Disentangling the roles of spatial and environmental variables in shaping benthic algal assemblages in rivers of central and northern China

Abstract

Benthic algae were collected from central and northern Chinese rivers to test the hypothesis that geographic location has significant contributions in shaping algal assemblages. We used Moran's eigenvector maps (MEM) to model spatial components and variation partitioning to quantify the influences of spatial and environmental variables on regional patterns of algal richness and community composition, respectively. We found that variation in algal richness was attributed to MEM component 2, 8, and 9 and the quadratic term of N-NO 3 . Regarding abundance data, latitude, longitude, and MEM component 1, 2, and 7 were important spatial variables. Although P-PO 4 , pH, and annual mean temperature were significant environmental variables influencing algal community composition, they were all spatially structured. Among the total explained variance in both algal metrics, spatial proportions were higher than that of environmental variables. We also found that abundant species of Achnanthidium minutissimum, Cocconeis placentula, Cymbella delicatula, Cymbella affinis, Cymbella turgidula, and Synedra ulna displayed clear spatially related patterns. In conclusion, the contributions of spatial and environmental variables to regional variation of algal assemblages are scaledependent. As for our study scale (*1,000 km), spatial control may be more important. Since spatial effects could obscure local environmental impacts on algal communities, appropriate study scale and statistical methods should be taken into account in algal bioassessment. We recommend inclusion of both algal richness and community composition in study of algal Handling Editor: Piet Spaak

Contribution of microspatial factors to benthic diatom communities

“…4). This was in accordance with Abbreviations of environmental variables as in Table 2 Hydrobiologia (2014) 732:49-60 55 Passy (2007) and Tang et al (2013), but not consistent with Potapova & Charles (2002), Urrea & Sabater (2009), Smucker & Vis (2011 and Göthe et al (2013). For example, Passy (2007) found that the unique contribution of spatial dataset (16%) to the explained diatom variance was higher than that of environmental variables (9%).…”

“…For example, Passy (2007) found that the unique contribution of spatial dataset (16%) to the explained diatom variance was higher than that of environmental variables (9%). Tang et al (2013) observed that spatial proportions were higher than that of environmental variables among the total explained variance in both algal metrics. However, Urrea & Sabater (2009) got a less influence of spatial factors (6.1%) than physicalchemistry variables (11.3%) on diatom community.…”

“…However, except for Tang et al (2013), no study to our knowledge has been reported in Asian countries such as China. More important ecological information could be lost if spatial variables are ignored, which could subsequently block biomonitoring endeavors by decreasing the strength of relationships between biotic and environmental variables (Smucker & Vis, 2011).…”

“…However, biomonitoring using diatoms often focuses on the local environmental conditions without regard to processes operating at larger spatial scales, although relationships between diatom community and large-scale land use variables were recently examined (Hill et al, 2000;Pan et al, 2004;Wang et al, 2005;Smucker & Vis, 2009). Spatial factors such as altitude or geographic location can also play important and potentially confounding roles in diatom distribution (Urrea & Sabater, 2009;Smucker & Vis, 2011;Tang et al, 2013).…”

Multi-scale temporal dynamics of epilithic algal assemblages: evidence from a Chinese subtropical mountain river network

“…Ten PCNM variables were obtained in the case of our study. Redundancy analysis (RDA) was then applied to detect relationships between algal relative abundance and PCNM variables (RDA-PCNM) (Borcard et al, 2011), accompanied by forward selection procedures for selecting important PCNM variables (Blanchet et al, 2008;Tang et al, 2013b). RDA was re-run with selected PCNM variables, and significant RDA axes were identified by the Monte Carlo permutation test (with 9,999 times).…”