Habitat segregation by fishes in western Taiwan rivers

Abstract

Summary Habitat segregation has been recognized as an important means of resource partitioning in river fish assemblages. Association between river fishes and habitat features were identified through principal component analysis (PCA). Fish and habitat data were collected in the Kaoping, Tsengwen, Choshui, and Tatu rivers from July 1997 to June 1999 in western Taiwan. Electrofishing was used to collect fishes in grids, and environment variables in the sampled areas were measured immediately after sampling. The first four PCs of depth, velocity, cover, substrate and water temperature variables explained 92.3% of the total variance in habitat characteristics. Varicorhinus barbatulus, Acrossocheilus formosanus and Tilapia mossambica in the upper right quadrant, indicated preference for deep‐water habitat. Species usually associated with shallow water habitat include several minnow species. Rhinogobius brunneus, Hemimyzon formosanum, Sinogastromyzon puliensis and Crossostoma lacustre distribution is centred in the upper and lower left, indicating preference for shallow water. Principal component (PC) scores on Zacco pachycephalus, Abbottina brevirostris and Hemiculter leucisculus appeared to have no special preference. Differences in habitat use exhibited by these species may be related to success in finding food and avoiding predators.

Effect of body size on microhabitat preferences in stream-dwelling fishes

“…However, some species showed significant correlations between body size and the distance from the stream bank. The massive presence of juveniles close to the stream bank can be related to their search for protection from predators (Rowe, Smith, & Baker, ; Yu & Lee, ), foraging habits (Crow, Closs, Waters, Booker, & Wallis, ; Persson & Greenberg, ), and occupation of suitable habitats (Lobón‐Cerviá & Rincón, ). In these species, the relationship between body size and microhabitat preference was confirmed, e.g., Mimagoniates microlepis and Scleromystax barbatus .…”

“…There is also evidence that individual fish use specific habitats in streams (Moyle & Baltz, ; Roff, ), and differences in the microhabitat structure allow the coexistence of the species (Gjelland, Bohn, & Amundsen, ). In stream‐dwelling fish assemblages, different individuals use different habitats, which allows resource partitioning (Herder & Freyhof, ; Leal, Junqueira, & Pompeu, ; Yu & Lee, ). Although individual fish select habitat types (Wootton, ), intraspecific variability in habitat use is poorly known.…”

Food resource partitioning between stream-dwelling Arctic charr Salvelinus alpinus (L.), Atlantic salmon Salmo salar L. and alpine bullhead Cottus poecilopus Heckel, 1836: an example of water column segregation

“…That said, habitat segregation may also be a key factor for food resource partitioning (e.g. Schoener, 1974;Jepsen et al, 1997;Yu & Lee, 2005), but resources can also be shared because species may adopt important adaptive features in order to reduce interspecific competition (e.g. Nakano et al, 1999;Kronfeld-Schor & Dayan, 2003;.…”

Spatial and temporal variation of fish assemblages and their associations to habitat variables in a mountain stream of north Tiaoxi River, China

“…Habitat variables, such as current velocity (Yu and Lee 2002), water temperature (Kadye et al 2008;Velázque-Velázquez et al 2008), distance to source (Vlach et al 2005), depth (Vlach et al 2005;Kadye et al 2008), stream width (Gerhard et al 2004), substrate (Vlach et al 2005;Kadye et al 2008), altitude (Magalhães et al 2002;Ferreira et al 2007; Kadye et al 2008), conductivity (Yu and Lee 2002), chlorophyll-a (Blanc et al 2001), climate (Magalhães et al 2002;Menni et al 2005;Ferreira et al 2007) et al, have all been shown to influence fish assemblages. To study fish species distribution and habitat requirements more accurately, the use of different uni-or multivariate methods to has increased over the last 20 years (Lepš and Šmilauer 2003) such as regression models (Filipe et al 2004), Canonical Correspondence Analysis (CCA) (Ferreira et al 2007;Jin et al 2010), Principle Component Analysis (PCA) and Non-metric Multi-Dimensional Scaling (NMDS) (Yu and Lee 2005;Jin et al 2007;Gerhard et al 2004).…”