Jamie Colquhoun

Habitat specialization in tropical continental shelf demersal fish assemblages

The implications of shallow water impacts such as fishing and climate change on fish assemblages are generally considered in isolation from the distribution and abundance of these fish assemblages in adjacent deeper waters. We investigate the abundance and length of demersal fish assemblages across a section of tropical continental shelf at Ningaloo Reef, Western Australia, to identify fish and fish habitat relationships across steep gradients in depth and in different benthic habitat types. The assemblage composition of demersal fish were assessed from baited remote underwater stereo-video samples (n = 304) collected from 16 depth and habitat combinations. Samples were collected across a depth range poorly represented in the literature from the fringing reef lagoon (1–10 m depth), down the fore reef slope to the reef base (10–30 m depth) then across the adjacent continental shelf (30–110 m depth). Multivariate analyses showed that there were distinctive fish assemblages and different sized fish were associated with each habitat/depth category. Species richness, MaxN and diversity declined with depth, while average length and trophic level increased. The assemblage structure, diversity, size and trophic structure of demersal fishes changes from shallow inshore habitats to deeper water habitats. More habitat specialists (unique species per habitat/depth category) were associated with the reef slope and reef base than other habitats, but offshore sponge-dominated habitats and inshore coral-dominated reef also supported unique species. This suggests that marine protected areas in shallow coral-dominated reef habitats may not adequately protect those species whose depth distribution extends beyond shallow habitats, or other significant elements of demersal fish biodiversity. The ontogenetic habitat partitioning which is characteristic of many species, suggests that to maintain entire species life histories it is necessary to protect corridors of connected habitats through which fish can migrate.

A Standardised Vocabulary for Identifying Benthic Biota and Substrata from Underwater Imagery: The CATAMI Classification Scheme

Imagery collected by still and video cameras is an increasingly important tool for minimal impact, repeatable observations in the marine environment. Data generated from imagery includes identification, annotation and quantification of biological subjects and environmental features within an image. To be long-lived and useful beyond their project-specific initial purpose, and to maximize their utility across studies and disciplines, marine imagery data should use a standardised vocabulary of defined terms. This would enable the compilation of regional, national and/or global data sets from multiple sources, contributing to broad-scale management studies and development of automated annotation algorithms. The classification scheme developed under the Collaborative and Automated Tools for Analysis of Marine Imagery (CATAMI) project provides such a vocabulary. The CATAMI classification scheme introduces Australian-wide acknowledged, standardised terminology for annotating benthic substrates and biota in marine imagery. It combines coarse-level taxonomy and morphology, and is a flexible, hierarchical classification that bridges the gap between habitat/biotope characterisation and taxonomy, acknowledging limitations when describing biological taxa through imagery. It is fully described, documented, and maintained through curated online databases, and can be applied across benthic image collection methods, annotation platforms and scoring methods. Following release in 2013, the CATAMI classification scheme was taken up by a wide variety of users, including government, academia and industry. This rapid acceptance highlights the scheme’s utility and the potential to facilitate broad-scale multidisciplinary studies of marine ecosystems when applied globally. Here we present the CATAMI classification scheme, describe its conception and features, and discuss its utility and the opportunities as well as challenges arising from its use.

The sponge gardens of Ningaloo Reef, Western Australia

Preliminary results from biodiversity surveys in the deeper waters of Ningaloo Marine Park, Western Australia revealed that while much of the area is composed of sediments and rhodolith fields with low densities of macro- epibenthos, locally dense and extensive filter feeding communities exist. They were distinctly dominated by demosponges, both in biomass and diversity. A subsample of dominant taxa determined by fresh weight yielded 155 different demosponge species from over 350 transects between 18-102 m depth. Data from three successive years of sampling indicated that only a few species were ubiquitous, suggesting that as minor species are identified the cumulative species list will significantly exceed the present species record. This implies greatly enhanced biodiversity values associated with Ningaloo Marine Park, complementing records attributed to the shallow coral reef environment. The richness of the observed filter feeding communities adds additional weight to the increasing perception of Australia as a global hotspot for Porifera biodiversity.

Towards image-based marine habitat classification

It is now fairly routine to quasi-automatically generate acoustic bathymetry and optical mosaics from properly instrumented Autonomous Underwater Vehicles (AUVs). However, further analysis and interpretation of gathered data is needed to address tasks such as habitat characterization and monitoring. This analysis stage is performed by human experts which limits the amount and speed of data processing. While it is unlikely that machines will match humans at fine-scale classification, machines can now perform preliminary, coarser classification to provide timely and relevant feedback to assist human decisions and enable adaptive AUV behavior. This paper presents a preliminary investigation into using a state-of-art object recognition system to classify marine habitat imagery based on labeled examples. We show that performance for such approaches can suffer with typical underwater imagery and present some of the causes for this. We propose modifications that make such a system suitable for automated coarse habitat classification and discuss experiences and results with three applications. The first corresponds to towed imagery from Ningaloo and Scott Reef, Western Australia. The second corresponds to AUV imagery near Hydrographers passage, Queensland. The third application demonstrates adaptive surveying using the output of the modified classification system.

Topic-based habitat classification using visual data

It is now common to quasi-automatically generate acoustic bathymetry and optical mosaics from instrumented Autonomous Underwater Vehicles (AUVs). However, further analysis and interpretation of gathered data is needed to address tasks such as habitat characterization and monitoring. This analysis stage is performed by human experts which limits the amount and speed of data processing. While it is unlikely that machines will match humans at fine-scale classification, machines can now perform preliminary, coarser classification to provide timely and relevant feedback to assist human decisions and enable adaptive AUV behavior. This paper presents a preliminary investigation into using a ‘bag of features’ object recognition system for unsupervised clustering of marine habitat imagery. In addition to directly using the high dimensional signature vectors, we also perform clustering based on a low dimensional topic model of the images. We use an AUV transect in the Great Barrier Reef that covers distinct habitat to illustrate the behavior of hierarchical clustering using both representations. Results suggest that both approaches generate clusters of images that are easily recognizable by humans, with significant computational gains to be made by using a topic-based model.

Effective Benthic Surveying with Autonomous Underwater Vehicles

The finite velocity and battery life of an AUV impose constraints on the extents of surveys and the spatial density of data recorded. Using a Gaussian process approach, we develop a method for quantifying the survey error resulting from spatial undersampling of the sample field. We also show how the Gaussian process model can be used to predict the information gain from a proposed AUV action. These techniques are demonstrated using a real world data-set collected during deployments at Ningaloo Marine Park, Western Australia.