Abstract

Myrionecta rubra patchiness in a Mexican coastal lagoon was studied. The 3 objectives were to (1) characterize the spatial distribution of M. rubra patches through time; (2) characterize and model the spatial distribution of M. rubra at scales ranging from m to km, and from 1 wk to more than 1 yr; and (3) to place the patchiness patterns of M. rubra into an ecological context. Geostatistical analysis was applied to data collected from simple and nested sampling grids in different seasons; autocorrelation analysis was used to detect temporal regularities over 55 wk. Classical statistics were applied to data from 10 sites in the lagoon to identify trends relating ciliate abundance to environ- mental conditions. Patches were detected and characterized using empirical variograms and mod- elled by omnidirectional Gaussian and exponential functions. For most of the analysis variance was low in the nugget parameter, indicating a strong spatial resolution of the data, and the range param- eter indicated that M. rubra formed patches of 10, 20, 80, 130, and 170 m. Spatial analysis using hier- archical grids produced a more detailed assessment of patches than single grids alone. Conditional simulation of patches indicated the presence of a >2 km patch covering most of the western lagoon. Patch densities varied from between 4 and 700 cells ml -1 . M. rubra abundance exhibited a temporal, pulse-like pattern; autocorrelation revealed a 13 wk periodicity. At the lagoonal level, multiple regression revealed a trend towards higher abundance in the north-west of the lagoon and a decrease during the dry season. Finally, we speculate on the forces causing heterogeneity at large (>1000 m), meso (100 to 1000 m), and fine (1 to 100 m) scales by examining physical-chemical envi- ronmental factors and physiological behavioural properties of the ciliate and its potential predators. We propose that M. rubra patches originate by fragmentation of larger patches, growth of smaller patches, and physical-behavioural aggregation of cells.

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