS is greater in FMU W13, as more restrictions have been placed on the W13 PFMS with regards to ECA and habitat constraints. The PFMS for both FMUs have a higher conifer harvest level at the beginning of the planning horizon representing the carryover volume. In all scenarios used in this analysis, deciduous harvest level remains almost constant and is not shown in this section.

Scenario Number Conifer MHA in W13 Conifer AAC in W13 % Reduction

60008 Mostly 65 361,000 n/a

63005 Some 65 332,000 8%

63004 No 65 in RSA 299,000 17%



Figure 42. Baseline and PFMS conifer harvest level for FMU W11

Figure 43. Baseline and PFMS conifer harvest level for FMU W13

-

20,000

40,000

60,000

80,000

100,000

120,000

140,000

2017 2037 2057 2077 2097 2117 2137 2157 2177 2197

Har

vest

Le

vel (

m3

/yr)

Year of TSA

64006 - PFMS 50003 - Base

-

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

2017 2037 2057 2077 2097 2117 2137 2157 2177 2197

Har

vest

Le

vel (

m3

/yr)

Year of TSA

64006 - PFMS 50003 - Base




As shown in section 4.5, removing RSA curves results in a decrease in conifer harvest level from the baseline. Scenario 52001 shows the conifer harvest level without RSA curves, while scenario 52002 shows the harvest level if RSA curves are used for 20 years and then removed. FMU W11 (Figure 44) has a smaller decrease than FMU W13 (Figure 45).

Figure 44. RSA conifer harvest level for FMU W11

Figure 45. RSA conifer harvest level for FMU W13

-

20,000

40,000

60,000

80,000

100,000

120,000

140,000

2017 2037 2057 2077 2097 2117 2137 2157 2177 2197

Har

vest

Le

vel (

m3

/yr)

Year of TSA

64006 - PFMS 50003 - Base 52001 - No RSA 52002 - No RSA after 20 yrs

-

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

2017 2037 2057 2077 2097 2117 2137 2157 2177 2197

Har

vest

Le

vel (

m3

/yr)

Year of TSA

64006 - PFMS 50003 - Base 52001 - No RSA 52002 - No RSA after 20 yrs



Scenario 54001 shows that the change in the MHA to 80 years old has little change from the baseline in W11 (Figure 46) and approximately an 8% decrease in conifer harvest volume in FMU W13 (Figure 47). Scenario 54002 shows the reduction from baseline when RSA curves are removed and the MHA is set to 80 years old. In both FMU’s, the conifer harvest level is almost the same as only removing the RSA curves. The similar trend is shown when RSA is allowed for 20 years and then removed when the MHA is at 80 years, as represented by Scenario 54003.

Figure 46. MHA and RSA conifer harvest level for FMU W11

Figure 47. MHA and RSA conifer harvest level for FMU W13

-

20,000

40,000

60,000

80,000

100,000

120,000

140,000

2017 2037 2057 2077 2097 2117 2137 2157 2177 2197

Har

vest

Le

vel (

m3

/yr)

Year of TSA

64006 - PFMS 50003 - Base 52001 - No RSA

52002 - No RSA after 20 yrs 54001 - MHA 80 54002 - MHA 80 and no RSA

54003 - MHA 80 and no RSA after 20 yrs

-

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

2017 2037 2057 2077 2097 2117 2137 2157 2177 2197

Har

vest

Le

vel (

m3

/yr)

Year of TSA

64006 - PFMS 50003 - Base 52001 - No RSA

52002 - No RSA after 20 yrs 54001 - MHA 80 54002 - MHA 80 and no RSA

54003 - MHA 80 and no RSA after 20 yrs




4.9 Black-throated Green Warbler

The initial scenarios completed using GoA’s bird habitat models, demonstrated that habitat levels of all birds species modeled with the exception of the black-throated green warbler (BTGW) in FMU W13 achieved the target of a maximum 15% reduction from current conditions over the planning horizon. The BTGW habitat dropped only slightly below the target (less than 20% reduction). A target was applied to a Patchworks scenario to determine the ACC impact of achieving the 15% target of the BTGW. The reduction was approximately 1,200 m3/yr of conifer AAC. This result was presented to the PDT on October 20, 2016 and the target was retained in all subsequent scenarios including in the PFMS.

PFMS Datasets 57


5. PFMS Datasets

The input and output datasets used in the Woodstock and Patchworks models are described in this section. The datasets are only included in the submission to GOA technical review staff.

5.1 Woodstock The files for the Woodstock model that was used to create the Patchworks model are located in the following directory;

zAnnexVI_TSA\Data\Patchworks\Analysis\Models\Patchworks\Round6\Tracks_nov29\Woodstock

In this directory are the Woodstock model components that are required to build a Patchworks model, but do not include an areas file from the final landbase that would allow the creation of a Woodstock scenario. The Landscape, Lifespan, Yields, Actions and Transitions sections are the most important for determining the model dynamics.

PW_TSA_20160901.pri – Main Woodstock control file. Contains links to all other files

5.2 Patchworks

5.2.1 Pin File

zAnnexVI_TSA\Data\Patchworks\Analysis\Models\Patchworks\Round6\Analysis_nov29\ p755_Round6_nov29.pin

The pin file controls the formulation of the model. It determines the input files, the patch targets and the length of the planning horizon. It uses the information in the tracks directory.

58 PFMS Datasets



5.2.2 Tracks

zAnnexVI_TSA\Data\Patchworks\Analysis\Models\Patchworks\Round6\Tracks_nov29

The files in the tracks directory contain most of the information needed to open a Patchworks model.

Accounts information – used to define summary targets

■ Accounts.csv – used by the model, includes manually entered targets

■ Protoaccounts.csv – default of matrix generator

Base Patchworks files – system files to define the model matrix

■ Blocks.csv

■ Curves.csv

■ Features.csv

■ Products.csv

■ Strata.csv

■ Tracknames.csv

■ Treatments.csv

Groups files - define groups to allow finer control of targets

■ Groups.csv

■ Other groups

Topology files – define the spatial distance of polygons from each other

■ topology_5_10_Forested_Active.csv – operable landbase only – used for block size patches

■ topology_5_200_Forested.csv – forested landbase – used for patches defining groups of blocks

XML files – created from Woodstock model and used to generate base patchworks files

■ Round6_20161129.xml – raw file from Woodstock to XML conversion

5.2.3 Landbase zAnnexVI_TSA\Data\Patchworks\Analysis\Models\Patchworks\Round6\data\ TSA_Forested_20161129.shp

The modeling landbase shapefile as used in the final PFMS is located in this directory.

PFMS Datasets 59


5.3 Patchworks PFMS Outputs

5.3.1 Standard Patchworks Outputs

zAnnexVI_TSA\Data\Patchworks\Analysis\Models\Patchworks\Round6\ analysis_nov29\MWFP_64006\scenario\

The standard Patchworks files when a scenario is saved are contained in this directory. These are used when re-loading an existing scenario into Patchworks. The three files that are critical are;

Schedule.csv – contains the timing and treatment of every action

Schedule_operators.csv – is a modified schedule for the first 20 years which contains the operator for each harvested polygon

TargetStatus.csv – contains a list of targets that are being controlled

TargetSummary.csv – contains the minimum and maximum values and weightings, as well as the achieved values for each target

Calculation of AAC from model outputs can be done from the targetSummary.csv file. Using the product.W11.Vol.mlb.Con and product.W11.Vol.mlb.Dec targets for W11 and product.W13.Vol.mlb.Con and product.W13.Vol.mlb.Dec targets for W13, the 200 year average (periods 2 to 41) divided by five to obtain the raw annual harvest level. This is then rounded down to the nearest 100 m3/year to arrive at the AAC.

5.3.2 Target Files

zAnnexVI_TSA\Data\Patchworks\Analysis\Models\Patchworks\Round6\ analysis_nov29\MWFP_64006\targets\

The files in this directory contain the same information as the targetsummary.csv file, but are split into one file for each target.

5.3.3 Future Forest Condition

zAnnexVI_TSA\Data\Patchworks\Analysis\Models\Patchworks\Round6\ analysis_nov29\MWFP_64006\Future_Forest_Condition_5_10\

The files in this directory describe the future forest condition in every period of the model. These contain the information as required in section 5.10 in the Alberta Forest Management Planning Standard, Version 4.1.

60 PFMS Datasets



5.3.4 Harvest Schedule

zAnnexVI_TSA\Data\Patchworks\Analysis\Models\Patchworks\Round6\ analysis_nov29\MWFP_64006\Harvest_Schedule_5_11\

The files in this directory describe the harvested stands in every period of the model. These contain the information as required in section 5.11 in the Alberta Forest Management Planning Standard, Version 4.1.

5.3.5 SHS Shapefile

zAnnexVI_TSA\Data\SHS\MWFP_64006_shs_operators.shp

This is the final SHS shapefile for the PFMS, including operator assignment for each block.

Appendix I – Q6 Response 61


Appendix I – Q6 Response



6. Q6 Response

As part of the GoA review of the Millar Western DFMP submission, a request for further information on TSA analysis (Question 6) has been returned to MWFP. Additional information on three sensitivity analyses, the 70 year harvest schedule and an AAC comparison were requested. This document provides MWFP’s response for the “ASAP” and “must be done” questions. With respect to the “nice to have” request, if it is not entirely necessary, then it is likely that no additional work will be done on this.

6.1 Sensitivity Analysis Common Answers The GOA identified a number of questions related to the regeneration sensitivity analysis (runs 50003, 52001, 52002), the carry forward Sensitivity analysis (runs 50003, 50006, 50007) and the minimum harvest age sensitivity analysis (runs 60008, 63005, 63004). Several of the questions for the three different sensitivity analyses have the same answer and are addressed below:

6.1.1 What Model was Used? All sensitivity analyses used the Patchworks model, and provided a spatial output.

6.1.2 SYU Assumptions All scenarios used the same SYUs, which are FMU W11 and FMU W13. Separate harvest levels are generated for each FMU.

6.1.3 Planning Horizon All scenarios used a 200 year planning horizon.

6.1.4 Growing Stock All scenarios used the same growing stock constraint as the PFMS. Keep in mind that Patchworks cannot set a hard limit on the growing stock, particularly the non-declining component. Also, this does not mean the same result was achieved, only that they were constrained in the same way.

64 Appendix I – Q6 Response



6.1.5 Was Total Harvest Volume Maximized? For all scenarios, total coniferous and total deciduous volumes were maximized within the Patchwork modeling context. Mathematical maximization is not possible. Total overall volume was not constrained.

6.1.6 Harvest Activities All scenarios used the same harvest activities except where noted in the regeneration curve sensitivity analysis where post harvest transitions were altered.

6.1.7 Seral Stage Constraints The seral stage constraints are the same as the PFMS for all sensitivity analysis scenarios.

6.1.8 Even Flow Constraints Even flow in patchworks in not an absolute even flow. Some scenarios did not achieve a perfect even flow, due to time constraints.

6.1.9 Any other constraints or assumptions used (what are the differences and similarities from the PFMS Run 64006)

Sensitivity analyses, as used in this plan, are based upon scenarios that are comparable to each other, and not to the PFMS or any other scenario not expressly reported on in the individual analysis. Sensitivity analysis were completed and presented to the PDT for acceptance during the development of the PFMS. There are too many variables that change in creating the final PFMS to track back to each sensitivity analysis.

As a result, the PFMS scenario employs some constraints that are not present in the sensitivity scenarios. Most of the changes were to the SHS and had little strategic impact. Changes included, but are not limited to, the following:

1. Modification of the sequence for barred owl targets and songbird targets.

2. Modification of the sequence for watershed ECA values in the north portion of FMU W13.

3. Modification of the sequence resulting from operational review of the 20 year SHS.

6.2 Regeneration Curve Sensitivity Analysis

The questions for the regeneration curve sensitivity where the answers are unique for this analysis are as follows:

6.2.1 Even Flow Constraints Even flow targets were applied to total conifer and total deciduous harvest volumes. In scenario 52002, the harvest was forced to follow the same sequence as 50003 for the first 20 years. It was set to be even flow after this point, although it did increase in the latter half of the analysis in both FMU’s. This prompted the decision to report on the first 100 years instead of the full 200 years, as it would more accurately reflect the difference in the medium term between the scenarios.



6.2.2 Minimum Harvest Ages The minimum harvest ages are the same as the PFMS.

6.2.3 Yield Curves Used Yield curves for all scenarios are the same as used in the PFMS. The only change is that scenario 52001 and 52002 did not use the RSA or Juvenile curves.

6.2.4 Provide an update of Annex VI Table 11 that uses harvest volumes averaged for years 21 to 200. Provide an explanation if volumes reported for run 50003 in Table 11 do not end up matching volumes reported for run 50003 in Table 12.

There appears to be two typo errors for Scenario 50003:

1. Table 11 FMU W13 Deciduous volume should read 203,608 and not 103,823. This is supported by Figure 34 in Annex VI.

2. Table 12 FMU W13 Coniferous volume should read 378,000 and not 376,000. This is consistent with other sensitivity analyses reporting this scenario.

These typos did not affect the % change values presented in the tables.

Table 1. (Original Table 11.) Harvest volumes for the three scenarios

Table 2. Corrected Table 11 with correct volumes for scenario 50003

50003 52001 52002

m3/yr m3/yr m3/yr

W13

Conifer 377,936 285,846 271,795 -5%

Deciduous 103,823 189,284 196,346 4%

W11

Conifer 103,823 91,299 89,076 -2%

Deciduous 121,167 120,516 120,887 0%

% change

from 52001


50003 52001 52002

m3/yr m3/yr m3/yr

W13

Conifer 377,936 285,846 271,795 -5%

Deciduous 203,608 189,284 196,346 4%

W11

Conifer 103,823 91,299 89,076 -2%

Deciduous 121,167 120,516 120,887 0%

% change

from 52001


66 Appendix I – Q6 Response



Table 3. Table 11 using years 21-200

Table 4. (Original Table 12). Carryover volume sensitivity analysis

6.3 Carry Forward Sensitivity Analysis

The questions for the carry forward sensitivity analysis where the answers are unique for this analysis are as follows:

6.3.1 Even Flow Constraints Each scenario uses even flow once the carryover volumes are complete, except for scenario 50003 which is even flow (within tolerances) for 200 years.

6.3.2 Minimum Harvest Ages The minimum harvest ages are the same as the PFMS.

6.3.3 Yield Curves Used Yield curves for all scenarios are the same as used in the PFMS.

6.4 Minimum Harvest Age Sensitivity Analysis

The questions for the minimum harvest age sensitivity analysis where the answers are unique for this analysis are as follows:

6.4.1 Even Flow Constraints Each scenario uses even flow for the full 200 years.

50003 52001 52002

m3/yr m3/yr m3/yr

W13

Conifer 378,139 285,960 276,054 -3%

Deciduous 203,568 189,401 197,376 4%

W11

Conifer 104,766 90,287 88,728 -2%

Deciduous 121,199 120,821 121,042 0%


% change

from 52001

Scenario # Conif Decid Conif Decid

m3/yr % drop m3/yr % drop m3/yr % drop m3/yr % drop

Normal 50003 376,000 204,000 105,000 122,000 376,000 204,000 105,000 122,000

Carryover to

25% max 50006 454,000 256,614 132,629 149,637 368,000 2% 203,000 0% 102,000 3% 121,000 1%

Full carryover

request 50007 463,160 284,231 158,599 149,637 368,000 2% 203,000 0% 99,000 6% 121,000 1%

Five year harvest Harvest volume, years 21-200

W11W13

Conif Decid Conif Decid

W13 W11



6.4.2 Minimum Harvest Ages The minimum harvest ages for each scenario are specified in Annex VI within the sensitivity analysis section.

6.4.3 Yield Curves Used Yield curves for all scenarios are the same as used in the PFMS.

6.5 70 Year Harvest Schedule There were 113 records identified that are harvested twice in the 70 year SHS. This creates issues when linking the spatial polygons to a flat file with two rows for a single polygon. To alleviate this issue, a new shapefile has been created for the SHS from years 21-70. As there is already a SHS 1-20 shapefile submitted, the entire span of years 1-70 is captured in the two shapefiles. Both the existing 1-20 SHS and the new 21-70 SHS have the same fields as the previous submission.

68



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