Biodiversity and Ecosystem Informatics - BDEI - Planning Workshop on Biodiversity and Ecosystem Informatics for the Indian River Lagoon, Florida

This proposal solicits funding to organize and conduct a planning workshop that will establish and facilitate research on the informatics needed to address complex issues of biodiversity and ecosystem processes within the Indian River Lagoon. This workshop will provide the opportunity and resources for collaboration and discussion among scientists from diverse fields of biodiversity, ecological sciences, remote sensing, geographic information systems, computer science and intelligent systems. The topics to be discussed will include investigation of novel computational intelligence techniques for modeling, prediction, analysis and database management of the disparate and complex data for the Indian River Lagoon. The explicit products of the proposed workshop will be a white paper and technical report, a formal research agenda that incorporates informatics into existing and planned research, and preparation of a competitive proposal based on the recommendations and preliminary work defined by the workshop

Simulated Response of St. Joseph Bay, Florida, Seagrass Meadows and Their Belowground Carbon to Anthropogenic and Climate Impacts

Seagrass meadows are degraded globally and continue to decline in areal extent due to human pressures and climate change. This study used the bio-optical model GrassLight to explore the impact of climate change and anthropogenic stressors on seagrass extent, leaf area index (LAI) and belowground organic carbon (BGC) in St. Joseph Bay, Florida, using water quality data and remotely-sensed sea surface temperature (SST) from 2002 to 2020. Model predictions were compared with satellite-derived measurements of seagrass extent and shoot density from the Landsat images for the same period. The GrassLight-derived area of potential seagrass habitat ranged from 36.2 km2 to 39.2 km2, averaging 38.0 ± 0.8 km2 compared to an observed seagrass extent of 23.0 ± 3.0 km2 derived from Landsat (range = 17.9–27.4 km2). GrassLight predicted a mean seagrass LAI of 2.7 m2 leaf m−2 seabed, compared to a mean LAI of 1.9 m2 m−2 estimated from Landsat, indicating that seagrass density in St. Joseph Bay may have been below its light-limited ecological potential. Climate and anthropogenic change simulations using GrassLight predicted the impact of changes in temperature, pH, chlorophyll a, chromophoric dissolved organic matter and turbidity on seagrass meadows. Simulations predicted a 2–8% decline in seagrass extent with rising temperatures that was offset by a 3–11% expansion in seagrass extent in response to ocean acidification when compared to present conditions. Simulations of water quality impacts showed that a doubling of turbidity would reduce seagrass extent by 18% and total leaf area by 21%. Combining climate and water quality scenarios showed that ocean acidification may increase seagrass productivity to offset the negative effects of both thermal stress and declining water quality on the seagrasses growing in St. Joseph Bay. This research highlights the importance of considering multiple limiting factors in understanding the effects of environmental change on seagrass ecosystems

Effective strategies to manage dredge related threats to tropical seagrass systems based on seagrass ecological requirements

Major dredging projects have the potential to impact on tropical seagrass communities through direct removal and burial and indirectly through turbid dredge plumes reducing the amount of light available to seagrasses. This is a major concern in Australia and elsewhere in the Indo Pacific region where substantial expansion of tropical ports associated with the resources boom is occurring. In the majority of cases managing the impacts from turbid plumes has focussed on a turbidity threshold that has not been related to the true light requirements of the various seagrass species potentially impacted. Here we report on the value of an approach based on determining the minimum light requirements of species, their resilience to impacts and ability to recover and designing a dredge mitigation approach that is focussed on maintaining critical windows of light to support seagrass growth and longer term survival. Results show the value of experimentally determining locally relevant ecological requirements and the importance of understanding the relationships between light requirements, tidal exposure, shifts in spectral quality of light, seasonality and capacity for species to recover from light stress in determining ecologically relevant triggers. This information combined with a robust toolkit for assessing sub-lethal light stress provides an effective dredge mitigation strategy to protect seagrasses

The Vezo communities and fisheries of the coral reef ecosystem in the Bay of Ranobe, Madagascar

Madagascar, a country whose extraordinary levels of endemism and biodiversity are celebrated globally by scientists and laymen alike, yet historically has received surprisingly little research attention, is the setting of the present dissertation. Here, I contribute to the need for applied research by: 1) focusing on the most intensely fished section of the Toliara Barrier Reef, the Bay of Ranobe; 2) characterizing the marine environment, the human population, and the fisheries; and 3) collecting the longest known time-series of data on fisheries of Madagascar, thereby providing a useful baseline for future analyses. In Chapter 1, the bathymetry of the Bay was characterized following a unique application of the boosted regression tree classifier to the RGB bands of IKONOS imagery. Derivation of water depths, based on DOS-corrected images, following a generic, log-transformed multiple linear regression approach produced a predictive accuracy of 1.28 m, whereas model fitting performed using the boosted regression tree classifier, allowing for interaction effects (tree complexity= 2), provided increased accuracy (RMSE= 1.01 m). Estimates of human population abundance, distribution, and dynamics were obtained following a dwelling-unit enumeration approach, using IKONOS Panchromatic and Google Earth images. Results indicated, in 2016, 31,850 people lived within 1 km of the shore, and 28,046 people lived within the 12 coastal villages of the Bay. Localized population growth rates within the villages, where birth rates and migration are combined, ranged from 2.96% - 6.83%, greatly exceeding official estimates of 2.78%. Annual pirogue counts demonstrated a shift in fishing effort from south to the north. Gear and boat (pirogue) profiles were developed, and the theoretical maximum number of fishermen predicted (n= 4,820), in 2013, from a regression model based on pirogue lengths (R2= 0.49). Spatial fishing effort distribution was mapped following a satellite-based enumeration of fishers-at-sea, resulting in a bay-wide estimate of intensity equaling 33.3 pirogue-meters km-2. Landings and CPUE were characterized, with respect to finfish, by family, species, gear, and village. Expansion of landings to bay-wide fisheries yields indicated 1,885.8 mt year-1 of mixed fisheries productivity, with an estimated wholesale value of 1.64 million USD per annum

Opportunities for seagrass research derived from remote sensing : a review of current methods

Seagrass communities provide critical ecosystem and provisioning services for both human populations and a wide range of associated species globally. However, it has been reported that seagrass area is decreasing at a rapid rate in many parts of the world, mostly due to anthropogenic activities including global change (pollution and climate change). The aim of this review article is to highlight the range of current tools for studying seagrasses as well as identify the benefits and limitations of a range of remote sensing and traditional methodologies. This paper provides a discussion of the ecological importance of seagrass meadows, and recent trends and developments in seagrass research methods are discussed including the use of satellite images and aerial photographs for seagrass monitoring and various image processing steps that are frequently utilised for seagrass mapping. The extensive use of various optical, Radar and LiDAR data for seagrass research in recent years has also been described in detail. The review concludes that the recent explosion of new methods and tools available from a wide range of platforms combined with the recent recognition of the importance of seagrasses provides the research community with an excellent opportunity to undertake a range of timely research. This research should include mapping the extent and distribution of seagrasses, identifying the drivers of change and factors that confer resilience, as well as quantification of the ecosystem services provided. Whilst remotely sensed data provides an important new tool it should be used in conjunction with traditional methods for validation and with a knowledge of the limitations of results and careful interpretation

Ecological forecasting: new tools for coastal and marine ecosystem management

Policy makers, natural resource managers, regulators, and the public often call on scientists to estimate the potential ecological changes caused by both natural and human-induced stresses, and to determine how those changes will impact people and the environment. To develop accurate forecasts of ecological changes we need to: 1) increase understanding of ecosystem composition, structure, and functioning, 2) expand ecosystem monitoring and apply advanced scientific information to make these complex data widely available, and 3) develop and improve forecast and interpretative tools that use a scientific basis to assess the results of management and science policy actions. (PDF contains 120 pages

Providing a Framework for Seagrass Mapping in United States Coastal Ecosystems Using High Spatial Resolution Satellite Imagery

Seagrasses have been widely recognized for their ecosystem services, but traditional seagrass monitoring approaches emphasizing ground and aerial observations are costly, time-consuming, and lack standardization across datasets. This study leveraged satellite imagery from Maxar\u27s WorldView-2 and WorldView-3 high spatial resolution, commercial satellite platforms to provide a consistent classification approach for monitoring seagrass at eleven study areas across the continental United States, representing geographically, ecologically, and climatically diverse regions. A single satellite image was selected at each of the eleven study areas to correspond temporally to reference data representing seagrass coverage and was classified into four general classes: land, seagrass, no seagrass, and no data. Satellite-derived seagrass coverage was then compared to reference data using either balanced agreement, the Mann-Whitney U test, or the Kruskal-Wallis test, depending on the format of the reference data used for comparison. Balanced agreement ranged from 58% to 86%, with better agreement between reference- and satellite-indicated seagrass absence (specificity ranged from 88% to 100%) than between reference- and satellite-indicated seagrass presence (sensitivity ranged from 17% to 73%). Results of the Mann-Whitney U and Kruskal-Wallis tests demonstrated that satellite-indicated seagrass percentage cover had moderate to large correlations with reference-indicated seagrass percentage cover, indicative of moderate to strong agreement between datasets. Satellite classification performed best in areas of dense, continuous seagrass compared to areas of sparse, discontinuous seagrass and provided a suitable spatial representation of seagrass distribution within each study area. This study demonstrates that the same methods can be applied across scenes spanning varying seagrass bioregions, atmospheric conditions, and optical water types, which is a significant step toward developing a consistent, operational approach for mapping seagrass coverage at the national and global scales. Accompanying this manuscript are instructional videos describing the processing workflow, including data acquisition, data processing, and satellite image classification. These instructional videos may serve as a management tool to complement field- and aerial-based mapping efforts for monitoring seagrass ecosystems

Feasibility Study for an Aquatic Ecosystem Earth Observing System Version 1.2.

International audienceMany Earth observing sensors have been designed, built and launched with primary objectives of either terrestrial or ocean remote sensing applications. Often the data from these sensors are also used for freshwater, estuarine and coastal water quality observations, bathymetry and benthic mapping. However, such land and ocean specific sensors are not designed for these complex aquatic environments and consequently are not likely to perform as well as a dedicated sensor would. As a CEOS action, CSIRO and DLR have taken the lead on a feasibility assessment to determine the benefits and technological difficulties of designing an Earth observing satellite mission focused on the biogeochemistry of inland, estuarine, deltaic and near coastal waters as well as mapping macrophytes, macro-algae, sea grasses and coral reefs. These environments need higher spatial resolution than current and planned ocean colour sensors offer and need higher spectral resolution than current and planned land Earth observing sensors offer (with the exception of several R&D type imaging spectrometry satellite missions). The results indicate that a dedicated sensor of (non-oceanic) aquatic ecosystems could be a multispectral sensor with ~26 bands in the 380-780 nm wavelength range for retrieving the aquatic ecosystem variables as well as another 15 spectral bands between 360-380 nm and 780-1400 nm for removing atmospheric and air-water interface effects. These requirements are very close to defining an imaging spectrometer with spectral bands between 360 and 1000 nm (suitable for Si based detectors), possibly augmented by a SWIR imaging spectrometer. In that case the spectral bands would ideally have 5 nm spacing and Full Width Half Maximum (FWHM), although it may be necessary to go to 8 nm wide spectral bands (between 380 to 780nm where the fine spectral features occur -mainly due to photosynthetic or accessory pigments) to obtain enough signal to noise. The spatial resolution of such a global mapping mission would be between ~17 and ~33 m enabling imaging of the vast majority of water bodies (lakes, reservoirs, lagoons, estuaries etc.) larger than 0.2 ha and ~25% of river reaches globally (at ~17 m resolution) whilst maintaining sufficient radiometric resolution

Seagrass interaction with heavy metals at Pulai River Estuary

Environmentalists have raised their concerns that pollution from development along Pulai River Estuary will have an impact on marine ecosystem. In 1994 eleven seagrass species were found in the area. However, when this study were conducted in 2011 only seven seagrass species were identified at the area, namely Enhalus acoroides, Halophila minor, Halophila spinulosa, Halophila ovalis, Thalassia hemprichii, Halodule uninervis and Cymodocea serrulata. The seagrass can uptake metals and therefore plays the role as bioindicator. Field work was conducted between July 2011 and April 2014 where seagrass, water and sediment were collected for analysis. The samples were analysed using Perkin Elmer Atomic Absorption Spectrophotometer Model AAnalyst 400 for copper (Cu), cadmium (Cd), and lead (Pb). Flow Injection Mercury System Perkin Elmer model FIMS 100 was used for mercury (Hg) and arsenic (As) analysis. Analysis of variance and Pearson’s correlation coefficients of metal concentrations were carried out using Statistical Package for the Social Science (SPSS) for seagrass tissues, seawater and sediment. Esri ArcGIS software was used to determine the metals distribution. The seagrass percent covers on the seagrass bed were determined by transect method. The study shows that Halophila minor was the most abundant species covering Pulai seagrass bed at 27% followed by Halophila ovalis (18%), Halophila spinulosa (8.8%), Enhalus acoroides (6.4%), Thallasia hemprichii (5.3%), Cymodocea serrulata (1%), and Halophila uninervis (0.3%). Among the seven seagrass species found, Halophila ovalis have the highest accumulation of metal and indicates positive significant correlation to translocation of metal in seagrass tissues, hence it meets the criteria to be selected as a bioindicator. Mapping using Esri ArcGIS, shows the metals distribution originated from land use. Monitoring conducted on 4th of April, 2014 indicated that land reclamation for Forest City has changed the condition of seagrass bed hydrodynamic and trophic state from upper-mesotrophic to light-eutrophic. Quantitative water, sediment and seagrass fugacity/equivalance mass balanced model was developed to describe the movement pattern of metals that ends up in the seagrass bed. Estimation rates of As, Cu, Cd, Hg and Pb concentration in seawater are at 3.18 µg/L, 32.35 µg/L, 39.94 µg/L, 4.99 µg/L and 99.86 µg/L, respectively for 1 day

The Use of Remote Sensing for Coral Reef Mapping in Support of Integrated Coastal Zone Management: A Case Study in the NW Red Sea - Volume I

Worldwide, coral reefs are rapidly degrading due to the combined negative effects of human activities and global change. Even though the Red Sea is a very suitable natural environment for coral reef growth, their status also has rapidly deteriorated since the 1970s. Coral reefs are especially affected in the NW Red Sea, primarily due to coastal development projects supporting the booming tourism industry. Integrated coastal zone management (ICZM), therefore, is urgently needed to protect the coral reefs and conserve these valuable natural resources for future generations. Effective ICZM necessitates sound baseline information concerning the current status of the coral reefs and the actual human activities taking place, as well as a tool to monitor changes in both elements. Fulfilling these requirements using in situ observations alone is both time- and labour-intensive and, therefore, often financially too demanding, especially for developing countries. Here, remote sensing may bring the solution as it synoptically collects data over large areas in a cost-efficient way. This work has proven the usefulness of passive, optical remote sensing from spaceborne platforms to collect and monitor the required data and support an effective ICZM. Based on Landsat 7 ETM+ and QuickBird data, accurate information has been collected on the bathymetric structure of the coral reef seabed, its geomorphological zonation, and the distribution of the main marine coastal habitats. The possibility to monitor changes in these elements as well as in the coastal development has also been confirmed. These remote sensing derived products have subsequently been analysed and integrated with auxiliary datasets in a GIS to develop valuable decision-support products such as a risk assessment map and a multi-use marine protected area zoning plan. To support ICZM, remote sensing is best integrated in a multi-level sampling approach in which detailed in situ observations are complemented with more broad-scale, regional information derived from remote sensing data analysis. As such, information-based decisions can be made, augmenting the success of the ICZM. This not only counts for the specific study area but is likely necessary for the sustainable development of coral reef coastal zones worldwide