Decision making for the conservation and management of coral reef biodiversity requires an understanding of spatial variability and distribution of reef habitat types. Despite the existence of very high-resolution remote sensing technology for nearly two decades, comprehensive assessment of coral reef habitats at national to regional spatial scales and at very high spatial resolution is still scarce. Here, we develop benthic habitat maps at a sub-national scale by analyzing large multispectral QuickBird imagery dataset covering ~686 km2 of the main shallow coral fringing reef along the southern border with Tanzania (4.68°S, 39.18°E) to the reef end at Malindi, Kenya (3.2°S, 40.1°E). Mapping was conducted with a user approach constrained by ground-truth data, with detailed transect lines from the shore to the fore reef. First, maps were used to evaluate the present management system’s effectiveness at representing habitat diversity. Then, we developed three spatial prioritization scenarios based on differing objectives: (i) minimize lost fishing opportunity; (ii) redistribute fisheries away from currently overfished reefs; and (iii) minimize resource use conflicts. We further constrained the priority area in each prioritization selection scenario based on optionally protecting the least or the most climate exposed locations using a model of exposure to climate stress. We discovered that spatial priorities were very different based on the different objectives and on whether the aim was to protect the least or most climate-exposed habitats. Our analyses provide a spatially explicit foundation for large-scale conservation and management strategies that can account for ecosystem service benefits.
Remote Sensing and GIS
Reef corals typically contain a number of pigments, mostly due to their symbiotic relationship with photosynthetic dinoflagellates. These pigments usually vary in presence and concentration and influence the spectral characteristics of corals. We studied the variations in pigment composition among seven Caribbean shallow-water Scleractinian corals by means of High Performance Liquid Chromatography (HPLC) analysis to further resolve the discrimination of corals. We found a total of 27 different pigments among the coral species, including some alteration products of the main pigments. Additionally, pigments typically found in endolithic algae were also identified. A Principal Components Analysis and a Hierarchical Cluster Analysis showed the separation of coral species based on pigment composition. All the corals were collected under the same physical environmental conditions. This suggests that pigment in the coral’s symbionts might be more genetically-determined than influenced by prevailing physical conditions of the reef. We further investigated the use of remote sensing reflectance (Rrs) as a tool for estimating the total pigment concentration of reef corals. Depending on the coral species, the Rrs and the total symbiont pigment concentration per coral tissue area correlation showed 79.5–98.5% confidence levels demonstrating its use as a non-invasive robust technique to estimate pigment concentration in studies of coral reef biodiversity and health.
The humpback whale population of New Caledonia appears to display a novel migratory pattern characterized by multiple directions, long migratory paths and frequent pauses over seamounts and other shallow geographical features. Using satellite-monitored radio tags, we tracked 34 whales for between 5 and 110 days, travelling between 270 and 8540 km on their southward migration from a breeding ground in southern New Caledonia. Mean migration speed was 3.53±2.22 km h−1, while movements within the breeding ground averaged 2.01±1.63 km h−1. The tag data demonstrate that seamounts play an important role as offshore habitats for this species. Whales displayed an intensive use of oceanic seamounts both in the breeding season and on migration. Seamounts probably serve multiple and important roles as breeding locations, resting areas, navigational landmarks or even supplemental feeding grounds for this species, which can be viewed as a transient component of the seamount communities. Satellite telemetry suggests that seamounts represent an overlooked cryptic habitat for the species. The frequent use by humpback whales of such remote locations has important implications for conservation and management.
This study aims to understand patterns, persistence and interrelationship between satellite derived oceanic variables. Time series near-synchronous sea surface height anomaly (SSHa), chlorophyll-a concentration (CC) and sea surface temperature (SST) derived from Topex/Poseidon altimeter, Oceansat-OCM and NOAA-AVHRR, respectively, were used for integrative signature analysis. Three dimensional surface and two dimensional spatial profiles of these variables were generated to understand the spatio-temporal variability. SST and SSHa were co-varying and CC shows an inverse correlation. The time series data analysis indicated bio-physical closely coupled processes. The patterns of variability in CC signatures were found to be associated with SSHa and SST signatures. High fish catch in terms of CPUE (catch-per-unit-effort) were found in low SSHa and corresponding high chlorophyll concentration area during the year 1998–2004 in the Northern Arabian Sea. SSHa signatures were detected earlier than CC and SST. Lower SSHa signatures were inferred as advanced information of the occurrence of productive sites in near future. This study would be useful to understand large scale bio-physical coupled processes for fishery resources exploration.
Mesodinium rubrum is a globally distributed nontoxic ciliate that is known to produce intense red-colored blooms using enslaved chloroplasts from its algal prey. Although frequent enough to have been observed by Darwin, blooms of M. rubrum are notoriously difficult to quantify because M. rubrum can aggregate into massive clouds of rusty-red water in a very short time due to its high growth rates and rapid swimming behavior and can disaggregate just as quickly by vertical or horizontal dispersion. A September 2012 hyperspectral image from the Hyperspectral Imager for the Coastal Ocean sensor aboard the International Space Station captured a dense red tide of M. rubrum (106 cells per liter) in surface waters of western Long Island Sound. Genetic data confirmed the identity of the chloroplast as a cryptophyte that was actively photosynthesizing. Microscopy indicated extremely high abundance of its yellow fluorescing signature pigment phycoerythrin. Spectral absorption and fluorescence features were related to ancillary photosynthetic pigments unique to this organism that cannot be observed with traditional satellites. Cell abundance was estimated at a resolution of 100 m using an algorithm based on the distinctive yellow fluorescence of phycoerythrin. Future development of hyperspectral satellites will allow for better enumeration of bloom-forming coastal plankton, the associated physical mechanisms, and contributions to marine productivity.
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.
This study assesses the ability of hyperspectral optical measurements to discriminate changes in the composition of phytoplankton communities in open-ocean non-bloom environments. A large set of in situ near-surface measurements, comprising phytoplankton pigment determinations and hyperspectral optical data of phytoplankton absorption coefficient, aph(λ), and remote-sensing reflectance, Rrs(λ), are used in the analysis. Measurements were collected in different ecological provinces in the Pacific and Atlantic Oceans with chlorophyll-a concentrations ranging from about 0.02 to 1.5 mg m− 3. Hierarchical cluster analysis was applied to measured spectra of aph(λ) and Rrs(λ) and the second-derivative spectra of these optical variables. The resulting optical-based classifications of the examined stations compared favorably (similarity index ≥ 0.73) with a classification of phytoplankton community composition calculated from pigment measurements. Similarities between pigment-based and optically-based classifications were better for the optical data of aph(λ) than Rrs(λ), with only slight improvements resulting from the use of the second derivative spectra as opposed to the non-differentiated spectra. An Empirical Orthogonal Function (EOF) analysis was applied to the optical spectra to examine the correlation of dominant modes of variability with several bio-optical and biogeochemical properties. This analysis supports the notion that the performance of the optical approach is strongly associated with the effects of differences in pigment composition, cell size, and intracellular pigment concentration among different phytoplankton communities on the optical properties of the ocean.
We assessed the temporal evolution of vegetation activity of mangroves in the Southeastern coastal of the Gulf of California (Mexico) through a multi-temporal analysis of Landsat TM images from 1990 to 2010 where time series of the Normalized Difference Vegetation Index (NDVI) were obtained. A multivariate regression analysis showed the presence of statistically significant negative trends of NDVI (low vegetation activity) in the coverage of mangrove forest, mangrove forest with pickleweed, and pickleweed; however, we did not found any meteorological variable (built time series of average minimum and maximum temperatures, and of accumulated rainfall) that controlled the observed trends. A pixel-by-pixel spatially distributed analysis of the temporal trends of NDVI, complemented by digitalization through photo interpretation of the shrimp farms present in the study area, showed a spatial relationship between the zones of greatest loss of vegetation activity (1990–2010) and the areas with greater proliferation of shrimp farms in the study area. Our study demonstrated the applicability of NDVI for the environmental assessment of mangroves. The relationship between changes in remote sensing indices and environmental variables allows for an efficient evaluation of the main environmental impacts, which can be used for coastal planning and management.
The recently declared Australian Commonwealth Marine Reserve (CMR) Network covers a total of 3.1 million km2 of continental shelf, slope, and abyssal habitat. Managing and conserving the biodiversity values within this network requires knowledge of the physical and biological assets that lie within its boundaries. Unfortunately very little is known about the habitats and biological assemblages of the continental shelf within the network, where diversity is richest and anthropogenic pressures are greatest. Effective management of the CMR estate into the future requires this knowledge gap to be filled efficiently and quantitatively. The challenge is particularly great for the shelf as multibeam echosounder (MBES) mapping, a key tool for identifying and quantifying habitat distribution, is time consuming in shallow depths, so full coverage mapping of the CMR shelf assets is unrealistic in the medium-term. Here we report on the results of a study undertaken in the Flinders Commonwealth Marine Reserve (southeast Australia) designed to test the benefits of two approaches to characterising shelf habitats: (i) MBES mapping of a continuous (~30 km2) area selected on the basis of its potential to include a range of seabed habitats that are potentially representative of the wider area, versus; (ii) a novel approach that uses targeted mapping of a greater number of smaller, but spatially balanced, locations using a Generalized Random Tessellation Stratified sample design. We present the first quantitative estimates of habitat type and sessile biological communities on the shelf of the Flinders reserve, the former based on three MBES analysis techniques. We contrast the quality of information that both survey approaches offer in combination with the three MBES analysis methods. The GRTS approach enables design based estimates of habitat types and sessile communities and also identifies potential biodiversity hotspots in the northwest corner of the reserve’s IUCN zone IV, and in locations close to shelf incising canyon heads. Design based estimates of habitats, however, vary substantially depending on the MBES analysis technique, highlighting the challenging nature of the reserve’s low profile reefs, and improvements that are needed when acquiring MBES data for small GRTS locations. We conclude that the two survey approaches are complementary and both have their place in a successful and flexible monitoring strategy; the emphasis on one method over the other should be considered on a case by case basis, taking into account the survey objectives and limitations imposed by the type of vessel, time available, size and location of the region where knowledge is required.
Modelling approaches have the potential to significantly contribute to the spatial management of the deep-sea ecosystem in a cost effective manner. However, we currently have little understanding of the accuracy of such models, developed using limited data, of varying resolution. The aim of this study was to investigate the performance of predictive models constructed using non-simulated (real world) data of different resolution. Predicted distribution maps for three deep-sea habitats were constructed using MaxEnt modelling methods using high resolution multibeam bathymetric data and associated terrain derived variables as predictors. Model performance was evaluated using repeated 75/25 training/test data partitions using AUC and threshold-dependent assessment methods. The overall extent and distribution of each habitat, and the percentage contained within an existing MPA network were quantified and compared to results from low resolution GEBCO models. Predicted spatial extent for scleractinian coral reef and Syringammina fragilissima aggregations decreased with an increase in model resolution, whereas Pheronema carpenteri total suitable area increased. Distinct differences in predicted habitat distribution were observed for all three habitats. Estimates of habitat extent contained within the MPA network all increased when modelled at fine scale. High resolution models performed better than low resolution models according to threshold-dependent evaluation. We recommend the use of high resolution multibeam bathymetry data over low resolution bathymetry data for use in modelling approaches. We do not recommend the use of predictive models to produce absolute values of habitat extent, but likely areas of suitable habitat. Assessments of MPA network effectiveness based on calculations of percentage area protection (policy driven conservation targets) from low resolution models are likely to be fit for purpose.