Scaling-up carbon and carbonate metabolism of coral reefs using in-situ data and remote sensing

Areal up-scaling at reef-scale of organic and inorganic metabolism is possible using in-situ measurements and remote sensing data providing the extent of each bottom type inside the reef. Using a SPOT image and published values of metabolism, the gross production (93,560᎒³ kg C year^-1), excess production (10,017᎒³ kg C year^-1) and calcification (165,348᎒³ kg CaCO₃ year^-1) over 35 km² of coral reef environment in Moorea Island (French Polynesia) are estimated. While the computations are straightforward, certain assumptions must be made in order to conduct the scaling exercise. The exercise is valid only if the metabolism of reef benthos is additive through increasing spatial scale. Despite the difficulty of quantitatively assessing our extrapolations, spatial additivity seems to represent the reality. The other limitation is that the reef must be considered as a closed system, in an equilibrium state supposedly accurately described by the few available in-situ measurements. To consider the reef an open system, long-term metabolic measurements coupled with knowledge of oceanic and land forcing processes are required. These theoretical considerations point to the necessity of integrated multi-scale studies based on both remote sensing and in-situ data in order to better understand the productivity and calcification of reefs in the current global change context.     

Spatial distribution of benthic microalgae on coral reefs determined by remote sensing

Understanding the ecological role of benthic microalgae, a highly productive component of coral reef ecosystems, requires information on their spatial distribution. The spatial extent of benthic microalgae on Heron Reef (southern Great Barrier Reef, Australia) was mapped using data from the Landsat 5 Thematic Mapper sensor, integrated with field measurements of sediment chlorophyll concentration and reflectance. Field-measured sediment chlorophyll concentrations, ranging from 23-1,153 mg chl a m^-2, were classified into low, medium, and high concentration classes (1-170, 171-290, and >291 mg chl a m^-2) using a K-means clustering algorithm. The mapping process assumed that areas in the Thematic Mapper image exhibiting similar reflectance levels in red and blue bands would correspond to areas of similar chlorophyll a levels. Regions of homogenous reflectance values corresponding to low, medium, and high chlorophyll levels were identified over the reef sediment zone by applying a standard image classification algorithm to the Thematic Mapper image. The resulting distribution map revealed large-scale (>1 km²) patterns in chlorophyll a levels throughout the sediment zone of Heron Reef. Reef-wide estimates of chlorophyll a distribution indicate that benthic microalgae may constitute up to 20% of the total benthic chlorophyll a at Heron Reef, and thus contribute significantly to total primary productivity on the reef.     

Review of coral reef ecosystem remote sensing

Coral reef communities face unprecedented pressures at local, regional and global scales as a consequence of climate change and anthropogenic disturbance. Remote sensing, from satellites or aircraft, is possibly the only means to measure the effects of such stresses at appropriately large spatial scales. In the past 30 years, remote sensing of coral reefs has made rapid progress. However, the current technology is still not mature enough to monitor complicated coral reef ecosystems. Compared with foreign research in this field, our work lags far behind. There are still deficiencies in many aspects, such as basic data collection, theoretical research and platform construction. In our nation, it is even unclear how coral reefs disperse and where they may be unhealthy. In this paper, general characteristics of coral reef ecosystems and spectral features of different reef benthos have been summarized, based initially on a review of relevant literature in recent years. Based on the spectral separability of different reef types or benthos, remote sensing can be used to monitor two aspects of coral reefs: (1) Measurement of the ecological properties of reefs. (2) Health assessment of the coral reef ecosystem. In the first part, optical remote sensing methods are widely used to map reef geomorphology and habitats or biotopes. The investigation of geomorphologic zonation has proven to be one of the most successful applications, as different geomorphologic zones are associated with characteristic benthic community structures and occur at spatial scales of tens to hundreds of meters, they are amenable to remote detection by moderate to high resolution sensors. With more and more attention on the ecological problems of coral reefs, a number of studies have used high resolution sensors to map reef communities. The number of classes distinguishable depends on many factors, including the platforms, resolution (spectral, spatial and temporal resolution) and environmental conditions (water depth, water clarity, surface roughness, etc.). Compared with deep water color remote sensing, or terrestrial remote sensing, three techniques for the measurement of reef ecological properties are examined in this paper: (1) Coral reef classification system using remote sensing. (2) Techniques of sea surface correction and water column correction. (3) Techniques of coral reef information extraction from images. In terms of the complexity of coral reef ecosystems, the current techniques still need further improvement or optimization. In the health assessment of coral reef ecosystems, there are two ways to carry out the monitoring using remote sensing: (1) Monitoring the pigment or symbiotic zooxanthellae contents in corals. (2) Measuring the environmental properties of reefs. The first way is theoretically feasible, but difficult to achieve in practice. Currently, most reef health assessments are carried out by measuring environmental parameters, including sea surface temperature, solar radiation, ultraviolet radiation, water color, wind speed and direction, rainfall, ocean acidification, sea level, etc., of which sea surface temperature has been routinely measured by NOAA to monitor coral bleaching. In addition to the contents above, this article puts forward five main prospects for development in the future: (1) Establishment of a coral reef classification system using remote sensing. (2) Satellite launch for monitoring coral reefs. (3) Theoretical and methodological development. (4) Establishment of a spectral database for different reef benthos. (5) Integrated application of multi-source remote sensing data. It is hoped that the information provided here will be a reference for subsequent similar studies.     

Three-dimensional species distribution modelling reveals the realized spatial niche for coral recruitment on contemporary Caribbean reefs

The three-dimensional structure of habitats is a critical component of species' niches driving coexistence in species-rich ecosystems. However, its influence on structuring and partitioning recruitment niches has not been widely addressed. We developed a new method to combine species distribution modelling and structure from motion, and characterized three-dimensional recruitment niches of two ecosystem engineers on Caribbean coral reefs, scleractinian corals and gorgonians. Fine-scale roughness was the most important predictor of suitable habitat for both taxa, and their niches largely overlapped, primarily due to scleractinians' broader niche breadth. Crevices and holes at mm scales on calcareous rock with low coral cover were more suitable for octocorals than for scleractinian recruits, suggesting that the decline in scleractinian corals is facilitating the recruitment of octocorals on contemporary Caribbean reefs. However, the relative abundances of the taxa were independent of the amount of suitable habitat on the reef, emphasizing that niche processes alone do not predict recruitment rates.     

Intra-specific niche partitioning obscures the importance of fine-scale habitat data in species distribution models

Geographic information systems (GIS) allow researchers to make cost-effective, spatially explicit predictions of species’ distributions across broad geographic areas. However, there has been little research on whether using fine-scale habitat data collected in the field could produce more robust models of species’ distributions. Here we used radio-telemetry data collected on a declining species, the North American wood turtle (Glyptemys insculpta), to test whether fine-scale habitat variables were better predictors of occurrence than land-cover and topography variables measured in a GIS. Patterns of male and female occurrence were similar in the spring; however, females used a much wider array of land-cover types and topographic positions in the summer and early fall, making it difficult for GIS-based models to accurately predict female occurrence at this time of year. Males on the other hand consistently selected flat, low-elevation, riparian areas throughout the year, and this consistency in turn led to the development of a strong GIS-based model. These results demonstrate the importance of taking a more sex-specific and temporally dynamic view of the environmental niche.     

Choosing the appropriate spatial resolution for monitoring coral bleaching events using remote sensing

Bleached corals provide a strong optical signal that suggests that remote sensing investigations of major bleaching events are feasible using airborne or satellite sensors. However, patchy coral cover, varying intensities of bleaching, and water depths are likely to limit the application of remote sensing techniques in monitoring and mapping coral bleaching. Today, satellite multispectral sensors routinely provide images of reefs from 4 m (Ikonos) to 30 m resolution (Landsat); however, the adequacy of these sensors for monitoring and mapping bleaching events remains unclear. To clarify these issues, scanned aerial photographs acquired during the 1998 bleaching event over the Great Barrier Reef (Australia) were analyzed at various spatial resolutions, from 10 cm to 5 m. We found that the accuracy of mapping bleaching is highly sensitive to spatial resolution. Highest accuracy was obtained at 10 cm resolution for detection of totally bleached colonies. At 1 m resolution, as much as 50% of the 10-cm resolution signal is lost, though the spatial patterns remain correctly described. Partially bleached (pale) corals are difficult to detect even in aerial surveys, leading to an underestimation of overall bleaching levels (total and partial bleaching) in aerial photos compared to in-situ surveys. If data volume and processing time are limiting factors, local variance analysis suggests that the optimal resolution necessary to capture spatial patterns of bleaching is in the range 40-80 cm.     

Habitat suitability modelling of an invasive plant with advanced remote sensing data

Aim  Lepidium latifolium (Brassicaceae; perennial pepperweed) is a noxious Eurasian weed invading riparian and wetland areas of the western USA. Understanding which sites are most susceptible to invasion by L. latifolium will allow more efficient management of this weed. We assessed the ability of advanced remote sensing techniques to develop habitat suitability models for L. latifolium .
Location  San Francisco Bay/Sacramento-San Joaquin River Delta, California, USA.
Methods  Lepidium latifolium distribution was mapped with hyperspectral image data of Rush Ranch Open Space Preserve, providing presence/absence data to train and validate habitat models. A high-resolution light detection and ranging digital elevation model was used to derive predictor environmental variables (distance to channel, distance to upland, elevation, slope, aspect and convexity). Aggregate decision tree models were used to predict the potential distribution of this species.
Results  Lepidium latifolium infested two zones: near the marshland–upland margin and along channels within the marsh. Topographical data, which are typically strongly correlated with wetland species distributions, were relatively unimportant to L. latifolium occurrence, although relevant microtopography information, particularly relative elevation, was subsumed in the distance to channel variable. The map of potential L. latifolium distribution reveals that Rush Ranch contains considerable habitat that it is susceptible to continued invasion.
Main conclusions  Lepidium latifolium invades relatively less stressful sites along the inundation and salinity gradients. Advanced remote sensing datasets were shown to be sufficient for species distribution modelling. Remote sensing offers powerful tools that deserve wider use in ecological research and management.     

Searching for the fundamental niche using individual‐based habitat selection modelling across populations

Strictly speaking, fundamental niches are inestimable. Nevertheless, ecologists attempt approximating them to understand species’ distribution and plasticity to environmental changes, with invaluable repercussions on both theoretical and applied ecology. So far, individual‐based habitat selection models only characterized realized niches of populations delimited by physical (e.g. fences), historical (colonization) and biotic (competition) barriers constraining access to a subset of resources available to the species. As populations with different realized niches share the same fundamental niche, we developed a novel framework to scale‐up response curves from population‐scale habitat selection models to approximate the species’ optimal habitat choices, unbiased by barriers constraining accessibility. We used GPS‐locations from 147 wild mountain reindeer Rangifer t. tarandus, belonging to 7 of the remaining populations scattered throughout the subspecies’ range. We linked individual choices to accessible habitat features using conditional‐logistic regression with log‐link function in a use‐available design. Focal variables were modeled using 2nd degree polynomials on log‐scale, which correspond to a Gaussian curve used to approximate the fundamental niche optimum (curve mean) and breadth (variance). Using both real and simulated data we demonstrate that robust approximations of a fundamental niche optimum and breadth can be estimated using a relatively small number of representative populations with relatively few individuals. While each classical realized niche model had strong predictive power for the focal population but poorly predicted across populations, the approximation of the fundamental niche allowed for robust inter‐population comparisons in habitat quality. The proposed approach brings individual‐based habitat selection models forward along the continuum from investigating the realized niche of a population towards investigating a species’ fundamental niche, and allows us to quantify empirically the relationship between realized and fundamental niches. This allows improving the understanding of differences in fitness among populations, the prediction of species’ distributions and plasticity to environmental changes, and suggestions for mitigation priorities.     

Application of multisensoral remote sensing data in the mapping of alkaline fens Natura 2000 habitat

The Biebrza River valley (NE Poland) is distinguished by largely intact, highly natural vegetation patterns and very good conservation status of wetland ecosystems. In 2013⿿2014, studies were conducted in the upper Biebrza River basin to develop a remote sensing method for alkaline fen classification ⿿ a protected Natura 2000 habitat (code 7230) ⿿ using remote sensing technologies. High resolution airborne true colour (RGB) and color infrared (CIR) orthophotomaps, the laser scanning point cloud and thermal day and night images were obtained in August 2013 in the Biebrza River basin. At the same time, botanical studies were conducted in this area using conventional phytosociological methods. The random forest classification method was used to distinguish patches (phytocoenoses) of alkaline fens in the study area. The developed method of wetland identification has an accuracy of 91.5%. The night land surface temperature (LST) appears to have the greatest indicator potential. The obtained inventory results were compared with the results of the traditional habitat 7230 mapping method, carried out in 2011⿿2013 by another team of authors, under the Management Plan (MP, 2014). The obtained results suggest that the developed method has a wide application in nature conservation. Remote sensing methods are alternatives to traditional methods and can be used to identify alkaline fens on a larger scale.     

Fine-scale spatial genetic structure and clonal distribution of the cold-water coral Lophelia pertusa

Determining the spatial genetic structure within and among cold-water coral populations is crucial to understanding population dynamics, assessing the resilience of cold-water coral communities and estimating genetic effects of habitat fragmentation for conservation. The spatial distribution of genetic diversity in natural populations depends on the species’ mode of reproduction, and coral species often have a mixed strategy of sexual and asexual reproduction. We describe the clonal architecture of a cold-water coral reef and the fine-scale population genetic structure (<35 km) of five reef localities in the NE Skagerrak. This study represents the first of this type of analysis from deep waters. We used thirteen microsatellite loci to estimate gene flow and genotypic diversity and to describe the fine-scale spatial distribution of clonal individuals of Lophelia pertusa. Within-population genetic diversity was high in four of the five reef localities. These four reefs constitute a genetic cluster with asymmetric gene flow that indicates metapopulation dynamics. One locality, the Säcken reef, was genetically isolated and depauperate. Asexual reproduction was found to be a highly important mode of reproduction for L. pertusa: 35 genetic individuals were found on the largest reef, with the largest clone covering an area of nearly 300 m².     

Seeing the unseen by remote sensing: satellite imagery applied to species distribution modelling

Remotely‐sensed proxies have been acknowledged as powerful tools for estimating species’ spatial distributions, whatever the taxonomic group being considered. Jiang et al. (2013) provide a robust example of seeing the unseen by remote sensing, predicting the distribution of epiphyllous liverworts from SPOT Vegetation remotely‐sensed data.     

Mapping migratory bird prevalence using remote sensing data fusion

Background

Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA.

Methodology and Principal Findings

A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy (“fusion”) models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species.

Conclusion and Significance

Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level.     

Integrating field surveys and remote sensing data to study distribution, habitat use and conservation status of the herpetofauna of the Comoro Islands

We studied the non-marine reptile and amphibian species of the volcanic Comoro archipelago in the Western Indian Ocean, a poorly known island herpetofauna comprising numerous microendemic species of potentially high extinction risk and widespread, non-endemic and often invasive taxa. According to our data, the Comoro islands are inhabited by two amphibian species and at least 28 species of reptiles although ongoing genetic studies and unconfirmed historical records suggest an even higher species diversity. 14 of the 28 currently recognized species of terrestrial reptiles (50%) and the two amphibians are endemic to a single island or to the Comoro archipelago. The majority of species are most abundant at low elevation. However, a few endemic species, like the gekkonid lizards Paroedura sanctijohannis and Phelsuma nigristriata, are more common in or even confined to higher altitudes. We created habitat maps from remotely sensed data in combination with detailed species distribution maps produced using comprehensive data from field surveys between 2000 and 2010, literature, and historical locality records based on specimens in zoological collections. Using these data, we assessed the conservation status of the endemic terrestrial reptiles and amphibians according to the IUCN Red List criteria. Our results show that although little area of natural forest remains on the Comoros, many species are abundant in degraded forest or plantations. Competition and predation by invasive species appears to be the most important threat factor for the endemic herpetofauna, together with habitat degradation and destruction, which further favours invasive species. We propose the status Endangered for three species, Vulnerable for one species, Near Threatened for six species, Least Concern for four and Data Deficient for two species. The endemic subspecies Oplurus cuvieri comorensis is proposed for the status Critically Endangered. Based on the results of this study, seven areas of importance for reptile and amphibian conservation on the Comoros are identified. This study shows how remote sensing data can contribute to increasing accuracy and objectiveness of conservation assessments.