An Intranuclear Microsporidium Associated with Acute Anemia in the Chinook Salmon,Oncorhynchus tshawytscha1

An intranuclear microsporidium is described from hemoblastic cells of the chinook salmon, Oncorhynchus tshawytscha. The infection is associated with an acute anemia in the fish. Up to 47% of the hemoblast nuclei were infected in anemic fish. The organisms, found only in spleen and kidney tissues, were 1–2 μm in diameter and consisted of vegetative and early sporulation forms. This microsporidium differs from known species which parasitize fish in its tissue location; however, the absence of mature spores and other life cycle stages precludes determination of its precise taxonomic identity.     

Behavioural response of fishes to increasing pH and dissolved oxygen: field and laboratory observations

• 1 Freshwater fish assemblages were sampled quantitatively before, during, and after naturally produced conditions of elevated pH and dissolved oxygen (DO) that occurred for 2–6h in two dense macrophyte beds. Field observations were followed by 3–4-h laboratory behavioural experiments in which banded killifish (Fundulus diaphanus), bluegill (Lepomis macrochirus), and striped bass (Morone saxatilis) were exposed to gradually increasing levels of: (i) pH; (ii) DO; and (iii) pH and DO.
• 2 Field results indicated that the macrophyte bed dominated by Hydrilla verticillata had a higher fish density than the mixed-species (H. verticillata and Vailisneria americana) bed, but beds did not differ significantly in fish species richness or biomass. Within beds, diel changes in fish species composition, richness, density and biomass were minor.
• 3 In the laboratory, avoidance of pH levels exceeding 9.5 was exhibited by all species tested. No significant response was observed to either DO increase treatments, or to pH and DO increase treatments.
• 4 Results suggest fishes do not avoid areas, such as dense macrophyte beds, when high pH levels (9.5–10.0) are accompanied by high DO levels (200–260% saturation).

     

Estimating the uncertainty in annual net ecosystem carbon exchange: spatial variation in turbulent fluxes and sampling errors in eddy-covariance measurements

Above forest canopies, eddy covariance (EC) measurements of mass (CO₂, H₂O vapor) and energy exchange, assumed to represent ecosystem fluxes, are commonly made at one point in the roughness sublayer (RSL). A spatial variability experiment, in which EC measurements were made from six towers within the RSL in a uniform pine plantation, quantified large and dynamic spatial variation in fluxes. The spatial coefficient of variation (CV) of the scalar fluxes decreased with increasing integration time, stabilizing at a minimum that was independent of further lengthening the averaging period (hereafter a ‘stable minimum’). For all three fluxes, the stable minimum (CV=9–11%) was reached at averaging times (τ_p) of 6–7 h during daytime, but higher stable minima (CV=46–158%) were reached at longer τ_p (>12 h) during nighttime. To the extent that decreasing CV of EC fluxes reflects reduction in micrometeorological sampling errors, half of the observed variability at τ_p=30 min is attributed to sampling errors. The remaining half (indicated by the stable minimum CV) is attributed to underlying variability in ecosystem structural properties, as determined by leaf area index, and perhaps associated ecosystem activity attributes. We further assessed the spatial variability estimates in the context of uncertainty in annual net ecosystem exchange (NEE). First, we adjusted annual NEE values obtained at our long‐term observation tower to account for the difference between this tower and the mean of all towers from this experiment; this increased NEE by up to 55 g C m^?2 yr^?1. Second, we combined uncertainty from gap filling and instrument error with uncertainty because of spatial variability, producing an estimate of variability in annual NEE ranging from 79 to 127 g C m^?2 yr^?1. This analysis demonstrated that even in such a uniform pine plantation, in some years spatial variability can contribute ～50% of the uncertainty in annual NEE estimates.