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RESEARCH ARTICLE
The expanded footprint of the Deepwater
Horizon oil spill in the Gulf of Mexico deep-sea
benthos
Michael G. ReuscherID
1¤
*, Jeffrey G. Baguley
2
, Paul A. MontagnaID
1
1Harte Research Institute for Gulf of Mexico Studies, Texas A&M University-Corpus Christi, Corpus Christi,
Texas, United States of America, 2Department of Biology, University of Nevada-Reno, Reno, Nevada,
United States of America
¤Current address: Department of Life Sciences, Texas A&M University-Corpus Christi, Corpus Christi,
Texas, United States of America
*michael.reuscher@tamucc.edu
Abstract
The 2010 Deepwater Horizon blowout off the coast of Louisiana caused the largest marine
oil spill on record. Samples were collected 2–3 months after the Macondo well was capped
to assess damage to macrofauna and meiofauna communities. An earlier analysis of 58 sta-
tions demonstrated severe and moderate damage to an area of 148 km
2
. An additional 58
archived stations have been analyzed to enhance the resolution of that assessment and
determine if impacts occurred further afield. Impacts included high levels of total petroleum
hydrocarbons (TPH) and polycyclic aromatic hydrocarbons (PAH) in the sediment, low
diversity, low evenness, and low taxonomic richness of the infauna communities. High nem-
atode to copepod ratios corroborated the severe disturbance of meiofauna communities.
Additionally, barium levels near the wellhead were very high because of drilling activities
prior to the accident. A principal component analysis (PCA) was used to summarize oil spill
impacts at stations near the Macondo well, and the benthic footprint of the DWH oil spill was
estimated using Empirical Bayesian Kriging (EBK) interpolation. An area of approximately
263 km
2
around the wellhead was affected, which is 78% higher than the original estimate.
Particularly severe damages to benthic communities were found in an area of 58 km
2
, which
is 142% higher than the original estimate. The addition of the new stations extended the
area of the benthic footprint map to about twice as large as originally thought and improved
the resolution of the spatial interpolation. In the future, increasing the spatial extent of sam-
pling should be a top priority for designing assessment studies.
Introduction
The three-months-long uncontrolled release of crude oil from the Macondo MC252 oil well
off the coast of Louisiana in the wake of the Deepwater Horizon (DWH) disaster had extensive
effects on shallow water and deep-sea ecosystems alike [1]. A substantial percentage of the esti-
mated 3.19 million gallons [2] of oil remained in the deep-sea [3]. Some of the crude oil was
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OPEN ACCESS
Citation: Reuscher MG, Baguley JG, Montagna PA
(2020) The expanded footprint of the Deepwater
Horizon oil spill in the Gulf of Mexico deep-sea
benthos. PLoS ONE 15(6): e0235167. https://doi.
org/10.1371/journal.pone.0235167
Editor: Ilaria Corsi, University of Siena, ITALY
Received: October 14, 2019
Accepted: June 9, 2020
Published: June 30, 2020
Copyright: ©2020 Reuscher et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: Data are available
from the GRIIDC database under the Unique
Dataset Identifier (UDI) R5.x272.000:0007, and the
Digital Object Identifier (DOI) 10.7266/FDKAJKNF.
Funding: Sample collection on R/V Gyre and R/V
Ocean Veritas during the Deepwater Horizon oil
spill response phase was funded by British
Petroleum (BP) and the National Oceanic and
Atmospheric Administration (NOAA) respectively.
Sample processing, data analysis, and production
of this paper were funded in part under contract
DG133C06NC1729 from NOAA’s Office of
Response and Restoration (OR&R) via subcontract
floating in mid-water plumes [4], where it aggregated with marine snow and settled on the sea-
floor [5]. Three important offshore and deep-sea habitats were studied as part of the Natural
Resource Damage Assessment (NRDA) and the Gulf of Mexico Research Initiative (GoMRI):
the pelagic realm [6,7], deep-sea corals [8,9], and the soft sediment benthos [10,11,12]. Each
one of these habitats suffered from a loss of biodiversity caused by the smothering effects of oil
as well as lethal and sublethal effects from a multitude of chemical compounds contained in
crude oil [13]. In addition, the use of industrial oil dispersants, such as Corexit A9500
1
,
increased the bioavailability of the oil [14] and introduced synergistic toxic effects to some of
the organisms [15,16,17].
In the soft bottom benthic realm, an area of 172 km
2
suffered from a loss of biodiversity, a
decrease in the number of taxa, and an increased nematode to copepod (N:C) ratio caused by
high levels of total petroleum hydrocarbons (TPH) and polycyclic aromatic hydrocarbons
(PAH) [10,18]. The most severe effects were found in the 24 km
2
zone immediately surround-
ing the wellhead, where peak TPH and PAH levels were compounded by high barium concen-
trations from drill cuttings [10]. One year after the spill the hydrocarbon contamination and
damage to the benthic fauna persisted, but signs of a mild recovery were detected [12,19].
Four years after the spill there were indicators that meiofauna had recovered some, as the nem-
atode to copepod ratio had decreased to background levels [20], but taxonomic richness was
still significantly lower in the affected areas indicating no recovery from the primary damaging
effects. Macrofauna still suffered from significantly lower diversity and taxa richness in 2014
[20].
During the original 2010 sampling, a total of 227 stations were sampled at water depths
ranging from 10 m to 2767 m [21]. However, because of time constraints, only 58 stations
were analyzed fully and used in the original estimate of the area of the deep sea that was dam-
aged by the spill [10]. Priority was given to stations collected to the southwest of the wellhead
because that is where the deep-sea plumes were detected. Shallow stations and stations to the
north and northeast were ignored. Taking into consideration the heterogenous spatial distri-
bution of the Deepwater Horizon fallout plume [22], the original footprint estimate of 148 km
2
based on benthos [10] was likely an underestimation [23] because hopane in sediment yields
estimates of 1,300 km
2
[6] and 1,800 km
2
[24], and radiocarbon in sediment yields estimates as
large as 24,000 km
2
[25]. For the present study, the footprint of the DWH oil on benthic
infauna was reevaluated by including an additional 58 stations for a more comprehensive list
of 116 stations sampled in 2010. With the newly included stations, this updated DWH foot-
print analysis extends over a larger area of the Gulf of Mexico and has improved spatial
resolution.
Materials and methods
Two research cruises (R/V Gyre, September 16—October 19, 2010, and R/V Ocean Veritas,
September 24—October 30, 2010) commenced with the collection of deep-sea sediment
samples, approximately two months after the Macondo oil well in Mississippi Canyon Block
252 (MC252) had been capped on 15 July 2010. Individual multicorer sample cores were set
aside to study macrofauna, meiofauna, and sediment chemistry to assess damage that the
hydrocarbon release had on the benthos. Marine benthic invertebrates were collected in the
Gulf of Mexico, as part of NOAA’s Office of Response and Restoration (ORR) contract
DG133C06NC1729. The study area was the northern central and northeastern Gulf of Mexico,
with the 116 sampling stations located on the continental shelf, the continental slope, and the
Sigsbee Abyssal Plain. Sampling locations covered a depth range from 32 to 2767 m and a dis-
tance from the wellhead from approximately 0.3 km to 265 km (S1 Table). Further details
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1050-TAMUCC and 1050-UNR from Industrial
Economics (IE), and through an internal transfer of
funds from NOAA’s Office of Response and
Restoration (OR&R) to NOAA’s National Centers
for Coastal Ocean Science (NCCOS), as part of the
DWH Natural Resource Damage Assessment.
Research was partially supported by a grant from
The Gulf of Mexico Research Initiative C-IMAGE III
award #SA 18-16 and RFP V/University of New
Hampshire award #SA-16-18; and data are publicly
available through the Gulf of Mexico Research
Initiative Information & Data Cooperative (GRIIDC)
at https://data.gulfresearchinitiative.org (DOI: 10.
7266/FDKAJKNF). This publication was made
partially possible by the National Oceanic and
Atmospheric Administration, Office of Education
Educational Partnership Program award
(NA16SEC4810009). Its contents are solely the
responsibility of the award recipient and do not
necessarily represent the official views of the U.S.
Department of Commerce, NOAA.
Competing interests: I have read the journal’s
policy and the authors of this manuscript have the
following competing interests: Ships for sample
collection was funded by British Petroleum. This
does not alter the authors’ adherence to all the
PLOS ONE policies on sharing data and materials,
as detailed online in the guide for authors. The
scientific results and conclusion of this publication,
as well as any views or opinions expressed herein,
are those of the authors and do not necessarily
represent the view of NOAA or any other natural
resource Trustee for the BP/Deepwater Horizon
NRDA.
about study design, sampling procedures, and sample processing were described previously
[10,19].
Macrofauna were identified to family level and meiofauna to higher taxonomic levels, rang-
ing from order to phylum. Higher taxonomic levels have proved to be reliable surrogates for
species level identification for the assessment of oil pollution impacts on benthic infauna com-
munities [26]. Using higher taxonomic levels in environmental impact studies has the advan-
tage that results may be obtained substantially faster. This was particularly important for the
Deepwater Horizon disaster where evidence for the extent of the damage to the natural
resources of the United States was crucial for the conclusion of legal proceedings and financial
settlements [1,27]. Measurements of macrofauna and meiofauna abundance (N) were stan-
dardized for an area of 1 m
2
and 10 cm
2
, respectively. Taxonomic richness (R) of meiofauna
was defined as number of taxa within the top 3 cm of sediment in a sampling core with 5.5 cm
inner diameter. Taxonomic richness of macrofauna was defined as number of taxa in the top
10 cm of sediment, averaged across three cores with 10 cm inner diameter. Infauna diversity
and evenness of distribution were calculated using Hill’s diversity number one (N1) [28] and
Pielou’s evenness index (J´) [29]. For macrofauna N1 and J´ values of individual cores were
averaged at each sampling station. All data are available in the S1 Table and the GRIIDC data-
base [30].
Chemical contaminant and sediment grain size data were collected in the same multicorer
drops as the infauna. Contaminant measurements were performed with the top 3 cm of sedi-
ment. Data were downloaded from the NRDA DIVER (Data Integration Visualization Explo-
ration and Reporting) website https://www.diver.orr.noaa.gov/deepwater-horizon-nrda-data
on 9 September 2016. This is the same data set reported on in the UAC (2010) report [31].
Methods for the chemical analyses are also described in the report and at http://www.nodc.
noaa.gov/deepwaterhorizon/ship.html.
The publicly available bathymetry map “World Ocean Base” by Esri was obtained from
http://www.arcgis.com/home/item.html?id=1e126e7520f9466c9ca28b8f28b5e500 on 4
December 2018. The seafloor contour shapefile was downloaded from the Gulf of Mexico
Coastal Ocean Observation System (GCOOS) website at http://gcoos.org/products/
topography/Shapefiles.html on 5 September 2018.
All biotic and chemical variables (X) were log-transformed using ln (X+1), except the N1
diversity index, which is already a log transformation of the Shannon diversity index H´. After
transformation, all variables were standardized to a normal distribution with a mean of 0 and
variance of 1 using the PROC STANDARD module contained in the SAS
1
software suite.
Raw and transformed data is provided in (S1 Table).
Principal components analysis (PCA) was used to classify the biological and environmental
variables. PCA was performed using the PROC FACTOR module contained in the SAS soft-
ware suite. The FACTOR analysis was run using the PCA method on the correlation matrix.
The principal component scores, which represented the DWH oil spill, were used to categorize
each station into five levels of impact, which were represented by different colors: red for
severely impacted, orange for moderately impacted, yellow for possible minor impacts, and
light and dark green for background levels. We used the Jenks natural breaks optimization
(Goodness of Variance Fit) [32] classes from our 2013 analysis [10] and assigned the newly
added stations to the different classes, based on their PC scores, which indicated oil spill
impacts. The stations were plotted in ArcMap 10.6 in the respective colors of their impact
level.
Empirical Bayesian Kriging (EBK) was used to predict the spatial extent of the DWH foot-
print. EBK is a powerful non-parametric interpolation method that does not assume normal
distribution of the data [33]. This method has been successfully implemented for spatial
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analyses of the DWH impact in recent studies [e.g. 34]. The spatial prediction maps for the
DWH impacts on the benthos were calculated in ArcMap 10.6, which has EBK implemented
in its ArcGIS Geostatistical Analyst [35]. The same five classes that were described above for
classification of sampling stations were implemented for classification of the spatially interpo-
lated impacts. The areal extent of the impacts was determined in ArcMap with the areal mea-
surement tool.
Non-metric multidimensional scaling (nMDS) was used to visualize similarities of macro-
fauna and meiofauna community structure among different sampling stations. Taxonomic
abundance for both macrofauna and meiofauna were imported into Primer 7 and square-root
transformed. This transformation is a standard procedure to increase the sensitivity of the
analysis for rare taxa [36]. A Bray-Curtis similarity matrix was generated and used for the
nMDS analysis. Individual stations were plotted as bubbles in the MDS ordinations. Bubble
size visualizes PAH44 concentrations, colors represent the five impact levels, as described
above. Stations with missing PAH44 concentrations were plotted as “X”. An analysis of simi-
larity (ANOSIM) was performed to identify which stations have similar community structure.
An analysis of similarity percentages (SIMPER) among species was performed to identify spe-
cies contributing to community structure similarity among groups of stations.
Results
The PCA analysis extracted eighteen orthogonal principal component (PC) factors. The first
five PCs had Eigenvalues greater than 1 and accounted for 80% of the variance in the dataset.
PC1 had an Eigenvalue of 6.1 and explained 34% of the variability. PC1 was driven by physical
sediment qualities and the availability of carbon, as mud content, sediment porosity, and con-
centrations of carbon and total organic carbon (TOC) had highly positive loadings (Fig 1A).
Additionally, PC1 was highly loaded by concentrations of several trace metals, including alu-
minum, chromium, mercury, vanadium, and, to a lesser degree, barium. None of the macro-
fauna or meiofauna metrics had high positive or negative loadings on PC1. PC2 had an
Eigenvalue of 3.9 and explained 21% of the variability. PC2 represents DWH-related contami-
nations with high positive loadings by PAH44, TPH, and barium concentrations (Fig 1A).
TPH and PAH44 are indicators of released petroleum, whereas barium is contained in drilling
muds and fluids. Several faunal metrics were highly correlated with the DWH contaminants
concentrations: meiofauna abundance and N:C ratio had high positive correlations, while
diversity and evenness of both meiofauna and macrofauna had high negative correlations to
the contaminants. In contrast, macrofauna abundance was unaffected, as its negative loading
on PC2 was near zero. Distance from the wellhead was correlated with PC2 station scores (Fig
2). PC3 had an Eigenvalue of 1.9 and explained 10% of the variability. PC3 revealed correla-
tions between the different faunal metrics and their relation to carbon concentrations. Carbon
concentrations were positively correlated to abundance of meiofauna and macrofauna, as well
as N:C ratios (Fig 1B). Conversely, evenness of community structure decreased with higher
carbon concentration, in both meiofauna and macrofauna. Interestingly, diversity of macro-
fauna was positively correlated with carbon concentrations, while meiofauna diversity showed
the opposite trend. PC4 had an Eigenvalue of 1.5 and accounted for 8% of the variability. PC5
had an Eigenvalue of 1.1 and explained 6% of the variability.
Seven of the nine stations located within the roughly 1 km radius (0.3–1.25 km) around the
wellhead were classified as severely impacted in the original study [10] and retain that classifi-
cation here (Fig 3a). These seven stations had high sediment concentrations of TPH (590–
5,023 μg/g), PAH44 (143–1,181 ng/g), barium (789–12,700 μg/g), and very high N:C ratios
(33–109). The high N:C ratio was caused by low copepod abundances at all the heavily
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Fig 1. Variable loads for the Principal Components (PC) analysis for abiotic and biotic variables. A) PC1 and PC2
B) PC2 and PC3; variables included: aluminium (Al), barium (Ba), total carbon (Carbon), chromium (Cr), mercury
(Hg), macrofauna evenness (Mac_J), macrofauna diversity (Mac_N1), macrofauna abundance (Mac_nm2), meiofauna
evenness (Mei_J), meiofauna diversity (Mei_N1), meiofauna abundance (Mei_nm2), mud content (Mud), nematode
to copepod ratio (NC), sum of 44 polycyclic aromatic hydrocarbons (PAH44), sediment porosity (Porosity), total
organic carbon (TOC), total petroleum hydrocarbons (TPH), and Vanadium (Va).
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impacted stations (n = 25.3–109.4 ind/10 cm
2
) and high numbers of nematodes at most
impacted stations. The highest nematode abundance was detected at a heavily impacted station
1 km to the southeast of the wellhead (n = 8,504.8 ind/10 cm
2
). The impacted stations also had
low taxonomic richness and diversity. The three most depauperate stations, located within a
0.65 km radius to the north of the spill, had only three meiofauna taxa present: nematodes,
harpacticoids, and polychaetes. The highest value for meiofauna taxonomic richness at the
seven highly impacted stations was six, compared to up to fourteen at unaffected sampling sta-
tions. Macrofauna showed similar trends with particularly low taxonomic richness (R = 4.3–
14.0) and diversity values (N1 = 2.35–9.91) at highly impacted sampling stations, compared to
maximum values at unaffected stations of R = 38.7 and N1 = 27.15, respectively. Two addi-
tional stations that were located 3 km from the wellhead were classified as severely impacted,
even though their contaminant concentrations were lower and faunal metrics less impaired
when compared to the seven stations described above (S1 Table). The closest moderately
impacted station was located 1 km to the northwest of the wellhead. Six stations within a 3 km
radius were also classified as moderately impacted. Moderate impacts from the DWH oil spill
were detected as far as 15 km to the southwest and 6.25 km to the northeast. The moderately
impacted stations typically had elevated contaminant concentrations and impaired faunal met-
rics, albeit less pronounced than the severly impacted stations.
Two heavily impacted stations had low TPH and PAH levels, indicating a different cause of
disturbance. One of these stations was located almost 30 km north of the spill site, in a depth
of 710 m (Fig 3B). Meiofauna and macrofauna at this sampling site were dominated by nema-
todes and cirratulid polychaetes, respectively. Thus, evenness and diversity values were among
the lowest of all stations, while the N:C ratio was the second highest. A shallow station, 12 km
off Grand Isle, Louisiana (Fig 3B) had the highest N:C ratio (381) and low values for macro-
fauna and meiofauna diversity and evenness. Twenty-seven stations were categorized as possi-
bly impacted because they fell within the range of uncertainty (S1 Table). One of them was
Fig 2. Station scores for PC1 and PC2 with symbols color-coded for distance from the wellhead. Red is nearest and
blue is farthest.
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Fig 3. Sampling stations plotted by the color of their impact level based on PC2 scores. Red: high impact; orange:
moderate impact; yellow: potential impact; light and dark green: background conditions. A) Stations near the MC252
wellhead. B) All sampling stations.
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only 0.5 km to the southeast of the wellhead. TPH levels at this station were elevated (898 μg/g)
but much lower compared to the other stations within a 1 km radius. Macrofauna abundance,
richness, and diversity, as well as meiofauna abundance and richness were very low at this sta-
tion, resembling the severely and moderately impacted stations near the wellhead. Conversely,
values for N:C ratio, meiofauna diversity, and evenness of both meiofauna and macrofauna
were comparable to stations with background conditions. It is noteworthy that TPH and
PAH44 levels on the central shelf and slope are, with few exceptions, much higher than in the
Eastern Gulf of Mexico. These higher concentrations may be caused by the abundant natural
hydrocarbon seeps, oil exploration, or the deposition of DWH hydrocarbons. Sixty stations
were representative of the natural background or pristine (light or dark green) category (S1
Table). Only one of these stations (LBNL4), located 7.5 km to the southwest of the spill site
and nested about halfway between moderately impacted stations, had slightly elevated levels of
TPH (183 μg/g). This station had a relatively diverse and evenly distributed faunal community
with a very low N:C ratio of 1.4. All stations on the Florida and Alabama continental shelf and
upper continental slope, which had not been included in our 2013 study, appeared to be unaf-
fected by the oil spill. In general, the far eastern stations had very low TPH and PAH44 con-
centrations, presumably because of the scarcity of natural seeps and oil extraction activities.
The nMDS ordination plots indicate a correlation between PAH44 concentrations and the
impact category zone of the individual sampling stations, as the largest bubbles were red and
the smallest ones green (Fig 4). Both meiofauna and macrofauna nMDS plots had a gradual
turnover in composition consistent with impact levels and PAH44 concentrations: the heavily
impacted stations with high PAH levels were all located on top of the ordination plot, followed
by orange and yellow stations, and the green stations at the bottom. There were differences in
the community structure among the five categories for macrofauna (ANOSIM, p <0.001) and
meiofauna (ANOSIM, p <0.001). Generally, the severely impacted group (1) was different,
and the moderately and uncertain impact groups (2 and 3) were similar, and the pristine
group (5) was always distinct (Table 1). The two heavily impacted stations, which did not have
elevated TPH or PAH levels but very high N:C ratios, resembled the strongly impacted station
in the meiofauna nMDS (Fig 4A). Nematode contribution to the meiofauna community
declined from the severe to pristine station groups: severe = 83%, moderate = 69%, uncer-
tain = 62%, no impact = 56%, and pristine = 53% (SIMPER). Another station (D046S), which
was located 97 km southeast of the spill site and placed in the category “possibly impacted”,
clustered with one of the heavily impacted stations (Fig 4A). Meiofauna communities at this
station were dominated by nematodes (N:C ratio = 46), including very low numbers for taxo-
nomic richness (5), diversity, and evenness. The lack of elevated TPH and PAH concentrations
means that the nematode dominance at this station was unrelated to the DWH oil spill. Addi-
tionally, this station had a relatively rich, diverse, and taxonomically evenly distributed macro-
fauna community. In the macrofauna nMDS plot the uncertain-impact station (D034S),
located only 0.5 km from the spill site, resembled the severely impacted stations (Fig 4B),
because of its low taxonomic richness and diversity and its similarity in the number of dorvil-
leid and paraonid polychaetes (S2 Table). Station FFMT1, while not clustering with any severe
or moderate impact station, was close to the top of the macrofauna nMDS plot (Fig 4B). This
relatively shallow station in the Mississippi Trough had low values for abundance, richness,
and diversity, but no evidence for DWH oil was found at this station. In general, dominance in
macrofauna communities declined from severe to pristine station groups, as documented by
the number of taxa that had a 70% cumulative contribution to the overall abundance: severe = 6
taxa, moderate = 12 taxa, uncertain = 14 taxa, no impact = 18 taxa, and pristine = 18 taxa (SIM-
PER, S2 Table).
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The footprint of the Deepwater Horizon oil spill, as determined by Kriging interpolation
analysis, was 321 km
2
(Table 2). The most severe impacts were found in an area of approxi-
mately 58 km
2
around the Macondo well (Fig 5A). The footprint area of 321 km
2
includes only
the red and orange zones around the Macondo oil well, but not the coastal areas further to the
north, which were likely affected by hypoxia of the “dead zone” (Fig 5B). The shape of the
impact zone around the wellhead was approximately elliptical, stretched in an approximate
southwest to northeast direction.
Fig 4. Multidimensional scaling (MDS) plots of benthic infauna community structure. Colors represent impact
levels as defined in Figs 2and 3, and the size of the bubbles represents PAH44 concentrations (ppb). An X indicates
PAH44 concentration was missing. Station labels were included for those explicitly mentioned in the text. A)
Meiofauna. B) Macrofauna.
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Discussion
The cluster of severely and moderately impacted stations around the Macondo oil well is a
strong indication of the devastating effects of the DWH oil spill on the diversity of the benthic
infauna. The locations of moderately impacted stations indicate that the DWH oil spread fur-
ther to the northeastern and southwestern directions from the well than previously thought
and causing damage to meiofauna and macrofauna assemblages in an area of approximately
321 km
2
. This estimate is considered conservative because it does not take into account the
many patches of oil that are expected to be scattered throughout the northern Gulf of Mexico
[37].
The loss of taxonomic richness at the affected stations was most likely caused by a combina-
tion of smothering [5] and the toxicity of contaminants, such as PAH44 and Barium [38]. This
is evidenced by the PCA analysis, which indicates that DWH related contaminants (TPH,
PAH44, and barium) were negatively correlated with taxonomic richness and diversity. Con-
versely, meiofauna abundance and N:C ratios were positively correlated with contaminant
concentrations. The increase of opportunistic nematodes is a well-known phenomenon in dis-
turbed and polluted habitats [39,40,41]. Additionally, many sensitive macrofauna taxa disap-
peared at affected stations [42], wheras tolerant taxa, such as dorvilleid polychaetes of the
genus Ophryotrocha, thrived [12]. Our findings corroborate previous studies that found simi-
lar patterns of injuries in deep-sea corals [43] and infauna associated with these corals [11]
near the DWH spill site.
The shallow station (1.20) off the coast of Grand Isle, Louisiana, USA, which had by far the
highest N:C ratio among all stations, had no sign of oil contamination. The disturbance of the
infauna was likely caused by hypoxic conditions, which are common along the Louisiana coast
during summer months [44]. Hypoxia causes local extinction of sensitive organisms, while
Table 1. Results from ANOSIM test for differences in community structure among impact classification groups. Classification and color scheme as defined in Figs
3–5. Groups underlined are not different in community structure at the p = 0.05 level.
Taxa Impact Classification Group
A) Macrofauna
Group 1 2 3 4 5
Color Red Orange Yellow Light Green Green
Classification Severe Moderate Uncertain None Pristine
B) Meiofauna
Group 1 2 3 4 5
Color Red Orange Yellow Light Green Green
Classification Severe Moderate Uncertain None Pristine
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Table 2. Dimensions of difference in impact zones between the 2013 and current studies. The colors refer to the impact zones in Figs 3–5. The predicted area of the
orange zone excludes areas affected by causes other than the DWH oil spill, e.g., hypoxia or other drilling activity.
Zone (Map Color) Based on Montagna et al. 2013 Based on current study
Area (km
2
) Stations Area (km
2
) Stations
Severe Impact (Red) 24 8 58 11
Moderate Impact (Orange) 148 14 263 18
Uncertain Impact (Yellow) 13,497 13 23,063 27
No Impact (Light green) 33,760 16 58,785 40
Pristine (Dark green) 22,718 7 29,609 20
Total 70,147 58 111,778 116
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Fig 5. Benthic footprint maps determined by Kriging interpolation. Red: high impact; orange: moderate impact;
yellow: potential impact; light and dark green: background conditions. A) Areanear the MC252 wellhead. B) Entire
sampling area.
https://doi.org/10.1371/journal.pone.0235167.g005
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tolerant taxa may thrive [45]. The disturbance identified in the heavily impacted station 30 km
north of the wellhead was also not related to DWH, as spill-related contaminants were not
detected at high concentrations. Instead, there are records about three exploratory wells that
were drilled between 0.7 km and 1.65 km from the station in 2005. Most likely meiofauna and
macrofauna were disturbed during these operations and had not recovered five years later. It is
known that drilling activities can cause long-term detectable effects in the benthos [41,46].
The newly included stations in the northeastern Gulf of Mexico off Alabama and Florida
did not exhibit signs of contamination. Their meiofauna and macrofauna communities were
among the most species-rich and diverse. This leads to the conclusion that no widespread
impacts on the benthic infauna of the Florida and Alabama shelf occurred after the DWH oil
spill, even though other researchers found some evidence that the strong upwelling in 2010 did
transport some of the oil to the Florida continental shelf [47].
The principal component, that was indicative of the oil spill changed from PC1 in our
2013 footprint assessment [10] to PC2 in our current study. This is an effect caused by the
increase of stations included in the present study. The newly included stations increased the
geographical coverage, the depth range, and many of the variables included in the PCA analy-
sis. Therefore, the natural background accounted for more of the total variance than the
DWH impacts. However, this does not mean that impacts of the DWH are considered less
prevalent.
Compared to our 2013 study [10], our estimation of the area where the benthos was affected
nearly doubled from 172 km
2
to 321 km
2
(Table 2). Estimations of area sizes of severe and
moderate impacts increased from 24 km
2
to 58 km
2
and 148 km
2
to 263 km
2
, respectively.
This updated areal extent does not include the extensive moderately impacted coastal area, nor
the small affected area 30 km north of the wellhead, which had other causes for their distur-
bances, as discussed above. The increased spatial resolution and the spatial interpolation
method used in the present study make us confident that the expanded benthic footprint iden-
tified here is more realistic than our previous conservative estimation. However, the large size
of the impacted area and the patchy distribution of the sequestered DWH hydrocarbons [22–
24] makes it difficult to recreate the true spatial extent of the DWH impact on benthic infauna
communities.
A total of 227 stations were sampled during the three 2010 cruises, but benthic macrofauna
were sampled at only 171 of the stations during two of the cruises (R/V Gyre and R/V Ocean
Veritas). Thus there are another 55 stations that remain unanalyzed. It is interesting to specu-
late if analyzing these additional samples might change the area of the footprint. We do not
believe it would for two reasons. First, all but one of the the remaining stations are north of the
spill area in shallow water (<170 m) where neither the surface slick nor the deep sea plumes
were present. The last station is very far to the northeast beyond the DeSoto Canyon. Second,
the PAH44 concentrations at these stations was very low ranging from 7 to 177 ng/g
(mean = 60, standard deviation = 42) [21].
We conclude that spatial analyses of environmental footprints should have a sampling
design in place that covers a large area and includes many sampling stations to improve
spatial resolution of the assessment. In the light of time and money constraints that are
innate to any environmental assessment, we advocate that future complex environmental
footprint assessments prioritize the optimization of spatial extent over multiple replicate
samples from a single sampling station [48,49]. Additionally, the substantial temporal vari-
ability of benthic communities in the northern Gulf of Mexico should be taken into consid-
eration [50].
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Supporting information
S1 Table. Station locations, data, and Principal Component (PC) scores.
(XLSX)
S2 Table. Macrofauna community similarity analysis (SIMPER results).
(XLSX)
Acknowledgments
The data of this study are publicly available through the Gulf of Mexico Research Initiative
Information & Data Cooperative (GRIIDC) at https://data.gulfresearchinitiative.org (DOI: 10.
7266/FDKAJKNF).
Numerous current and former technicians of the Ecosystem & Modeling group at the
Harte Research Institute spent many hours in the wet lab to process the benthic samples and
on their microscopes to identify all macrofauna organisms, including Noe C. Barrera, Robert
Gutierrez, Larry J. Hyde, Richard D. Kalke, Kate A. Lavelle (now Texas Commission of Envi-
ronmental Quality), Elani K. Morgan, Adelaide C.E. Rhodes (now University of Vermont),
Melissa Rohal (now Texas Commission of Environmental Quality), and Travis W. Washburn
(now University of Hawaii).
Author Contributions
Conceptualization: Paul A. Montagna.
Data curation: Jeffrey G. Baguley, Paul A. Montagna.
Formal analysis: Michael G. Reuscher, Paul A. Montagna.
Funding acquisition: Paul A. Montagna.
Investigation: Michael G. Reuscher, Jeffrey G. Baguley.
Methodology: Paul A. Montagna.
Project administration: Jeffrey G. Baguley, Paul A. Montagna.
Resources: Jeffrey G. Baguley, Paul A. Montagna.
Software: Paul A. Montagna.
Supervision: Paul A. Montagna.
Validation: Michael G. Reuscher, Jeffrey G. Baguley, Paul A. Montagna.
Visualization: Michael G. Reuscher, Paul A. Montagna.
Writing – original draft: Michael G. Reuscher.
Writing – review & editing: Michael G. Reuscher, Jeffrey G. Baguley, Paul A. Montagna.
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