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Abstract ContextLand-cover class definitions are scale-dependent. Up-scaling categorical data must account for that dependence, but most decision rules aggregating categorical data do not produce scale-specific class definitions. However, non-hierarchical, empirically derived classification systems common in phytosociology define scale-specific classes using species co-occurrence patterns. ObjectivesEvaluate tradeoffs in class precision and representativeness when up-scaling categorical data across natural landscapes using the multi-dimensional grid-point (MDGP)-scaling algorithm, which generates scale-specific class definitions; and compare spectral detection accuracy of MDGP-scaled classes to ‘majority-rule’ aggregated classes. MethodsVegetation maps created from 2-m resolution WorldView-2 data for two Everglades wetland areas were scaled to the 30-m Landsat grid with the MDGP-scaling algorithm. A full-factorial analysis evaluated the effects of scaled class-label precision and class representativeness on compositional information loss and detection accuracy of scaled classes from multispectral Landsat data. ResultsMDGP‐scaling retained between 3.8 and 27.9% more compositional information than the majority rule as class-label precision increased. Increasing class-label precision and information retention also increased spectral class detection accuracy from Landsat data between 1 and 8.6%. Rare class removal and increase in class-label similarity were controlled by the class representativeness threshold, leading to higher detection accuracy than the majority rule as class representativeness increased. ConclusionsWhen up-scaling categorical data across natural landscapes, negotiating trade-offs in thematic precision, landscape-scale class representativeness and increased information retention in the scaled map results in greater class-detection accuracy from lower-resolution, multispectral, remotely sensed data. MDGP-scaling provides a framework to weigh tradeoffs and to make informed decisions on parameter selection. 

PremiseThe southern Florida Everglades landscape sustains wetlands of national and international importance. Sawgrass (Cladium jamaicense), the dominant macrophyte in the Everglades, has two phenotypes that vary in size and density between Everglades marl prairies and peat marshes. Marl prairies have recently been hypothesized to be a newly formed habitat developed after European colonization as a result of landscape‐scale hydrologic modifications, implying that sawgrass marl phenotypes developed in response to the marl habitat. We examined whether sawgrass wetland phenotypes are plastic responses to marl and peat soils. MethodsIn a common‐mesocosm experiment, seedlings from a single Everglades population were grown outdoors in field‐collected marl or peat soils. Growth and morphology of plants were measured over 14 mo, while soil and leaf total nitrogen, total phosphorus, total carbon, and plant biomass and biomass allocation were determined in a final harvest. ResultsSawgrass plant morphology diverged in marl vs. peat soils, and variations in morphology and density of mesocosm‐grown plants resembled differences seen in sawgrass plants growing in marl and peat habitats in Everglades wetlands. Additionally, sawgrass growing in marl made abundant dauciform roots, while dauciform root production of sawgrass growing in peat was correlated with soil total phosphorus. ConclusionsSawgrass from a single population grown in marl or peat soils can mimic sawgrass phenotypes associated with marl vs. peat habitats. This plasticity is consistent with the hypothesis that Everglades marl prairies are relatively new habitats that support plant communities assembled after European colonization and subsequent landscape modifications.