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1308 Landscape Ecol 2017 32 1307 1325, and validation computing capacity model applica Fig 1 Time line of milestones in computing capacity forest c. bility to real world problems and ability to integrate dynamics model development and forest inventory data. collection Moores Law Moore 1965 backed by 40 years. biological social and economic drivers of change As of empirical evidence suggests the number of transistors on an. computing and data resources improved a new class integrated circuit i e a computer chip will double about every. of spatially explicit forest landscape models emerged 2 years leading to exponential increases in computing capacity. Conclusions We are at a point of great opportunity in Sources Wikipedia contributors 2016a b for transistor counts. personal communication with Dennis May US Forest Service. development and application of forest dynamics mod for forest inventory milestones 18 Feb 2016. els Past limitations in computing capacity and in data. suitable for model calibration or evaluation are becom than timber production has demanded greater use of. ing less restrictive Forest landscape models in partic spatially explicit modelling methods that permit. ular are ready to transition to a central role supporting landscape scale analyses in addition to tree and. forest management planning and policy decisions stand scale considerations that were emphasized in. Recommendations Transitioning forest landscape the early decades of forest dynamics modelling. models to a central role in applied decision making We briefly summarize progress and milestones in the. will require greater attention to evaluating perfor evolution of forest growth and yield models and forest. mance building application support staffs expanding landscape models from the 1930s to the present with. the included drivers of change and incorporating emphasis on how progress has been linked to computing. metrics for social and economic inputs and outputs capacity and data availability We subsequently identify. specific actions needed to support future forest land. Keywords Process model Individual tree model scape model development and application. Gap model Model validation Ecosystem services,LANDIS TreeMig Forest Vegetation Simulator. Forest growth and yield models, Introduction The initial impetus for modeling forest dynamics was to. estimate timber yields over time and thereby improve. Forecasting forest change is essential to forest man efficiency of timber production The development of. agement and over the past century the suite of variable density yield equations MacKinney et al. quantitative modeling tools available to aid forest 1937 initiated the era of statistical or empirical. management decision making has become increas growth and yield modeling Regression models appli. ingly sophisticated quantitative spatially explicit and cable to disturbed stands predicted future yield or. inclusive of multiple drivers of forest change Moser future periodic growth as a function of current stand. 1980 Mladenoff and Baker 1999 Mladenoff 2005 conditions and time However early regression models. The development and application of forest dynamics were limited to those that could be fit using a. models has historically been constrained by the mechanical calculator and calibration data describing. availability of computing capacity observational data forest change over time were in short supply Fig 1. on forest change and supporting software e g for Subsequent forest dynamics models incorporated. geographic information systems or GIS e g Moser systems of differential equations describing stand. 1980 Ek et al 1988 Leary 1988 Mladenoff 2004 growth over time as the first derivative of yield over. Risser and Iverson 2013 However in recent decades time for even aged and uneven aged stands e g. model developers and users benefited from remarkable Clutter 1963 Moser and Hall 1969 Increased avail. advances in modeling approaches computing capac ability of detailed forest inventory data supported the. ity the body of observational data from which to development of complex systems of differential or. calibrate and test predictive models and knowledge difference equations that simultaneously modeled. about impacts of exogenous disturbances on forests change in individual components of stand growth. These advances removed some barriers to modeling Beers 1962 Moser 1974 including ingrowth growth. forest dynamics but they have also increased expec of surviving trees mortality and harvest During the. tations for model access performance and relevance same period diameter distribution models were. to emerging issues Growing interest in issues other developed that modeled change over time in the. Landscape Ecol 2017 32 1307 1325 1309,Integrated circuit transistor count. Maximum number of transistors on an integrated circuit billions. technologies,IBM 12 core,8 core Itanium,6 core Xeon.
Motorola Intel,Mechanical adding Electronic,6089 Itanium. machine and slide desktop,rule calculator Intel 80386 Intel Pen um. 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020,Spa ally explicit landscape models. Process models,Stand component models, Timeline of milestones in forest dynamics model development. Variable shading indcates rela ve intensity of ac vity. Individual tree models,Gap and succession models,Diameter distribu on models.
Stand scale variable density compa ble growth and yield equa ons. Stand scale variable density yield equa ons,Normal yield tables. U S Implementa on of con nuous forest inventory CFI with permanent remeasured plots. Widespread use of eld data recorders to improve data quality. Timeline of milestones in US Forest Service inventory methods. Rela onal databases for rapid data access,GPS used for precise plot loca ons. Na onal database standardiza on,Inventory cycle 5yr east 10yr west. Measure 20 000 plots year,Web access to all data,1310 Landscape Ecol 2017 32 1307 1325. parameters of a diameter distribution e g the Weibull manipulating geographically referenced data at the. distribution and provided great flexibility in summa landscape scale and lack of computing capacity to. rizing products by size class Clutter and Bennett model landscape scale spatially explicit changes for. 1965 Burkhart 1971 Bailey and Dell 1973 landscapes thousands of hectares in extent To some. Empirical individual tree based growth models degree those limitations have been overcome for. flourished in the 1970s and 1980s with advent of the modeling landscapes encompassing thousands of. personal computer They were intuitive in predicting hectares Crookston and Stage 1991 but not for. growth and survival over time for individual trees on applications spanning millions of hectares as is the. inventory plots and cumulative change for the stands case with some of the forest landscape models. and landscapes represented by the inventory plots e g discussed in the following section. Arney 1972 Stage 1973 Ek and Monserud 1974, Leary 1979 but they did not model spatially explicit.
landscape processes such as spread of fire or patho Spatially interactive forest landscape models. gens Early development of empirical individual tree. based growth models was constrained by limited data Forest landscape models FLMs simulate forest stand. and computing capacity but since the mid 1980s dynamics in conjunction with interactive forest land. empirical individual tree distance independent mod scape processes in a spatially explicit i e mapped. els exemplified by the forest vegetation simulator framework Scheller and Mladenoff 2007 He 2008. FVS have gradually become the dominant method Although many types of forest dynamic models can be. ology for making operational site specific estimates spatially explicit FLMs additionally model forest. of future forest change over time with or without landscape processes spatial and stochastic processes. harvesting fire or climate effects Crookston et al that include seed dispersal natural disturbances such. 2010 US Forest Service 2016b The notable excep as fire spread windstorms avalanches insect and. tion is for planted conifers where stand scale models disease propagation and human influences such as. continue to dominate applications e g Burkhart and forest harvesting fuel treatment and climate change. Tome 2012 FLMs divide the simulated landscape into sites or. FVS model variants have been calibrated and points or raster cells the smallest unit of spatial. validated for most regions of the U S and are readily resolution with forest dynamics simulated for each. linked to forest inventory databases to set initial forest site and spatially interactive forest landscape pro. conditions and to enable localized model calibration cesses simulated over all or a subset of sites With. and validation That process has been facilitated by a current technology sites typically range from 0 1 to. permanent U S Forest Service support staff serving 300 ha in size for landscapes ranging from 105 to 109. private and public land managers The outcome is a ha in extent By simultaneously modelling site scale. mature modeling technology that is documented forest dynamics and landscape processes across. validated integrated with forest inventory systems mapped forest landscapes FLMs introduced a new. and routinely applied to support on the ground forest paradigm for modeling forest dynamics. planning and silvicultural decisions Outputs of pro Because the computation loads for FLMs increase. jected forest conditions include tabular and graphical nonlinearly with increasing landscape size and com. summaries as well as three dimensional visualizations plexity FLMs typically simplify site scale processes. There has been a propensity to increase the spatial e g competition at the tree level in order to make. extent of applications of growth and yield models and landscape scenario analyses possible within the con. to integrate landscape scale processes e g seed fines of available computational capabilities Mlade. dispersal spread of wildfire insects diseases climate noff and Baker 1999 Mladenoff 2004 In contrast to. change and management for non timber forest forest growth and yield models that track the species. attributes e g wildlife habitat over time Dixon number and size of each tree on site or stand early. 2002 Rebain 2010 Initially such efforts were FLMs did not explicitly simulate site scale forest. hampered by a lack of field inventory data or remotely dynamics Rather variables from simulated forest. sensed data describing initial forest conditions seam landscape processes were used as a surrogate for those. lessly across a landscape lack of software suitable for dynamics The elapsed time since last fire for. Landscape Ecol 2017 32 1307 1325 1311, example was used to represent the age of trees on a FLM is a natural progression that is facilitated by. site or the amount of accumulated fuel Baker et al advances in computing capacity. 1991 Li et al 1997 Cary 1998 The original LANDCLIM tracks the number of trees and. LANDIS model Mladenoff et al 1996 Mladenoff biomass by species and age cohort Schumacher. and He 1999 used species age cohorts as a measure of et al 2004 It is unique in introducing gap model. forest structure and site occupancy Recent FLMs dynamics into simulated site scale dynamics by. incorporate more quantitative information at each site including the interactions of abiotic variables soil. e g seed number total biomass number of trees by water and climate and biotic variables tree size. age or height class or in some cases individual trees Stand scale resource competition is modeled as shad. with explicit positions thus increasing the detail of ing and by a growth and density dependent mortality. site scale forest dynamics Alternative FLMs differ in function based on site biomass relative to maximum. how they balance the level of detail for each site and or potential biomass Additionally large scale distur. the complexity of modeled forest landscape processes bances such as wind throw wildfires and bark beetle. given practical constraints on computing and the infestations are modeled Results are evaluated in. mechanisms needed to simulate robustly the processes terms of ecosystem services e g protection against. of interest The following are specific examples natural biohazards Bugmann et al 2014. TreeMig tracks species specific seed densities in iLand represents a novel approach to integrate. the seedbank and tree population densities in height functional structural and spatial processes and their. classes at each cell Lischke et al 2006 It captures interactions through an individual based model frame. dynamic within stand heterogeneity in terms of work Seidl et al 2012 The model tracks the location. species composition and vertical and horizontal stand and attributes of each individual tree within a cell The. structure Besides modeling recruitment growth approach is coupled with physiology based resource. mortality and light competition as in gap models use modeling including competition for light via an. TreeMig includes processes and interactions essen upscaled shade surface and is embedded in a robust. tial for modeling landscape dynamics seed produc scaling framework to address landscape level dynam. tion seed density regulation and seed dispersal ics iLand represents a FLM with exceptional detail at. Thus the model simulates patterns and shifts in the individual cell lev. The past and future of modeling forest dynamics from growth and yield curves to forest landscape models Stephen R Shi ey Hong S He Heike Lischke Wen J Wang Wenchi Jin Eric J Gustafson Jonathan R Thompson Frank R Thompson III William D Dijak Jian Yang Received 2 May 2016 Accepted 1 June 2017 Published online 12 June 2017 Springer Science Business Media B V

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