Environmental and Ecological Statistics

, Volume 17, Issue 4, pp 411–436 | Cite as

Preliminary prioritization based on partial order theory and R software for compositional complexes in landscape ecology, with applications to restoration, remediation, and enhancement

  • Wayne Myers
  • Ganapati Patil


The present purpose is to provide convenient computational capability and visualizations for preliminary partial or progressive prioritization based largely on concepts of partial order theory and implemented in R software as illustrated in a context of conservation and sustainable stewardship across landscapes with ecosystem services as a complex multidimensional domain that must be placed in public and private perspective in pursuit of multi-resource management. Practical perspective is promoted by graphic visualization with local partial order modeling (LPOM) methods for screening of settings and scenarios involving interactions of ecosystem elements as evidenced by environmental indicators. ORDIT ordering and precedence plots arise from ascribed advantage as an outcome of a rating regime. Representative ranks constitute criteria drawn from the rank distribution for the case in question. Distal data are determined with regard to remediation and retention. Median mismatches reflect interplay of indicators appearing as isolated instances in plotting patterns. A suggested strategy to circumvent computational constraints is partitioning the pool of cases into collectives by clustering, pursuing classes of partitions, and then prioritizing in particular partitions. When prime prospects have been obtained, detailed determinations can be done with partial ordering procedures involving Hasse diagrams and similarly complex constructs that are difficult to apply with many cases and/or indicator criteria.


Landscape ecology Environmental indicators Partial order Ordination Visualization Prioritization Remediation R software Computational complexity 


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Penn State Institutes of Energy and EnvironmentPenn State UniversityUniversity ParkUSA
  2. 2.Center for Statistical Ecology & Environmental Statistics, Department of StatisticsPenn State UniversityUniversity ParkUSA

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