@Article{Hoffman_LandscapeEcol_20131001,
 author		= {Forrest M. Hoffman and Jitendra Kumar and Richard T. Mills and William W. Hargrove},
 title		= {Representativeness-based Sampling Network Design for the {S}tate of {A}laska},
 journal	= LandscapeEcol,
 volume		= 28,
 number		= 8,
 pages		= {1567--1586},
 doi		= {10.1007/s10980-013-9902-0},
 day		= 1,
 month		= oct,
 year		= 2013,
 abstract	= {Resource and logistical constraints limit the frequency and extent of environmental observations, particularly in the Arctic, necessitating the development of a systematic sampling strategy to maximize coverage and objectively represent environmental variability at desired scales.  A quantitative methodology for stratifying sampling domains, informing site selection, and determining the representativeness of measurement sites and networks is described here.  Multivariate spatiotemporal clustering was applied to down-scaled general circulation model results and data for the State of Alaska at 4~km$^2$ resolution to define multiple sets of ecoregions across two decadal time periods.  Maps of ecoregions for the present (2000--2009) and future (2090--2099) were produced, showing how combinations of 37 characteristics are distributed and how they may shift in the future.  Representative sampling locations are identified on present and future ecoregion maps.  A representativeness metric was developed, and representativeness maps for eight candidate sampling locations were produced.  This metric was used to characterize the environmental similarity of each site.  This analysis provides model-inspired insights into optimal sampling strategies, offers a framework for up-scaling measurements, and provides a down-scaling approach for integration of models and measurements.  These techniques can be applied at different spatial and temporal scales to meet the needs of individual measurement campaigns.}
}