Estimation of nested spatial patterns and seasonal variation in the longitudinal distribution of Sicyopterus japonicus in the Datuan Stream, Taiwan by using geostatistical methods

Abstract

This study attempts to determine the scale-dependent hierarchical spatial variation and longitudinal distributions of Sicyopterus japonicus year round. The distribution of S. japonicus in the Datuan Stream in northern Taiwan was surveyed during the fall and winter 2007, as well as the spring and summer of 2008. The spatial structure of S. japonicus density was modeled using geostatistics. The longitudinal distributions of S. japonicus density were then estimated using kriging and hydrology distance with nested variogram models. Variography results indicate that nested variogram models could reflect the hierarchical structure in the spatial variation of seasonal S. japonicus density, with the small, median, and large ranges representing three nested scales. Models for the four seasons were consistent in that they shared the same shape of variogram models with various ranges and sill values. This model shape consistency implies stationary spatial correlations in the longitudinal fish distribution across the four seasons. The Kriging geostatistical method based on the multiple scales nested variogram models also provided robust estimates of S. japonicus densities at unsampled sections. We conclude that S. japonicus densities exhibit hierarchical patterns and variation in the four seasons along the study stream. Geostatistical methods with a nested variograms and hydrological distance are a highly effective means of delineating the hierarchical structure in longitudinal patterns of S. japonicus density in each season, providing estimates of the S. japonicus density for hierarchically structured spatial distributions and expanding knowledge of S. japonicus beyond the limits imposed by spatial and temporal scales.

Uncertainty in Various Habitat Suitability Models and Its Impact on Habitat Suitability Estimates for Fish

“…Consistent with previous studies, Sicyopterus japonicas, an amphidromous gobiid, was the focal species, as it has often been identified as a habitat quality indicator species [5,[31][32][33]. [5,32] have described the study area and the method for collecting data in more detail. Figure 1 presents the study area and sampling locations as well as the number of Sicyopterus japonicas observed.…”

“…V and D were classified using the classification criterion that was proposed by [32]. Both the projected current velocity and water depth across the study area were inputted into the species distribution models.…”

“…The random sample splitting explained about 37.0% of the variation between the 6000 outputs (6 SDMs × 1000 iterations), while model structure explained about 63.0%, indicating that the uncertainty that originated in sampling of data had less of an effect on the variability of the projected HSI but was still responsible for one third of the variability The variability in HSI that was introduced by the sample data is attributable to deviations introduced by the collection of data concerning fish occurrences and the small sample size [18]. Point electrofishing was performed while moving upstream to minimize time spent in water [5,32]. Since these data were collected manually, some untraceable errors may have arisen during their collection.…”

Integrating Social Values and Ecosystem Services in Systematic Conservation Planning: A Case Study in Datuan Watershed

“…Overall, the total length of Datuan Stream is 14.5 km and the watershed covers 15.68 km 2 , with an average slope of 7.5%. Additionally, it is the third largest stream within Tamsui District and is managed at the national level [54]. Datuan Watershed maintains a subtropical monsoon climate with northeastern and northern monsoons in the winter, and southeastern and eastern monsoons in the summer.…”

Spatiotemporal analysis of olive flowering using geostatistical techniques

“…Kriging is an interpolation procedure widely used to analyse the spatial behaviour for a given variable in many of the natural sciences, including geology (Mendes and Ribeiro, 2010;Pérez-Rodríguez et al, 2007), zoology (Lin et al, 2011), botany (Fortin et al, 1989), ecology (Robertson, 1987), and climatology (Bajat et al, 2013). Over the last few years, a number of papers have addressed the use of geostatistical techniques coupled with GIS in phenological and aerobiological researches (e.g.…”