Passive flow through an unstalked intertidal ascidian: Orientation and morphology enhance suspension feeding in Pyura stolonifera

Passive flow is believed to increase the gains and reduce the costs of active suspension feeding. We used a mixture of field and laboratory experiments to evaluate whether the unstalked intertidal ascidian Pyura stolonifera exploits passive flow. We predicted that its orientation to prevailing currents and the arrangement of its siphons would induce passive flow due to dynamic pressure at the inhalant siphon, as well as by the Bernoulli effect or viscous entrainment associated with different fluid velocities at each siphon, or by both mechanisms. The orientation of P. stolonifera at several locations along the Sydney-Illawarra coast (Australia) covering a wide range of wave exposures was nonrandom and revealed that the ascidians were con- sistently oriented with their inhalant siphons directed into the waves or backwash. Flume experiments using wax mod- els demonstrated that the arrangement of the siphons could induce passive flow and that passive flow was greatest when the inhalant siphon was oriented into the flow. Field exper- iments using transplanted animals confirmed that such an orientation resulted in ascidians gaining food at greater rates, as measured by fecal production, than when oriented perpendicular to the wave direction. We conclude that P. stolonifera enhances suspension feeding by inducing pas- sive flow and is, therefore, a facultatively active suspension feeder. Furthermore, we argue that it is likely that many other active suspension feeders utilize passive flow and, therefore, measurements of their clearance rates should be made under appropriate conditions of flow to gain ecolog- ically relevant results.

Sperm environment affects offspring quality in broadcast spawning marine invertebrates

The provisioning of offspring can have far-reaching consequences for later life in a wide range of organisms and generally this provisioning is thought to be under maternal influence or control. In experiments with a broadcast-spawning ascidian, we found that the size of offspring was determined by egg size and the abundance of sperm present during fertilization. Larger eggs were fertilized at low sperm concentrations, whilst smaller eggs were successfully fertilized at high sperm concentrations. These differences in fertilized egg size resulted in differences in the development rate, hatching success and mean size of the subsequent larvae. Our results suggest that, in contrast to females that reproduce by other mating systems, free-spawning mothers lack some control over the provisioning of offspring. Furthermore, because males can alter the sperm environment, they can exert paternal (non-genetic) control over key offspring characteristics.

Corner Inlet and Nooramunga Habitat Mapping Project – Part 2 : High resolution mapping and change detection analysis using 2011 Rapid Eye Satellite Image compared to 2009 ALOS Satellite Image

Building on a habitat mapping project completed in 2011, Deakin University was commissioned by Parks Victoria (PV) to apply the same methodology and ground-truth data to a second, more recent and higher resolution satellite image to create habitat maps for areas within the Corner Inlet and Nooramunga Marine and Coastal Park and Ramsar area. A ground-truth data set using in situ video and still photographs was used to develop and assess predictive models of benthic marine habitat distributions incorporating data from both RapidEye satellite imagery (corrected for atmospheric and water column effects by CSIRO) and LiDAR (Light Detection and Ranging) bathymetry. This report describes the results of the mapping effort as well as the methodology used to produce these habitat maps.

Overall accuracies of habitat classifications were good, with error rates similar to or better than the earlier classification (>73 % and kappa values > 0.58 for both study localities). The RapidEye classification failed to accurately detect Pyura and reef habitat classes at the Corner Inlet locality, possibly due to differences in spectral frequencies. For comparison, these categories were combined into a ‘non-seagrass’ category, similar to the one used at the Nooramunga locality in the original classification. Habitats predicted with highest accuracies differed from the earlier classification and were Posidonia in Corner Inlet (89%), and bare sediment (no-visible seagrass class) in Nooramunga (90%). In the Corner Inlet locality reef and Pyura habitat categories were not distinguishable in the repeated classification and so were combined with bare sediments. The majority of remaining classification errors were due to the misclassification of Zosteraceae as bare sediment and vice versa. Dominant habitats were the same as those from the 2011 classification with some differences in extent. For the Corner Inlet study locality the no-visible seagrass category remained the most extensive (9059 ha), followed by Posidonia (5,513 ha) and Zosteraceae (5,504 ha). In Nooramunga no-visible seagrass (6,294 ha), Zosteraceae (3,122 ha) and wet saltmarsh (1,562 ha) habitat classes were most dominant.

Change detection analyses between the 2009 and 2011 imagery were undertaken as part of this project, following the analyses presented in Monk et al. (2011) and incorporating error estimates from both classifications. These analyses indicated some shifts in classification between Posidonia and Zosteraceae as well as a general reduction in the area of Zosteraceae. Issues with classification of mixed beds were apparent, particularly in the main Posidonia bed at Nooramunga where a mosaic of Zosteraceae and Posidonia was seen that was not evident in the ALOS classification. Results of a reanalysis of the 1998-2009 change detection illustrating effects of binning of mixed beds is also provided as an appendix.

This work has been successful in providing baseline maps at an improved level of detail using a repeatable method meaning that any future changes in intertidal and shallow water marine habitats may be assessed in a consistent way with quantitative error assessments. In wider use, these maps should also allow improved conservation planning, advance fisheries and catchment management, and progress infrastructure planning to limit impacts on the Inlet environment.