|Abstract or Summary
- Quantifying the distribution, abundance, and diversity of nearshore organisms over large areas presents problems to scientists and resource managers constrained by time, personnel, and funding. For example, no method currently exists to statistically
extrapolate biological transect data from small to large spatial scales. Ecological
responses caused by interacting physical and biological processes operate across multiple
scales of space and time. At large scales (100-1000 km, decades to centuries) physical
processes may dominate the structuring of nearshore communities, while at smaller scales (1 - 10 in, minutes to hours) biological processes may become more important in determining organism distributions. Climatic variations delineate global habitats near one end of the space/time continuum, while competition for space and food determines nearshore community structure at the opposite end. Delineating coastal habitats at intermediate spatial scales becomes complex, requiring multiple parameters at each increment through the space/time continuum. The objective of this study was to develop a coastal classification system spanning spatial scales from 10 in to 1,000's km based on a suite of physical factors linked to causal processes associated with ecological responses in the nearshore environment. Complex shorelines can be partitioned into relatively discrete horizontal and vertical polygons with generally homogeneous morphodynamic attributes. The attributes of each unit are described and quantified, thus allowing statistical calculations for parametric or
spatial distribution modelling of nearshore habitats. In 1994 - 1995, the 138 km Cook Inlet shoreline of Lake Clark National Park was classified using this system. Queries of the GIS database show the total area, length and width of each intertidal habitat type, to a minimum resolution of 10 meters horizontally, as defined by alongshore polygon attributes such as wave runup, substrate character, slope angle and aspect. The methods developed in this study have application to oil spill damage assessments, inventory and monitoring programs, and global change studies when economical or logistical constraints dictate a reliance on data collected from relatively localized areas, but when there is a need to extrapolate to broad spatial scales.
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