The Tolerance of Benthic Infauna to Fine-Grained Organic Rich Sediments in a Shallow Subtropical Estuary
by
Daniel Christopher Hope
A thesis submitted to the College of Engineering at Florida Institute of Technology in partial fulfillment of the requirements for the degree of
Master of Science in Biological Oceanography
Melbourne, Florida December, 2016
We the undersigned committee hereby approve the attached thesis, “The Tolerance of Benthic Infauna to Fine-Grained Organic Rich Sediments in a Shallow Subtropical Estuary,” by Daniel Christopher Hope.
______Kevin Johnson, Ph.D. Associate Professor Department of Ocean Engineering and Sciences
______John Trefry, Ph.D. Professor Department of Ocean Engineering and Sciences
______Jon Shenker, Ph.D. Associate Professor Department of Biological Sciences
______Stephen Wood Associate Professor and Department Head Department of Ocean Engineering and Sciences
Abstract
The Tolerance of Benthic Infauna to Fine-Grained Organic Rich Sediments in a Shallow Subtropical Estuary
Author: Daniel Christopher Hope
Advisor: Kevin Johnson, Ph.D.
Fine-grained organic-rich sediments (FGORS) from anthropogenic impacts are a growing concern for bays and estuaries around the world. This study explores the relationships between infaunal community diversity and species’ abundances with FGORS in the Indian River Lagoon and its tributaries. To examine these potential relationships, infauna were collected monthly using a Petit Ponar grab at
16 stations in the central Indian River Lagoon from October 2015 to August 2016.
Abundant taxa in these sediments include polychaete worms (e.g., the polychaete
Nereis succinea), molluscs (e.g., clam Parastarte triquetra), and arthropods (e.g., the tanaid Leptochelia dubia) with densities as high as 5.3x104 m-2 (L. dubia in July
2016). Increasing organic matter (OM) in the sediments was inversely correlated with species richness (r2 = 0.74; p-value < 0.001), densities (r2 = 0.72; p-value <
0.001), and diversity (r2 = 0.80; p-value < 0.001). Other infaunal community and population data showed similar relationships with silt-clay (%), sediment porosity,
iii
and dissolved oxygen (mg/L). Two thresholds of OM and correlated environmental parameters are discussed: an impairment threshold at 2% OM, above which infauna decrease precipitously, and a critical threshold at 10% OM above which infauna are generally absent.
iv
Table of Contents
List of Figures ...... vi List of Tables ...... vii Acknowledgement ...... viii Dedication ...... ix Introduction ...... 1 Methods ...... 9 Field Collection ...... 9 Laboratory Procedures and Statistical Analysis ...... 11 Results ...... 13 Discussion ...... 25 Conclusion ...... 38 References ...... 39
v
List of Figures
Figure 1 — Map of Sampling Stations...... 11 Figure 2 — Organic Matter Distribution ...... 14 Figure 3 — Regression of Basic FGORS Parameters ...... 15 Figure 4 — Regression of Species Richness vs. FGORS Parameters ...... 21 Figure 5 — Regression of Infaunal Density vs. FGORS Parameters ...... 22 Figure 6 — Regression of Infaunal Diversity vs. FGORS Parameters ...... 23 Figure 7 — Regression of Specific Taxonomic Groupings vs. Organic Matter...... 24 Figure 8 — Determined Thresholds With ANOVA graphs...... 28 Figure 9 — Plot of Richness, Density, and Diversity Against Increasing OM...... 34
vi
List of Tables
Table 1 — GPS Coordinates for Stations ...... 11 Table 2 — Dominant Infaunal Species and Abundances...... 18
vii
Acknowledgement
First and foremost, I would like to give my appreciation to Dr. Kevin Johnson who reached out to me from the beginning and unfailingly guided me through my research and writing. My thanks extends to my committee members Dr. John Trefry and Dr. Jon Shenker for kindly aiding me and providing years of knowledge to my research. I would also like to thank my two great lab mates, Tony Cox and Angelica Zamora-Duran, for their patience and perseverance in the lab and through the many sampling days in the muck. My deepest gratitude goes out to the state legislature through Brevard County for providing the funding with which I was allowed to pursue my dreams. I would like to thank Florida Tech for being such a great school with incredible professors, endless opportunities and amazing friends. Finally, I would like to thank my wife Melissa, who with unwavering support, never stops believing in me.
viii
Dedication
I wish to dedicate this to my parents who showed me what’s important in life, and to my wife who is always there to help me dream big.
ix 1
Introduction
Estuaries and coastal systems throughout the world are accumulating high organic sediments from various sources such as runoff and sewage (Trefry et al.
1990), terrestrial litter (Hedges et al. 1997, Trefry et al. 2007) and oil and industrial waste (Gray et al. 1979). Sedimented organic matter, although a potential food source for bottom dwellers (Kharlamenko et al. 2001, Coull 1999), is harmful to natural ecosystems in large amounts (Hyland et al. 2005, Pearson and Rosenberg
1978). Anaerobic bacterial processes can further degrade a system via mass decomposition of organic matter, resulting in hypoxia or anoxia (Viaroli et al.
2008), which is exacerbated when the water is static, deep, and warm (Diaz and
Rosenberg 1995). These conditions foster sulfur reducing bacteria which release hydrogen sulfide (H2S). This toxic dissolved gas can saturate the benthic environment, making hypoxic habitats even more hostile (Wang and Chapman
1999).
Fine-Grained Organic-Rich Sediment (FGORS) buildup in estuaries is
heavily influenced by the degree of mixing and flushing in the system. Puente and
Diaz (2015) note that high-energy bodies of water stir up sediments more
2 frequently. This prevents small particles from settling and, consequently, there are fewer negative sedimentary impacts on benthic infauna. This mixing also oxygenates the bottom water. In contrast, low energy estuaries allow fine particles and organic matter to settle and accumulate. Because of the small interstitial spaces between fine particles, water movement within sediments is reduced, contributing to weaker penetration of oxygen (Byers and Grabowski 2014). Low energy conditions allow water column stratification, which can perpetuate hypoxia and foster the accumulation of H2S in waters near the benthos.
Organic matter also has a strong affinity for sediments and its concentration tends to vary with particle surface area (Milliman 1994, Hedges and Kail 1995,
Pelletier et al. 2011). Therefore, the amount of organic matter present likely correlates with the abundance of smaller particles such as silt and clay (Thompson and Lowe 2004). Small particles not only attract organic matter, but also attract and bind toxicants, such as copper (Benton 1995), and other contaminants from anthropological inputs (Swartz 1985; Gray 1979; Gray and Mirza 1979). In a number of ways, FGORS create a stressful environment for benthic infauna.
Invertebrate infauna found in FGORS play positive ecological roles in the benthic ecosystem. Some are filter feeders and deposit eaters (Dauer 1993, Lopez and Levinton 1978), others are detritivores (Whitlatch 1981; Levine 1998) and carnivores (Peterson 1979). Because they live and die in the sediments, infauna are a major source of sediment oxidation by particle cycling and bioturbation (Gibson
3 et al. 2001). Also, the process of feeding on and digesting the sediments causes denitrification, remineralization and sediment recycling (Aller 1994; Rhoads 1974).
Diaz and Rosenberg (1995) mention that without deposit feeders and detritivores to work sediments, large bacterial mats can form on the benthos which shortens food chains and impedes energy transfer to higher trophic levels. Because they are near the base of estuarine food webs (Chen et al. 2016, Coull, 1999), they form a crucial connection between benthic primary production and higher trophic levels in the system. Many coastal fish populations, for example, feed directly on these bottom dwellers at some time in their development (Newell et al. 1998; Elliot et al. 2007).
The change from healthy to disturbed sediments impacts species composition and abundance (Dauer 1993; Pearson and Rosenberg 1978). Healthy, non-polluted sediments house an abundant number of species with a wide variety of taxonomic groups including annelids, molluscs and crustaceans. Dauer (1993) generalizes that contaminated benthic sediments result in lower species richness, abundance and diversity; sensitive species will die off as pollution-tolerant species take over, and this is a common pattern (Magni et al. 2008; Hyland et al. 2005;
Gray 1979). The decrease in species richness and abundance is due to the many stresses the polluted environment brings to species less adapted to these stresses.
Examples of this can be anoxia or toxicity, competition for food or space, reduced predator avoidance, or the physical difference in the sediment (Gray 1979).
4 Shallow dwelling and short lived, opportunistic species tend to be more tolerant of organically rich sediments (Gray 1979). For example, when sensitive species die off as pollution rises, high fecundity species tend to overtake polluted coastal systems as conditions favor r-selected species (Tsutsumi 1978). Clonal animals also have an advantage because they develop quickly and can colonize a larger space in a smaller amount of time compared to sexually produced young (Jackson 1986).
Other species are able to change their life history strategies to provide a colonization advantage in local disturbed or polluted areas. For example, when other species grow less abundant, some polychaetes (i.e., Capitella sp., Polydora sp.) may elect to brood, rather than release, their larvae in order to colonize nearby sediments (Gray 1980).
Phylum specific research has been done to understand the thresholds and tolerances of certain species in FGOR sediments. Gray et al. (2002) summarizes that crustaceans and echinoderms are the most sensitive to changes, then annelids, and finally molluscs. In slightly polluted sediments, the echinoderms, Amphiura filiformis, and Hemipholis elongata seem to withstand lower oxygen and hydrogen sulfide levels than many other healthy-sediment species (Rosenberg et al. 1991,
Christensen et al. 2000). In heavier polluted areas, molluscs can withstand high physiological stress when dealing with hypoxia and H2S (Bayne, 1976), but in fine- grained sediments their feeding mechanisms can get clogged by fine particulates
5 (Pearson 1980). As this kills off filter feeders, sediment feeders thrive and become more abundant as their food and space increase (Amjad and Gray 1983).
Other species take advantage of organic-rich sediments. Capitella capitata is a cosmopolitan species, tolerant to a wide range of conditions (Gray et al. 2002,
Pearson and Rosenberg 1978). Other annelids such as Nereis sp. and Polydora sp. can also withstand stresses, outcompete less tolerant species, and are found co- existing with Capitella capitata in organically rich sediments (Dauer 1993). The foraminiferan Ammonia parkinsoniana, an organism that lives in organic-rich bays and estuaries around the world, can withstand low oxygen and high organic sediments (Carpotondi 2015). For all species, however, there presumably is a threshold of hypoxia and FGORS tolerance (Hyland et al. 2005; Magni et al.
2008). In the most extreme polluted conditions, even the best-adapted infaunal animals will die, while fungi and bacteria overtake the sediments (Rosenberg et al.
1991; Casalduero 2001). In a study done by Pearson and Rosenberg (1978), the authors produced a model to predict how infaunal abundance, species richness, and biomass respond to a gradient of organic pollution. The model expresses in general that, as organic matter increases, species richness and diversity decrease. In places where organic matter was highest, the model predicted that no infaunal species should be found.
Benthic infauna have commonly been used to monitor water and sediment quality (Gray 1980; Carvalho et al. 2006; Afli et al. 2008). Infaunal animals live
6 in, and sometimes feed on, sediments and thus may respond rapidly and predictably to altered environmental conditions. (Dauer 1993; Hyland et al. 2005). Many infauna have limited mobility (Weisberg et al. 1997) and are essentially trapped in degrading environmental conditions, forced to adapt or die. As pollution alters sediments and stresses infauna, community composition will change to reflect adaptive tolerances and trophic strategies (Dauer 1993).
Some recent research has attempted to quantitatively examine the impact of organic sediments on infauna and pinpoint any thresholds (Hyland et al. 2005;
Magni et al. 2008). This task is made more challenging because analogous estuaries globally show a wide range of organic content in sediments (0%-25%)
(Magni et al. 2008). Nonetheless, attempts to characterize organic sediment effects and identify thresholds have been made. The term “muck” has emerged from these efforts and has become a popular term for describing FGORS. Trefry et al. (1990) defines muck as having water content greater than 50% by weight, at least 10% organic matter by dry weight, and at least 60% fine sediments (silt plus clay) by dry weight. In an alternate definition, Hyland et al. (2005) characterizes a “highly polluted state” where sediment organic matter (OM) content is greater than 10.5%.
Making predictions about ecological effects, Magni et al. (2008) concluded that sediments with greater than 11% OM will exhibit a “major impoverishment of benthic assembly”. As a way to evaluate and quantify the sediment, this study will
7 apply the muck definition as OM greater than 10% to define extreme cases of organic pollution, or “muck”.
The Indian River Lagoon (IRL) is plagued by many of the same stresses afflicting other estuaries. It makes up 40% of the coastline on the east coast of
Florida with a range of 250 km. Because of its length and its geographical location through transitional climates, it is considered one of the most diverse estuaries in
North America (Gilmore 1995). The lagoon serves as a sink for agricultural run-off from the interior of the state and supports a significant human population along its length. Consequently, the IRL is highly impacted by anthropogenic nutrient inputs and the buildup of FGORS in the lagoon is one result of, and contributor to, the estuary’s nutrient problems. This is a concern to the local marine environment and subsequently a large portion of the Florida economy, with an annual Indian River
Lagoon economic value of 3.7 billion dollars (Garland 2016). Anthropogenic nutrients and FGORS threaten seagrass growth, promote an algae-dominated system, and can contribute to fish kills.
This study examines the occurrence of FGORS sediments in selected tributaries to the Indian River Lagoon (Brevard County, Florida) and characterizes the associated infaunal invertebrate communities. Pearson and Rosenberg (1978) and Gray (1981) point out that infauna are absent from extreme organic sediments and it follows that sediments with intermediate organic material will support intermediate diversity and organism abundance.
8 I hypothesize that: 1) Infaunal species richness, density, and abundances will be inversely correlated with sediment that contains fine-grained, organic rich particles. 2) There will be an absence of infauna at a threshold of sediment organic matter.
9 Methods
Field Collection
Infauna were collected monthly at 16 sampling stations (3 replicate grabs per station per sampling date) in Florida’s central Indian River Lagoon (IRL) from
May 2015 to August 2016. Two adjacent tributary systems 4.5 km apart in the IRL were sampled at various stations in each with additional nearby stations in the IRL proper (fig. 1 and table 1).
Grabs (n=3 per sampling station listed in fig. 1 and table 1) were collected monthly, haphazardly around each station’s coordinates. Grab samples were collected using a Wildco Petite Ponar (6” scoop) grab (Rhoads and Germano, 1986;
Burd et al. 2000). The water depth above each sediment grab is between 30 cm to
180 cm, except for the muck locations (TCM and CCM) where the water is 3 m deep. Total sediment grab volumes were recorded to enable calculations of organism densities and grab penetration depth. Sediments were then sifted through a 0.5 mm sieve (Eleftheriou and Holme 1984, Ruso et al. 2007) and retained sediments and organisms were labeled and kept for lab analysis. A fourth, unsifted grab was collected at each station for sediment characterization, including % water content, % organics, and % silt and clays.
10 A YSI (Yellow Springs Instruments) gauge was used to measure the temperature, salinity, dissolved oxygen and oxygen saturation at every location, with measurements taken at the surface and the bottom in the water column.
A B
N N
Figure 1. A. Turkey Creek and adjacent sampling stations. Red = Turkey Creek Muck stations (TCM). Yellow = Turkey Creek stations (TC). Green = Turkey Creek Lagoon stations (TCL). B. Crane Creek and adjacent stations. Orange = Crane Creek Muck sampling stations (CCM). Blue = Crane Creek Lagoon sampling stations (CCL).
11 Table 1. GPS coordinates for stations shown in Figure 1 with associated abbreviations. Sampling Station Abbr. GPS coordinates of stations Turkey Creek #1 TC 1 28° 2'20.09"N 80°34'50.65"W Turkey Creek #2 TC 2 28° 2'18.39"N 80°34'54.03"W Turkey Creek #3 TC 3 28° 2'16.07"N 80°34'54.41"W Turkey Creek #4 TC 4 28° 2'13.25"N 80°34'53.82"W Turkey Creek Lagoon #1 TCL 1 28° 2'33.97"N 80°34'54.21"W Turkey Creek Lagoon #2 TCL 2 28° 2'26.47"N 80°34'48.16"W Turkey Creek Lagoon #3 TCL 3 28° 1'49.46"N 80°34'33.28"W Turkey Creek Lagoon #4 TCL 4 28° 1'38.55"N 80°34'28.94"W Crane Creek Lagoon #1 CCL 1 28° 4'35.89"N 80°35'52.77"W Crane Creek Lagoon #2 CCL 2 28° 4'24.25"N 80°35'54.38"W Crane Creek Lagoon #3 CCL 3 28° 4'15.29"N 80°35'51.48"W Crane Creek Lagoon #4 CCL 4 28° 4'07.11"N 80°35'46.42"W Turkey Creek Muck #1 TCM 1 28° 2'13.01"N 80°34'48.23"W Turkey Creek Muck #2 TCM 2 28° 2'08.51"N 80°34'49.64"W Crane Creek Muck #1 CCM 1 28° 4'39.16"N 80°36'04.52"W Crane Creek Muck #2 CCM 2 28° 4'35.77"N 80°36'11.31"W
Laboratory Procedures and Statistical Analysis
Sediment samples were frozen pending the identification and counting of organisms via stereomicroscopy (8 – 35x magnification). All organisms were counted and identified to the most specific taxonomic level possible. Some hard- shelled organisms (e.g., Ostracoda and Foraminifera) were identified using morphological details revealed via scanning electron microscopy (SEM). Molluscs, crustaceans, and annelids were identified via gross morphology, cross-referenced with known distributions and physiological tolerances (Smithsonian 2014; WoRMS
2016).
12 The fourth grab sample collected at each station was used to characterize the sediments. To determine water content, subsamples were weighed before and after baking at 105° C for 24 hours (Heiri et al. 2001). Organic content was determined via the mass loss-on-ignition (LOI) method, where ground dried sediment is weighed before and after baking at 550° C for 4 hours (Dean 1974;
Heiri et al. 2001). To determine silt and clay content, about 25 grams of sediments
(Birchenough and Frid 2009) was weighed, sifted through a 63 µm mesh (Burd et al. 2000; Hyland et al. 2005), and then re-weighed to calculate the loss of silt and clay particles (<63 µm).
To determine the degree of correlations between populations and communities and FGORS, the average number of species or individuals were regressed against organic sediment characteristics using the statistical program R.
Where appropriate, data were log10 +1 transformed. An ANCOVA was used to determine a difference in slopes between the three taxonomic groupings (molluscs, annelids, and crustaceans) and each was compared using a t-test and a Bonferroni correction to account for the three comparisons (p = 0.05/3 or 0.016).
13 Results
Sandier sediments were generally found at stations located in the IRL proper, whereas fine-grained organic-rich sediments (FGORS) were found at stations located in the tributaries, especially in channels where this type of sediment was concentrated. The organic content of sediment ranged from 0.3% to 25.9%
(fig. 2). Six stations were consistently sandy and contained organic matter < 1% with an average of 0.65% OM: CCL 2, 3, 4, and TCL 1, 3, 4. Six stations consisted of a darker, organically richer sediment: TC 1, 2, 3, 4; CCL 1; and TCL 2. The majority of those stations ranged from 1% to 10% OM, but TC 3 fluctuated between 3% (Oct. 2015) to 13% OM (Aug. 2016) and TC 4 fluctuated from 4%
(Nov. 2015) to 23% OM (Dec. 2015). The other four stations (TCM 1 and 2, and
CCM 1 and 2) always contained black organically-rich sediments consisting of OM
> 10%. Sandy stations (0.3% to 1% OM) were 0.5 m to 1 m in water depth while more organic-rich stations (>10% OM) were 1-5 m.
14 A B
N N
Figure 2. Crane Creek (A) and Turkey Creek (B) sampling stations in the Indian River Lagoon color coded according to organic matter distribution. Green stations ranged between 0.3 – 2%, yellow stations between 1 – 10%, and red stations had organic matter > 10% OM.
Sediment organic matter (fig. 3A) correlated strongly with silt-clay content
(p < 0.001). Organic matter in turn had an inverse logarithmic correlation with dissolved oxygen (p < 0.001, fig. 3B). Accordingly, the most organic-rich stations
(TCM 1 and 2 and CCM 1 and 2) were anoxic most of the year (mg/L < 0.05), with the exceptions of January and February. Sediment porosity (fig. 3C & D) also showed strong correlation with OM and silt-clay (p < 0.001).
15
100 18 A B 16 y = -0.21ln(x) + 1.34 R² = 0.54 80 14 12 60
10 clay(%)
- 8
40 DO DO (mg/L) Silt 6
20 4 y = 4.19x + 0.45 2 R² = 0.93 0 0 0 5 10 15 20 25 0 5 10 15 20 25 30 OM (%) OM (%)
1.0 1.0 C 0.9 D 0.9 0.8 0.8 0.7 0.7 0.6 0.6
0.5 0.5 Porosity 0.4 Porosity 0.4 0.3 0.3 0.2 0.2 2 0.1 y = 12.83ln(x) + 52.67 0.1 y = -7E-05x + 0.012x + 0.46 R² = 0.88 R² = 0.91 0.0 0.0 0 5 10 15 20 25 0 20 40 60 80 100 OM (%) Silt-Clay(%)
Figure 3. Regressions of basic FGORS indicator parameters against each other and dissolved oxygen (n = 175). (A) Silt-clay content (% dry weight) displaying a linear regression vs. organic matter (% dry weight). Organic matter increased as silt-clay increased. (B) Dissolved oxygen vs. organic matter presents an inverse logarithmic correlation. (C) Porosity against organic matter was best represented by a logarithmic line, while (D) porosity was best represented by a second degree polynomial line against silt-clay.
16 Temperature was coldest in January (14.8° C) and rose to a high in August
(33.5° C) with an 11 month mean of 25.8° C. Although the temperature did not correlate with DO, cooler water has higher dissolved oxygen holding capacity.
Bottom salinity varied from 3.5 to 32.8 with an 11 month mean of 22.5.
Seventy-four species of infaunal animals were found in the course of this study. These represented eight phyla: Annelida (22 species), Crustacea (23 species), and Mollusca (24 species), Echinodermata (2 species), and one species from each of the following phyla: Chordata, Nematoda, and Sipuncula. One species of foraminifera, Ammonia parkinsoniana, was also found in the samples and are included in the statistical analyses. Molluscs, crustaceans, and annelids make up
29.3%, 30.6%, and 32% of total species found, respectively, or collectively 91.9% of species overall. Species with the highest average observed density (± 1SE) include the clam, Mulinia lateralis (12,000 per m2 ± 2,400 in April, 2016), the tanaid crustacean, Leptochelia dubia (53,000 per m2 ± 14,000 in June, 2016), and the polychaete, Diopatra cuprea (3,800 per m2 ± 1,300 in May, 2016). These densities were found at CCL 2, CCL 2, and TCL 4, respectively. 25 species were found to average 10,000 total organisms m-2 or greater over the course of this study
(table 2). The highest average species richness recorded was 19 at CCL 3 in May,
2016.
17 A few animals captured in benthic grabs were not infauna, but water column organisms apparently near the surface of the sediment and thus captured in the grab. These organisms, a crab zoea, a megalopa, and the larval anchovy
Anchoa mitchilli, are not included in the analysis.
Table 2. Dominant benthic infaunal species with total abundance greater than 10,000 individuals m-2 in the duration of the study. Patterns of absence and maximum occurrences (# m-1 ± SE) in non-muck locations (Turkey Creek - TC, Turkey Creek Lagoon - TCL, Crane Creek Lagoon - CCL). Months of absence are listed when the species was completely absent from all locations. For maximum occurrence, the location and sample month are given. With rare exceptions discussed in the text, all species were completely absent from muck locations (CCM, TCM), which are not listed or discussed here. High Density High Density
Species Complete Absence Window (# m-1 ± SE) High Sample Month
Density
Leptochelia dubia Oct. Nov. Dec. and August 53,000 ± 14,000 TCL July
Peratocytheridea Almost always present in non-muck 12,000 ± 3,900 TCL July
setipunctata
Parastarte triquetra Always present in non-muck 3,900 ± 860 CCL January
Mulinia lateralis Almost always present in non-muck 7,400 ± 1,700 CCL April
Ammonia parkinsoniana October 10,000 ± 6,400 TC May
Nereis succinea Always present in non-muck 2,500 ± 520 CCL August
18
Unidentified Tanaid Almost always present in non-muck 1,100 ± 650 TCL July
Oxyurostylis smith October and August 2,600 ± 710 CCL January
Acteocina canaliculata Almost always present in non-muck 2,000 ± 390 TCL April
Unidentified Gammarid Nov. Feb. Mar. and April 1,600 ± 480 CCL July
Amphipod A
Unidentified Polychaete A Oct. and June 1,800 ± 810 TC December
Unidentified Gammarid Nov. Jan. Feb. Mar. and April 1,600 ± 480 CCL July
Amphipod B
Unidentified Polychaete B Almost always present in non-muck 590 ± 270 CCL May
Paradiopatra hispanica Almost always present in non-muck 610 ± 220 TCL April
Japonactaeon May, June, July 990 ± 270 CCL January Punctostriatus Glycera A Almost always present in non-muck 500 ± 250 CCL January
Pectinaria Gouldii Almost always present in non-muck 740 ± 380 TC May
19
Diopatra cuprea Almost always present in non-muck 180 ± 80 CCL May
Hemipholis elongate July 330 ± 310 CCL April
Eusirus cuspidatus Jan. Feb. Jul. and August 300 ± 150 CCL May
Capitella capitata Nov. Apr. and August 410 ± 220 TCL August
Haminoaea succinea July 190 ± 120 TCL December
Unidentified Polychaete C Nov. Dec. Apr. and August 400 ± 240 CCL June
Hargeria rapax Nov. Dec. Mar. and August 270 ± 270 TCL July
Amygdalum papyrium Dec. Feb. Mar. Apr. and June 150 ± 100 TC July
20
21 Regression of species richness against basic FGORS parameters yields an
inverse logarithmic correlation, consistent with OM % (fig. 4A), silt-clay content
(fig. 4B), and water % (fig. 4C). Species richness showed a positive correlation
with DO (fig. 4D, p < 0.001 for all four correlations).
20 20 A 18 y = -0.30ln(x) + 2.34 B 18 y = -0.069ln(x) + 2.33 R² = 0.74 16 16 R² = 0.77 14 14 12 12 10 10 8
Richness 8 Richness 6 6 4 4 2 2 0 0 0 5 10 15 20 25 30 0 10 20 30 40 50 60 70 80 90 100 OM (%) Silt (%)
20 20 C 18 D 18 16 16 14 14 12 12 10 10
8 8 Richness 6 Richness 6 4 4 y = 1.05x + 2.02 2 y = -19.18x + 18.16 2 R² = 0.69 R² = 0.55 0 0 0.00 0.20 0.40 0.60 0.80 1.00 0 2 4 6 8 10 12 14 16 Porosity DO (mg/L)
Figure 4. Regressions of species richness against each of the basic FGORS indicator parameters and dissolved oxygen (n = 175). (A) Species richness displaying an inverse logarithmic relationship with organic matter (% dry weight), and (B) silt-clay content. Richness was negatively correlated with and (C) water % (by weight). (D) Species richness has a positive relationship with dissolved oxygen.
22 Regression of infaunal community density against basic FGORS parameters yields an inverse logarithmic correlation, a pattern seen with OM % (fig. 5A), silt- clay content (fig. 5B), and water % (fig. 5C). Infaunal community density showed a positive correlation with DO (fig. 5D, p < 0.001 for all four correlations).
80000 80000 A y = -0.31ln(x) + 9.11 B y = -0.14ln(x) + 9.19 70000 70000
R² = 0.72 R² = 0.75
)
) 2 60000 2 60000
50000 50000
40000 40000 30000 30000
20000 20000
Density Density (# m per Density Density m (# per 10000 10000 0 0 0 5 10 15 20 25 30 0 20 40 60 80 100 OM (%) Silt - Clay (%)
C 80000 80000 y = -28000x + 25000 D 70000 70000 y = 1300x + 2200
) R² = 0.23 )
2 R² = 0.14 60000 2 60000 50000 50000 40000 40000 30000 30000
20000 20000
Density Density (# m per Density Density (# m per 10000 10000 0 0 0.00 0.20 0.40 0.60 0.80 1.00 0 2 4 6 8 10 12 14 16 18 Porosity DO (mg/L)
Figure 5. Regressions of infaunal community density (average organisms per 225 cm2) against each of the basic FGORS indicator parameters and dissolved oxygen (n = 175). (A) Infaunal community density displaying an inverse logarithmic relationship with organic matter (% dry weight) and (B) silt-clay content. Porosity (C) yielded a negative linear relationship with density and DO (D) has a positive linear relationship with density.
23 Regression of Shannon-Weiner diversity against the basic FGORS parameters yields an inverse logarithmic correlation, a pattern seen with OM %
(fig. 6A), silt-clay content (fig. 6B), and porosity (fig. 6C). Infaunal diversity showed a positive correlation with DO (fig. 6D, p < 0.001 for all four correlations).
2.5 2.5 A y = -0.25ln(x) + 0.52 B y = -0.057ln(x) + 0.48 R2 = 0.80 R² = 0.80 2 2
1.5 1.5
1 1
Diversity Diversity
0.5 0.5
0 0 0 5 10 15 20 25 0 20 40 60 80 100 OM (%) Silt - Clay (%)
C 2.5 D 2.5
2 2
1.5 1.5
1 Diversity Diversity 1
0.5 0.5 y = -2.94x + 2.87 y = 0.16x + 0.41 R² = 0.63 R² = 0.49 0 0 0.00 0.20 0.40 0.60 0.80 1.00 0 5 10 15 20 Porosity DO (mg/L)
Figure 6. Regressions of Shannon-Weiner diversity against each of the basic FGORS indicator parameters and dissolved oxygen (n = 175). (A) Species diversity displaying an inverse logarithmic relationship with organic matter % (dry weight). Diversity has a negative linear relationship with silt – clay and porosity but has a positive linear relationship with DO.
24 Molluscs, crustaceans and annelids made up almost 92% of all observed organisms. 24 mollusc species, 23 crustacean species, and 22 annelid species were identified. Crustaceans were the most abundant taxa in terms of population sizes, while annelids were the least abundant among the three major phyla. Molluscs, crustaceans and annelids all showed decreasing abundance as OM% increased (p <
0.001, fig. 7). Annelids however, were significantly less responsive to OM % increase relative to the other two phyla (shallower slope, p < 0.016).
3.5 Crustaceans y = -0.08x + 1.49, r2= 0.55 Linear (Crustaceans)
) 3 Annelids y = -0.06x + 1.22, r2= 0.60 Linear (Annelids) 2 Molluscs y = -0.08x + 1.59, r2= 0.64 Linear (Molluscs) 2.5
2
1.5
1
Log Density (# per 225 cm per Density 225 (# Log 0.5
0 0 2 4 6 8 10 12 14 16 18 20 22 24 26
OM (%) Figure 7. Log-transformed densities (average individuals per 225 cm2) of crustaceans, annelids, and molluscs vs. OM % (n = 175). The regression slope for annelids is significantly shallower than both molluscs and crustaceans.
25 Discussion
The purpose of this study was to determine the impact of organic sediments on infaunal diversity and abundance in a shallow, diverse estuary. We also examined differences in major taxonomic groups (crustaceans, molluscs, and annelids) in responding to changes in sediment organic content. The results present a repeating pattern regarding infaunal communities in relation to sediment conditions. The relationships found may be useful in the characterization of polluted ecosystems and management decisions for eutrophic estuaries including the Indian River Lagoon.
We found the basic FGORS parameters (silt-clay content, OM %, and porosity) to be inter-correlated and these factors are known to influence benthic communities (Lawless and Seitz 2014; Robertson et al. 2015). Sediment conditions can also influence water column chemistry, evidenced by the correlations of
FGORS characteristics and dissolved oxygen in bottom water. Because organic matter has an influence on the biology and chemistry of the benthos, it is considered as a potential proxy for benthic community health, taking correlated environmental conditions into account.
A rapid drop in infaunal species richness, abundance and diversity results from stressful sediment conditions. As silts and clays accumulate, OM binds to the
26 increased sediment surface areas (Milliman 1994, Hedges and Kail 1995). Bacterial processes then decompose the OM, lowering DO and raising H2S (Wang and
Chapman 1999). These increasingly lethal conditions push out sensitive organisms, altering community composition and curtailing sediment bioturbation, a mechanism of sediment oxygenation, nutrient cycling, and remineralization (Mermillod-
Blondin et al. 2004, Giblin et al. 1997, Aller 1994). As this cycle escalates stressful conditions, macroinfaunal life can be driven out altogether (Gray 1979). Sediments with the highest silt-clay and organic matter contents, and the lowest dissolved oxygen, accumulate in the deeper pockets or channels, and it is in those locations where conditions for life become most stressful. The four deepest stations, with the highest FGORS scores, are part of channels dredged for navigational purposes. It has been observed by others that deep channels act as sediment traps for accumulating silts, clays, and organic particles (Puente and Diaz 2015, De Facto et al. 2004). These “traps” hinder hydrodynamic movements needed for continual water renewal and dissolved oxygen penetration. Reduced water movement and low oxygen penetration can further compound the effect of sedimentation and decreasing DO. Low DO, coupled with high amounts of silt-clay and OM, increases the production of toxic hydrogen sulfides, particularly in static water. In high concentrations, hydrogen sulfide not only permeates fine bottom sediments, but can diffuse into the overlying water column (Luther et al. 1991) and likely influence the occurrence of epibenthic and infaunal animals. The sandier stations
27 (> 85% sand) were located in shallower locations in the IRL proper, away from the dredged channels. The inverse logarithmic relationships of biological data with adverse sediment conditions are consistent with the common sense understanding that diverse and healthy ecosystems usually thrive with abundant oxygen and low toxicity.
The abundances and diversity indicators all drop rapidly in the same region of increasing organic matter content. The lower transition point, or impairment threshold, of OM sensitivity is close to 2% OM (fig. 8). At 2% OM and below, species richness stays above 4, with a maximum high of 19 (fig. 8A). Certain species in the IRL thrive in those conditions and reach numbers exceeding 65,000 organisms m-2 (fig. 8C). Above 2% OM, richness drops rapidly and abundances never exceed 1,300 individuals m-2, a 5-fold decrease. An upper or critical threshold occurs around 10% OM (fig. 8), above which very few or no organisms live. We thus propose two thresholds, the impairment threshold at 2% OM where organisms cease to thrive, and a critical threshold at 10% OM where benthic populations are absent or nearly so. These thresholds are also supported by
ANOVA. Binned data of 0 – 2%, > 2% to 10% and >10% show significant results with richness, abundance, and diversity (fig. 8E, 8D, and 8F).
A 20 B 80000 C 2.5 18
) 70000 16 2 2 60000 14 12 50000 1.5 10 40000 8 1
Richness 30000 6 Diversity Density Density (# m per 20000 4 0.5 2 10000 0 0 0 0 2 4 6 8 10121416 18 20222426 0 2 4 6 8 101214161820222426 0 2 4 6 8 101214161820222426 OM (%) OM (%) OM (%)
D 12 F 2
) E
A 2 A 10 15000 A 1.5 8 10000 B 6 B 1
Richness 4 5000 B Diversity 0.5 2 C C A
0 per Densitym (# 0 0 0 - 2% > 2 to > 10% 0 - 2% > 2 to > 10% 0 - 2% > 2 to > 10% OM 10% OM OM OM 10% OM OM OM 10% OM OM
Figure 8. Scatter plots of (A) richness, (B) density, and (C) diversity against organic matter % with proposed visual thresholds (striped lines). The different letters on each of the bar graphs (D, E, F) suggest significant differences between the binned data using ANOVA.
28
29
Other benthic infaunal studies show analogous thresholds, sometimes quantifying organic content as Total Organic Carbon (TOC). For comparison purposes, the calculation outlined by Leong and Tanner (1999) was used by dividing TOC from other studies by 0.33 to get OM. The upper threshold of 10%
OM is similar to patterns found by Hyland et al. (2005) and Magni et al. (2009), who proposed thresholds of 10.5% and 8.4% OM, respectively. Kodama et al.
(2012) noted in Tokyo Bay that diversity and species density were lowest in their
11.3% OM treatment, of the highest OM levels in the bay. It may be no coincidence that 10% OM is the threshold used to by Trefry et al. (1990) to define
“muck” in the Indian River Lagoon. As so defined, where there is muck present in the Indian River Lagoon, benthic communities are, for all practical purposes, absent. Hyland et al. (2005) and Magni et al. (2009) both found 3% OM to be the threshold where organic matter starts to impair benthic populations. These thresholds appear important for most standard community measurements, including diversity (Magni et al. 2009), species richness (Hyland et al. 2005), and abundances (fig. 8). Our impairment threshold of 2% is on the lower side of thresholds suggested in the literature, but in this ecosystem a decline of the biological community is evident, particularly in richness and densities. The critical threshold of 10% is consistent with other research and represents a point of serious pollution and highly degraded infaunal community health. With regard to
30 mitigation efforts to restore natural benthic sediments, or at least reduce organic pollution, significant decreases in sediment organic matter will foster improved community richness.
The relationship of abundance and diversity with organic content has been observed and modeled (Pearson and Rosenberg 1978, Warwick 1986, Gray 1979).
A model frequently referenced is the Pearson Rosenberg (P-R) model (Pearson and
Rosenberg 1978), which shows a pattern of early increasing richness and abundance before decreasing in response to polluted sediments. Corroborating studies show species richness and diversity increasing with OM in the range of
0.5% to 1.5% (Hyland 2005, Magni et al. 2009), up to 3% in the case of Kodoma et al. (2012), before decreasing. These studies show a pattern very similar to a log- normal distribution (Gray and Pearson 1982, Gray and Mizra 1979). The P-R model, however, does not state the quantitative amount of OM increase and is not universally applicable in all marine systems (Maurer 1993, Heip 1995). Puente and
Diaz (2015) suggests that the P-R model needs to be revised for coastal systems because the energy of the system needs to be taken into account, explaining that systems with less energy means more sedimentation and less dissolved oxygen.
Hypoxia, or low dissolved oxygen, may be contributing to patterns in this study. Because organic decay can decrease dissolved oxygen in the bottom waters, it can be difficult to determine whether OM or DO is the primary direct influence.
When oxygen is not limiting, infaunal species adapted for existence in high organic
31 sediments will gain an advantage over more environmentally sensitive competitors
(Gray 1982, Pearson and Rosenberg 1978). For example, the cosmopolitan species
Capitella sp. and Polydora sp. seem to thrive in polluted sediments (Gray 1982,
Gray et al. 2002, Pearson and Rosenberg 1978) and can occur in high numbers
(Tsutsumi 1987). However, species tolerant to organic sediment conditions, including bio-indicator species, rapidly decline with low oxygen. In our study,
Polydora sp. was altogether absent, and Capitella sp. occurred in low numbers, even though they are both endemic to this region of the Indian River Lagoon
(Grizzle 1984). Low oxygen conditions associated with muck sediments may explain the absence of these important species from our high organic stations (Gray et al. 2002). Grizzle (1984) concludes that pollution-indicator species (Capitella capitata being one of them in the IRL) are as intolerant of oxygen extremes as non- indicator species. This supports conclusions (Gray et al. 2002, Diaz and Rosenberg
1995, Kodama et al. 2012) that low dissolved oxygen could be the main limiting factor inhibiting infaunal communities. This study found critically low DO concentrations close to the benthos. Comparing this study to published studies, OM of 9.2% (Kodama et al. 2012) and exceeding 10.5% (Hyland et al. 2005) could coincide to DO of 2.1 mg/L or less. Low dissolved oxygen in this study was even less, and more pervasive. While some studies found episodic hypoxic conditions that can greatly stress organisms (Kodama et al. 2012, Lim et al. 2006, Baustian and Rabalais 2009), benthic infauna usually recover. However, in this study,
32 hypoxic conditions in muck-associated bottom water lasted almost year round, leaving little or no window for recovery of the benthic community.
The Indian River Lagoon, being a microtidal, wind driven system with little water renewal (Smith 1993), might have different benthic responses to organic enrichment, especially in relatively deeper areas (3 to 5 meters) with less mixing.
Hyland’s (Hyland et al. 2005) mean species richness shows a rise in species richness until around 1.5% OM before it drops, but the pattern used to represent the southeastern United States, which includes the Indian River Lagoon system, shows differently. There, richness starts highest at the lowest OM levels and declines from that point forward, a pattern consistent with the present study. Our research suggests benthic infauna have the highest richness, abundance, and diversity in sediments where organic matter is low, even less than 1% OM (fig. 9). Species richness was also greater. Hyland et al. (2005) reports mean global species richness of 5.3 in sediments with 3% OM or less, 4.2 with OM of 3-11.5%, and 2.4 with
OM exceeding 11.5%. In contrast, this study found species richness of 9.1, 3.5, and
0.2 in those same OM ranges, respectively. The 156-mile long Indian River Lagoon was a relatively pristine estuary until about 60 - 80 years ago (Trefry et al. 1990) when coastal development rose significantly (Clark, 1995; De Freese, 1991), changing the state of the lagoon from relatively low nutrient system to a highly eutrophic system. Similar anthropogenic impacts are reported in the Eastern
Mediterranean Sea. Since that region is naturally oligotrophic and diverse
33 (Tortonese 1985, Danovaro et al. 1995), benthic species there might also be hypersensitive to organic enrichment (Hyland et al. 2005). While it is usually uncertain whether ecosystems have adapted to pollution or anthropogenic stressors, if one assumes that systems can respond to pollution as they would a disturbance
(Gray et al. 1979, Freedman 1995, Whomersley 2010), then productivity needs to be factored in according to Kondoh’s (2001) model. High productivity yields higher richness in intermediate disturbed systems represented by a log-normal distribution, while in low productivity systems, richness decreases as disturbance increases, creating a pattern similar to this present study. According to this model, the decrease in infaunal richness in the IRL could be a result of low productivity, but that was not addressed by our data.
34 A 12 10 8
6
Richness 4 2 0 <0.75 <1.5 <3 <4.5 <6 <7.5 <9 <10.5 <12 ≥12
B 18000
) 16000 2 14000 12000 10000 8000 6000 4000
Density (# per m per (# Density 2000 0 <0.75 <1.5 <3 <4.5 <6 <7.5 <9 <10.5 <12 ≥12
1.8 C 1.6 1.4 1.2 1 0.8
Diversity 0.6 0.4 0.2 0 <0.75 <1.5 <3 <4.5 <6 <7.5 <9 <10.5 <12 ≥12 OM %
Figure 9. Plot of mean (A) species richness, (B) density, and (C) diversity versus increasing organic matter percentage in select groupings, showing a comparative view in contrast with the P-R model and other analogous studies. Error bars represent standard error (n = 175).
35 Sandy, oxygenated stations hosted diverse communities, while muck had little or no life. While environmental parameters discussed above are the ultimate drivers of distributions patterns, what are the biological response mechanisms underlying these patterns? The two most likely hypotheses for the absence of macrofauna in muck goes back to the inception of supply-side ecology and the debate over planktonic supply vs. post-settlement processes as the underlying mechanism of patchy distributions (Connell 1985). One possibility is that settling infauna sense hostile conditions, either in the demersal water column (low DO or
H2S) or upon contact with sediment (particle sizes or organic content), and postpone settlement while awaiting friendlier benthic conditions. An alternative possibility is that infauna settle on muck and then shortly crawl away or die. The combination of the geography of the Indian River Lagoon and the pattern of winds and currents determining the direction of the water flow could also impact settlement or recruitment (Arnold et al. 2005). Turkey Creek and Crane Creek, where the muck stations in this study are located, have narrow openings to the IRL proper and this could limit recruitment into the creeks (Johnson 1995). Also, if species are sensitive to fresh water inflow, might have to navigate through a salt wedge to settle in the creeks.
On occasion, a few individuals were found in muck. This tended to occur when that species’ abundance in neighboring sandy habitats were unusually high.
For example, at TCM 2 in July, the crustacean Leptochelia dubia was
36 extraordinarily abundant, in fact the highest density of any one species throughout this study with a density of 53452 m-2 ± 14044 (density ± SE, July, TCL4). It was under these circumstances that 25 L. dubia were found in a single muck grab. On another occasion, the gastropod mollusc Japonactaeon punctostriatus was found in
January at TCM 1 in two out of the three grabs (three individuals each). That same month, J. punctostriatus populations were peaking nearby at 466 (± 207) individuals m-2 (TC stations 1-4 collectively). The organisms found in the muck could have come from settling planktonic settlement or benthic overspill from neighboring populations, especially if the currents and winds delivered them there with favorable dissolved oxygen. Generally, however, organisms were not found in the muck stations.
Crustaceans are the most abundant group in the sediment containing less organic matter (< 2% OM), followed by molluscs, then annelids. As organic matter increases, all taxa decrease in abundance, but the annelid decline is less rapid (fig.
7), suggesting annelids may be slightly less sensitive to organic matter and the co- occurring stressors than either crustaceans or molluscs. Annelids are known to be opportunistic feeders, and a large number of species are deposit feeders (Gray
1979, Tsutsumi 1987, Pearson and Rosenberg 1978). Dauer (1993) suggests trophic and life history strategies shift with sediment composition. An increase in sediment organic content would stress species better adapted for healthy, oxygenated sediments (Levinton 1972, Bayne 2004). OM and DO (fig. 3B) could also have a
37 profound effect on the community shift. Crustaceans, especially juveniles, are sensitive to low oxygen (Miller et al., 1995) and their growth can be stunted with
DO levels under 2 to 3.5 mg/l (Gray et al., 2002). The highest abundances of the crustaceans Leptochelia dubia, and Peratocytheridea setipunctata (table 2) were found in sandy sediments with moderate to high DO throughout the year.
Crustaceans as a phylum exceeded 20,000 organisms m-2, much higher densities than the collective numbers of molluscs or annelids. Molluscs in general can be resilient to lower dissolved oxygen levels (Gray et al. 2002). Gray et al. (2002) in reviewing hypoxia, concluded that crustaceans were most sensitive, followed by annelids, and finally molluscs. This is likely due to the respective metabolic adaptations of the three phyla (Bayne 1976, Gäde and Ellington 1983). Conditions correlating with OM content (small particles, H2S, porosity) may have a larger impact on molluscs than low oxygen levels alone. Very fine organic sediments can negatively impact filter-feeding organisms (Gray 2002, Nepote et al. 2016) and, more specifically, may clog mollusc feeding mechanisms (Pearson 1980). While molluscs may resist physiological stress related to hypoxia, fine-grained organic sediments present additional stressful conditions that contribute to their decline and ultimate failure.
38 Conclusion
Infaunal community characteristics, including richness, density and diversity, showed inverse logarithmic relationships with FGORS parameters. This pattern, not a traditional log-normal distribution, could result from hypoxia in boundary bottom water. We identified an impairment threshold at 2% OM where infauna begin a precipitous decline in all major community characteristics. At the higher end of the spectrum, 10% OM marked a critical threshold where infauna were generally absent. Using organic matter as a tool to assess benthic health can help environmental strategists and managers restore ecosystems. For the Indian River
Lagoon, reducing bottom sediments to <2% organic content will enhance richness, abundance, and diversity.
39 References
Afli, A., Ayari, R., & Zaabi, S. (2008). Ecological quality of some Tunisian coast
and lagoon locations, by using benthic community parameters and biotic
indices. Estuarine, Coastal and Shelf Science, 80(2), 269-280.
Aller, R. C. (1994). Bioturbation and remineralization of sedimentary organic
matter: effects of redox oscillation. Chemical Geology, 114(3), 331-345.
Amjad, S., & Gray, J. S. (1983). Use of the nematode-copepod ratio as an index of
organic pollution. Marine Pollution Bulletin, 14(5), 178-181.
Arnold, W. S., Hitchcock, G. L., Frischer, M. E., Wanninkhof, R., & Sheng, Y. P.
(2005). Dispersal of an introduced larval cohort in a coastal
lagoon. Limnology and Oceanography, 50(2), 587-597.
Baustian, M. M., & Rabalais, N. N. (2009). Seasonal composition of benthic
macroinfauna exposed to hypoxia in the northern Gulf of Mexico. Estuaries
and Coasts, 32(5), 975-983.
Bayne, B. L. (2004). Phenotypic flexibility and physiological tradeoffs in the
feeding and growth of marine bivalve molluscs. Integrative and
Comparative Biology, 44(6), 425-432.
40 Bayne, B. L. (Ed.). (1976). Marine mussels: their ecology and physiology (Vol.
10). Cambridge University Press.
Birchenough, S. N., & Frid, C. L. (2009). Macrobenthic succession following the
cessation of sewage sludge disposal. Journal of Sea Research, 62(4), 258-
267.
Burd, B., Macdonald, R., & Boyd, J. (2000). Punctuated recovery of sediments and
benthic infauna: a 19-year study of tailings deposition in a British Columbia
fjord. Marine Environmental Research, 49(2), 145-175.
Byers, J. E., & Grabowski, J. H. (2014). Soft-sediment communities. Marine
Community Ecology. Sinauer, 227-249.
Carvalho, S., Gaspar, M. B., Moura, A., Vale, C., Antunes, P., Gil, O., ... & Falcao,
M. (2006). The use of the marine biotic index AMBI in the assessment of
the ecological status of the Óbidos lagoon (Portugal). Marine Pollution
Bulletin, 52(11), 1414-1424.
Casalduero, G. F. (2001). Biondicators. Tools for the impact assessment of
aquaculture activities on the marine communities. Cahiers Options
Méditerranéennes (CIHEAM).
41 Chen, H., Hagerty, S., Crotty, S. M., & Bertness, M. D. (2016). Direct and indirect
trophic effects of predator depletion on basal trophic levels. Ecology, 97(2),
338-346.
Christensen, A. B., & Colacino, J. M. (2000). Respiration in the burrowing
brittlestar, Hemipholis elongata Say (Echinodermata, Ophiuroidea): a study
of the effects of environmental variables on oxygen uptake. Comparative
Biochemistry and Physiology Part A: Molecular & Integrative
Physiology, 127(2), 201-213.
Clark, Kerry B. "Rheophilic/oligotrophic lagoonal communities: through the eyes
of slugs (Mollusca: opisthobranchia)." Bulletin of marine science 57.1
(1995): 242-251.
Connell, Joseph H. "The consequences of variation in initial settlement vs. post-
settlement mortality in rocky intertidal communities." Journal of
experimental marine biology and ecology 93.1-2 (1985): 11-45.
Coull, B. C. (1999). Role of meiofauna in estuarine soft-bottom habitats. Australian
Journal of Ecology, 24(4), 327-343.
42 Danovaro, R., Della Croce, N., Eleftheriou, A., Fabiano, M., Papadopoulou, N.,
Smith, C., & Tselepides, A. (1995). Meiofauna of the deep Eastern
Mediterranean Sea: distribution and abundance in relation to bacterial
biomass, organic matter composition and other environmental
factors. Progress in Oceanography, 36(4), 329-341.
Dauer, D. M. (1993). Biological criteria, environmental health and estuarine
macrobenthic community structure. Marine Pollution Bulletin, 26(5), 249-
257.
De Freese, D. E. (1991). Threats to biological diversity in marine and estuarine
ecosystems of Florida. Coastal Management, 19(1), 73-101.
Dean Jr, W. E. (1974). Determination of carbonate and organic matter in calcareous
sediments and sedimentary rocks by loss on ignition: comparison with other
methods. Journal of Sedimentary Research, 44(1).
Diaz, R. J., & Rosenberg, R. (1995). Marine benthic hypoxia: a review of its
ecological effects and the behavioural responses of benthic
macrofauna. Oceanography and marine biology. An annual review, 33,
245-03.
43 Eleftheriou, A., & Holme, N. A. (1984). Macrofauna techniques. Methods for the
study of marine benthos, 140-216.
Gäde, G., & Ellington, W. R. (1983). The anaerobic molluscan heart: adaptation to
environmental anoxia. Comparison with energy metabolism in vertebrate
hearts. Comparative Biochemistry and Physiology Part A:
Physiology, 76(3), 615-620.
Giblin, A. E., Hopkinson, C. S., & Tucker, J. (1997). Benthic metabolism and
nutrient cycling in Boston Harbor, Massachusetts. Estuaries, 20(2), 346-
364.
Gibson, R. N., Barnes, M., & Atkison, R. J. A. (2001). Functional group ecology in
softsediment marine benthos: the role of bioturbation. Oceanogr Mar Biol
Annu Rev, 39, 233-267.
Gray, J. S., Waldichuk, M., Newton, A. J., Berry, R. J., Holden, A. V., & Pearson,
T. H. (1979). Pollution-induced changes in populations [and
discussion]. Philosophical Transactions of the Royal Society B: Biological
Sciences, 286(1015), 545-561.
Gray, J. S. (1982). Effects of pollutants on marine ecosystems. Netherlands Journal
of Sea Research, 16, 424-443.
44 Gray, J. S. (1980). Why do ecological monitoring?. Marine Pollution
Bulletin, 11(3), 62-65.
Gray, J. S., & Mirza, F. B. (1979). A possible method for the detection of pollution-
induced disturbance on marine benthic communities. Marine Pollution
Bulletin, 10(5), 142-146.
Gray, J. S., & Pearson, T. H. (1982). Objective Selection of Sensitive Species
Indicative of Pollution-Induced Change in Benthic Communities. I.
Comparative Methodology. Marine ecology progress series.
Oldendorf, 9(2), 111-119.
Gray, J. S., Wu, R. S. S., & Or, Y. Y. (2002). Effects of hypoxia and organic
enrichment on the coastal marine environment. Marine Ecology Progress
Series, 238, 249-279.
Gray, J. S. (1981). The ecology of marine sediments (Vol. 2). CUP Archive.
Grizzle, R. E. (1984). Pollution indicator species of macrobenthos in a coastal
lagoon. Marine ecology progress series. Oldendorf, 18(3), 191-200.
Hargrave, B. T., Holmer, M., & Newcombe, C. P. (2008). Towards a classification
of organic enrichment in marine sediments based on biogeochemical
indicators. Marine Pollution Bulletin, 56(5), 810-824.
45 Hedges, J. I., Keil, R. G., & Benner, R. (1997). What happens to terrestrial organic
matter in the ocean?. Organic geochemistry, 27(5), 195-212.
Hedges, J. I., & Keil, R. G. (1995). Sedimentary organic matter preservation: an
assessment and speculative synthesis. Marine chemistry, 49(2), 81-115.
Heip, C. (1995). Eutrophication and zoobenthos dynamics. Ophelia, 41(1), 113-
136.
Heiri, O., Lotter, A. F., & Lemcke, G. (2001). Loss on ignition as a method for
estimating organic and carbonate content in sediments: reproducibility and
comparability of results. Journal of paleolimnology, 25(1), 101-110.
Hyland, J., Balthis, L., Karakassis, I., Magni, P., Petrov, A., Shine, J., & Warwick,
R. (2005). Organic carbon content of sediments as an indicator of stress in
the marine benthos. Marine Ecology Progress Series, 295, 91-103.
Johnson, D. R. (1995). Wind forced surface currents at the entrance to Chesapeake
Bay: their effect on blue crab larval dispersion and post-larval
recruitment. Bulletin of Marine Science, 57(3), 726-738.
46 Kharlamenko, V. I., Kiyashko, S. I., Imbs, A. B., & Vyshkvartzev, D. I. (2001).
Identification of food sources of invertebrates from the seagrass Zostera
marina community using carbon and sulfur stable isotope ratio and fatty
acid analyses. Marine Ecology Progress Series, 220, 103-117.
Kodama, K., Lee, J. H., Oyama, M., Shiraishi, H., & Horiguchi, T. (2012).
Disturbance of benthic macrofauna in relation to hypoxia and organic
enrichment in a eutrophic coastal bay. Marine environmental research, 76,
80-89.
Kondoh, M. (2001). Unifying the relationships of species richness to productivity
and disturbance. Proceedings of the Royal Society of London B: Biological
Sciences, 268(1464), 269-271.
Lawless, A. S., & Seitz, R. D. (2014). Effects of shoreline stabilization and
environmental variables on benthic infaunal communities in the Lynnhaven
River System of Chesapeake Bay. Journal of Experimental Marine Biology
and Ecology, 457, 41-50.
Leong, L. S., & Tanner, P. A. (1999). Comparison of methods for determination of
organic carbon in marine sediment. Marine Pollution Bulletin, 38(10), 875-
879.
47 Levin, L. A., & Gage, J. D. (1998). Relationships between oxygen, organic matter
and the diversity of bathyal macrofauna. Deep Sea Research Part II:
Topical Studies in Oceanography, 45(1), 129-163.
Levinton, J. (1972). Stability and trophic structure in deposit-feeding and
suspension-feeding communities. American Naturalist, 472-486.
Lim, H. S., Diaz, R. J., Hong, J. S., & Schaffner, L. C. (2006). Hypoxia and benthic
community recovery in Korean coastal waters. Marine Pollution
Bulletin, 52(11), 1517-1526.
Lopez, G. R., & Levinton, J. S. (1987). Ecology of deposit-feeding animals in
marine sediments. Quarterly Review of Biology, 235-260.
Luther, G. W., Church, T. M., & Powell, D. (1991). Sulfur speciation and sulfide
oxidation in the water column of the Black Sea. Deep Sea Research Part A.
Oceanographic Research Papers, 38, S1121-S1137.
Magni, P., Tagliapietra, D., Lardicci, C., Balthis, L., Castelli, A., Como, S., &
Pessa, G. (2009). Animal-sediment relationships: Evaluating the ‘Pearson–
Rosenberg paradigm in Mediterranean coastal lagoons. Marine Pollution
Bulletin, 58(4), 478-486.
48 Magni, P., De Falco, G., Como, S., Casu, D., Floris, A., Petrov, A. N., & Perilli, A.
(2008). Distribution and ecological relevance of fine sediments in organic-
enriched lagoons: the case study of the Cabras lagoon (Sardinia,
Italy). Marine Pollution Bulletin, 56(3), 549-564.
Maurer, D., Robertson, G., & Gerlinger, T. (1993). San Pedro Shelf California:
Testing the Pearson-Rosenberg Model (PRM). Marine Environmental
Research, 35(4), 303-321.
Mermillod-Blondin, F., Rosenberg, R., François-Carcaillet, F., Norling, K., &
Mauclaire, L. (2004). Influence of bioturbation by three benthic infaunal
species on microbial communities and biogeochemical processes in marine
sediment. Aquatic Microbial Ecology, 36(3), 271-284.
Miller, D. C., Poucher, S. L., Coiro, L., Rego, S., & Munns, W. (1995). Effects of
hypoxia on growth and survival of crustaceans and fishes of Long Island
Sound. New York Sea Grant Inst., Stony Brook, NY (USA). 92, 1995.
Milliman, J. D. (1994). Organic matter content in US Atlantic continental slope
sediments: decoupling the grain-size factor. Deep Sea Research Part II:
Topical Studies in Oceanography, 41(4-6), 797-808.
49 Nepote, E., Bianchi, C. N., Morri, C., Ferrari, M., & Montefalcone, M. (2016).
Impact of a harbour construction on the benthic community of two shallow
marine caves. Marine Pollution Bulletin.
Newell, R. C., Seiderer, L. J., & Hitchcock, D. R. (1998). The impact of dredging
works in coastal waters: a review of the sensitivity to disturbance and
subsequent recovery of biological resources on the sea bed. Oceanography
and Marine Biology: An Annual Review, 36, 127-178.
Pearson, T. H. (1980). Marine pollution effects of pulp and paper industry
wastes. Helgoländer Meeresuntersuchungen, 33(1), 340.
Pearson, T. H., & Rosenberg, R. (1978). Macrobenthic succession in relation to
organic enrichment and pollution of the marine environment. Oceanogr.
Mar. Biol. Ann. Rev, 16, 229-311.
Puente, A., & Diaz, R. J. (2015). Response of benthos to ocean outfall discharges:
does a general pattern exist?. Marine pollution bulletin, 101(1), 174-181.
Rhoads, D. C. (1974). Organism-sediment relations on the muddy sea floor.
Oceanogr. Mar. Biol. Ann. Rev., 12, 263-300.
Rhoads, D. C., & Germano, J. D. (1986). Interpreting long-term changes in benthic
community structure: a new protocol. Hydrobiologia, 142(1), 291-308.
50 Robertson, B. P., Gardner, J. P., & Savage, C. (2015). Macrobenthic–mud relations
strengthen the foundation for benthic index development: A case study from
shallow, temperate New Zealand estuaries. Ecological Indicators, 58, 161-
174.
Rosenberg, R., Hellman, B., & Johansson, B. (1991). Hypoxic tolerance of marine
benthic fauna. Marine ecology progress series. Oldendorf, 79(1), 127-131.
Ruso, Y. D. P., De la Ossa Carretero, J. A., Casalduero, F. G., & Lizaso, J. S.
(2007). Spatial and temporal changes in infaunal communities inhabiting
soft-bottoms affected by brine discharge. Marine Environmental
Research, 64(4), 492-503.
Smith, N. P. (1993). Tidal and nontidal flushing of Florida’s Indian River
Lagoon. Estuaries, 16(4), 739-746.
Paul, E., V. (2014, May 2). Indian River Lagoon Species Inventory. Retrieved from
http://www.sms.si.edu/irlspec/Species_Rpts.htm
Thompson, B., & Lowe, S. (2004). Assessment of macrobenthos response to
sediment contamination in the San Francisco Estuary, California,
USA. Environmental toxicology and Chemistry, 23(9), 2178-2187.
51 Tortonese, E. (1985). Distribution and ecology of endemic elements in the
Mediterranean fauna (fishes and echinoderms). In Mediterranean marine
ecosystems (pp. 57-83). Springer US.
Trefry, J. H., Trocine, R. P., & Woodall, D. W. (2007). Composition and sources of
suspended matter in the Indian River Lagoon, Florida. Florida
Scientist, 70(4), 363.
Trefry, J. H., Metz, S., Trocine, R. P., Iricanin, N., Burnside, D., Chen, N., &
Webb, B. (1990). Design and operation of a muck sediment survey. Special
publication SJ.
Tsutsumi, H. (1987). Population dynamics of Capitella capitata (Polychaeta;
Capitellidae) in an organically polluted cove. Mar. Ecol. Prog. Ser, 36, 139-
149.
Viaroli, P., Bartoli, M., Giordani, G., Naldi, M., Orfanidis, S., & Zaldivar, J. M.
(2008). Community shifts, alternative stable states, biogeochemical controls
and feedbacks in eutrophic coastal lagoons: a brief overview. Aquatic
Conservation: Marine and Freshwater Ecosystems, 18(S1), S105-S117.
52 Wang, F., & Chapman, P. M. (1999). Biological implications of sulfide in
sediment—a review focusing on sediment toxicity. Environmental
Toxicology and Chemistry, 18(11), 2526-2532.
Warwick, R. (1986). A new method for detecting pollution effects on marine
macrobenthic communities. Marine biology, 92(4), 557-562.
Weisberg, S. B., Ranasinghe, J. A., Dauer, D. M., Schaffner, L. C., Diaz, R. J., &
Frithsen, J. B. (1997). An estuarine benthic index of biotic integrity (B-IBI)
for Chesapeake Bay. Estuaries, 20(1), 149-158.
Whitlatch, R. B. (1981). Animal-sediment relationships in intertidal marine benthic
habitats: some determinants of deposit-feeding species diversity. Journal of
Experimental Marine Biology and Ecology, 53(1), 31-45.
Whomersley, P., Huxham, M., Bolam, S., Schratzberger, M., Augley, J., &
Ridland, D. (2010). Response of intertidal macrofauna to multiple
disturbance types and intensities–an experimental approach. Marine
environmental research, 69(5), 297-308.
WoRMS Editorial Board (2016). World Register of Marine Species. Available
from http://www.marinespecies.org at VLIZ. Accessed 2016-03-30