Resource Occurrence and Productivity in Existing and Proposed Wind Energy Lease Areas on the Northeast US Shelf

Abstract

States in the Northeast United States have the ambitious goal of producing more than 22 GW of offshore wind energy in the coming decades. The infrastructure associated with offshore wind energy development is expected to modify marine habitats and potentially alter the ecosystem services. Species distribution models were constructed for a group of fish and macroinvertebrate taxa resident in the Northeast US Continental Shelf marine ecosystem. These models were analyzed to provide baseline context for impact assessment of lease areas in the Middle Atlantic Bight designated for renewable wind energy installations. Using random forest machine learning, models based on occurrence and biomass were constructed for 93 species providing seasonal depictions of their habitat distributions. We developed a scoring index to characterize lease area habitat use for each species. Subsequently, groups of species were identified that reflect varying levels of lease area habitat use ranging across high, moderate, low, and no reliance on the lease area habitats. Among the species with high to moderate reliance were black sea bass (Centropristis striata), summer flounder (Paralichthys dentatus), and Atlantic menhaden (Brevoortia tyrannus), which are important fisheries species in the region. Potential for impact was characterized by the number of species with habitat dependencies associated with lease areas and these varied with a number of continuous gradients. Habitats that support high biomass were distributed more to the northeast, while high occupancy habitats appeared to be further from the coast. There was no obvious effect of the size of the lease area on the importance of associated habitats. Model results indicated that physical drivers and lower trophic level indicators might strongly control the habitat distribution of ecologically and commercially important species in the wind lease areas. Therefore, physical and biological oceanography on the continental shelf proximate to wind energy infrastructure development should be monitored for changes in water column structure and the productivity of phytoplankton and zooplankton and the effects of these changes on the trophic system.

Full text

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ORIGINAL RESEARCH
published: 21 April 2021
doi: 10.3389/fmars.2021.629230
Edited by:
Mary C. Fabrizio,
College of William & Mary,
United States
Reviewed by:
Ana B. Bugnot,
The University of Sydney, Australia
Robert Isdell,
College of William & Mary,
United States
*Correspondence:
Kevin D. Friedland
kevin.friedland@noaa.gov
Specialty section:
This article was submitted to
Marine Conservation
and Sustainability,
a section of the journal
Frontiers in Marine Science
Received: 13 November 2020
Accepted: 16 March 2021
Published: 21 April 2021
Citation:
Friedland KD, Methratta ET,
Gill AB, Gaichas SK, Curtis TH,
Adams EM, Morano JL, Crear DP,
McManus MC and Brady DC (2021)
Resource Occurrence
and Productivity in Existing
and Proposed Wind Energy Lease
Areas on the Northeast US Shelf.
Front. Mar. Sci. 8:629230.
doi: 10.3389/fmars.2021.629230
Resource Occurrence and
Productivity in Existing and
Proposed Wind Energy Lease Areas
on the Northeast US Shelf
Kevin D. Friedland1*, Elizabeth T. Methratta2, Andrew B. Gill3, Sarah K. Gaichas4,
Tobey H. Curtis5, Evan M. Adams6, Janelle L. Morano7, Daniel P. Crear8,
M. Conor McManus9and Damian C. Brady10
1National Marine Fisheries Service, Narragansett, RI, United States, 2IBSS Corporation, in Support of NOAA National Marine
Fisheries Service, Woods Hole, MA, United States, 3Centre for Environment, Fisheries and Aquaculture Science, Lowestoft,
United Kingdom, 4National Marine Fisheries Service, Woods Hole, MA, United States, 5Atlantic Highly Migratory Species
Management Division, National Marine Fisheries Service, Gloucester, MA, United States, 6Biodiversity Research Institute,
Portland, ME, United States, 7Department of Natural Resources and the Environment, Cornell University, Ithaca, NY,
United States, 8ECS Federal, in Support of Atlantic Highly Migratory Species Management Division, National Marine
Fisheries Service, Silver Spring, MD, United States, 9Division of Marine Fisheries, Rhode Island Department of Environmental
Management, Jamestown, RI, United States, 10 School of Marine Sciences, University of Maine, Walpole, ME, United States
States in the Northeast United States have the ambitious goal of producing more than
22 GW of offshore wind energy in the coming decades. The infrastructure associated
with offshore wind energy development is expected to modify marine habitats
and potentially alter the ecosystem services. Species distribution models were
constructed for a group of fish and macroinvertebrate taxa resident in the Northeast
US Continental Shelf marine ecosystem. These models were analyzed to provide
baseline context for impact assessment of lease areas in the Middle Atlantic Bight
designated for renewable wind energy installations. Using random forest machine
learning, models based on occurrence and biomass were constructed for 93 species
providing seasonal depictions of their habitat distributions. We developed a scoring
index to characterize lease area habitat use for each species. Subsequently, groups of
species were identified that reflect varying levels of lease area habitat use ranging across
high, moderate, low, and no reliance on the lease area habitats. Among the species
with high to moderate reliance were black sea bass (Centropristis striata), summer
flounder (Paralichthys dentatus), and Atlantic menhaden (Brevoortia tyrannus), which
are important fisheries species in the region. Potential for impact was characterized
by the number of species with habitat dependencies associated with lease areas and
these varied with a number of continuous gradients. Habitats that support high biomass
were distributed more to the northeast, while high occupancy habitats appeared to be
further from the coast. There was no obvious effect of the size of the lease area on
the importance of associated habitats. Model results indicated that physical drivers and
lower trophic level indicators might strongly control the habitat distribution of ecologically
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Friedland et al. Wind Energy Area Fish Habitat
and commercially important species in the wind lease areas. Therefore, physical and
biological oceanography on the continental shelf proximate to wind energy infrastructure
development should be monitored for changes in water column structure and the
productivity of phytoplankton and zooplankton and the effects of these changes on
the trophic system.
Keywords: wind energy, fisheries, habitat, monitoring, temperature, zooplankton
INTRODUCTION
The provision of ecosystem goods and services is intersecting
with the rapid development of the offshore wind industry in
continental shelf seas, an effort in energy generation designed
in part to ameliorate the effects of climate change (Causon and
Gill, 2018;UNEP, 2019). Shelf seas account for the majority of
seafood production of the World Ocean (Costanza et al., 2014),
which raises particular concern about the impacts that energy
infrastructure will have on fisheries and dependent communities
(Hooper et al., 2015;Carpenter, 2020). Therefore, we have a
growing need to develop assessment methods to understand
the intersection of fisheries resources with potential offshore
wind energy areas.
Interactions between wind energy production and fisheries
resources occur across trophic levels and life stages, during
each phase of energy infrastructure development, and through
biotic and abiotic pathways (Boehlert and Gill, 2010;Pezy et al.,
2020). The exploration and construction phases of offshore wind
development bring periodic elevated noise levels to the marine
environment through increased vessel traffic, seismic survey
methods, and, in most cases of fixed foundation turbines, impulse
pile driving (Wahlberg and Westerberg, 2005;Hatch et al., 2008).
Acoustic changes to the marine environment can cause sublethal
physiological effects (Popper and Hawkins, 2019) and mortality,
as well as changes in movement, behavior, habitat utilization,
and migration patterns for numerous marine taxa (Boehlert and
Gill, 2010;Brandt et al., 2018). Once installed and operating,
wind turbine foundations create new hard bottom habitats
that enhance the recruitment of native and non-native benthic
invertebrates (De Mesel et al., 2015). The resulting artificial
reefs attract fish species seeking food and refuge (Wilhelmsson
et al., 2006). Shells and other biogenic materials associated with
reef organisms are deposited in the surrounding environment,
increasing sediment organic content and nutrient concentrations,
and thus modifying benthic community composition (Wilding,
2014). Furthermore, the network of power cables associated with
wind farms emits electromagnetic fields, which have the potential
to affect behavior and movement of commercially valued and
migratory species (Hutchison et al., 2020).
Localized hydrodynamic regime changes at the scale of
individual turbines and wind farms occur as currents pass
structures, modifying downstream turbulence, surface wave
energy, and upwelling patterns (Bakhoday-Paskyabi et al.,
2018). Much larger scale effects (∼80 km from structures) on
hydrodynamics and vertical stratification are possible through
the impact of wind wakes and dynamic coupling of the ocean
and atmospheric systems (Carpenter et al., 2016). Physical and
biological oceanographic processes are directly linked through
numerous mechanisms, including the vertical and horizontal
transport of macro- and micro- nutrients to primary producers,
and changes in the distribution of suspended particulates, and the
effect of this suspended matter on the depth of the photic zone.
Altered hydrodynamic patterns could affect primary production
as well as upper trophic levels. These conceptual linkages have
been demonstrated with empirical data in the southern North
Sea that revealed increased vertical mixing at an offshore wind
farm resulting in the transport of nutrients to the surface mixed
layer and subsequent uptake by phytoplankton in the photic
zone (Floeter et al., 2017). Changes in water column properties
(water temperature, dissolved oxygen, and suspended matter
concentration) have also been linked to altered zooplankton
community structure at offshore wind farms in China (Wang
et al., 2018). Increased primary production could have important
implications for the productivity of bivalves and other macro-
benthic suspension feeders, representing a major component of
artificial reef communities that form on turbine foundations
(Slavik et al., 2019;Mavraki et al., 2020). In total, these effects may
propagate to upper trophic levels, particularly predatory fish on
and around the turbines (Pezy et al., 2020).
Assessing the effects of offshore wind on fisheries resources
requires that we know what to measure, what survey designs
to use, and how to coordinate that information with existing
surveys that support regional stock assessments (Wilding, 2014;
Methratta, 2020). Our traditional view of how fish habitat
is defined is rapidly changing; there is expanding evidence
suggesting that fish habitats can be determined by biological
variables related to primary and secondary production patterns
(Weber et al., 2018;Mazur et al., 2020). This highlights the need to
continue, or expand, current sampling efforts related to the water
column parameters such as phytoplankton and zooplankton and
suspended sediment material.
Currently in the United States, the offshore wind energy
developers are required by the Federal Agency responsible for
permitting and management of the offshore waters [the Bureau
of Ocean Management (BOEM)], to consider essential fish
habitat (EFH) designations developed by the National Marine
Fisheries Service (NMFS) for fishery management plans to help
assess species and habitat impacts. While EFH has benefits
in its availability and mostly standardized development across
species, it is a comparatively coarse representation of a given
species’ distribution and habitat reliance, and can be based on
sparse or discontinuously collected observations (Moore et al.,
2016). EFH may encompass the broad range of a species’
distribution and its habitats, but does not generally discern if
there are focal or highly preferred habitat areas within that
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distribution, or if there are seasonal shifts in distribution.
Species distribution models (SDMs) can complement EFH by
providing higher resolution distribution probabilities within EFH
and identify influential environmental parameters and habitat
suitability. Various types of SDMs have been developed using
fishery-dependent or -independent data to both hindcast and
forecast species distributions and habitat suitability relative
to spatial fisheries management needs (Hazen et al., 2018;
Crear et al., 2020).
Offshore wind energy capacity is developing rapidly across
the Northeast US Continental Shelf ecosystem (NES), with most
projects currently in a pre-construction planning and assessment
phase. There are considerable data gaps and a need for additional
baseline information to support the assessment of these impacts
on marine resources in this highly productive region. The
goal of this study was to characterize the use of wind energy
lease areas by fish and macroinvertebrate species sampled in
resource abundance surveys on the NES and identify species
with a high dependency on lease area habitats. These species
might be considered for prioritized attention and research by
fisheries management. The characterization was based on habitat
or SDMs developed using machine learning techniques. These
models provided species occurrence probability and biomass
productivity at spatial scales related to BOEM lease areas. The
relative importance of habitats was characterized for use by
each species, including the change in dependency on these
habitats over time, and the biological and physical aspects of the
ecosystem that shaped habitat. The models in this study draw
on a range of lower trophic level variables to provide context
for identifying ecosystem properties that would merit monitoring
before, during, and after installing energy-generating structures.
MATERIALS AND METHODS
Study System
We studied the distribution of fish and macroinvertebrates
occurring in the NES, a well-studied marine system along the
western boundary of the North Atlantic Ocean. We fit SDMs
estimating occupancy and biomass habitats onto a 0.1-degree
grid, termed the estimation grid (Figure 1A). The boundaries
of eleven wind energy lease areas are identified as either existing
lease areas, E1–E7, or proposed areas, P1–P4. Each lease area will
be composed of a number of parcels of varying size (boundaries
as of May 2020). A single convex hull was drawn around the
parcels of each lease area and was the basis for spatial data
extraction representing that area; hence, this was exclusive of
any cable corridors. Since depth distribution is an important
factor, bathymetric relief and key depth contours are shown
in Figure 1B, along with identifying areas within the NES
commonly referred to in the text.
Survey Data Response Variables
The basis of the study was a series of SDMs incorporating habitat
features for taxa captured in a fishery-independent bottom trawl
survey conducted in the NES. The bottom trawl survey has
been conducted by the Northeast Fisheries Science Center each
year since 1963 in the fall and spring since 1968, occupying
FIGURE 1 | Map of the study system showing the estimation grid (red dots) for habitat models (A) and bathymetry (B). Existing (black outlines) and proposed (red
outlines) energy lease sites on the Northeast US Shelf are labels E1 through E7 and P1 through P4, respectively. Bathymetric relief, key depth contours, areas of
significance within the NES are also presented.
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upward of 300 stations during each season and is based on a
random stratified design (Desprespatanjo et al., 1988). Catches
were standardized for various correction factors related to vessels
and gears used in the time series (Miller et al., 2010). The survey
catch provided the binary response of presence or absence for
each taxa as the response variable in classification models, and
catch per unit (log10[CPUE kg tow−1+ 1]) of biomass used as
continuous variables in the regression models that can be thought
of as a biomass habitat metric (see Data Availability Statement).
Though time series of catch data extend back to the 1960s, the
time series of the analysis was limited to the period 1976–2018
due the availability of other data described below.
Predictor Variables
Physical and biological environmental data used as predictor
variables included dynamic variables that changed annually with
recurring sampling and static variables that were held constant
over time. The suite of predictors can be summarized over five
general categories (Table 1). Physical environmental variables,
including surface and bottom water temperature and salinity
were collected contemporaneously with survey trawl samples
with Conductivity/Temperature/Depth (CTD) instruments (see
Data Availability Statement). Temperature and salinity were
initially tested as dynamics variables; however, salinity was found
to be a weak predictor and was applied as a static variable,
which enabled training and fitting the models over the period
1976–2018. Depth of the survey station (meters) was a static
variable in the analysis.
In addition to the dynamic station temperature variables,
remote sensing sea surface temperature (SST) fields were used
to derive a complementary set of static physical environment
variables. SST fields from the MODIS Terra sensor were used
to generate monthly mean SST data and monthly gradient
magnitude, or frontal fields of the SST (see Data Availability
Statement). There are many methods used to identify fronts
(Belkin and O’Reilly, 2009) in oceanographic data that usually
utilize some focal filter to reduce noise and then identify gradient
magnitude with a Sobel filter. Calculations were performed
in R using the “raster” package (version 2.6-7) by applying
a three by three mean focal filter and a Sobel filter to
generate xand yderivatives, which were then used to calculate
gradient magnitude.
Benthic terrain descriptors included a series of static variables
that characterize the shape and complexity of the substrate.
Most benthic terrain variables were derived from the depth
measurements, such as vector ruggedness, rugosity, and slope
(Table 2). Other variables described the substrate itself, such as
benthic sediment grain size. The vorticity of benthic currents was
also considered a benthic terrain variable. These variables were
sampled to match the position of a survey trawl.
Biological covariates included predictor variables representing
lower trophic level primary and secondary production. Primary
production variables were monthly chlorophyll concentration
static variables developed from a multi-sensor remote sensing
data source. These data were merged using the Garver, Siegel,
Maritorena Model algorithm (Maritorena et al., 2010) obtained
from the Hermes GlobColour website and provided data over the
period of 1997–2016 (see Data Availability Statement). As with
the remote sensing SST data, monthly gradient magnitude (i.e.,
chlorophyll frontal fields) were also developed. Both the SST and
chlorophyll concentration variables were sampled to match the
position of a survey trawl.
Secondary production variables were based on zooplankton
abundances measured by the Ecosystem Monitoring Program
(EcoMon), which conducts shelf-wide bimonthly random-
stratified surveys of the NES (Kane, 2007). Zooplankton are
collected obliquely through the water column to a maximum
depth of 200 m using paired 61-cm Bongo samplers equipped
with 333-micron mesh nets (see Data Availability Statement).
We used the density estimates (number per 100 m3) of
the 18 most abundant taxonomic categories and a biomass
indicator (settled bio-volume) as potential predictor variables
(Table 3). The zooplankton time series has some missing values,
which were ameliorated by summing data over 5-year time
steps for each seasonal period and interpolating a complete
field using ordinary kriging. For example, zooplankton data
for spring 2000 would include the available data from tows
made during the period 1998–2002. The zooplankton variables
were sampled to match the date (season) and position of
the survey trawl.
TABLE 1 | Summary of predictor variables used in the development of spring and fall presence/absence and biomass habitat models.
Predictor variable categories Description Number
Physical environment variables Physical and oceanographic variables including depth (DEPTH), surface and bottom temperature
(ST_SD, BT_SD), and surface and bottom salinity (SS_SD, BS_SD) derived from surveys.
5
Benthic terrain descriptors A series of variables that characterize the structure of benthic habitats, most of which are based on
bathymetry data. See Table 2 for details.
19
Secondary production variables Abundance of zooplankton taxa and a zooplankton biomass index (settled bio-volume) composed
mostly of copepod species. Some taxa only identified to family or others to a general category. See
Table 3 for details.
19
Remote sensing Primary production variables Remote sensed measurements of monthly mean chlorophyll concentration; and, the gradient
magnitude or frontal data for the same fields [CHL_(R for raw data, F for frontal gradient magnitude)_(XX
from 01 to 12 for month)].
24
Remote sensing Physical environment variables Remote sensed measurements of monthly mean SST; and, the gradient magnitude or frontal data for
the same fields [SST_(R for raw data, F for frontal gradient magnitude)_(XX from 01 to 12 for month)].
24
Number refers to number of variables.
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TABLE 2 | Summary of benthic terrain predictor variables used in the development of spring and fall presence/absence and biomass habitat models.
Variable Notes References
COMPLEXITY Terrain Ruggedness Index, the difference in elevation values from a center cell and
the eight cells immediately surrounding it. Each of the difference values are squared
to make them all positive and averaged. The index is the square root of this average.
Riley et al., 1999
NAMERA_BPI BPI is a second order derivative of the surface depth using the TNC Northwest
Atlantic Marine Ecoregional Assessment (“NAMERA”) data with an inner radius = 5
and outer radius = 50.
Lundblad et al., 2006
NAMERA_VRM Vector Ruggedness Measure (VRM) measures terrain ruggedness as the variation in
three-dimensional orientation of grid cells within a neighborhood based the TNC
Northwest Atlantic Marine Ecoregional Assessment (“NAMERA”) data.
Hobson, 1972
PRCURV2KM,
PRCURV10KM
PRCURV 20KM
Benthic profile curvature at 2, 10, and 20 km spatial scales was derived from depth
data.
Winship et al., 2018
RUGOSITY A measure of small-scale variations of amplitude in the height of a surface, the ratio
of the real to the geometric surface area.
Friedman et al., 2012
SEABEDFORMS Seabed topography as measured by a combination of seabed position and slope. http://www.northeastoceandata.org/
SLP2KM
SLP10KM
SLP20KM
Benthic slope at 2, 10, and 20 km spatial scales. Winship et al., 2018
SLPSLP2KM
SLPSLP10KM
SLPSLP20KM
Benthic slope of slope at 2, 10, and 20 km spatial scales Winship et al., 2018
SOFT_SED Soft-sediments is based on grain size distribution from the USGS usSeabed:
Atlantic coast offshore surficial sediment data.
http://www.northeastoceandata.org/
VORTFA
VORTSP
VORTSU
VORTWI
Benthic current vorticity at a 1/6 degree (approx. 19 km) spatial scale in fall (fa),
spring (sp), summer (su), and winter (wi).
Kinlan et al., 2016
TABLE 3 | Summary of zooplankton predictor variables used in the development
of spring and fall presence/absence and biomass habitat models.
Variable name Full name
ACARSP Acartia spp.
CALFIN Calanus finmarchicus
CHAETO Chaetognatha
CHAMZZ Centropages hamatus
CIRRZZ Cirripedia
CTYPZZ Centropages typicus
ECHINO Echinodermata
EVADNE Evadne spp.
GASZZZ Gastropoda
HYPERZ Hyperiidea
LARVAC Appendicularians
MLUCEN Metridia lucens
OITHSP Oithona spp.
PARAZZ Paracalanus parvus
PENILE Penilia spp.
PSEUDO Pseudocalanus spp.
SALPSZ Salpa
TLONGZ Temora longicornis
VOLUME Plankton bio-volume
Occupancy and Biomass Habitat Models
Seasonal SDMs were developed using the approach as reported
in Friedland et al. (2020); however, salinity variables were
static fields as opposed to dynamic. Random Forest models
based on occurrence were fit as classification models of the
presence or absence of taxa in a trawl tow and yielded an
occurrence probability; hereafter, these models are referred to
as presence/absence models and the estimates of habitat from
these models is referred to as occupancy habitat. Random
Forest models based on biomass were fit as regression models
of the catch rate of taxa in weight and yielded an index of
biomass; hereafter, these models are referred to as biomass
models and their output as biomass habitat. Random forest
machine learning models were fit (Cutler et al., 2007) using
the “randomForest” R package (version 4.6-14). Random forest
models can achieve comparable predictive power to other
statistical methods (Smolinski and Radtke, 2017). Prior to
fitting the model, the independent variables were tested for
multi-collinearity among the predictors using the multi-collinear
command from R package “rfUtilities” (version 2.1-5) with a
p-level of 0.1; highly correlated variables were eliminated from
the analysis. From this reduced set of predictors, the final model
variables were selected utilizing the model selection criteria
of Murphy et al. (2010) as implemented in rfUtilities. This
procedure ranks the importance of model variables based on
the change in mean squared error as a ratio of the maximum
model improvement error (termed MIR). A range of models
are fit and all variables with MIRs above a given threshold are
retained; the threshold is selected to minimize the number of
variables in the model while minimizing the mean squared error
and maximizing the variation explained (see Supplementary
Material for example R code). The presence/absence models were
evaluated for fit based on out-of-bag classification accuracy using
the AUC or Area Under the ROC Curve index using the “irr”
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package in R (version 0.84.1), applying an optimized classification
threshold probability. Models with an AUC of at least 0.73
were deemed satisfactory, a level associated with the lowest
performing model that was included in the study. Biomass model
regressions were evaluated for fit using the root mean squared log
error statistic based on the R package “Metrics” (version 0.1.4).
The 96 candidate species selected were consistently abundant
taxa from the survey, occurring in at least 150 trawl tows;
separate seasonal (spring and fall/autumn) models were fit
reflecting the two seasonal surveys. From this candidate list, a
subgroup of species with satisfactory presence/absence model
fits were used to estimate occupancy and biomass habitats
over the estimation grid for the same period of the training
data, 1976–2018.
Analysis Strategy
The study had four goals: (1) identify the species with habitats
overlapping the wind energy lease areas; (2) characterize the
relative importance of lease areas to species modeled in the study;
(3) characterize the change over time in habitat value to species in
the lease areas; and (4) determine which aspects of the ecosystem
were critical in shaping habitat in the lease areas.
To achieve the first goal, an index representing the reliance of
a species on lease area habitats was developed that utilized both
seasonal habitat scores (limited to the occurrence probabilities
only) for species and a relative score based on the ratio of
habitat scores within a lease area to overall habitat score for
the NES. A habitat score for a lease area was the median of
occurrence probability for a species that fell within a convex hull
that circumscribed a lease area, while the habitat score for the
NES was the median of occurrence probability over the entire
NES. Within a season, the index was incremented by one if
the median habitat score across the lease areas averaged >0.1, by
one if the average habitat score plus the 95% confidence interval
was >0.1, and by one if the habitat scores in at least one lease
area was >0.1. The 0.1 threshold was selected to represent those
species with a clear presence in an area. Similarly, the index was
incremented by one if the median ratio across the lease areas
averaged >1, by one if the average ratio plus the 95% confidence
interval was >1, and by one if the ratio in at least one lease area
was >1. For a species with a satisfactory model fit in a single
season, the index could range from 0 to 6, and for a species
with satisfactory models in both seasons, the index could range
from 0 to 12. The species were divided by their index scores
into four groups: “high reliance” for species with indices from 12
to 10; “moderate reliance” for species with indices from 9 to 6;
“low reliance” for species with indices from 5 to 1; “no reliance”
for species with indices of 0. High reliance species consistently
showed high utilization of lease areas in both seasons, moderate
reliance species generally showed high utilization in at least one
season, low reliance species generally showed low utilization in
both seasons, and no reliance species did not show utilization
in either season.
For the second goal, species occupancy and biomass habitat
were compared across lease areas and with respect to the
geographical position and size of the lease areas. For each lease
area, the number of species for which the ratio exceeded 0.7 was
summed, with the same exercise repeated for biomass habitat.
The threshold value of 0.7 was determined by testing values
from 1 to 0.5; the 0.7 level represented a breakpoint where lower
thresholds did not dramatically increase the number of species
included. The numbers of spring and fall species were averaged
to represent the relative roles of lease areas in respect to species
occurrence and biomass. In addition, species counts by model
and season were associated with four properties of the lease
areas: latitude, longitude, distance to the coast, and area of the
lease area. The correlations between species number and the four
properties were tested with Spearman rank order correlation.
For the third goal, trend in habitat use was evaluated using
a non-parametric test of time series (1976–2018) trend using
the R package “zyp” (version 0.10-1.1). We used the Yue et al.
(2002) method to estimate Theil-Sen slopes and performed an
auto-correlation corrected Mann–Kendall test of trend. The trend
in occupancy and biomass habitat was evaluated for the high
reliance species grouping and plotted against median occurrence
probability and biomass habitat scores.
For the fourth goal, variable importance in the
presence/absence models were evaluated for the models
associated with the species in the high reliance grouping.
Importance was based on five performance measures: the
number of times a variable was the root variable (i.e., variable
associated with the root node); the mean minimum node
depth for the variable; Gini index of node impurity decreases;
prediction accuracy decrease; and, the proportion of models
for which the variable was among the 10 highest ranked
variables. The first four of these indices was computed using
the “randomForestExplainer” R package (version 0.10.0); as
customary, the times a root variable was plotted against the mean
minimum depth variable and the Gini index was plotted against
the accuracy decrease variable. All the performance measures
were used in a principal components (PCs) analysis to provide
an overall rank of variables across species based on PC 1.
RESULTS
Species Models Included in the Analysis
The species modeled in this study included both finfish and
macroinvertebrate taxa. Of the initial candidate species, 93 taxa
had a seasonal presence/absence model with an AUC score of at
least 0.73 and were thus included in the study (Table 4). Based
on the performance of the seasonal presence/absence model,
complementary biomass model results were also considered. Not
all species with a satisfactory model fit in one season (i.e., spring
or fall) had a satisfactory fit in the other season. Hence, we had
model results for 83 taxa in the spring and 89 in the fall, with 80
taxa having models in both seasons.
Identification of Species Associated With
Wind Lease Areas
Using our index based on habitat scores and ratios of habitat
scores, we identified four groupings of species that reflected
the importance of the lease area habitats. Twenty species fell
within the criteria for the high reliance grouping (Table 5 and
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TABLE 4 | Species with presence/absence and biomass habitat models based on spring and fall survey data.
AUC RMSLE AUC RMSLE
Species Abbr Spring Fall Spring Fall Species Abbr Spring Fall Spring Fall
Sebastes fasciatus ACARED 0.93 0.94 0.15 0.14 Lophius americanus MONKFH 0.76 0.77 0.16 0.18
Alosa pseudoharengus ALEWIF 0.75 0.87 0.15 0.10 Triglops murrayi MOUSCL 0.84 0.84 0.01 0.02
Aspidophoroides
monopterygius
ALLFSH 0.85 0.85 0.00 0.00 Prionotus carolinus NORSEA 0.85 0.85 0.11 0.11
Hippoglossoides
platessoides
AMEPLA 0.91 0.94 0.10 0.10 Sphoeroides maculatus NPUFFR 0.87 0.90 0.00 0.03
Alosa sapidissima AMESHA 0.81 0.06 Macrozoarces americanus OCPOUT 0.79 0.82 0.15 0.08
Homarus americanus AMLOBS 0.79 0.77 0.15 0.17 Merluccius albidus OFFHAK 0.91 0.93 0.05 0.04
Squatina dumeril ANGSHR 0.93 0.91 0.05 0.08 Pollachius virens POLLOC 0.82 0.86 0.19 0.17
Peristedion miniatum ARMSEA 0.88 0.88 0.02 0.02 Cancer irroratus RCKCRA 0.80 0.76 0.06 0.04
Argentina silus ATLARG 0.88 0.84 0.04 0.02 Decapterus punctatus RDSCAD 0.83 0.03
Gadus morhua ATLCOD 0.84 0.89 0.22 0.18 Ger yon quinquedens REDCRA 0.84 0.84 0.03 0.03
Micropogonias
undulatus
ATLCRO 0.89 0.95 0.04 0.15 Urophycis chuss REDHAK 0.80 0.82 0.15 0.16
Hippoglossus
hippoglossus
ATLHAL 0.84 0.84 0.07 0.07 Etrumeus teres RHERRI 0.80 0.13
Clupea harengus ATLHER 0.76 0.90 0.21 0.14 Trachurus lathami ROSCAD 0.84 0.03
Scomber scombrus ATLMAC 0.77 0.77 0.19 0.10 Leucoraja garmani ROSSKA 0.93 0.93 0.04 0.04
Brevoortia tyrannus ATLMEN 0.85 0.85 0.04 0.03 Dasyatis centroura RTSTIG 0.83 0.14
Melanostigma
atlanticum
ATLPOU 0.86 0.84 0.01 0.00 Ammodytes dubius SANDLA 0.82 0.75 0.09 0.05
Menidia menidia ATLSIL 0.89 0.01 Stenotomus chrysops SCUPZZ 0.90 0.91 0.10 0.16
Anarhichas lupus ATLWOL 0.85 0.86 0.09 0.06 Hemitripterus americanus SEARAV 0.80 0.81 0.14 0.11
Dipturus laevis BARSKA 0.89 0.88 0.09 0.11 Placopecten magellanicus SEASCA 0.84 0.84 0.11 0.13
Anchoa mitchilli BAYANC 0.94 0.92 0.04 0.11 Lumpenus maculatus SHANNY 0.90 0.01
Centropristis striata BLABAS 0.86 0.86 0.07 0.07 Chlorophthalmus agassizi SHORTP 0.90 0.93 0.01 0.01
Helicolenus
dactylopterus
BLAROS 0.90 0.90 0.06 0.06 Illex illecebrosus SHTSQD 0.88 0.81 0.06 0.16
Callinectes sapidus BLUCRA 0.74 0.89 0.00 0.02 Merluccius bilinearis SILHAK 0.81 0.82 0.17 0.17
Pomatomus saltatrix BLUEFI 0.88 0.85 0.05 0.18 Etropus microstomus SMAFLO 0.87 0.79 0.01 0.01
Alosa aestivalis BLUHER 0.76 0.88 0.09 0.05 Mustelus canis SMODOG 0.92 0.89 0.09 0.17
Zenopsis conchifera BUCDOR 0.89 0.91 0.04 0.04 Malacoraja senta SMOSKA 0.89 0.88 0.07 0.07
Peprilus triacanthus BUTTER 0.86 0.77 0.12 0.23 Majidae SPICRA 0.73 0.01
Scyliorhinus retifer CHADOG 0.95 0.94 0.04 0.04 Squalus acanthias SPIDOG 0.79 0.80 0.32 0.28
Scomber japonicus CHUBMA 0.74 0.00 Urophycis regia SPOHAK 0.88 0.84 0.09 0.13
Raja eglanteria CLESKA 0.92 0.92 0.07 0.09 Leiostomus xanthurus SPOTZZ 0.84 0.94 0.02 0.13
Conger oceanicus CONGEL 0.82 0.03 Anchoa hepsetus STRANC 0.93 0.10
Tautogolabrus
adspersus
CUNNER 0.84 0.87 0.05 0.05 Morone saxatilis STRBAS 0.90 0.87 0.10 0.08
Brosme brosme CUSKZZ 0.89 0.88 0.09 0.09 Prionotus evolans STRSEA 0.89 0.89 0.06 0.08
Lepophidium
profundorum
FAWMEL 0.89 0.89 0.04 0.04 Paralichthys dentatus SUMFLO 0.84 0.90 0.11 0.13
Monacanthus hispidus FILEFS 0.75 0.01 Tautoga onitis TAUTOG 0.84 0.03
Paralichthys oblongus FOUFLO 0.87 0.83 0.11 0.13 Amblyraja radiata THOSKA 0.88 0.89 0.12 0.14
Enchelyopus cimbrius FRBERO 0.89 0.88 0.03 0.02 Lopholatilus
chamaeleonticeps
TILEFI 0.92 0.85 0.03 0.01
Citharichthys arctifrons GULFLO 0.87 0.86 0.02 0.03 Cynoscion regalis WEAKFI 0.84 0.93 0.03 0.12
Melanogrammus
aeglefinus
HADDOC 0.85 0.84 0.20 0.21 Maurolicus weitzmani WEITZP 0.78 0.75 0.01 0.01
Myxine glutinosa HAGFIS 0.84 0.87 0.02 0.02 Urophycis tenuis WHIHAK 0.87 0.88 0.13 0.14
Cancer borealis JONCRA 0.74 0.74 0.05 0.05 Scophthalmus aquosus WINDOW 0.83 0.85 0.12 0.13
Ovalipes ocellatus LADCRA 0.86 0.87 0.01 0.04 Pseudopleuronectes
americanus
WINFLO 0.88 0.87 0.13 0.14
(Continued)
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TABLE 4 | Continued
AUC RMSLE AUC RMSLE
Species Abbr Spring Fall Spring Fall Species Abbr Spring Fall Spring Fall
Urophycis chesteri LGFINH 0.92 0.92 0.02 0.02 Leucoraja ocellata WINSKA 0.81 0.88 0.23 0.17
Leucoraja erinacea LITSKA 0.83 0.86 0.23 0.19 Glyptocephalus
cynoglossus
WITFLO 0.84 0.90 0.09 0.09
Myoxocephalus
octodecemspinosus
LONSCU 0.89 0.88 0.13 0.13 Cryptacanthodes
maculatus
WRYMOU 0.88 0.88 0.02 0.02
Doryteuthis pealeii LONSQD 0.89 0.85 0.12 0.22 Limanda ferruginea YELFLO 0.87 0.88 0.12 0.12
Cyclopterus lumpus LUMPFI 0.79 0.83 0.04 0.04
Presence/absence model performance statistic is Area under the ROC curve (AUC) index, biomass model performance statistic is Root Mean Squared Log Error (RMSLE)
index. Species name abbreviations (Abbr) referred to elsewhere.
see Supplementary Material). Since the minimum score for
this grouping was 10, all high reliance species had both spring
and fall model data included in the study. The high reliance
grouping included demersal and pelagic fish and invertebrate
species, including cephalopods (squid) and decapods (crab).
There were 31 species in the moderate reliance group, some
of which were only considered for one season. Like the high
reliance group, there were finfish and invertebrates among the
taxa in the moderate reliance group and many of the single season
species were small pelagic taxa. Twenty-six species were in the
low use grouping, many of which fell within the single season
category. Finally, there were 16 taxa in the no-reliance group;
while they can occur in the lease areas their reliance was below
the required threshold.
Utilization of Lease Areas
Habitat in the lease areas varied widely between depictions based
on the output of the presence/absence and biomass models.
Averaged over season and lease areas and with ratios exceeding
0.7, approximately 38 species used the lease areas as occupancy
habitat compared to 42 species that utilized the areas as biomass
habitat (Figures 2A,B). The areas with the highest number
of species making use of the lease areas as occupancy habitat
included E1 and P3 and among the lowest were areas E2 through
E5, which were located inshore. The areas with the highest
number of species using the lease areas as biomass habitat
included E1 and P1, two of the more northerly lease areas. The
lease areas with the lowest number of species making use of
the lease areas as biomass habitat included E2 through E5, the
same areas with low numbers of species using the lease areas as
occupancy habitat.
When species counts were related to parameters reflecting the
position and size of the lease areas, distinct trends emerged. The
number of species with ratios >0.7 for presence/absence models
were uncorrelated with latitude in the spring but correlated
in the fall season (Figures 3A,C). However, there appears
to be a stronger relationship between the number of species
and latitude for the biomass habitat, which is significant in
both seasons (Figures 3B,D). Though not explicitly tested with
the correlation coefficient, biomass model responses appeared
non-linear and suggested lower counts at middle latitudes.
There was significant correlation with longitude of the lease
areas for both presence/absence and biomass models in both
seasons (Figures 4A–D), suggesting higher species counts with
more eastern lease areas. Counts from spring presence/absence
models were positively correlated with distance to the coast
of the lease areas (Figures 5A,C); however, the relationship
in the fall data is less developed. The biomass model counts
were also positively related to distance to the coast, although
these relationships were non-significant (Figures 5B,D). Size of
the lease area may be playing a role in species counts; however,
the correlations for presence/absence and biomass models were
relatively weak (Figure 6).
Retrospective Change in Lease Area
Habitat Use by Species
Most taxa classified as high reliance species with respect to
lease area utilization were also increasingly dependent on the
lease area habitats. For presence/absence model output, 80 and
89% of species had medians of significant habitat time series
trends that were positive for spring and fall models, respectively
(Figures 7A,B). Notably, among the species with the greatest
increases in occurrence probability in the spring were Urophycis
regia,Leucoraja ocellata, Paralichthys dentatus, and Squalus
acanthias; in fall, Citharichthys arctifrons, Raja eglanteria, U.
regia, and Centropristis striata were among the species with
the greatest increases in occurrence probability. In both spring
and fall, Glyptocephalus cynoglossus and Limanda ferruginea had
negative trends in occurrence probability. For biomass model
output, 65 and 60% of species in spring and fall, respectively, had
increasing trends in biomass habitat index scores (Figures 8A,B).
In spring, there were exceptional increases in biomass habitat for
Leucoraja erinacea and S. acanthias; however, many species had
moderate increases in biomass habitat scores in fall, including
L. erinacea, P. carolinus, S. acanthias, R. eglanteria, P. dentatus,
Placopecten magellanicus, and U. regia.
Model Variable Importance as Indicators
of Habitat
The different variable classes contributed to presence/absence
models in a hierarchical fashion when considering the times
a root, mean minimum depth, Gini decreases, and accuracy
decrease variable performance measures. Physical and biological
variables had a larger influence on the model fits than benthic
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TABLE 5 | Species groups and their relative reliance on wind lease areas, based on the sum of habitat indices where Spring is the sum of habitat score indices triggered
for spring distributions and Fall, likewise to spring.
High Moderate Low No
Species Spring Fall Total Species Spring Fall Total Species Spring Fall Total Species Spring Fall Total
BUTTER 6 6 12 ATLCRO 3 6 9 ATLSIL 5 0 5 ACARED 0 0 0
CLESKA 6 6 12 ATLMEN 6 3 9 RTSTIG 0 5 5 ALLFSH 0 0 0
FOUFLO 6 6 12 LADCRA 3 6 9 SPICRA 0 5 5 AMEPLA 0 0 0
GULFLO 6 6 12 OCPOUT 6 3 9 BLUCRA 1 3 4 ATLHAL 0 0 0
LITSKA 6 6 12 SCUPZZ 3 6 9 FAWMEL 1 3 4 ATLWOL 0 0 0
LONSQD 6 6 12 SMAFLO 5 4 9 JONCRA 1 3 4 CUSKZZ 0 0 0
NORSEA 6 6 12 NPUFFR 2 6 8 OFFHAK 1 3 4 HAGFIS 0 0 0
RCKCRA 6 6 12 SILHAK 4 4 8 ROSCAD 0 4 4 LUMPFI 0 0 0
SPOHAK 6 6 12 SPOTZZ 2 6 8 TILEFI 3 1 4 MOUSCL 0 0 0
SUMFLO 6 6 12 AMLOBS 3 4 7 ATLPOU 0 3 3 POLLOC 0 0 0
WINDOW 6 6 12 ATLHER 6 1 7 BARSKA 1 2 3 REDCRA 0 0 0
WINFLO 6 6 12 ATLMAC 6 1 7 CHUBMA 3 0 3 SHANNY 0 0 0
WINSKA 6 6 12 BLUEFI 1 6 7 ATLCOD 2 0 2 SMOSKA 0 0 0
BAYANC 5 6 11 BLUHER 6 1 7 CONGEL 2 0 2 THOSKA 0 0 0
BLABAS 5 6 11 LONSCU 4 3 7 RDSCAD 0 2 2 WHIHAK 0 0 0
SANDLA 6 5 11 MONKFH 4 3 7 WEITZP 2 0 2 WITFLO 0 0 0
YELFLO 6 5 11 REDHAK 4 3 7 WRYMOU 1 1 2
SEASCA 5 5 10 SHTSQD 4 3 7 AMESHA 0 1 1
SMODOG 4 6 10 STRSEA 1 6 7 ATLARG 0 1 1
SPIDOG 6 4 10 ALEWIF 5 1 6 BLAROS 0 1 1
ANGSHR 0 6 6 CUNNER 0 1 1
ARMSEA 3 3 6 FILEFS 0 1 1
BUCDOR 3 3 6 FRBERO 1 0 1
CHADOG 3 3 6 HADDOC 0 1 1
RHERRI 0 6 6 LGFINH 1 0 1
ROSSKA 3 3 6 TAUTOG 0 1 1
SEARAV 4 2 6
SHORTP 3 3 6
STRANC 0 6 6
STRBAS 6 0 6
WEAKFI 0 6 6
Total is the sum of spring and fall indices. Groupings from high to no reliance are divisions intended to reflect the role of lease area habitats to individual species.
FIGURE 2 | Mean of spring and fall species counts with lease area to NES
ecosystem ratio of >0.7 for occurrence (A) and biomass (B) model output.
See Figure 1 for area abbreviations.
terrain variables. Variable classes with a higher times a root
and lower mean minimum depth score (upper left quadrant,
Figure 9A) are indicative of variables of greater importance.
Likewise, variable classes with large Gini decrease and accuracy
decrease values (upper right quadrant, Figure 9B), are indicative
of variables that are more important in explaining species’
variation in occupancy. The spring models suggest physical,
primary production, and secondary production variable classes
were more important than the benthic terrain habitat variables.
For the fall models, however, primary and secondary production
variables were of greater significance than the physical variables
since they occur more frequently in the key quadrants
(Figures 9C,D). For both sets of seasonal models, the terrain
variables made the lowest contribution.
The PC analysis supported the role of temperature and
depth in defining fish and macroinvertebrate habitat, but also
suggested primary and secondary production variables were
critical. The first dimension of the spring and fall PCAs
explained 76.9 and 83.2% of the variance in variable importance
indices, respectively. For both models, the second dimension
explained <15% of the variance, and was not considered in
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FIGURE 3 | Species counts with lease area to ecosystem ratio of >0.7 versus centroid latitude of the respective lease area for spring occurrence (A) and biomass
(B) models and fall occurrence (C) and biomass (D) models. Spearman rank order correlations, rs, noted in panel titles with significant correlations marked with
asterisk. See Figure 1 for area abbreviations.
further analyses. For spring models, the highest PC scores
were found with bottom temperature and depth; however,
among the top twenty variables were 15 primary and secondary
production variables (Figure 10A). Only one terrain variable
was among the top variables. For fall models, depth and
bottom temperature were the top variables, with 17 primary
and secondary production variables among the top 20 variables
(Figure 10B). No terrain variables were among the top 20
variables from the fall models.
DISCUSSION
The characterization of occupancy and biomass habitat of fish
and macroinvertebrate species allowed the identification of taxa
with high dependency on habitats that overlap the wind lease
areas in the NES ecosystem. This raises the potential for impact
of offshore wind development on these candidate taxa, where
impact is broadly defined as the potential direct effects for
some species on their habitat and subsequently their growth and
reproduction (Stenberg et al., 2015;Raoux et al., 2017). However,
it may also represent indirect effects in that wind farms may
change use patterns in the lease areas, such as limiting fishery
access and thus reducing fishing mortality for select species
(Ashley et al., 2014;Coates et al., 2016). Among the species with
the highest fisheries landings in the Middle Atlantic Bight (MAB)
in the most recent decade were Atlantic menhaden (B. tyrannus),
sea scallops (P. magellanicus), squids (Doryteuthis pealeii, Illex
illecebrosus), Atlantic croaker (Micropogonias undulatus), scup
(Stenotomus chrysops), spiny dogfish (S. acanthias), summer
flounder (P. dentatus), and striped bass (Morone saxatilis),
which were all species with a high to moderate reliance on the
lease area habitats. This information can aid ongoing efforts
to characterize cumulative risk to fishery species by a range
of environmental factors and human activities, including non-
fisheries uses (Hobday et al., 2011;Holsman et al., 2017). In
particular, summer flounder, scup, and spiny dogfish are among
federally managed species evaluated annually in an ecosystem-
level risk assessment, and summer flounder fisheries have been
found to face multiple other risks (Gaichas et al., 2018).
Despite the importance of multiple species to specific fisheries
in the MAB, some were not reliant on the lease areas. For
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FIGURE 4 | Species counts with lease area to ecosystem ratio of >0.7 versus centroid longitude of the respective lease area for spring occurrence (A) and biomass
(B) models and fall occurrence (C) and biomass (D) models. Spearman rank order correlations, rs, noted in panel titles with significant correlations marked with
asterisk. See Figure 1 for area abbreviations.
example, three taxa with high landings in the MAB, blue crab
(Callinectes sapidus), surf clam (Spisula solidissima), and ocean
quahogs (Arctica islandica), occur in the lease areas, but do not
appear in either the high or moderate reliance use groups. This
is likely because the clam species are infauna and not effectively
sampled in bottom trawl surveys, and blue crab provide high
regional landings, but are mostly an inshore estuarine taxon.
Despite low habitat use across most of the lease areas, some
taxa play pivotal roles in the NES ecosystem. For example,
Atlantic cod (Gadus morhua), which only meets the criteria of
low reliance, has been an ecologically pivotal species supporting
regional groundfish fisheries and has declined in recent years
due to overfishing and changing climate conditions (Pershing
et al., 2015). Hence, even if cod interactions were limited to a
small segment of the overall lease area, it would seem prudent to
monitor and study the effects of habitat change on cod biology.
Ongoing monitoring is comprehensive in the lease areas for
specific fisheries species and ecosystem aspects; however, other
components are characterized less well, such as benthic infauna,
birds, bats, marine mammals, or highly migratory megafauna.
Results here for finfish could potentially be expanded using data
from other dedicated surveys. Advances in acoustic and satellite
tracking can now identify important foraging and migration
habitats for highly migratory tunas, billfishes, and sharks (Wilson
et al., 2005;Curtis et al., 2018) and multiple species of seabirds
(Montevecchi et al., 2012), and bats, which have been detected
up to 21 km offshore (Sjollema et al., 2014). Expanding analyses
could be particularly valuable for cetaceans, which were found
to occur in higher than expected numbers in lease areas off
southern New England (Stone et al., 2017). Habitat models
provide critical baselines for assessing potential overlap of many
species with wind lease areas (Roberts et al., 2016); in particular,
understanding habitat use by endangered North Atlantic right
whales in proximity to lease areas (Davis et al., 2017). In the
absence of standardized fishery-independent survey data, fishery-
dependent catch data may be a useful alternative for developing
distribution models for species not well-represented in this study
(Hazen et al., 2018).
Climate-driven changes in species distribution may play an
important role in changing dependency of species on lease
area habitats over time. Center of gravity distribution for most
species on the NES have shifted to higher latitudes, which in
a practical sense, represents an along-shelf movement from
the southwest to the northeast (Nye et al., 2009;Kleisner et al.,
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FIGURE 5 | Species counts with lease area to ecosystem ratio of >0.7 versus centroid distance to the coast of the respective lease area for spring occurrence (A)
and biomass (B) models and fall occurrence (C) and biomass (D) models. Spearman rank order correlations, rs, noted in panel titles with significant correlations
marked with asterisk. See Figure 1 for area abbreviations.
2016). Concomitant with shifting distributions, habitat use for
most taxa have expanded over recent decades (Friedland et al.,
2020), suggesting changing climate conditions have expanded
the range of useful, or at least tolerable, habitat for many
species. Occupancy habitat, reflected by the change in occurrence
probability, has increased for most high reliance species within
the lease areas. The increasing importance of these habitats is
likely the result of ranging dynamic environmental variables like
SST and secondary productivity that likely represent currently
optimal climate refugia (Poloczanska et al., 2013;Ban et al.,
2016). Moreover, these changes could shift more southerly species
into these communities (Lurgi et al., 2012;Barceló et al., 2016).
Secondary production variables reflect climatological production
zones, with some taxa displaying similar distribution shifts as fish.
Specifically, many copepod species have experienced a shift in
their center of gravity to the northeast, which would reinforce
the putative effects of temperature in increased habitat values
on a latitudinal basis (Friedland et al., 2019). Finally, factors
further complicating our understanding of climate impacts are
changes to the habitat and the related ecosystem components
within the lease blocks due to energy development itself that will
have species-specific outcomes (Langhamer, 2012).
Current marine spatial planning for offshore wind considers
the wind resource, the seabed type for installation, the location
of designated areas and other key marine users, such as
navigation and military uses. Fisheries grounds and vessel
transit routes are considered to varying degrees. However, the
relative importance of those locations to the actual fisheries
species (as demonstrated here) and life history association
(Barbut et al., 2020) is generally lacking. As more offshore
wind locations are chosen and the extent of the lease
areas increases, there will be a need to also understand
the temporal use of the areas by the fisheries species and the
life stage that may be dependent on the areas (Birchenough
and Degraer, 2020). The migratory connections between life
history stage habitats (Buscher et al., 2016) and the availability
of alternative habitat require consideration when assessing
potentially significant impact on species, such as population
or community change (Boehlert and Gill, 2010). Determining
the changes that do occur will need to be considered over
the appropriate time scale for cohort recruitment of species
and spatially may be within the jurisdiction of other states,
or countries. Larger developments will necessarily have more
subsea cables, longer installation times and larger turbine
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FIGURE 6 | Species counts with lease area to ecosystem ratio of >0.7 versus area of the respective lease area for spring occurrence (A) and biomass (B) models
and fall occurrence (C) and biomass (D) models. Spearman rank order correlations, rs, noted in panel titles with significant correlations marked with asterisk. See
Figure 1 for area abbreviations.
structures, spaced further apart than currently sized turbines.
Hence, the installation of the offshore wind infrastructure will
need to be incorporated into the future analysis of habitat
association and productivity of fisheries species in terms
of spatial extent and the length of time that the fisheries
may be affected.
With the advent of floating turbines, the future interactions
between marine resources and energy development may change
more toward offshore fisheries, particularly in the Gulf of Maine.
With moving offshore, there will be gradients of potential
habitat importance and perhaps differential impacts. With
this in mind, the assessment of occurrence and productivity
of fisheries species will remain a requirement to provide
explanatory variables that determine the species association
in these new offshore areas. Fish attraction (of different life
stages) to the structures and connectivity linked to prevailing
currents will likely need determination and occurrence of pelagic
predators would be expected. Moving wind turbine structures
offshore also increases the likelihood of cables intersecting with
migratory species routes, including electromagnetically sensitive
fauna such as elasmobranchs, sturgeons, and marine turtles
(Hutchison et al., 2020).
Physical (temperature and depth) and biological (primary and
secondary production) oceanographic variables were important
for determining habitat occupancy for upper trophic level species
in this study. Temperature and depth are well known to be
strong determinants of fish distribution and abundance in marine
ecosystems (Murawski and Finn, 1988). Primary and secondary
production form the base of the marine food web and are
associated with fisheries yields (Friedland et al., 2012;Stock et al.,
2017). Both physical and biological oceanographic variables are
likely to be affected by the operation of offshore wind farms
(Floeter et al., 2017;Bakhoday-Paskyabi et al., 2018). However,
few studies have directly demonstrated linkages between physical
and biological oceanographic variables at these facilities. One of
the first studies to empirically take on these questions reported
increased vertical mixing, doming of the thermocline (i.e., rising
of the thermocline to replace the surface mixed layer), and
transport of nutrients to the surface mixed layer followed by
uptake by phytoplankton in the photic zone (Floeter et al., 2017).
Coupled with our findings, this would suggest that wind farm
effects on phytoplankton and zooplankton might extend to upper
trophic level impacts, potentially modifying the distribution and
abundance of finfish and invertebrates. The spatial scale of
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FIGURE 7 | Median occurrence probability of species during the study period versus change in probability as Theil–Sen slope for essential species in existing and
proposed lease areas during spring (A) and fall (B). Only data associated with significant slopes were used in the calculations; sample size expressed with the size of
plotting symbol with maximum of 11 reflecting total number of lease areas in the study. See Table 4 for species abbreviations.
these effects remains unknown but could range from localized
within individual farms to broader spatial scales (Carpenter
et al., 2016;Bakhoday-Paskyabi et al., 2018). The implications
of these effects for fisheries production and commercial fisheries
economics could be significant; hence, we suggest that the effect
of wind farms on oceanographic variables be considered in
environmental impact analyses.
A well-designed regional wind farm monitoring program
is essential to understanding how offshore wind development
affects marine ecosystems, and the design of strategic and
meaningful assessments to properly contextualize impacts is
required (Lindeboom et al., 2015). With respect to our case
study, there is currently much discussion about how to conduct
monitoring focusing on variables that are sensitive to change,
that are sampled with current gear and technologies, and that
are indicative of wind farm effects on the biological community
and the ecosystem as a whole. To date, limited attention in
the research community has been given to how changes in
oceanographic variables due to offshore wind farm operation are
linked to effects on biological processes. Given the importance
of oceanographic variables in determining habitat occupancy
of upper trophic levels revealed in our study, we recommend
that comprehensive monitoring of these variables be conducted
in conjunction with biological monitoring. Specifically, to be
cognizant of the factors related to primary and secondary
production and how they are linked to patterns of natural
resource abundance and distribution.
In addition to regional monitoring, research programs are also
under development in the Northeast US led by a new science
entity, the Responsible Offshore Science Alliance, which seeks
to address research questions through cross-sector collaboration
and hypothesis-driven questions development (Dannheim et al.,
2020). The findings of this study could inform the development
of such research priorities and questions. For example, we
can foresee questions related to feeding behavior and diet
of species at various life stages and sizes and the associated
impacts of wind farm structures. The results of this study show
black sea bass (Centropristis striata) are highly dependent on
habitat in wind areas in both the spring and fall. Because of
its attraction to structural habitat and reef formations, black
sea bass has previously garnered attention as a species that
may benefit from the installation of turbine foundations. As
phytoplankton and zooplankton are important drivers of habitat
occupancy, species that are planktivorous such as Atlantic
menhaden (Brevoortia tyrannus), may be sensitive to any changes
in biological oceanography caused by wind farm operation. These
findings could be used to develop ecosystem simulation models
that couple changes in physical and biological oceanography
to explore a range of bottom up forcing scenarios caused
by wind farm operation and how they might affect upper
trophic levels (Pezy et al., 2020). Knowing how the range of
primary and secondary productivity values are linked to upper
trophic levels would be extremely useful in parameterizing and
testing such models.
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Friedland et al. Wind Energy Area Fish Habitat
FIGURE 8 | Median biomass index during the study period versus change in biomass index as Theil–Sen slope for essential species in existing and proposed lease
areas during spring (A) and fall (B). Only data associated with significant slopes were used in the calculations; sample size expressed with the size of plotting symbol
with maximum of 11 reflecting total number of lease areas in the study. See Table 4 for species abbreviations.
Species’ occupancy and biomass spatial predictions provide
a basis for quantifying baseline conditions for wind energy
areas of the NES. However, our methodological framework
could have broad applicability to other parts of the world
with comparable data availability that are also experiencing
similar growth in offshore energy development or other
impacts. As part of developers’ construction plans, they are
charged with understanding the environment they plan to
utilize. Their pre-construction monitoring needs to take into
account the variability in baseline estimates (Witt et al.,
2012). The model predictions in our study provide a method
for testing the tradeoffs of defining baseline year lengths
for species of high exploitation and ecosystem significance.
The results also highlight how the relationship between areas
and species can change through time, as many of the
high reliance species have increased dependency over time
(Figure 7). Many of these taxa are experiencing regional
shifts, often northeastward (Bell et al., 2015;Kleisner et al.,
2017), suggesting that the historical significance of these
areas for species should not be the sole metric used to
understand prospective wind farm impacts. Using SDMs with
forecasted environmental data and fit with the appropriate
rigor (Briscoe et al., 2019), may provide increased insight into
future interactions between offshore wind development and
the fish community.
The study presented here utilized data from NOAA’s
long-term bottom trawl survey and ecosystem monitoring
programs. In the Northeast US, wind development areas
overlap with these, and a number of scientific surveys
representing more than 315 years of cumulative survey
effort, which are executed by NOAA ships and aircraft.
Information gathered from these surveys represents one of the
most comprehensive data sets on marine ecosystems in the
world (Desprespatanjo et al., 1988). In addition to making
predictive modeling studies such as this one possible, these
surveys support fisheries assessment and management process,
protected species assessment and remediation, ecosystem-
based fisheries management, and regional and national climate
assessments, as well as a number of regional, national, and
international science activities (Smith, 2008). Within offshore
wind facility areas, survey operations will be curtailed or
eliminated under current vessel and aircraft capacities, safety
requirements, and monitoring protocols. For example, in the
case of the bottom-trawl survey, a stratified-random sampling
methodology will no longer be possible because wind energy
areas will not be sampled with the current vessel and gear
specifications. The same limitation will affect ecosystem survey
work. The inability to conduct sampling inside of wind
areas will lead to survey bias, a reduction in information,
increased uncertainty in stock assessments, and poorly informed
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Friedland et al. Wind Energy Area Fish Habitat
FIGURE 9 | The mean number of times a variable was the root node variable versus the mean minimum depth of a variable in a tree for spring (A) and fall (C)
models; and, the mean decrease in the Gini index of node impurity versus the mean accuracy decrease if a variable were to be removed for spring (B) and fall (D)
models. Variables are color coded by their class: physical (black), primary production (green), secondary production (blue), and benthic terrain complexity (red).
Symbol size is scaled by the number of species models the variable was selected.
FIGURE 10 | The top twenty variables across all spring (A) and fall (B) models of high reliance species based on principal component 1 variable scores as an index
of importance. Physical, primary production, secondary production, and terrain variables are represented in black, green, blue, and red, respectively. See Tables 1–3
for variable abbreviations.
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Friedland et al. Wind Energy Area Fish Habitat
management decisions (Boenish et al., 2020). Maintaining
the integrity of long-term scientific assessments through
development and implementation of novel survey design and
gear types is essential to ensure the compatibility of data collected
inside and outside of wind farms and of historical data with
future data sets.
DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included
in the article/Supplementary Material, further inquiries can be
directed to the corresponding author/s.
AUTHOR CONTRIBUTIONS
KF led the analysis with primary drafting of the manuscript by
KF, EM, AG, SG, and TC. EA, JM, DC, MM, and DB contributed
to the drafting and editing of the manuscript. All authors
contributed to the article and approved the submitted version.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fmars.
2021.629230/full#supplementary-material
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Disclaimer: The views expressed herein are those of the authors and do not
necessarily reflect those of their agencies.
Conflict of Interest: DC was employed by ECS Federal, and EM was employed by
IBSS Corporation.
The remaining authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a potential
conflict of interest.
Copyright © 2021 Friedland, Methratta, Gill, Gaichas, Curtis, Adams, Morano,
Crear, McManus and Brady. This is an open-access article distributed under the
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Frontiers in Marine Science | www.frontiersin.org 19 April 2021 | Volume 8 | Article 629230