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.

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.

Quantitative experimental comparison of single-beam, sidescan, and multibeam benthic habitat maps

Map comparison is a relatively uncommon practice in acoustic seabed classification to date, contrary to the field of land remote sensing, where it has been developed extensively over recent decades. The aim here is to illustrate the benefits of map comparison in the underwater realm with a case study of three maps independently describing the seabed habitats of the Te Matuku Marine Reserve (Hauraki Gulf, New Zealand). The maps are obtained from a QTC View classification of a single-beam echosounder (SBES) dataset, manual segmentation of a sidescan sonar (SSS) mosaic, and automatic classification of a backscatter dataset from a multibeam echosounder (MBES). The maps are compared using pixel-to-pixel similarity measures derived from the literature in land remote sensing. All measures agree in presenting the MBES and SSS maps as the most similar, and the SBES and SSS maps as the least similar. The results are discussed with reference to the potential of MBES backscatter as an alternative to SSS mosaic for imagery segmentation and to the potential of joint SBES–SSS survey for improved habitat mapping. Other applications of map-similarity measures in acoustic classification of the seabed are suggested.

New approaches to predicting the distribution and dynamics of seafloor habitats

Marine spatial planners and managers need timely and accurate maps describing biological communities at the seafloor. This thesis employs state of the art sonar equipment and seafloor imaging techniques to answer some fundamental questions about how these communities are distributed through space and time.

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 management.

AUV location detection in an enclosed environment

Normally, experiments are done in a controlled environment so that different systems under test can be isolated. The added benefit is that the sensors used are a lot more accurate under controlled conditions. In the experiments perform on underwater robot localization, this was not the case. The sonar localization equipment use perform flawlessly in open water as it was designed to do, but poorly in an indoor pool. It is believed that the sonar had too much power causing too many reflections in the enclosed space. Unfortunately the experiments are better done in a pool so as to control the elements under test. This paper is the search to improve the equipment's accuracy in an enclosed environment by attempting to reduce the power of the sonar via mechanical means.

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.

The drowned apostles: the longevity of sea stacks over eustatic cycles

Cliffed rocky coasts are erosional environments, the remnants of which can be preserved as sea stacks as the shoreline retreats. These sea stacks form spectacular landscapes, such as the iconic Twelve Apostles in Victoria, Australia. However, they are ephemeral features formed on a centennial scale, continually eroding and collapsing, meaning that coasts characterised by sea stacks often have fewer features than when first described. The question arises then as to the longevity of such features and whether they can be preserved over eustatic cycles. The modern Twelve Apostles, of which 8 are still standing, are comprised of the Miocene Port Campbell Limestone and reach 45 m above sea level. Recent multibeam sonar data show five features around 6 km offshore, in 40-50 m water depth that appear to be relict sea stacks. Based on the morphology and geology of both the modern and drowned Apostles, it is inferred that the drowned and modern stacks evolved in a similar manner. While the modern sea stacks have an average height of 45 m, the drowned stacks have an average height of 4 m, suggesting a much greater age and also the possibility of multiple exposures to subaerial processes. The drowned stacks lay 655 m seaward of a drowned cliff averaging 14 m high which likely represents a former interstadial shoreline. This is much greater than the 91 m average distance between stack and cliff for the Modern Apostles, which may imply a more prolonged period of erosion along the drowned coastline.

Ocean governance and risk management

The high seas have always engendered a range of emotions and reactions from humans. Curiosity, fear, even terror, of this great expanse of ocean which cover 70 % of Earth the blue planet. Yet the sheer size of the oceans and the difficulty of transporting across them meant the high seas were largely ignored by the vast majority of humans for centuries. Humans were largely confined to land with the only interest in the seas being as trade routes and the defence of the land. In fact all the way up to the last quarter of the twentieth century a nations territorial sea extended only three nautical miles off shore the distance that a cannon ball could be fired.

This almost casual relationship to the oceans changed dramatically in the 1960s and 1970s as technology played an ever icnreasing role in the exploitation of the natural resources of the seas. Fishing was made far easier by being able to use sophisticated sonar systems to detect the fish and by advanced nets and vessels. But it was probably the technological ability to first find and then extract oil and gas off shore on continental shelfs, and at increasing depths, which stimulated interest in exploiting marine resources. Dreams of other deep sea mineral resources (e.g. manganese nodules) simply fuelled interest in the oceans, not to mentino some of the pharmaceuticals that were being discovered.

Contrasting patterns of population connectivity between regions in a commercially important mollusc Haliotis rubra: integrating population genetics, genomics and marine LiDAR data

Estimating contemporary genetic structure and population connectivity in marine species is challenging, often compromised by genetic markers that lack adequate sensitivity, and unstructured sampling regimes. We show how these limitations can be overcome via the integration of modern genotyping methods and sampling designs guided by LIDAR and SONAR datasets. Here we explore patterns of gene flow and local genetic structure in a commercially harvested abalone species (Haliotis rubra) from South Eastern Australia, where the viability of fishing stocks is believed to be dictated by recruitment from local sources. Using a panel of microsatellite and genome-wide SNP markers we compare allele frequencies across a replicated hierarchical sampling area guided by bathymetric LIDAR imagery. Results indicate high levels of gene flow and no significant genetic structure within or between benthic reef habitats across 1400 km of coastline. These findings differ to those reported for other regions of the fishery indicating that larval supply is likely to be spatially variable, with implications for management and long-term recovery from stock depletion. The study highlights the utility of suitably designed genetic markers and spatially informed sampling strategies for gaining insights into recruitment patterns in benthic marine species, assisting in conservation planning and sustainable management of fisheries.