Lori J. Morris

Setting seagrass depth, coverage, and light targets for the Indian River Lagoon system, Florida

Seagrass protection and restoration in Florida’s Indian River Lagoon system (IRLS) is a mutual goal of state and federal programs. These programs require, the establishment of management targets indicative of seagrass recovery and health. We used three metrics related to seagrass distribution: areal coverage, depth limit, and light requirement. In order to account for the IRLS’s spatial heterogeneity and temporal variability, we developed coverage and depth limit targets for each of its 19 segments. Our method consisted of two steps: mapping the union of seagrass coverages from all availabe mapping years (1943, 1986, 1989, 1992, 1994, 1996, and 1999) to delineate wherever seagrass had been mapped and determining the distribution of depth limits based on 5,615 depth measurements collected on or very near the deep-edge boundary of the union coverage. The frequency distribution of depth limits derived from the union coverage, along with the median (50th percentile) and maximum (95th percentile) depth limits, serve as the seagrass depth targets for each segment. The median and maximum depth targets for the IRLS vary among segments from 0.8 to 1.8 and 1.2 to 2.8 m, respectively.Halodule wrightii is typically the dominant seagrass species at the deep-edge of IRLS grass beds. We set light requirement targets by using a 10-yr record of light data (1990–1999) and the union coverage depth limit distributions from the most temporally stable seagrass segments. The average annual light requirement, based on the medians of the depth limit distributions, is 33 ± 17% of the subsurface light. The minimum annual light requirement, based on of the 95th percentile of the depth distributions, is 20 ± 14%; the minimum growing season light requirement (March to mid September) is essentially the same (20 ± 13%). Variation in depth limits and light requirements, is probably due to factors other than light that influence the depth limit of seagrasses (e.g., competition, physical disturbance). The methods used in this study are robust when applied to large or long-term data sets and can be applied to other estuaries where grass beds are routinely monitored and mapped.

The Demise and Recovery of Seagrass in the Northern Indian River Lagoon, Florida

Seagrass both disappeared and recovered within 4 yr in one region of northern Indian River Lagoon (IRL). For the specific area referred to as Turnbull Bay, a relatively pristine area of the IRL, over 100 ha of seagrass completely disappeared from 1996 to 1997 and then recovered by 2000. Based on lagoon-wide mapping from aerial photographs taken every 2–3 years since 1986, coverage of seagrass in Turnbull Bay declined from 124 ha in 1989 to 34 ha by 1999 and increased to 58 ha in 2003. Bi-annual monitoring of fixed seagrass transects tells a more detailed story. Species composition along the Turnbull transect shifted fromHalodule wrightii toRuppia maritima beginning in 1995, and macroalgal abundance increased. By the summer of 1997, seagrass completely disappeared along the transect, as well as in most of the surrounding areas in Turnbull Bay; macroalgae covered much of the sediment surface. No significant water quality changes were detected. Light attenuation and suspended solid values did increase after the seagrass disappeared. Porewater sulfide concentrations, taken after all the grass was gone in 1997, were high (2,000 μM), but did improve by 1998 (1,200 μM). Seagrass recovery was rapid and occurred in the reverse sequence of species loss. Seedlings ofR. maritima were the first colonizers, then patches ofH. wrightii appeared. In 2000,Halophila engelmannii returned in the deeper water (>0.6m). By the summer of 2000, the beds had completely recovered. We conclude that this demise was a natural event caused by a long-term buildup of seagrass biomass and a thick (10–15 cm) layer of organic detritus and ooze. We surmise that such a crash and subsequent recovery may be a natural cycle of decline and recovery within this semirestricted, poorly-flushed area. The frequency of this cycle remains uncertain.

Evaluating Hyperspectral Imager for the Coastal Ocean (HICO) data for seagrass mapping in Indian River Lagoon, FL

Differentiation between benthic habitats, particularly seagrass and macroalgae, using satellite data is complicated because of water column effects plus the presence of chlorophyll-a in both seagrass and algae that result in similar spectral patterns. Hyperspectral imager for the coastal ocean data over the Indian River Lagoon, Florida, USA, was used to develop two benthic classification models, SlopeRED and SlopeNIR. Their performance was compared with iterative self-organizing data analysis technique and spectral angle mapping classification methods. The slope models provided greater overall accuracies (63–64%) and were able to distinguish between seagrass and macroalgae substrates more accurately compared to the results obtained using the other classifications methods.

Monitoring of the 2011 Super Algal Bloom in Indian River Lagoon, FL, USA, Using MERIS

During the spring of 2011 an unprecedented “Super” algal bloom formed in the Indian River Lagoon (IRL), with Chlorophyll a (Chl a) concentrations over eight times the historical mean in some areas and lasted for seven months across the IRL. The European Space Agency’s MEdium Resolution Imaging Spectrometer (MERIS) platform provided multispectral data at 665 and 708 nm, which was used to quantify the phytoplankton Chl a by fluorescence while minimizing the effects of other water column constituents. The three objectives were to: (1) calibrate and validate two Chl a algorithms using all available MERIS data of the IRL from 2002 to 2012; (2) determine the accuracy of the algorithms estimation of Chl a before, during, and after the 2011 super bloom; and (3) map the 2011 algal bloom using the Chl a algorithm that was proven to be effective in other similar estuaries. The chosen algorithm, Normalized Difference Chlorophyll Index (NDCI), was positively correlated with the in-situ measurements, with an R2 value of 0.798. While there was a significant (62.9 ± 25%) underestimation of Chl a using MERIS NDCI, the underestimation appears to be consistent across the data and mostly in the estimations of lower concentrations, suggesting that a qualitative or ratio analysis is still valid. Analysis of the application of the NDCI processed MERIS data provided additional insights that the in-situ measurements were unable to record. The time series MERIS Chl a maps along with in-situ water quality monitoring data depicted that the 2011 IRL bloom started after a heavy rainfall in March 2011 and peaked in October 2011 after a decrease in temperature. The bloom collapse also coincided with heavy rainfall and rapidly decreasing temperatures and salinity through October to November 2011.

Acoustic Detection and Mapping of Muck Deposits in the Indian River Lagoon, Florida

ABSTRACT Foster, G.; Riegl, B.M.; Foster, K.A., and Morris, L.J., 2018. Acoustic detection and mapping of muck deposits in the Indian River Lagoon, Florida. Locating significant deposits of muck (semifluid, fine-grained, organic-rich sediment) is an important aspect of estuarine conservation and management. The 38-kHz signal of a dual-frequency, single-beam acoustic survey of drift macroalgae was repurposed post hoc to locate and measure the horizontal and vertical extent of muck deposits within Indian River Lagoon, Florida. Raw echo returns were segmented into 5-cm strata of echo intensity, and a series of postprocessing algorithms were written to identify the characteristic pattern of backscatter associated with muck. Twenty-three deposits thicker than 0.5 m were identified within the 283-km2 survey area, nearly all of which were found within depressions of the Indian River (IR). The quantity of muck was estimated at 1.87 × 106 m3, roughly four times the quantity slated for removal from the Eau Gallie River and Elbow Creek (Florida) in 2016. The quantity of muck within the 110-km traverse of the IR Intracoastal Waterway (ICW) was estimated at 5.31 × 106 m3. Muck deposits within the 31-km traverse of the Banana River (BR) ICW were deeper (μ = 1.22 vs. 0.51 m), but muck volume was difficult to estimate because of the uncertain boundaries (i.e. channel width) of the BR ICW. The decision of whether to remove the significant volumes of muck within the 23 deposits and the ICW must consider ecological impact relative to concentrations of muck at the discharge of tributaries. Nonetheless, this extra layer of information was achieved with only a modest increase of surveying and postprocessing effort. Synergies such as this will be important in an era of monitoring and management cost constraints.