Habitat classification of temperate marine macroalgal communities using bathymetric LiDAR

Here, we evaluated the potential of using bathymetric Light Detection and Ranging (LiDAR) to characterise shallow water (<30 m) benthic habitats of high energy subtidal coastal environments. Habitat classification, quantifying benthic substrata and macroalgal communities, was achieved in this study with the application of LiDAR and underwater video groundtruth data using automated classification techniques. Bathymetry and reflectance datasets were used to produce secondary terrain derivative surfaces (e.g., rugosity, aspect) that were assumed to influence benthic patterns observed. An automated decision tree classification approach using the Quick Unbiased Efficient Statistical Tree (QUEST) was applied to produce substrata, biological and canopy structure habitat maps of the study area. Error assessment indicated that habitat maps produced were primarily accurate (>70%), with varying results for the classification of individual habitat classes; for instance, producer accuracy for mixed brown algae and sediment substrata, was 74% and 93%, respectively. LiDAR was also successful for differentiating canopy structure of macroalgae communities (i.e., canopy structure classification), such as canopy forming kelp versus erect fine branching algae. In conclusion, habitat characterisation using bathymetric LiDAR provides a unique potential to collect baseline information about biological assemblages and, hence, potential reef connectivity over large areas beyond the range of direct observation. This research contributes a new perspective for assessing the structure of subtidal coastal ecosystems, providing a novel tool for the research and management of such highly dynamic marine environments. © 2014 by the authors; licensee MDPI, Basel, Switzerland.

Assessment of habitat representation across a network of marine protected areas with implications for the spatial design of monitoring

Networks of marine protected areas (MPAs) are being adopted globally to protect ecosystems and supplement fisheries management. The state of California recently implemented a coast-wide network of MPAs, a statewide seafloor mapping program, and ecological characterizations of species and ecosystems targeted for protection by the network. The main goals of this study were to use these data to evaluate how well seafloor features, as proxies for habitats, are represented and replicated across an MPA network and how well ecological surveys representatively sampled fish habitats inside MPAs and adjacent reference sites. Seafloor data were classified into broad substrate categories (rock and sediment) and finer scale geomorphic classifications standard to marine classification schemes using surface analyses (slope, ruggedness, etc.) done on the digital elevation model derived from multibeam bathymetry data. These classifications were then used to evaluate the representation and replication of seafloor structure within the MPAs and across the ecological surveys. Both the broad substrate categories and the finer scale geomorphic features were proportionately represented for many of the classes with deviations of 1-6% and 0-7%, respectively. Within MPAs, however, representation of seafloor features differed markedly from original estimates, with differences ranging up to 28%. Seafloor structure in the biological monitoring design had mismatches between sampling in the MPAs and their corresponding reference sites and some seafloor structure classes were missed entirely. The geomorphic variables derived from multibeam bathymetry data for these analyses are known determinants of the distribution and abundance of marine species and for coastal marine biodiversity. Thus, analyses like those performed in this study can be a valuable initial method of evaluating and predicting the conservation value of MPAs across a regional network.

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's 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.

Forests of the sea: predictive habitat modelling to assess the abundance of canopy forming kelp forests on temperate reefs

 Large brown seaweeds (kelps) form forests in temperate and boreal marine systems that serve as foundations to the structure and dynamics of communities. Mapping the distributions of these species is important to understanding the ecology of coastal environments, managing marine ecosystems (e.g., spatial planning), predicting consequences of climate change and the potential for carbon production. We demonstrate how combining seafloor mapping technologies (LiDAR and multibeam bathymetry) and models of wave energy to map the distribution and relative abundance of seaweed forests of Ecklonia radiata can provide complete coverage over hundreds of square kilometers. Using generalized linear mixed models (GLMMs), we associated observations of E. radiata abundance from video transects with environmental variables. These relationships were then used to predict the distribution of E. radiata across our 756.1km2 study area off the coast of Victoria, Australia. A reserved dataset was used to test the accuracy of these predictions. We found that the abundance distribution of E. radiata is strongly associated with depth, presence of rocky reef, curvature of the reef topography, and wave exposure. In addition, the GLMM methodology allowed us to adequately account for spatial autocorrelation in our sampling methods. The predictive distribution map created from the best GLMM predicted the abundance of E. radiata with an accuracy of 72%. The combination of LiDAR and multibeam bathymetry allowed us to model and predict E. radiata abundance distribution across its entire depth range for this study area. Using methods like those presented in this study, we can map the distribution of macroalgae species, which will give insight into ecological communities, biodiversity distribution, carbon uptake, and potential sequestration.

Predictive mapping of abalone fishing grounds using remotely-sensed LiDAR and commercial catch data

Defining the geographic extent of suitable fishing grounds at a scale relevant to resource exploitation for commercial benthic species can be problematic. Bathymetric light detection and ranging (LiDAR) systems provide an opportunity to enhance ecosystem-based fisheries management strategies for coastally distributed benthic fisheries. In this study we define the spatial extent of suitable fishing grounds for the blacklip abalone (Haliotis rubra) along 200 linear kilometers of coastal waters for the first time, demonstrating the potential for integration of remotely-sensed data with commercial catch information. Variables representing seafloor structure, generated from airborne bathymetric LiDAR were combined with spatially-explicit fishing event data, to characterize the geographic footprint of the western Victorian abalone fishery, in south-east Australia. A MaxEnt modeling approach determined that bathymetry, rugosity and complexity were the three most important predictors in defining suitable fishing grounds (AUC = 0.89). Suitable fishing grounds predicted by the model showed a good relationship with catch statistics within each sub-zone of the fishery, suggesting that model outputs may be a useful surrogate for potential catch.

Spatial abundance patterns and recruitment of a virus-affected commercial mollusc fishery

 Infectious pathogens figure prominently among those factors threatening marine wildlife. Mass mortality events caused by pathogens can fundamentally alter the structure of wild fish stocks and depress recruitment rates and yield. In the most severe instances, this can precipitate stock collapses resulting in dramatic economic losses to once valuable commercial fisheries. An outbreak of a herpes-like virus among commercially fished abalone populations in the south-west fishery of Victoria, Australia, during 2006-2007, has been associated with high mortality rates among all cohorts. Long-term records from fishery-independent surveys of blacklip abalone Haliotis rubra (Leach) enabled abundance from pre- and post-viral periods to be analysed to estimate stock density and biomass. The spatial distribution of abundance in relation to physical habitat variables derived from high-resolution bathymetric LiDAR data was investigated. Significant differences were observed in both measures between pre- and post-viral periods. Although there was some limited evidence of gradual stock improvement in recent years, disease-affected reefs have remained below productivity rates prior to the disease outbreak suggesting a reduction in larval availability or settlement success. This was corroborated by trends in sublegal sized blacklip abalone abundance that has yet to show substantial recovery post-disease. Abundance data were modelled as a function of habitat variables using a generalised additive model (GAM) and indicated that high abundance was associated with complex reef structures of coastal waters (<15 m). This study highlights the importance of long-term surveys to understand abalone recovery following mass mortality and the links between stock abundance and seafloor variability.

Nearshore-offshore-basin species diversity and body size variation patterns in Late Permian (Changhsingian) brachiopods

 B.V. Body size is a fundamental and defining character of an organism, and its variation in space and time is generally considered to be a function of its biology and interactions with its living environment. A great deal of body size related ecological and evolutionary research has been undertaken, mostly in relation to extant animals. Among the many body size-related hypotheses proposed and tested, the size-bathymetry relationship is probably the least studied. In this study, we compiled a global body size dataset of Changhsingian (Late Permian, ca. 254. Ma-252. Ma) brachiopod species from low-latitude areas (30°S-30°N) and analyzed their species diversity and body size distribution patterns in relation to the nearshore-offshore-basin bathymetric gradient. The dataset contained 1768 brachiopod specimens in 435 species referred to 159 genera and 9 orders, from 135 occurrences (localities) of 18 different palaeogeographic regions. Treating the whole of the Changhsingian Stage as a single time slice, we divided the nearshore-offshore-basin bathymetric gradient into three broad depth-related environments: nearshore, offshore and basinal environments, and compared how the species diversity and body size varied along this large-scale bathymetric gradient.Here, we report an array of complex patterns. First, we found a clear overall inverse correlation between species diversity and water depth along the nearshore-offshore-basin gradient, with most species concentrating in the nearshore environment. Second, when the median sizes of all low-latitude brachiopod species from the three environments were compared, we found that there was no significant size difference between the nearshore and offshore environments, suggesting that neither the wave base nor the hydrostatic pressure exerts a critical influence on the body size of brachiopods. On the other hand, the median sizes of brachiopods from the nearshore environment and, to a lesser extent, the offshore environment were found to be significantly larger than that of basinal brachiopods. This trend of significant size reduction in basinal brachiopods mirrors the relative low species diversity in the basinal environment, and neither can be easily explained by the tendency of decreasing food availability towards deeper sea environments. Rather, both trends are consistent with the hypothesis of an expanding Oxygen Minimum Zone (OMZ) in the bathyal (slope to deepsea) environments, where hypoxic to anoxic conditions are considered to have severely restricted the diversification of benthos and favored the relative proliferation of small-sized brachiopods. Finally, a significant difference was also found between eurybathic and stenobathic species in their body size response to the nearshore-offshore-basin gradient, in that eurybathic species (species found in all three environments) did not tend to change their body size significantly according to depth, whereas stenobathic forms (species restricted to a single environment) exhibit a decline in body size towards the basinal environment. This pattern is interpreted to suggest that bathymetrically more tolerant species are less sensitive to depth control with respect to their body size change dynamics, in contrast to stenobathic species which tend to grow larger in shallower water depths.

Accounting for uncertainty in volumes of seabed change measured with repeat multibeam sonar surveys

Seafloors of unconsolidated sediment are highly dynamic features; eroding or accumulating under the action of tides, waves and currents. Assessing which areas of the seafloor experienced change and measuring the corresponding volumes involved provide insights into these important active sedimentation processes. Computing the difference between Digital Elevation Models (DEMs) obtained from repeat Multibeam Echosounders (MBES) surveys has become a common technique to identify these areas, but the uncertainty in these datasets considerably affects the estimation of the volumes displaced. The two main techniques used to take into account uncertainty in volume estimations are the limitation of calculations to areas experiencing a change in depth beyond a chosen threshold, and the computation of volumetric confidence intervals. However, these techniques are still in their infancy and, as a result, are often crude, seldom used or poorly understood. In this article, we explored a number of possible methodological advances to address this issue, including: (1) using the uncertainty information provided by the MBES data processing algorithm CUBE, (2) adapting fluvial geomorphology techniques for volume calculations using spatially variable thresholds and (3) volumetric histograms. The nearshore seabed off Warrnambool harbour - located in the highly energetic southwest Victorian coast, Australia - was used as a test site. Four consecutive MBES surveys were carried out over a four-months period. The difference between consecutive DEMs revealed an area near the beach experiencing large sediment transfers - mostly erosion - and an area of reef experiencing increasing deposition from the advance of a nearby sediment sheet. The volumes of sediment displaced in these two areas were calculated using the techniques described above, both traditionally and using the suggested improvements. We compared the results and discussed the applicability of the new methodological improvements. We found that the spatially variable uncertainty derived from the CUBE algorithm provided the best results (i.e. smaller confidence intervals), but that similar results can be obtained using as a fixed uncertainty value derived from a reference area under a number of operational conditions.

Accounting for habitat and seafloor structure characteristics on southern rock lobster (Jasus edwardsii) assessment in a small marine reserve

Where to place marine protected areas (MPAs) and how much area they should cover are some of the most basic questions when designing MPAs. Based on the theory of island biogeography, larger reserves are likely to protect more species and individuals but smaller reserves have been shown to positively influence populations. In this study, we assess a localised population of the ecologically and economically important southern rock lobster (Jasus edwardsii) inside and outside a small reserve. We used standardised fishery assessment trapping methods to sample J. edwardsii populations inside a reserve and an adjacent area outside the reserve. The population characteristics of the captured individuals were compared inside and outside the reserve using t tests (male size, female size,number of reproductive females, number of individuals and biomass), and we found that there were significantly greater numbers and larger individuals and biomass inside the reserve. However, many assessments of MPA effectiveness are confounded by differences in habitat. To account for possible differences in habitat, we collected multibeam bathymetry data to allow us to characterise seafloor structure and video data to assign each sampling location to a biotope class based on macroalgae assemblages. Then, using generalised linear models (GLMs), we assessed differences in populations while accounting for habitat. The GLMs revealed that there was still a significant difference in populations inside the reserve despite habitat differences inside and outside the reserve. We demonstrate a methodological approach to provide a baseline data set to assess MPA effectiveness through time and measure how habitat may respond to indirect consequences of fishing or other human impacts at the species or ecosystem level. We also highlight some of the limitations in sampling design and data availability common in MPA studies and resulting implications for assessment.