Mary A. Young

Multivariate Prediction of Rockfish Habitat Suitability in Cordell Bank National Marine Sanctuary and Del Monte Shalebeds, California, USA

Due to the decline of fisheries throughout the world, there is an ever-increasing demand among fisheries managers for more and better data regarding the distribution and abundance of commercially important fishes. Along the Pacific coast of North America, there are insufficient stock data for most rockfish species, which compose one of the most valuable commercial and recreational fisheries in California. One approach being explored for increasing our understanding of fish distribution patterns and potentially generating stock assessment data over large areas is the use of habitat-based assessment. The general hypothesis is that because rockfish are not randomly distributed across habitats, it should be possible to model and predict their distribution and abundance based on habitat maps and biological data. Furthermore, to the extent that these models are robust and portable, they should be applicable across a variety of locations and physical settings. We attempt to test these hypotheses using predictive models created for two species of rockfish [Sebastes flavidus (yellowtail) and S. rosaceus (rosy)] within Cordell Bank National Marine Sanctuary (CBMNS). These models, created as part of a recent study, were applied to and tested against distribution data from a previous study of the two species in question at Del Monte shalebeds in Monterey Bay, California. The general linear models (GLMs) were created using rugosity, slope, aspect, depth, and topographic position index analyses of bathymetric digital elevation along with presence/absence data for the two species of rockfish. The model for S. flavidus generated at CBNMS proved to be at least as efficient at predicting yellowtail rockfish distribution at Del Monte as in the setting in which it was created, while the model for S. rosaceus failed to predict rosy rockfish distribution at Del Monte with any reliability.

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.

Applications of unmanned aerial vehicles in intertidal reef monitoring

Monitoring of intertidal reefs is traditionally undertaken by on-ground survey methods which have assisted in understanding these complex habitats; however, often only a small spatial footprint of the reef is observed. Recent developments in unmanned aerial vehicles (UAVs) provide new opportunities for monitoring broad scale coastal ecosystems through the ability to capture centimetre resolution imagery and topographic data not possible with conventional approaches. This study compares UAV remote sensing of intertidal reefs to traditional on-ground monitoring surveys, and investigates the role of UAV derived geomorphological variables in explaining observed intertidal algal and invertebrate assemblages. A multirotor UAV was used to capture <1 cm resolution data from intertidal reefs, with on-ground quadrat surveys of intertidal biotic data for comparison. UAV surveys provided reliable estimates of dominant canopy-forming algae, however, understorey species were obscured and often underestimated. UAV derived geomorphic variables showed elevation and distance to seaward reef edge explained 19.7% and 15.9% of the variation in algal and invertebrate assemblage structure respectively. The findings of this study demonstrate benefits of low-cost UAVs for intertidal monitoring through rapid data collection, full coverage census, identification of dominant canopy habitat and generation of geomorphic derivatives for explaining biological variation.

Combining pixel and object based image analysis of ultra-high resolution multibeam bathymetry and backscatter for habitat mapping in shallow marine waters

Habitat mapping data are increasingly being recognised for their importance in underpinning marine spatial planning. The ability to collect ultra-high resolution (cm) multibeam echosounder (MBES) data in shallow waters has facilitated understanding of the fine-scale distribution of benthic habitats in these areas that are often prone to human disturbance. Developing quantitative and objective approaches to integrate MBES data with ground observations for predictive modelling is essential for ensuring repeatability and providing confidence measures for habitat mapping products. Whilst supervised classification approaches are becoming more common, users are often faced with a decision whether to implement a pixel based (PB) or an object based (OB) image analysis approach, with often limited understanding of the potential influence of that decision on final map products and relative importance of data inputs to patterns observed. In this study, we apply an ensemble learning approach capable of integrating PB and OB Image Analysis from ultra-high resolution MBES bathymetry and backscatter data for mapping benthic habitats in Refuge Cove, a temperate coastal embayment in south-east Australia. We demonstrate the relative importance of PB and OB seafloor derivatives for the five broad benthic habitats that dominate the site. We found that OB and PB approaches performed well with differences in classification accuracy but not discernible statistically. However, a model incorporating elements of both approaches proved to be significantly more accurate than OB or PB methods alone and demonstrate the benefits of using MBES bathymetry and backscatter combined for class discrimination.

Optimal soil carbon sampling designs to achieve cost-effectiveness: a case study in blue carbon ecosystems

Researchers are increasingly studying carbon (C) storage by natural ecosystems for climate mitigation, including coastal ‘blue carbon’ ecosystems. Unfortunately, little guidance on how to achieve robust, cost-effective estimates of blue C stocks to inform inventories exists. We use existing data (492 cores) to develop recommendations on the sampling effort required to achieve robust estimates of blue C. Using a broad-scale, spatially explicit dataset from Victoria, Australia, we applied multiple spatial methods to provide guidelines for reducing variability in estimates of soil C stocks over large areas. With a separate dataset collected across Australia, we evaluated how many samples are needed to capture variability within soil cores and the best methods for extrapolating C to 1 m soil depth. We found that 40 core samples are optimal for capturing C variance across 1000s of kilometres but higher density sampling is required across finer scales (100–200 km). Accounting for environmental variation can further decrease required sampling. The within core analyses showed that nine samples within a core capture the majority of the variability and log-linear equations can accurately extrapolate C. These recommendations can help develop standardized methods for sampling programmes to quantify soil C stocks at national scales.

Comparing repeated sidescan sonar measurements of benthic squid egg beds in Monterey Bay, CA

The squid Loligo opalescens lays its eggs in capsules attached to a flat sandy substrate. These capsules are usually clustered in so‐called egg beds. Earlier work in Monterey Bay, CA [Foote et al., J. Acoust. Soc. Am. 119, 844–856 (2006)] established the acoustic detectability of such egg beds. This work has now been extended in a new study performed in June 2006. An EdgeTech sidescan sonar was towed at nominal 5‐m height over the bottom. Measurements were made at 400 kHz, horizontal beamwidth 0.5 deg, along six parallel transects, with adjacent centerlines separated by 30 m. The swath width along each transect was 100 m. The transects were repeated on 10, 13, and 14 June. Data from areas where egg beds were densely concentrated, confirmed by drop video camera, have been excerpted. Comparisons of corresponding data, which are generally very strong, are quantified. The question is asked as to whether such sidescan sonar data can be used differentially to measure egg‐laying success or hatching over rather s...