Assessing spatial and temporal patchiness of the autotrophic ciliate Myrionecta rubra: a case study in a coastal lagoon

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 environmental conditions. Patches were detected and characterized using empirical variograms and modelled 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 parameter indicated that M. rubra formed patches of 10, 20, 80, 130, and 170 m. Spatial analysis using hierarchical 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 environmental 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.

Role of ciliates and other microzooplankton in the Irminger Sea (NW Atlantic Ocean)

“…Spatial distribution of plankton abundance, biomass, and production were assessed by geostatistical analysis (for an overview of this method see Bulit et al 2003Bulit et al , 2004. The spatial structure of abundance was determined using the empirical variogram as the basic tool (Goovaerts 1997).…”

“…The empirical variogram is a mathematical function that allows description of the spatial variability of abundance, biomass, and production in terms of variance between pairs of samples at increasing distance. Empirical variograms were modelled to predict values at non-visited locations using ordinary kriging techniques (see Bulit et al 2003Bulit et al , 2004. Omnidirectional spherical and Gaussian models were fit to the empirical variograms, using the weighted least-squares method (Cressie 1993), and their parameters were used for kriging contour maps.…”

Spatial structure of the abiotic environment and its association with sapling community structure and dynamics in a cloud forest

“…This method provides the minimum variance prediction error, which reduces the uncertainty of the estimated values (Bulit et al 2004).…”

“…For a spatial variable Z, the spatially structured component (SSC) is defined as the percentage of the total variation of Z that is explained by the spatial structure. It is calculated as follows (Bulit et al 2004):…”

Scaling patterns of plankton diversity: a study of ciliates in a tropical coastal lagoon

“…Surprisingly, although spatial and temporal variation in plankton diversity is regularly investigated in surveys, experiments, and models (Dolan et al, 2006b), there remains an inadequate provision of methods for predicting spatial patchiness of plankton diversity, although inroads are being made to this end (see Beaugrand & Ibañez, 2002;Kienel & Kumke, 2002). Here we, therefore, develop and apply a suite of complementary geostatistical and multiple-regression analysis tools, previously used to approach the study of patchiness of plankton abundance (Bulit et al, 2003(Bulit et al, , 2004Díaz-Á valos et al, 2006), to assess diversity. Not only can these methods be applied to predict spatial and temporal patterns of diversity, they can also provide error estimates (i.e., confidence limits) associated with these predictions, and they can assess how spatial structure of environmental factors may drive diversity patchiness.…”