Abstract
Forest managers are faced with the difficulty of promoting multiple ecosystem services (ES) with an incomplete understanding of the complex ways these ES interact. We developed a quantitative model to help managers better understand the effect of different management options on eight ES over time. These ES were (1) Mexican spotted owl ( Strix occidentalis lucida) habitat, (2) the harvest of merchantable timber, (3) the harvest of woody biomass for potential conversion to bioenergy, (4) northern goshawk ( Accipiter gentilis) habitat, (5) scenic beauty, (6) fire hazard reduction, (7) carbon storage, and (8) restoration of pre-European settlement forest structure represented through larger quadratic mean diameters (QMD). We integrated production functions for these eight ES with a forest growth-and-yield model to simulate the effects of three management actions over a 45-year period in northern Arizona, USA. We scaled ES values based on their observed maximum and minimum values as a means to compare achievement levels between ES. These scaled ES values were input into a goal-programming model to identify the optimal management regimes given a corresponding set of five different management objectives. The combination of simulation modeling with goal-programming is a novel approach for evaluating ES responses to management and for optimizing ES attainment objectives. To illustrate the value of this novel approach, we designed management objectives to reflect the likely goals a forest manager may choose for each ES in this region. ES goals were distinguished at the forest- and stand-level spatial extents. Results demonstrate the flexibility of the ES optimization model to plan for a variety of situations and preferences. For instance, two ES management goals viewed as conflicting are the protection of Mexican Spotted Owl habitat and the reduction of fire hazard risk. One ES optimization scenario (Scenario 5) produces a plan that reduces area considered as having a high fire hazard from 56% to 7% of the study area in 2015 without any reduction in owl habitat quality throughout the time horizon. We recommend that our methodology be applied and developed further by those in both the research and applied fields of ecosystem sciences.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 11-27 |
| Number of pages | 17 |
| Journal | Ecological Modelling |
| Volume | 324 |
| DOIs | |
| State | Published - Mar 24 2016 |
Fingerprint
Keywords
- Ecosystem services
- Forest management and planning
- Forest Vegetation Simulator
- Goal-programming
- Optimization modeling
ASJC Scopus subject areas
- Ecological Modeling
Cite this
Managing for ecosystem services in northern Arizona ponderosa pine forests using a novel simulation-to-optimization methodology. / Bagdon, Benjamin A.; Huang, Ching-Hsun; Dewhurst, Stephen M.
In: Ecological Modelling, Vol. 324, 24.03.2016, p. 11-27.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Managing for ecosystem services in northern Arizona ponderosa pine forests using a novel simulation-to-optimization methodology
AU - Bagdon, Benjamin A.
AU - Huang, Ching-Hsun
AU - Dewhurst, Stephen M
PY - 2016/3/24
Y1 - 2016/3/24
N2 - Forest managers are faced with the difficulty of promoting multiple ecosystem services (ES) with an incomplete understanding of the complex ways these ES interact. We developed a quantitative model to help managers better understand the effect of different management options on eight ES over time. These ES were (1) Mexican spotted owl ( Strix occidentalis lucida) habitat, (2) the harvest of merchantable timber, (3) the harvest of woody biomass for potential conversion to bioenergy, (4) northern goshawk ( Accipiter gentilis) habitat, (5) scenic beauty, (6) fire hazard reduction, (7) carbon storage, and (8) restoration of pre-European settlement forest structure represented through larger quadratic mean diameters (QMD). We integrated production functions for these eight ES with a forest growth-and-yield model to simulate the effects of three management actions over a 45-year period in northern Arizona, USA. We scaled ES values based on their observed maximum and minimum values as a means to compare achievement levels between ES. These scaled ES values were input into a goal-programming model to identify the optimal management regimes given a corresponding set of five different management objectives. The combination of simulation modeling with goal-programming is a novel approach for evaluating ES responses to management and for optimizing ES attainment objectives. To illustrate the value of this novel approach, we designed management objectives to reflect the likely goals a forest manager may choose for each ES in this region. ES goals were distinguished at the forest- and stand-level spatial extents. Results demonstrate the flexibility of the ES optimization model to plan for a variety of situations and preferences. For instance, two ES management goals viewed as conflicting are the protection of Mexican Spotted Owl habitat and the reduction of fire hazard risk. One ES optimization scenario (Scenario 5) produces a plan that reduces area considered as having a high fire hazard from 56% to 7% of the study area in 2015 without any reduction in owl habitat quality throughout the time horizon. We recommend that our methodology be applied and developed further by those in both the research and applied fields of ecosystem sciences.
AB - Forest managers are faced with the difficulty of promoting multiple ecosystem services (ES) with an incomplete understanding of the complex ways these ES interact. We developed a quantitative model to help managers better understand the effect of different management options on eight ES over time. These ES were (1) Mexican spotted owl ( Strix occidentalis lucida) habitat, (2) the harvest of merchantable timber, (3) the harvest of woody biomass for potential conversion to bioenergy, (4) northern goshawk ( Accipiter gentilis) habitat, (5) scenic beauty, (6) fire hazard reduction, (7) carbon storage, and (8) restoration of pre-European settlement forest structure represented through larger quadratic mean diameters (QMD). We integrated production functions for these eight ES with a forest growth-and-yield model to simulate the effects of three management actions over a 45-year period in northern Arizona, USA. We scaled ES values based on their observed maximum and minimum values as a means to compare achievement levels between ES. These scaled ES values were input into a goal-programming model to identify the optimal management regimes given a corresponding set of five different management objectives. The combination of simulation modeling with goal-programming is a novel approach for evaluating ES responses to management and for optimizing ES attainment objectives. To illustrate the value of this novel approach, we designed management objectives to reflect the likely goals a forest manager may choose for each ES in this region. ES goals were distinguished at the forest- and stand-level spatial extents. Results demonstrate the flexibility of the ES optimization model to plan for a variety of situations and preferences. For instance, two ES management goals viewed as conflicting are the protection of Mexican Spotted Owl habitat and the reduction of fire hazard risk. One ES optimization scenario (Scenario 5) produces a plan that reduces area considered as having a high fire hazard from 56% to 7% of the study area in 2015 without any reduction in owl habitat quality throughout the time horizon. We recommend that our methodology be applied and developed further by those in both the research and applied fields of ecosystem sciences.
KW - Ecosystem services
KW - Forest management and planning
KW - Forest Vegetation Simulator
KW - Goal-programming
KW - Optimization modeling
UR - http://www.scopus.com/inward/record.url?scp=84954551560&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84954551560&partnerID=8YFLogxK
U2 - 10.1016/j.ecolmodel.2015.12.012
DO - 10.1016/j.ecolmodel.2015.12.012
M3 - Article
AN - SCOPUS:84954551560
VL - 324
SP - 11
EP - 27
JO - Ecological Modelling
JF - Ecological Modelling
SN - 0304-3800
ER -