Encapsulating model complexity and landscape-scale analyses of state-and-transition simulation models: an application of ecoinformatics and juniper encroachment in sagebrush steppe ecosystems

Michael S. O'Donnell; Michael S. O'Donnell

doi:10.3934/environsci.2015.3.464

AIMS Environmental Science

2015, Volume 2, Issue 3: 464-493. doi: 10.3934/environsci.2015.3.464

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Research article Special Issues

Encapsulating model complexity and landscape-scale analyses of state-and-transition simulation models: an application of ecoinformatics and juniper encroachment in sagebrush steppe ecosystems

Michael S. O'Donnell ^,

.S. Geological Survey, Fort Collins Science Center, 2150 Centre Ave., Bldg. C., Fort Collins, CO 80526, USA

Received: 28 January 2015 Accepted: 18 May 2015 Published: 08 June 2015

State-and-transition simulation modeling relies on knowledge of vegetation composition and structure (states) that describe community conditions, mechanistic feedbacks such as fire that can affect vegetation establishment, and ecological processes that drive community conditions as well as the transitions between these states. However, as the need for modeling larger and more complex landscapes increase, a more advanced awareness of computing resources becomes essential. The objectives of this study include identifying challenges of executing state-and-transition simulation models, identifying common bottlenecks of computing resources, developing a workflow and software that enable parallel processing of Monte Carlo simulations, and identifying the advantages and disadvantages of different computing resources. To address these objectives, this study used the ApexRMS® SyncroSim software and embarrassingly parallel tasks of Monte Carlo simulations on a single multicore computer and on distributed computing systems. The results demonstrated that state-and-transition simulation models scale best in distributed computing environments, such as high-throughput and high-performance computing, because these environments disseminate the workloads across many compute nodes, thereby supporting analysis of larger landscapes, higher spatial resolution vegetation products, and more complex models. Using a case study and five different computing environments, the top result (high-throughput computing versus serial computations) indicated an approximate 96.6% decrease of computing time. With a single, multicore compute node (bottom result), the computing time indicated an 81.8% decrease relative to using serial computations. These results provide insight into the tradeoffs of using different computing resources when research necessitates advanced integration of ecoinformatics incorporating large and complicated data inputs and models.
- STSM,
- high-throughput computing,
- high-performance computing,
- ecoinformatics,
- sagebrush steppe,
- juniper encroachment
Citation: Michael S. O'Donnell. Encapsulating model complexity and landscape-scale analyses of state-and-transition simulation models: an application of ecoinformatics and juniper encroachment in sagebrush steppe ecosystems[J]. AIMS Environmental Science, 2015, 2(3): 464-493. doi: 10.3934/environsci.2015.3.464

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Abstract

State-and-transition simulation modeling relies on knowledge of vegetation composition and structure (states) that describe community conditions, mechanistic feedbacks such as fire that can affect vegetation establishment, and ecological processes that drive community conditions as well as the transitions between these states. However, as the need for modeling larger and more complex landscapes increase, a more advanced awareness of computing resources becomes essential. The objectives of this study include identifying challenges of executing state-and-transition simulation models, identifying common bottlenecks of computing resources, developing a workflow and software that enable parallel processing of Monte Carlo simulations, and identifying the advantages and disadvantages of different computing resources. To address these objectives, this study used the ApexRMS® SyncroSim software and embarrassingly parallel tasks of Monte Carlo simulations on a single multicore computer and on distributed computing systems. The results demonstrated that state-and-transition simulation models scale best in distributed computing environments, such as high-throughput and high-performance computing, because these environments disseminate the workloads across many compute nodes, thereby supporting analysis of larger landscapes, higher spatial resolution vegetation products, and more complex models. Using a case study and five different computing environments, the top result (high-throughput computing versus serial computations) indicated an approximate 96.6% decrease of computing time. With a single, multicore compute node (bottom result), the computing time indicated an 81.8% decrease relative to using serial computations. These results provide insight into the tradeoffs of using different computing resources when research necessitates advanced integration of ecoinformatics incorporating large and complicated data inputs and models.

References

[1]	Bestelmeyer BT, Moseley K, Shaver PL, et al. (2010) Practical guidance for developing state-and-transition models. Rangelands 32: 23-30.
[2]	Frid L, Hanna D, Korb N, et al. (2013) Evaluating alternative weed management strategies for three Montana landscapes. Invasive Plant Sci Manag 6: 48-59. doi: 10.1614/IPSM-D-11-00054.1
[3]	Costanza JK, Terando AJ, Mckerrow AJ, et al. (2015) Modeling climate change, urbanization, and fire effects on Pinus palustris ecosystems of the southeastern U.S. J Environ Manage 151: 186-199. doi: 10.1016/j.jenvman.2014.12.032
[4]	Halofsky JE, Hemstrom MA., Conklin DR, et al. (2013) Assessing potential climate change effects on vegetation using a linked model approach. Ecol Modell 266: 131-143. doi: 10.1016/j.ecolmodel.2013.07.003
[5]	Wilson T, Sleeter B, Sleeter R, et al. (2014) Land-use threats and protected areas: a scenario-based, landscape level approach. Land 3: 362-389. doi: 10.3390/land3020362
[6]	Daniel CJ, Frid L. Predicting landscape vegetation dynamics using state-and-transition simulation models. Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14-16, 2011. 2012. p. 5-22.
[7]	Booker K, Huntsinger L, Bartolome JW, et al. (2013) What can ecological science tell us about opportunities for carbon sequestration on arid rangelands in the United States? Glob Environ Chang 23: 240-51. doi: 10.1016/j.gloenvcha.2012.10.001
[8]	Bagchi S, Briske DD, Wu XB, et al. (2012) Empirical assessment of state-and-transition models with a long-term vegetation record from the Sonoran Desert. Ecol Appl 22: 400-411. doi: 10.1890/11-0704.1
[9]	Creutzburg MK, Halofsky JS, Hemstrom MA. Using state-and-transition models to project cheatgrass and juniper invasion in southeastern Oregon sagebrush steppe. Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14-16, 2011. 2012. p.73-84.
[10]	Strand EK, Vierling LA, Bunting SC (2009) A spatially explicit model to predict future landscape composition of aspen woodlands under various management scenarios. Ecol Modell 220: 175-191. doi: 10.1016/j.ecolmodel.2008.09.010
[11]	Chambers JC, Bradley BA, Brown CS, et al. (2014) Resilience to stress and disturbance, and resistance to Bromus tectorum L. invasion in cold desert shrublands of western North America. Ecosystems 17: 360-375.
[12]	Steele CM, Bestelmeyer BT, Burkett LM, et al. (2012) Spatially explicit representation of state-and-transition models. Rangel Ecol Manag 65: 213-222. doi: 10.2111/REM-D-11-00047.1
[13]	Bestelmeyer BT, Goolsby DP, Archer SR (2011) Spatial perspectives in state-and-transition models: a missing link to land management? J Appl Ecol 48: 746-757. doi: 10.1111/j.1365-2664.2011.01982.x
[14]	ApexRMS. SyncroSim. SyncoSim ST-Sim software. 2014. Available from: http://wiki.syncrosim.com/
[15]	Evers LB, Miller RF, Doescher PS, et al. (2013) Simulating current successional trajectories in sagebrush ecosystems with multiple disturbances using a state-and-transition modeling framework. Rangel Ecol Manag 66: 313-329. doi: 10.2111/REM-D-11-00220.1
[16]	Vajda A. Programming Many-Core Chips. 2011th ed. Springer; 2011.
[17]	Gropp W, Lusk E, Skjellum A. Using MPI: portable parallel programming with the message-passing interface (Scientific and Engineering Computation). 3rd ed. The MIT Press; 2014.
[18]	Hennessy JL, Patterson DA. Computer architecture: a quantitative approach. 5th ed. Elsevier; 2012.
[19]	Schauer B (2008) Multicore processors-a necessity. ProQuest Discovery Guides 1-14.
[20]	Chapman MT (2005) The benefits of dual-core processors in high-performance computing. White Paper 18.
[21]	Microsoft Developer Network. Memory limits for Windows releases. Available from: http://msdn.microsoft.com/en-us/library/aa366778.aspx
[22]	nixCraft. Maximum memory and CPU limitations for Linux server. Available from: http://www.cyberciti.biz/tips/maximum-memory-and-cpu-limitations-for-linux-server.html
[23]	Flynn MJ (1972) Some computer organizations and their effectiveness. IEEE Trans Comput 100: 948-960.
[24]	Rauber T, Runger G. Parallel programming for multicore and cluster systems. Springer-Verlag, Berlin, Heidelberg; 2010.
[25]	Darema F. The SPMD Model: Past, Present and Future. In: Cotronis Y, Dongarra J, editors. In Recent Advances in Parallel Virtual Machine and Message Passing Interface. Springer Berlin Heidelberg; 2001. p. 1.
[26]	Chang C-C, Czajkowski G, Eicken T Von, Kesselman C. Evaluating the Performance Limitations of MPMD Communication. ACM/IEEE SC 1997 Conf. 1997; 1-10.
[27]	El-Rewini H, Abd-El-Barr M. Advanced computer architecture and parallel processing (Wiley Series on Parallel and Distributed Computing). 1st ed. Wiley-Interscience; 2005.
[28]	Patterson DA, Hennessy JL. Computer organization and design. 5th ed. Green T, McFadden N, editors. Morgan Kaufmann; 2013.
[29]	Silva LME, Buyya R. Parallel programming models and paradigms. High Performance Cluster Computing: Programming and Applications, Volume 2. 1st ed. Prentice Hall; 1999. p. 4-27.
[30]	Navarro CA, Hitschfeld-Kahler N, Mateu L (2014) A survey on parallel computing and its applications in data-parallel problems using GPU architectures. Commun Comput Phys 15: 285-329.
[31]	Center for High Throughput Computing U of W-M. HTCondor^TM Version 8.0.1 manual. 2013. Available from:http://research.cs.wisc.edu/htcondor/
[32]	ApexRMS. Getting started. Sample data, ST-Sim-SpatialSample-V2-1-0. 2014. Available from: http://wiki.syncrosim.com/index.php?title=Getting_Started
[33]	Karl JW, Laliberte AS, Rango A. Spatial dependence of predictions from image segmentation: a methods to determine appropriate scales for producing land-management information. Int Arch Photogramm Remote Sens Spat Inf Sci 2007; XXXVIII (4/C7).
[34]	Miller RF, Rose JA (1999) Fire history and western juniper encroachment in sagebrush steppe. J Range Manag 52: 550-559. doi: 10.2307/4003623
[35]	Baruch-Mordo S, Evans JS, Severson JP, et al. (2013) Saving sage-grouse from the trees: a proactive solution to reducing a key threat to a candidate species. Biol Conserv 167: 233-241. doi: 10.1016/j.biocon.2013.08.017
[36]	Petersen SL, Stringham TK, Roundy BA. (2009) A process-based application of state-and-transition models: a case study of western juniper (Juniperus occidentalis) encroachment. Rangel Ecol Manag 62: 186-192. doi: 10.2111/06-171.1
[37]	Bates JD, Sharp RN, Davies KW (2014) Sagebrush steppe recovery after fire varies by development phase of Juniperus occidentalis woodland. Int J Wildl Fire 23: 117-130. doi: 10.1071/WF12206
[38]	Rowland MM, Suring LH, Tausch RJ, et al. (2010) Dynamics of western juniper woodland expansion into sagebrush communities in central Oregon. Communities 16: 13.
[39]	Kachergis EJ, Knapp CN, Fernandez-Gimenez ME, et al. (2013) Tools for resilience management: multidisciplinary development of state-and-transition models for northwest Colorado. Ecol Soc 18: 39.
[40]	Xamarin. Mono project. Cross platform, open source.NET framework. 2015. Available from: http://www.mono-project.com/
[41]	SchedMD. Simple Linux resource utility manager workload manager. Software and Online Help. 2014. Available from: http://slurm.schedmd.com/
[42]	Tatham S. PuTTY. PuTTY. 2014. Available from: http://www.putty.org/
[43]	Prikryl M. WinSCP free SFTP, SCP, and FTP client for Windows. WinSCP 5.5.6 released. 2014. Available from: http://winscp.net/eng/index.php
[44]	CycleComputing. Cycle Computing, Better answers. Faster. CycleServer. 2014. Available from: http://www.cyclecomputing.com/
[45]	Massie M, Contributors. Ganglia monitoring system. 2014. Available from: http://ganglia.sourceforge.net/
[46]	Conservancy SF. Samba, Opening Windows to a wider world. 2015. Available from: https://www.samba.org/
[47]	Karp AH, Flatt HP (1990) Measuring parallel processor performance. Commun ACM 33: 539-543. doi: 10.1145/78607.78614
[48]	Moturi CA, Maiyo SK (2012) Use of MapReduce for data mining and data optimization on a web portal. Int J Comput Appl 56: 39-43.
[49]	Zhang X, Yan Y. Modeling and characterizing heterogeneous parallel networks computing of workstations of Texas at San Antonio. Parallel and Distributed Processing, 1995 Proceedings Seventh IEEE Symposium on IEEE. 1995. p. 25-34.
[50]	Oracle. Oracle technology network. Oracle VM VirtualBox. 2014. Available from: http://www.oracle.com/technetwork/server-storage/virtualbox/downloads/index.html
[51]	National Aeronautics and Space Administration. Open Government Initiative. NASA Nebula Cloud Computing Platform. 2015. Available from: http://www.nasa.gov/open/plan/nebula.html
[52]	Amazon. Amazon Web Services. Amazon EC2. 2015. Available from: http://aws.amazon.com/ec2/
[53]	Microsoft. Microsoft Azure. The Cloud for Modern Business. 2015. Available from: http://www.windowsazure.com/en-us/
[54]	Google. Google Cloud Platform. Google App Engine: Platform as a Service. 2015. Available from: https://developers.google.com/appengine/
[55]	Eri T, Mahmood Z, Puttini R. Cloud computing: concepts, technology & architecture. Prentice Hall; 2013.
[56]	Alford T, Morton G. The economics of cloud computing. Booz Allen Hamilton. 2011. Available from: http://broadcast.rackspace.com/hosting_knowledge/whitepapers/Cloudonomics-The_Economics_of_Cloud_Computing.pdf
[57]	Kondo D, Javadi B, Malecot P, Cappello F, et al. Cost-benefit analysis of cloud computing versus desktop grids. 2009 IEEE International Symposium on Parallel & Distributed Processing. 2009. p. 1-12.
[58]	Nanath K, Pillai R (2013) A model for cost-benefit analysis of cloud computing. J Int Technol Inf Manag 22: 93-118.
[59]	GDAL. GDAL-Geospatial data abstraction library. Open Source Geospatial Foundation. 2015. Available from: http://www.gdal.org/
[60]	SQLite. An Asynchronous I/O module for SQLite. 2015. Available from: http://www.sqlite.org/asyncvfs.html

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