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The primary objective of this study was to develop a climate-sensitive modular-based structural stand density management model (SSDMM) for red pine (Pinus resinosa Aiton) plantations situated within the western Great Lakes—St. Lawrence and south-central Boreal Forest Regions of Canada. For a given climate change scenario (e.g., representative concentration pathway (RCP)), geographic location (longitude and latitude), site quality (site index) and crop plan (e.g., initial espacement density and subsequent thinning treatments), the resultant hierarchical-based SSDMM consisting of six integrated modules, enabled the prediction of a multitude of management-relevant performance metrics over rotational lengths out to the year 2100. These metrics included productivity measures (e.g., mean annual volume, biomass and carbon increments), volumetric yield estimates (e.g., total and merchantable volumes), pole and log product distributions (e.g., number and size distribution of pulp and saw logs, and utility poles), biomass production and carbon sequestration outcomes (e.g., oven-dried masses of above-ground components and associated carbon mass equivalents), recoverable end-product volumes and associated monetary values (e.g., volumes and economic worth estimates of recovered chip and dimensional lumber products extractable via stud and randomized length mill processing protocols), and crop tree fibre attributes reflective of end-product potential (e.g., wood density, microfibril angle, and modulus of elasticity). The core modules responsible for quantifying stand dynamics and structural change were developed using 491 tree-list measurements and 146 stand-level summaries obtained from 98 remeasured permanent sample plots situated within 21 geographically separated plantation-based initial spacing and thinning experiments distributed throughout southern and north-central Ontario. Computationally, the red pine SSDMM and associated algorithmic analogue (1) produced mathematically compatible stem and end-product volume estimates, (2) accounted for density-dependent as well as density-independent mortality losses, response delay following thinning and genetic worth effects, (3) enabled end-users to specify merchantability standards (log and pole dimensions), product degrade factors and cost profiles, and (4) addressed climate change impacts on rotational yield outcomes by geo-referencing RCP-specific effects on stand dynamical processes via the deployment of a climate-driven biophysical site-based height-age model. In summary, the provision of the red pine SSDMM and its unique ability to account for locale-specific climate change effects on crop planning forecasts inclusive of utility pole production, should be of consequential utility as the complexities of silvicultural decision-making intensify during the Anthropocene.
The primary objective of this study was to develop a climate-sensitive modular-based structural stand density management model (SSDMM) for red pine (Pinus resinosa Aiton) plantations situated within the western Great Lakes—St. Lawrence and south-central Boreal Forest Regions of Canada. For a given climate change scenario (e.g., representative concentration pathway (RCP)), geographic location (longitude and latitude), site quality (site index) and crop plan (e.g., initial espacement density and subsequent thinning treatments), the resultant hierarchical-based SSDMM consisting of six integrated modules, enabled the prediction of a multitude of management-relevant performance metrics over rotational lengths out to the year 2100. These metrics included productivity measures (e.g., mean annual volume, biomass and carbon increments), volumetric yield estimates (e.g., total and merchantable volumes), pole and log product distributions (e.g., number and size distribution of pulp and saw logs, and utility poles), biomass production and carbon sequestration outcomes (e.g., oven-dried masses of above-ground components and associated carbon mass equivalents), recoverable end-product volumes and associated monetary values (e.g., volumes and economic worth estimates of recovered chip and dimensional lumber products extractable via stud and randomized length mill processing protocols), and crop tree fibre attributes reflective of end-product potential (e.g., wood density, microfibril angle, and modulus of elasticity). The core modules responsible for quantifying stand dynamics and structural change were developed using 491 tree-list measurements and 146 stand-level summaries obtained from 98 remeasured permanent sample plots situated within 21 geographically separated plantation-based initial spacing and thinning experiments distributed throughout southern and north-central Ontario. Computationally, the red pine SSDMM and associated algorithmic analogue (1) produced mathematically compatible stem and end-product volume estimates, (2) accounted for density-dependent as well as density-independent mortality losses, response delay following thinning and genetic worth effects, (3) enabled end-users to specify merchantability standards (log and pole dimensions), product degrade factors and cost profiles, and (4) addressed climate change impacts on rotational yield outcomes by geo-referencing RCP-specific effects on stand dynamical processes via the deployment of a climate-driven biophysical site-based height-age model. In summary, the provision of the red pine SSDMM and its unique ability to account for locale-specific climate change effects on crop planning forecasts inclusive of utility pole production, should be of consequential utility as the complexities of silvicultural decision-making intensify during the Anthropocene.
The objectives of this study were to access and exemplify the potential utility of a climate-sensitive modular-based structural stand density management model (SSDMM) developed for red pine (Pinus resinosa Aiton) in crop planning decision making. Firstly, the model’s predictive ability was assessed using a retrospective validation approach without consideration of climate change effects. Although limited in scope and applicability, the preliminary results revealed that the magnitude of the mean prediction error for the principal determinates governing stand development did not exceed ±15%. Secondly, the potential utility of the model was illustrated within a spatial-based forest management planning context for a range of climate change scenarios. These exemplifications included three conventional crop plan simulations (initial spacing (IS), IS plus one commercial thinning (CT) treatment, and IS plus two CTs) developing under three climate change scenarios (1971–2000 climate norms, and 4.5 and 8.5 representative concentration pathways) over 75-year rotations (2022–2097) at three geographically diverse locales (north-eastern (Kirkland Lake), north-central (Thessalon), and north-western (Thunder Bay) Ontario, Canada). Resultant developmental indices and (or) productivity metrics were contrasted in terms of (1) regional-specific differences in temporal stand dynamical patterns and rotational yields with increasing climatic change severity, and (2) silvicultural effectiveness of the crop plans within and across locales for each climate change scenario. Climate-wise, although the results revealed marginal regional differences across a multitude of rotational outcome metrics, declines in mean tree size and merchantable volume productivity, and most importantly utility pole production within unthinned plantations, were among the most consequential and consistent negative outcomes associated with climate-induced site productivity declines. Silviculturally, crop plans that included thinning treatments relative to their counterparts that did not, yielded trees of greater mean size and were able to maintain utility pole production status while not achieving similar levels of site occupancy or volumetric productivity. Management-wise, maintenance of pole production status along with concurrent increases in fiscal worth even in light of climate change outweighed the marginal decline in volumetric productivity that was associated with the thinning regimes. In summary, the validation results provided a measure of predictive performance relative to the underlying calibration data set whereas the exemplifications illustrated the model’s potential operational utility in spatial-based forest management planning. For managers aspiring to maintain the historical productivity legacy of red pine through optimal density management decision making while acknowledging prediction uncertainty when forecasting stand development trajectories under climate change, the SSDMM provides an optional decision-support tool for designing climate-smart crop plans during the Anthropocene.
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