Table PCR primers used in this study

Table 1.
PCR primers used in this Nintedanib study.
DesignationSequence (5′–3′)Reference
For deletional mutagenesis
aad9 BamFCGGGATCCTTGATTTTCGTTCGTGAATAC[14]
aad9 XbaRGCTCTAGATTATAATTTTTTTAATCTGTTATTTAAATAG[14]
spd729KOu/EcoFGAATTCGAATTAGCCAAGGATATAAGThis study
spd729KOu/XmaRCCCGGGATCTTTAGGAGATGTTATACGThis study
spd729KOd/XbaFTCTAGATCTGATGGTACAATGATAGTTAGTTTGTCThis study
spd729KOd/HindRAAGCTTGAATTTTATCAGCAATTTCACCThis study
PlyKOu/EcoFGAATTCGTAGCTCTTTATTTGCCTTTTCCThis study
PlyKOu/BamRGGATCCTCGATAACAACAAACTCATCGGThis study
PlyKOd/XbaFTCTAGAGGACAATACAGAAGTGAAGGCThis study
PlyKOd/HindRAAGCTTCTAGTCATTTTCTACCTTAThis study
aad9 F2GGAGGATGATTCCACGGTACCATTThis study
aad9 R2GGGAGAGAATTTTGTTAGCAGTTCGTThis study
For RT-PCR and real-time RT-PCR
spd729KOu/EcoFCATATGTCAGCACAAATTACGATTAACCAThis study
spd729KOu/XmaRCCCGGGCAAGACATCATCGTCACTCACCThis Nintedanib study
D39 Cps2A FwTGCGGGCATTTATGGAGTTGThis study
D39 Cps2A RvATCGGCTAGTGAGTAGCGTTThis study
D39 Cps2E orders FwGGTTCCTTTGATTCGAAAGGATGThis study

Qui Upper Psa Upper Oli Upper Gyr Upper Ins

Qui Upper 0.442 0.002
Psa Upper 0.381 0.002
Oli Upper 0.286 0.009
Gyr Upper 0.238 0.041
Ins Upper 0.234 0.041
Table options
We preceded the search for the best parsimonious RDA model by excluding the mostly highly correlated environmental variables (see variance inflation factor in Section 2.4). We considered a full model, including the set of environmental variables shown in Table 1, except temperature. Temperature PH-797804 a more appropriate variable for seasonal studies (e.g. Silva et al., 2006) or temporal gradients (McLusky, 1993). The variance inflation factors (vif) applied to the constrained variables in a full RDA model (based on sites centroid for the species data set) yielded the highest vif value (146) for sediment mud content. We recomputed the full model without mud as PH-797804 an environmental variable. The remaining environmental variables showed weak linear dependencies. The highest vif value (7.29) in this case corresponded to TN. The resulting full RDA model was significant (P < 0.05) and explained 94% of the observed variance ( Table 4).

Several studies recorded species decline from the

Several studies recorded species decline from the outer to the innermost fjord basin in temperate, sub Arctic and Arctic fjords (Hardangerfjord – Brattegard, 1966; Sydisfjördur – Hansen and Ingolfsson, 1993; Knight Inlet – Farrow et al., 1983; Hornsund fjord – Görlich et al., 1987; Kongsforden – Wlodarska-Kowalczuk and Pearson, 2004; Port Valdez – Blanchard et al., 2010). These patterns were often attributed to substrate changes along the fjord due to change in sedimentation rate gradient caused by glacier suspension release or river outflow. In addition, variability in factors like exposure, salinity, air and water temperature as well as biological factors including predation or TW37 for space is believed to influence species number in a fjordic environment (Brattegard, 1966; Hansen and Ingolfsson, 1993; Smith and Witman, 1999; Barnes and Kuklinski, 2004).
Also along the bathymetric range of the fjordic system variation in number of species and composition was recorded for several groups of organisms. This includes both mobile benthic organisms (Fosså and Brattegard, 1990) as well as sessile biota (Smith and Witman, 1999; Kuklinski et al., 2005). Observed patterns in a depth gradient were explained by differences in sediment properties, salinity differences, substrate heterogeneity, food preferences and food availability (Fosså and Brattegard, 1990; Smith and Witman, 1999; Kuklinski et al., 2006).