Wave exposure as a predictor of benthic habitat distribution on high energy temperate reefs

The new found ability to measure physical attributes of the marine environment at high resolution across broad spatial scales has driven the rapid evolution of benthic habitat mapping as a field in its own right. Improvement of the resolution and ecological validity of seafloor habitat distribution models has, for the most part, paralleled developments in new generations of acoustic survey tools such as multibeam echosounders. While sonar methods have been well demonstrated to provide useful proxies of the relatively static geophysical patterns that reflect distribution of benthic species and assemblages, the spatially and temporally variable influence of hydrodynamic energy on habitat distribution have been less well studied. Here we investigate the role of wave exposure on patterns of distribution of near-shore benthic habitats. A high resolution spectral wave model was developed for a 624 km2 site along Cape Otway, a major coastal feature of western Victoria, Australia. Comparison of habitat classifications implemented using the Random Forests algorithm established that significantly more accurate estimations of habitat distribution were obtained by including a fine-scale numerical wave model, extended to the seabed using linear wave theory, than by using depth and seafloor morphology information alone. Variable importance measures and map interpretation indicated that the spatial variation in wave-induced bottom orbital velocity was most influential in discriminating habitat classes containing the canopy forming kelp Ecklonia radiata, a foundation kelp species that affects biodiversity and ecological functioning on shallow reefs across temperate Australasia. We demonstrate that hydrodynamic models reflecting key environmental drivers on wave-exposed coastlines are important in accurately defining distributions of benthic habitats. This study highlights the suitability of exposure measures for predictive habitat modeling on wave-exposed coastlines and provides a basis for continuing work relating patterns of biological distribution to remotely-sensed patterns of the physical environment

Benthic habitat mapping in coastal waters of south–east Australia

The Victorian Marine Mapping Project will improve knowledge on the location, spatial distribution, condition and extent of marine habitats and associated biodiversity in Victorian State waters. This information will guide informed decision making, enable priority setting, and assist in targeted natural resource management planning. This project entails benthic habitat mapping over 500 square kilometers of Victorian State waters using multibeam sonar, towed video and image classification techniques. Information collected includes seafloor topography, seafloor softness and hardness (reflectivity), and information on geology and benthic flora and fauna assemblages collectively comprising habitat. Computerized semi-automated classification techniques are also being developed to provide a cost effective approach to rapid mapping and assessment of coastal habitats.Habitat mapping is important for understanding and communicating the distribution of natural values within the marine environment. The coastal fringe of Victoria encompasses a rich and diverse ecosystem representative of coastal waters of South-east Australia. To date, extensive knowledge of these systems is limited due to the lack of available data. Knowledge of the distribution and extent of habitat is required to target management activities most effectively, and provide the basis to monitor and report on their status in the future.<br /

Warrnambool and Corangamite land suitability decision framework study: technical report

The Warrnambool-Corangamite Land Suitability and Decision Making Framework (the Project) is a joint project between the Corangamite Shire Council (CSC), the Warrnambool City Council (WCC), the Victorian Local Sustainability Accord, and Deakin University.The Project was developed with the objective to establish a land suitability and decision-making framework for the WCC and CSC that can be applied to improve the basis for regional and local planning. The Project aims to improve approaches to regional planning to preserve highly productive agricultural land, protect and enhance the environment, whilst supporting sustainable regional development and settlement

Mangrove and saltmarsh distribution mapping and land cover change assessment for south-eastern Australia from 1991 to 2015

Coastal wetland ecosystems, such as saltmarsh and mangroves, provide a wide range of important ecological and socio-economic services. A good understanding of the spatial and temporal distribution of these ecosystems is critical to maximising the benefits from restoration and conservation projects. We mapped mangrove and saltmarsh ecosystem transitions from 1991 to 2015 in south-eastern Australia, using remotely sensed Landsat data and a Random Forest classification. Our classification results were improved by the addition of two physical variables (Shuttle Radar Topographic Mission (SRTM), and Distance to Water). We also provide evidence that the addition of post-classification, spatial and temporal, filters improve overall accuracy of coastal wetlands detection by up to 16%. Mangrove and saltmarsh maps produced in this study had an overall User Accuracy of 0.82–0.95 and 0.81–0.87 and an overall Producer Accuracy of 0.71–0.88 and 0.24–0.87 for mangrove and saltmarsh, respectively. We found that mangrove ecosystems in south-eastern Australia have lost an area of 1148 ha (7.6%), whilst saltmarsh experienced an overall increase in coverage of 4157 ha (20.3%) over this 24-year period. The maps developed in this study allow local managers to quantify persistence, gains, and losses of coastal wetlands in south-eastern Australia

Integrating multibeam backscatter angular response, mosaic and bathymetry data for benthic habitat mapping

Multibeam echosounders (MBES) are increasingly becoming the tool of choice for marine habitat mapping applications. In turn, the rapid expansion of habitat mapping studies has resulted in a need for automated classification techniques to efficiently map benthic habitats, assess confidence in model outputs, and evaluate the importance of variables driving the patterns observed. The benthic habitat characterisation process often involves the analysis of MBES bathymetry, backscatter mosaic or angular response with observation data providing ground truth. However, studies that make use of the full range of MBES outputs within a single classification process are limited. We present an approach that integrates backscatter angular response with MBES bathymetry, backscatter mosaic and their derivatives in a classification process using a Random Forests (RF) machine-learning algorithm to predict the distribution of benthic biological habitats. This approach includes a method of deriving statistical features from backscatter angular response curves created from MBES data collated within homogeneous regions of a backscatter mosaic. Using the RF algorithm we assess the relative importance of each variable in order to optimise the classification process and simplify models applied. The results showed that the inclusion of the angular response features in the classification process improved the accuracy of the final habitat maps from 88.5% to 93.6%. The RF algorithm identified bathymetry and the angular response mean as the two most important predictors. However, the highest classification rates were only obtained after incorporating additional features derived from bathymetry and the backscatter mosaic. The angular response features were found to be more important to the classification process compared to the backscatter mosaic features. This analysis indicates that integrating angular response information with bathymetry and the backscatter mosaic, along with their derivatives, constitutes an important improvement for studying the distribution of benthic habitats, which is necessary for effective marine spatial planning and resource managemen

Retrospektive Analyse der parasitologischen Untersuchungsergebnisse eines privaten Untersuchungslabors : Intestinale, respiratorische und vektorübertragene Parasitosen bei Hunden und Katzen in Deutschland (2004 – 2006)

Es wurden Untersuchungsbefunde von Kotproben, die einem kommerziellen tierärztlichen Untersuchungslabor von Tierärzten in den Jahren 2004–2006 zugesandt worden waren, ausgewertet. Somit konnten aktuelle Daten die Häufigkeit des Vorkommens von Parasitosen des Verdauungs- und Atmungstrakts bei Hunden und Katzen in Deutschland liefern. Bei Hunden wurden mittels ZnCl2-NaCl-Flotationsverfahren Toxocara-Eier in 4,6 %, Oozysten von Isospora spp. (Isospora canis, Isospora burrowsi/ohioensis) in 4,5 %, Hakenwurmeier in 1,3 %, Eier von Trichuris vulpis in 0,9 %, Eier von Toxascaris leonina und Capillaria spp. in je 0,6 %, Taeniiden-Eier in 0,27 %, Neospora caninumähnliche Oozysten in 0,13 %, Sarcocystis-Sporozysten in 0,06 % sowie Strongyloides-Eier in < 0,01 % aller Kotproben (n = 53.693) nachgewiesen. Giardiaoder Cryptosporidium-spezifische Koproantigene wurden mittels Kopro-ELISA (n = 53.534; n = 1.554 Proben) in 22,8 % bzw. 10 % der Proben festgestellt. Mittels Baermann-Trichterverfahren waren Crenosoma vulpis-Larven in 2,2 % und Angiostrongylus vasorum-Larven in 1 % von 509 Proben nachzuweisen. In weniger als 0,01 % aller eingesandten Kotproben wurden makroskopisch Proglottiden von Taenia spp., Mesocestoides spp. oder Diplopylidium/Joyeuxiella spp. gefunden. Bei Katzen wurden im Flotationsverfahren Toxocara-Eier in 4,8 %, Oozysten von Isospora spp. (Isospora felis, Isospora rivolta) in 3,8 %, Capillaria spp.-Eier in 0,5 %, Toxoplasma gondii-ähnliche Oozysten in 0,4 %, Taeniiden-Eier in 0,2 %, Hakenwurmeier und Sarcocystis-Sporozysten in je 0,1% sowie Toxascaris-Eier in 0,04 % der Kotproben (n = 26.491) nachgewiesen. Giardia-(n = 26.092 Proben) oder Cryptosporidium-spezifische (n = 624 Proben) Koproantigene wurden mittels Kopro- ELISA in 15,4 % bzw. 8,3 % der Proben festgestellt. Larven von Aelurostrongylus abstrusus waren mittels Baermann-Trichterverfahren in 2,6 % von 114 Kotproben zu finden. Weniger als 0,1 % aller eingesandten Kotproben enthielten Proglottiden von Taenia spp., Mesocestoides spp., Dipylidium caninum oder Diplopylidium/Joyeuxiella spp. Die mit dem indirekten Immunfluoreszenz-Antikörper-Test ermittelte Seroprävalenz von T. gondii-spezifischen IgM-Antikörpern (Titer &#8805; 1:16) und IgG-Antikörpern (Titer &#8805; 1:64) betrug 6,2 % (n = 2.616 Seren) bzw. 38,3 % (n = 3.693 Seren). Mischinfektionen mit Einzellern, Nematoden und Cestoden konnten nachgewiesen werden. Bei den Giardia-Koproantigen positiven Hunden im Alter zwischen 3 und 6 Monate besteht eine erhöhte Wahrscheinlichkeit, dass sie auch I. burrowsi/ohioensis- Oozysten ausscheiden. Bei der gleichen Altersklasse ist es eher unwahrscheinlich, dass sie eine Mischinfektion mit T. canis und Giardien bzw. I. canis aufweisen. Katzen, die Ausscheider von Giardia-Koproantigen und T. cati-Eiern und jünger als 6 Monate sind, scheiden höchst wahrscheinlich auch keine I. felis-Oozysten über den Kot aus. Bei ganz jungen Katzen (< 3 Monate) ist eine Mischinfektion mit Giardien und T. cati nicht zu erwarten. Direktnachweise von Babesia canis, Mikrofilarien und Hepatozoon canis gelangen in 2,1%, 0,2% bzw. 0,05% der Giemsa-gefärbten Blutausstriche (n = 7.923). Mikrofilarien wurden in 4,5% der mit dem Knott-Test untersuchten Proben (n = 440) gefunden. Zirkulierendes Dirofilaria immitis-Antigen war mittels ELISA in 1,4% der Proben (n = 9.381) festzustellen. Mittels PCR wurden B. canis-DNA in 3,3% der Blutproben (n = 15.555) und Leishmania infantum-DNA in 11 % der eingesandten Proben (n = 301) nachgewiesen. Antikörper gegen L. infantum oder B. canis wurden in 23,5% bzw. 11,5% der mittels IFAT untersuchten Serumproben (n = 23.665 bzw. 2.653) festgestellt. Leishmanien-Antikörper wurden bei den aus dem Ausland stammenden Hunden (80,6 %) und bei 4 einheimischen Hunden ohne Reiseanamnese nachgewiesen. Im Blutausstrich konnten Babesien-Merozoiten häufiger bei den aus Deutschland stammenden Tieren als bei den in anderen europäischen Ländern geborenen Hunden diagnostiziert werden. Alle auf Babesien-DNA untersuchte Hunde (n=15) aus Deutschland ohne Auslandsaufenthalt waren positiv.Faecal samples from 53.693 dogs and 26.491 cats, routinely submitted to a private veterinary laboratory between 2004 and 2006 were examined using a ZnCl2-NaCl flotation method. Toxocara canis eggs were detected in 4,6 % of canine samples, followed concerning the frequency of occurrence by Isospora spp. oocysts (Isospora canis, Isospora burrowsi/ohioensis; 4,5 % ), hookworm eggs (1,3 %), Trichuris vulpis eggs (0,9 %), Toxascaris leonina and Capillaria spp. eggs (0,6 %), taeniid eggs (0,27 %), Neospora caninum-like oocysts (0,13 %), Sarcocystis spp. sporocysts (0,06 %) and Strongyloides spp. eggs (< 0,01 %). Giardia (n = 53.534) and Cryptosporidium (n = 1.554) coproantigens were detected by ELISA in 22,8 % and 10 % of canine samples, respectively. Crenosoma vulpis larvae were detected, using the Baermann technique, in 2,2 % and Angiostrongylus vasorum larvae in 1 % of a total of 509 samples, respectively. Tapeworms (Taenia spp., Mesocestoides spp. and Diplopylidium/Joyeuxiella spp.) were detected in < 0,01 % of the faecal samples . The following gastrointestinal parasites were recorded in feline faecal samples (prevalences in brackets): Toxocara cati (4,8 %), Isospora spp. (Isospora felis, Isospora rivolta; 3,8 %), Capillaria spp. (0,5 %), Toxoplasma gondii-like oocysts (0,4 %), taeniids (0,2 %), hookworms, Sarcocystis spp. (0,1%), and Toxascaris leonina (0,04 %). Giardia (n = 26.092 samples) and Cryptosporidium (n = 624 samples) coproantigens were detected in 15,4 % and 8,3 % feline samples, respectively, using ELISA. The prevalence of Aelurostrongylus abstrusus was 2,6 % in a total of 114 faecal samples detected by Baermann technique. The segments of Taenia spp., Mesocestoides spp., Dipylidium caninum and Diplopylidium/Joyeuxiella spp. were found in < 0,1 % of feline samples. IgM (&#8805; 1:16) and IgG (&#8805; 1:64) antibodies against T. gondii were found in 6,2 % of 2.616 sera and in 38,3 % of 3.693 sera, respectively, tested by indirect fluorescent antibody test (IFAT), respectively. 208 SUMMARY Multiple parasitisation by protozoan and helminths in canine and feline faecal samples was detected. Young dogs (3 – 6 months) positive for Giardia coproantigens were at increased risk to shed also I. burrowsi/ohioensis oocysts, compared to Giardia negative puppies. Kittens positive for Giardia coproantigen younger than 6 months are not at increased risk to shed also I. felis oocysts when compared to Giardia negative kittens. Very young cats (< 3 months) are also not at increased risk to be infected with multiple parasite species (Giardia and T. cati). Blood samples were taken from dogs with a history of importation from or travelling to countries in the south or south-east of Europe, or showing clinical signs related to some vector-borne diseases. The screening of Giemsa-stained smears of 7.923 blood samples resulted in detection of Babesia canis (2,1%), microfilariae (0,2%) and Hepatozoon canis (0,05%). Knott’s Test for microfilariae was positive in 4,5% of 440 dogs. Heartworm antigen was detected by ELISA in 1,4% of 9.381 samples. Of the 15.555 and 301 blood samples analysed, 3,3 % were positive for B. canis DNA and 11 % were positive for Leishmania infantum by PCR. Antibodies against L. infantum DNA and B. canis were detected in 23,5% (n = 23.665) and 11,5% (n = 2.653) of blood samples by IFAT, respectively. Antibodies against L. infantum were detected in 80,6 % of dogs imported from endemic areas and in 4 German dogs without travel history. Babesia trophozoites were found more frequently in Giemsa-stained smears from dogs born in Germany when compared to blood samples of dogs originating from south or south-east European countries. A total of 15 blood samples of German dogs which have never been abroad, was positive for Babesia DNA

High variability of Blue Carbon storage in seagrass meadows at the estuary scale

Seagrass meadows are considered important natural carbon sinks due to their capacity to store organic carbon (Corg) in sediments. However, the spatial heterogeneity of carbon storage in seagrass sediments needs to be better understood to improve accuracy of blue carbon assessments, particularly when strong gradients are present. We performed an intensive coring study within a sub-tropical estuary to assess the spatial variability in sedimentary Corg associated with seagrasses, and to identify the key factors promoting this variability. We found a strong spatial pattern within the estuary, from 52.16 mg Corg cm-3 in seagrass meadows in the upper parts, declining to 1.06 mg Corg cm-3 in seagrass meadows at the estuary mouth, despite a general gradient of increasing seagrass cover and seagrass habitat extent in the opposite direction. The sedimentary Corg underneath seagrass meadows came principally from allochthonous (non-seagrass) sources (~70-90%), while the contribution of seagrasses was low (~10-30%) throughout the entire estuary. Our results showed that Corg stored in sediments of seagrass meadows can be highly variable within an estuary, attributed largely to accumulation of fine sediments and inputs of allochthonous sources. Local features and the existence of spatial gradients must be considered in blue carbon estimates in coastal ecosystems

Corner inlet and Nooramunga habitat mapping project

Deakin University and the University of Tasmania were commissioned by Parks Victoria (PV) to create two updated habitat maps for areas within the Corner Inlet and Nooramunga Marine and Coastal Park and Ramsar area. The team obtained a ground-truth data set using in situ video and still photographs. This dataset was used to develop and assess predictive models of benthic marine habitat distributions incorporating data from both ALOS (Advanced Land Observation Satellite) imagery atmospherically corrected 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, returning overall accuracies &gt;73 % and kappa values &gt; 0.62 for both study localities. Habitats predicted with highest accuracies included Zosteraceae in Nooramunga (91 %), reef in Corner Inlet (80 %), and bare sediment (no-visible macrobiota/no-visible seagrass classes; both &gt; 76 %). The majority of classification errors were due to the misclassification of areas of sparse seagrass as bare sediment. For the Corner Inlet study locality the no-visible macrobiota (10,698 ha), Posidonia (4,608 ha) and Zosteraceae (4,229 ha) habitat classes covered the most area. In Nooramunga no-visible seagrass (5,538 ha), Zosteraceae (4,060 ha) and wet saltmarsh (1,562 ha) habitat classes were most dominant.In addition to the commissioned work preliminary change detection analyses were undertaken as part of this project. These analyses indicated shifts in habitat extents in both study localities since the late 1990s/2000. In particular, a post-classification analysis highlighted that there were considerable increases in seagrass habitat (primarily Zosteraceae) throughout the littoral zones and river/creek mouths of both study localities. Further, the numerous channel systems remained stable and were free of seagrass at both times. A substantial net loss of Posidonia in the Corner Inlet locality is likely but requires further investigation due to potential misclassifications between habitats in both the 1998 map (Roob et al. 1998) and the current mapping. While the unsupervised Independent Components Analysis (ICA) change detection technique indicated some changes in habitat extent and distribution, considerable areas of habitat change observed in the post-classification approach are questionable, and may reflect misclassifications rather than real change. A particular example of this is an apparent large decrease in Zosteraceae and increase in Posidonia being related to the classification of Posidonia beds as Zosteraceae in the 1998 mapping. Despite this, we believe that changes indicated by both the ICA and post-classification approaches have a high likelihood of being &lsquo;actual&rsquo; change. A pattern of gains and losses of Zosteraceae in the region north of Stockyard channel is an example of this. Further analyses and refinements of approaches in change detection analyses such as would improve confidence in the location and extent of habitat changes over this time period.This work has been successful in providing new baseline maps 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. <br /

Exploring spatiotemporal trends in commercial fishing effort of an abalone fishing zone: a GIS-based hotspot model

Assessing patterns of fisheries activity at a scale related to resource exploitation has received particular attention in recent times. However, acquiring data about the distribution and spatiotemporal allocation of catch and fishing effort in small scale benthic fisheries remains challenging. Here, we used GIS-based spatio-statistical models to investigate the footprint of commercial diving events on blacklip abalone (Haliotis rubra) stocks along the south-west coast of Victoria, Australia from 2008 to 2011. Using abalone catch data matched with GPS location we found catch per unit of fishing effort (CPUE) was not uniformly spatially and temporally distributed across the study area. Spatial autocorrelation and hotspot analysis revealed significant spatiotemporal clusters of CPUE (with distance thresholds of 100\u27s of meters) among years, indicating the presence of CPUE hotspots focused on specific reefs. Cumulative hotspot maps indicated that certain reef complexes were consistently targeted across years but with varying intensity, however often a relatively small proportion of the full reef extent was targeted. Integrating CPUE with remotely-sensed light detection and ranging (LiDAR) derived bathymetry data using generalized additive mixed model corroborated that fishing pressure primarily coincided with shallow, rugose and complex components of reef structures. This study demonstrates that a geospatial approach is efficient in detecting patterns and trends in commercial fishing effort and its association with seafloor characteristics