Testing the influence of survey method for building species distribution models of marine fishes

Accurate estimates of fish species occurrence are important to any species’ assessments and habitat suitability model. However, surveys of marine fishes are often biased by method. Surveys of marine fishes are often biased by method. Such bias could influence the interpretation of any habitat suitability model. With increasing emphasis on non-destructive sampling, underwater video techniques are commonly used without a thorough understanding of their advantages and disadvantages. This study compared data collected from baited remote underwater stereo-video systems and towed-video systems to provide occurrence data to develop habitat suitability models of nine temperate marine fishes. While numerous studies have compared modelling approaches in terms of model performance (i.e. via AUC or Kappa) the point of this paper was to highlight how very sensiblelooking, well-performing (based on AUC) models can provide different predictions of habitat suitability depending on which dataset is used.

Seafloor mapping data, Victoria

The collection includes three major data types

- Seafloor structure information: Collation of seafloor structure information (bathymetry and softness hardness) collected using multibeam sonar systems as part of the Victorian Marine Habitat Mapping Project and bathymetric light detection and ranging data (LiDAR) collected as part of the future coats program. The geographic is Victorian State waters (~1400km2 multibeam sonar & ~4000 km2 for LiDAR as of 2012

- Video information: ~800 linear kilometres of precisely georeferenced towed video data from depths of 10-100m in Victorian coastal waters. 300 baited stereo video drops in Hopkins and Discovery Bay site locations. All video has been classified to the lowest taxonomic resolution possible with the video systems employed and converted to spatial data layers in the GIS environment.

- Habitat maps: Predicted habitat models integrating seafloor structure and video information developed using classification algorithms and remote sensing technologies.

Are we predicting the actual or apparent distribution of temperate marine fishes?

Planning for resilience is the focus of many marine conservation programs and initiatives. These efforts aim to inform conservation strategies for marine regions to ensure they have inbuilt capacity to retain biological diversity and ecological function in the face of global environmental change – particularly changes in climate and resource exploitation. In the absence of direct biological and ecological information for many marine species, scientists are increasingly using spatially-explicit, predictive-modeling approaches. Through the improved access to multibeam sonar and underwater video technology these models provide spatial predictions of the most suitable regions for an organism at resolutions previously not possible. However, sensible-looking, well-performing models can provide very different predictions of distribution depending on which occurrence dataset is used. To examine this, we construct species distribution models for nine temperate marine sedentary fishes for a 25.7 km2 study region off the coast of southeastern Australia. We use generalized linear model (GLM), generalized additive model (GAM) and maximum entropy (MAXENT) to build models based on co-located occurrence datasets derived from two underwater video methods (i.e. baited and towed video) and fine-scale multibeam sonar based seafloor habitat variables. Overall, this study found that the choice of modeling approach did not considerably influence the prediction of distributions based on the same occurrence dataset. However, greater dissimilarity between model predictions was observed across the nine fish taxa when the two occurrence datasets were compared (relative to models based on the same dataset). Based on these results it is difficult to draw any general trends in regards to which video method provides more reliable occurrence datasets. Nonetheless, we suggest predictions reflecting the species apparent distribution (i.e. a combination of species distribution and the probability of detecting it). Consequently, we also encourage researchers and marine managers to carefully interpret model predictions.

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 >73 % and kappa values > 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 > 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 ‘actual’ 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.

Aspects of the biology of Galaxias maculatus

The biology of three landlocked and a riverine population of Galaxias maculatus were examined in western Victoria, Australia. All systems supported reproducing populations of these fish, including Lake Corangamite which had salinities that on occasion reached 82. Spawning sites in Lake Corangamite were located in adjacent tributaries and not in the main lake as was the case for other populations. The smallest fish were found in the fresh water Lake Purrumbete and the largest in the hypersaline Lake Corangamite. The size at which 50% of the population attained sexual maturity varied across sites, with fish maturing at a smaller size in Lake Purrumbete, followed by the Merri River, Lake Bullen Merri and Lake Corangamite. Condition was higher in the freshwater Lake Purrumbete and there was no relationship between condition and temperature, conductivity, turbidity and pH; but there was a positive relationship between condition and dissolved oxygen. Length frequency analysis suggested that the majority of fishes live for a year.

Changes in muscle composition during the development of diving ability in the Australian fur seal

During development the Australian fur seal transitions from a terrestrial, maternally dependent pup to an adult marine predator. Adult seals have adaptations that allow them to voluntarily dive at depth for long periods, including increased bradycardic control, increased myoglobin levels and haematocrit. To establish whether the profile of skeletal muscle also changes in line with the development of diving ability, biopsy samples were collected from the trapezius muscle of pups, juveniles and adults. The proportions of different fibre types and their oxidative capacity were determined. Only oxidative fibre types (Type I and IIa) were identified, with a significant change in proportions from pup to adult. There was no change in oxidative capacity of Type I and IIa fibres between pups and juveniles but there was a two-fold increase between juveniles and adults. Myoglobin expression increased between pups and juveniles, suggesting improved oxygen delivery, but with no increase in oxidative capacity, oxygen utilisation within the muscle may still be limited. Adult muscle had the highest oxidative capacity, suggesting that fibres are able to effectively utilise available oxygen during prolonged dives. Elevated levels of total creatine in the muscles of juveniles may act as an energy buffer when fibres are transitioning from a fast to slow fibre type.

Evaluation of four supervised learning methods for benthic habitat mapping using backscatter from multi-beam sonar

An understanding of the distribution and extent of marine habitats is essential for the implementation of ecosystem-based management strategies. Historically this had been difficult in marine environments until the advancement of acoustic sensors. This study demonstrates the applicability of supervised learning techniques for benthic habitat characterization using angular backscatter response data. With the advancement of multibeam echo-sounder (MBES) technology, full coverage datasets of physical structure over vast regions of the seafloor are now achievable. Supervised learning methods typically applied to terrestrial remote sensing provide a cost-effective approach for habitat characterization in marine systems. However the comparison of the relative performance of different classifiers using acoustic data is limited. Characterization of acoustic backscatter data from MBES using four different supervised learning methods to generate benthic habitat maps is presented. Maximum Likelihood Classifier (MLC), Quick, Unbiased, Efficient Statistical Tree (QUEST), Random Forest (RF) and Support Vector Machine (SVM) were evaluated to classify angular backscatter response into habitat classes using training data acquired from underwater video observations. Results for biota classifications indicated that SVM and RF produced the highest accuracies, followed by QUEST and MLC, respectively. The most important backscatter data were from the moderate incidence angles between 30° and 50°. This study presents initial results for understanding how acoustic backscatter from MBES can be optimized for the characterization of marine benthic biological habitats. © 2012 by the authors.

How long should we ignore imperfect detection of species in the marine environment when modelling their distribution?

The application of the 'ecosystem approach' to marine conservation management demands knowledge of the distribution patterns of the target species or communities. This information is commonly obtained from species distribution models (SDMs). This article explores an important but rarely acknowledged assumption in these models: almost all species may be present, but simply not detected by the particular survey method. However, nearly all of these SDM approaches neglect this important characteristic. This leads to the violation of a fundamental assumption of these models, which presuppose the detection of a species is equal to one (i.e. at each survey locality, a species is perfectly detected). In this article, the concept of imperfect detection is discussed, how it potentially influences the prediction of species' distributions is examined, and some statistical methods that could be used to incorporate the detection probability of species in estimates of their distribution are suggested. The approaches discussed here could improve the collection and interpretation of marine biological survey data and provide a coherent way to incorporate detection probability estimates in the modelling of species distributions. This will ultimately lead to an unbiased and more rigorous understanding of the distribution of species in the marine environment.

Detecting patterns of change in benthic habitats by acoustic remote sensing

Changes in benthic habitats occur as a result of natural variation or human-induced processes. It is important to understand natural fine-scale inter-annual patterns of change to separate these signals from patterns of long-term change. Describing change from an acoustic remote sensing standpoint has been facilitated by the recent availability of full coverage swath acoustic datasets, but is limited by cost pressures associated with multiple surveys of the same area. We studied the use of landscape transition analysis as a means to differentiate seemingly random patterns of habitat change from systematic signals of habitat transition at a shallow (10 to 50 m depth) 18 km2 site on the temperate Australian continental shelf in 2006 and 2007. Supervised classifications for each year were accomplished using inde pendently collected highresolution swath acoustic and video reference data. Of the 4 representative biotic clas ses considered, signals of directional systematic changes occurred be tween a kelp-dominated class, a sessile invertebrate-dominated class and a mixed class of kelp and sessile invertebrates. We provide a detailed analysis of the components of the traditional change detection cross tabulation matrix, allowing identification of the strongest signals of systematic habitat transitions. Iden tifying patterns of habitat change is an important first step toward understanding the processes that drive them.

Age-related differences revealed in Australian fur seal Arctocephalus pusillus doriferus gut microbiota

The gut microbiota of Australian fur seals (Arctocephalus pusillus doriferus) was examined at different age classes using fluorescent in situ hybridisation (FISH) and 16S rRNA gene pyrosequencing. The FISH results indicated that in the fur seal groups, the predominant phyla are Firmicutes (22.14-67.33%) followed by Bacteroidetes (3.11-15.45%) and then Actinobacteria (1.4-5.9%) consistent with other mammals. Phylum Proteobacteria had an initial abundance of 1.8% in the 2-month-old pups, but < 1% of bacterial numbers for the other fur seal age groups. Significant differences did occur in the abundance of Clostridia, Lactobacilli and Bifidobacteria between 2 months pups and 9 months pups and adult fur seals. Results from the 16S rRNA gene pyrosequencing supported the FISH data and identified significant differences in the composition of Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria, Verrucomicrobia and Fusobacteria at all ages. Class Clostridia in phylum Firmicutes dominates the microbiota of the 2 months and 9 months seal pups, whilst class Bacilli dominates the 6 months pups. In addition, a high level of dissimilarity was observed between all age classes. This study provides novel insight into the gut microbiota of Australian fur seals at different age classes.

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.

Influence of sea-ice structure on minke whale and other krill-predator distribution

This thesis investigated the extent to which sea-ice structure and complexity could be measured to identify suitable feeding habitat for krill-eating predators such as the Antarctic minke whale (Balaenoptera bonaerensis). The results of this study suggest that the distribution and movement of minke whales is limited by the structure of the sea ice and that the sea ice influences minke whales' habitat use more than their overall distribution. Similar relationships were found for other krill predators such as Adelie penguins and crabeater seals.

Vertical movements of North Sea Cod

Various air-breathing marine vertebrates such as seals, turtles and seabirds show distinct patterns of diving behaviour. For fish, the distinction between different vertical behaviours is often less clear-cut, as there are no surface intervals to differentiate between dives. Using data from acoustic tags (n = 23) and archival depth recorders attached to cod Gadus morhua (n = 92) in the southern North Sea, we developed a quantitative method of classifying vertical movements in order to facilitate an objective comparison of the behaviour of different individuals. This method expands the utilisation of data from data storage tags, with the potential for a better understanding of fish behaviour and enhanced individual based behaviour for improved ecosystem modelling. We found that cod were closely associated with the seabed for 90% of the time, although they showed distinct seasonal and spatial patterns in behaviour. For example, cod tagged in the southern North Sea exhibited high rates of vertical movement in spring and autumn that were probably associated with migration, while the vertical movements of resident cod in other areas were much less extensive and were probably related to foraging or spawning behaviours. The full reasons underlying spatial and temporal behavioural plasticity by cod in the North Sea warrant further investigation.