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RESEARCH ARTICLE
Mapping queen snapper (Etelis oculatus)
suitable habitat in Puerto Rico using ensemble
species distribution modeling
Katherine E. OverlyID
1,2
*, Vincent LecoursID
2,3
1Technical and Engineering Support Alliance, National Oceanic and Atmospheric Administration, National
Marine Fisheries Service, Southeast Fisheries Science Center, Panama City, Florida, United States of
America, 2School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, Florida,
United States of America, 3Laboratoire d’expertise et de recherche en ge
´ographie applique
´e, Universite
´du
Que
´bec àChicoutimi, Chicoutimi, Que
´bec, Canada
*katherine.overly@noaa.gov
Abstract
Queen snapper (Etelis oculatus) is of interest from an ecological and management perspec-
tive as it is the second most landed finfish species (by total pounds) as determined by Puerto
Rico commercial landings (2010–2019). As fishing activities progressively expand into
deeper waters, it is critical to gather data on deep-sea fish populations to identify essential
fish habitats (EFH). In the U.S. Caribbean, the critically data-deficient nature of this species
has made this challenging. We investigated the use of ensemble species distribution model-
ing (ESDM) to predict queen snapper distribution along the coast of Puerto Rico. Using
occurrence data and terrain attributes derived from bathymetric datasets at different resolu-
tions, we developed species distribution models unique to each sampling region (west,
northeast, and southeast Puerto Rico) using seven different algorithms. Then, we devel-
oped ESDM models to analyze fish distribution using the highest-performing algorithms for
each region. Model performance was evaluated for each ensemble model, with all depicting
‘excellent’ predictive capability (AUC >0.8). Additionally, all ensemble models depicted
‘substantial agreement’ (Kappa >0.7). We then used the models in combination with exist-
ing knowledge of the species’ range to produce binary maps of potential queen snapper dis-
tributions. Variable importance differed across spatial resolutions of 30 m (west region) and
8 m (northeast and southeast region); however, bathymetry was consistently one of the best
predictors of queen snapper suitable habitat. Positive detections showed strong regional
patterns localized around large bathymetric features, such as seamounts and ridges.
Despite the data-deficient condition of queen snapper population dynamics, these models
will help facilitate the analysis of their spatial distribution and habitat preferences at different
spatial scales. Our results therefore provide a first step in designing long-term monitoring
programs targeting queen snapper, and determining EFH and the general distribution of this
species in Puerto Rico.
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OPEN ACCESS
Citation: Overly KE, Lecours V (2024) Mapping
queen snapper (Etelis oculatus) suitable habitat in
Puerto Rico using ensemble species distribution
modeling. PLoS ONE 19(2): e0298755. https://doi.
org/10.1371/journal.pone.0298755
Editor: Vitor Hugo Rodrigues Paiva, MARE –
Marine and Environmental Sciences Centre,
PORTUGAL
Received: March 1, 2023
Accepted: January 31, 2024
Published: February 26, 2024
Copyright: ©2024 Overly, Lecours. 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: All relevant data are
within the manuscript and its Supporting
Information files.
Funding: This work was funded in part by the
National Oceanic and Atmospheric Administration,
National Marine Fisheries Service internal
Cooperative Research Program awarded to K.E.O.,
and the National Oceanic and Atmospheric
Administration, Deep Sea Coral Research and
Technology Program, Southeast Deep Coral
Initiative internal grant awarded to K.E.O. The
Introduction
Knowledge of the spatial distributions of marine species is necessary for the development and
implementation of management strategies for fisheries around the world. The shift towards
ecosystem-based fisheries management (EBFM) has become more mainstream in recent years,
focusing on habitat, ecosystem processes, and the sustainability of populations [1,2]. This
approach to fisheries management requires accurate ecological information on the spatial dis-
tribution of key species, and critical environmental variables that influence observed patterns
of habitat use [3]. We note that the term “habitat” has been used in many different ways by
marine scientists [4]; in this study, we are using the term “habitat” as the combination of envi-
ronmental characteristics, and in particular characteristics of the physical environment, that is
associated with the presence of a species at given spatial and temporal scales [4].
Identification, mapping and understanding Essential Fish Habitats (EFH) provide impor-
tant spatial information to support EBFM. EFH has been broadly defined in the Magnuson-
Stevens Fishery Conservation and Management Act as “waters and substrate necessary for fish
spawning, breeding, feeding or growth to maturity,” making it difficult to discern what in fact
is essential about EFH [5]. To begin to determine EFH for a species, basic distribution data
must be available for a species’ geographic range. In the U.S. Caribbean, a lack of ecological
information on deepwater fish species has made it difficult to define EFH for the suite of spe-
cies occupying those domains. However, recent improvements in habitat mapping technolo-
gies and underwater video systems have greatly advanced our ability to generate spatially-
explicit data, particularly for deepwater habitats [6,7]. Within U.S. waters, studies on the dis-
tribution and species assemblages of Caribbean deepwater habitats are limited when compared
to the Gulf of Mexico or other parts in the North Atlantic Ocean. Yet several species of deep-
water snapper, including queen snapper (Etelis oculatus), have previously been determined to
be undergoing overfishing, or their stock status is unknown within the U.S. Caribbean Exclu-
sive Economic Zone [8]. The life history of queen snapper is characterized by slow growth and
high longevity [9,10], similar to that of other deepwater fishes [11,12], thus rendering the spe-
cies vulnerable to, and likely slow to recover from, fishing pressure. This is particularly critical
for data-poor species for which management decisions will undoubtedly deal with uncertain
model parameters, spatial distribution models, relative abundance indices, and diet matrices
(in the case of EBFM). As such, identifying and understanding EFH is crucial for the long-
term biological and economic sustainability of fisheries and deepwater habitats along Puerto
Rico’s coast.
Queen snapper is of interest from an ecological and management perspective as it is a tar-
geted component of the commercially important deepwater snapper-grouper complex fishery
found throughout Puerto Rico and the Caribbean. In Puerto Rico, queen snapper is the fourth
most landed species, and the second most landed finfish (Fig 1) according to the National Oce-
anic and Atmospheric Administration (NOAA) Trip Interview Program data (2010–2019),
and in recent years the fishing effort targeting this species appears to have shifted to deeper
waters, possibly in response to stock depletion [13]. Yet, little is known of its fine-scale distri-
bution patterns and the habitats it utilizes. A study conducted by Cummings [14] indicated
that queen snapper is most abundant in areas characterized by rocky bottom habitat near oce-
anic islands, and Allen [15] noted their adult depth range of 130–450 m. However, researchers
onboard the NOAA exploratory research vessel Okeanos Explorer recently established a new
maximum depth for the species of 534 meters (m) with direct observations via a remotely
operated vehicle [16]. Knowledge of juvenile habitat and depth range is more limited. A study
conducted by Gobert et al. [17] noted several fish between 55–70 millimeter (mm) fork length
(FL) were found as deep as 490 m, whereas the smallest fish obtained by Overly [10] was 178
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funders had no role in study design, data collection
and analysis, decision to publish, or preparation of
the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
mm FL, or 4 years old, captured at a depth greater than 100 m. Larvae appear to move deeper
with ontogeny [18] and were found as deep as 100 m at 38 days old. Thus, it is difficult to dis-
cern if there are age-based differences in habitat use for queen snapper.
Effective management strategies for queen snapper in the U.S. Caribbean will require a
knowledge of their spatial distribution to not only define EFH, but also guide future fishery
surveys and identify exploited and unexploited regions. Consequently, the Caribbean Fishery
Management Council (CFMC) has prioritized investigations into the deepwater snapper-grou-
per complex, particularly the habitats it targets [19]. However, as a relatively deepwater species,
it is difficult to develop expansive occurrence datasets for queen snapper due to limited oppor-
tunities and costly field sampling. In addition, describing deepwater habitats is challenging
mainly due to technological hurdles associated with visually assessing deepwater benthos.
Marine habitat mapping has become a critical first step in EBFM [20] and can combine
environmental variables at sites of known species occurrence to predict a species’ distribution
in unsampled areas and explore habitat suitability [6,21]. In particular, species distribution
models (SDM) enable the exploration of species-environment relationships that can help infer
potential environmental or ecological requirements needed by a particular species. Presence-
only, or presence-background SDMs have gained traction in recent years as presence/absence
models are not necessarily well suited for marine species modeling [22,23]. Monk et al. [21]
indicate a bias towards falsely identifying absences in the marine environment due to the
explicit constraints surrounding absence data, primarily what constitutes a true absence versus
Fig 1. Deepwater finfish landings in Puerto Rico. Total Puerto Rico commercial landings, in pounds (lbs), by year for the top five reef-associated finfishes, spiny
lobster, and queen conch.
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a failure to detect. The issue of false absence can be exacerbated when sampling cryptic species
or taking into account the sampling bias (e.g., selectivity) of certain gear types [24]. Classifying
sites where a fish was not seen or caught as non-suitable habitat has the potential for inaccu-
racy due to bias surrounding the nature of sampling and thus estimating error. Presence-back-
ground modeling allows for estimating spatial distributions of a species’ niche habitat from
positive observations in the data. In addition, presence-background SDM is less sensitive to
small sample sizes (n <30) while still generating ecologically valuable models, which is of par-
ticular importance in the deep oceans where sampling and data are limited [21,25–29].
In more recent years, ensemble species distribution modeling (ESDM) has been used for
marine fishes and as an approach to marine benthic habitat mapping [27,30]. Outcomes dem-
onstrate that in addition to providing sounder results through measurements of uncertainty
[30,31], ESDMs also tended to outperform individual SDMs with increased precision mea-
sured by the area under the receiver operating characteristic curve (AUC) [29,32], and true
skill statistic (TSS) metrics [32]. The benefit of training multiple algorithms that differ in their
predictions is twofold: the ensemble model is typically more accurate than any individual
model on its own; and by combining models with varying structures, we can ensure diverse
classification results that focus solely on multiple classifications simultaneously [33]. In addi-
tion, ESDM enables the quantification of model uncertainty, a valuable product when models
are used to inform decision-making and management [34].
In this study, we used ESDM to predict areas of suitable habitat for queen snapper, and by
extension map their potential distribution along sections of the coast of Puerto Rico. The
objectives of this work were to: 1) develop robust habitat suitability and uncertainty maps for
each of the study regions; and 2) quantify species-environment relationships to evaluate the
potential of various environmental variables, and more specifically terrain variables, to act as
surrogates for queen snapper distribution. Our main goal through modeling is exploratory in
nature as we sought to identify areas where queen snapper habitat suitability is predicted to be
high (85%) and identify the available variables explaining queen snapper distribution the
most. The models developed in this study are intended to be used as a tool to identify potential
areas in which queen snapper may be found, in a more cost-effective way than intensive bio-
logical sampling. Analyzing habitat utilization and the distribution of queen snapper will not
only add to our limited knowledge regarding queen snapper habitat preferences, but results
could also be incorporated into spatial planning under EBFM and the start of determining
EFH for queen snapper.
Materials and methods
Species occurrence data
Queen snapper occurrence data were collected during a fishery-independent, video and hook
and line survey, conducted between 2018 and 2020 in depths ranging from 100–500 m (Fig 2).
Sites were selected using a stratified random sampling design that utilizes a combination of
depth gradient and habitat, with sites allocated by 50 m depth intervals along three classes of
habitat complexity: low, moderate, and high as defined by an ArcCord rugosity score developed
by the University of Miami Rosenstiel School of Marine, Atmospheric, and Earth Science [35].
The project utilized Puerto Rican commercial fishers to conduct camera and fishing gear
deployments. Each deployment consisted of two separate vertical lines at each selected site’s
coordinates, the first targeting queen snapper via hook and line fishing and the second the
associated habitat and species presence/absence via a video camera system. Each vertical line
was composed of monofilament and synthetic braided line rigged with a 4.5-kilogram (kg)
weight attached to the bottom of the line. The first line deployment incorporated 12 leaders
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with 9/0 Mustad Extra Wide1circle hooks baited with California squid (Loligo sp.). The fish-
ing line was deployed for a total bottom time of 15 minutes after the weight reached the sea-
floor. The second line, deployed at the same coordinates as the hook and line, consisted of two
baited hooks and the camera system. The camera system was deployed for a total bottom time
of five minutes after the weight hit the seafloor and was then retrieved. To sample the full
range of depths, a customized video camera system had to be created that not only allowed
sampling to a depth of 500 m, but also provided lighting as light penetration at mesophotic-
deep benthic reefs is limited. The video camera system consisted of a Golem Gear1housing
enclosing a GoPro HERO31high-definition camera, deepwater LED lights from Blue Robot-
ics1and Sartek1Industries, and an aluminum battery housing enclosing a lithium-ion bat-
tery (Fig 3). The system consists of several pieces of white marine-grade high-density
polyethylene sheets constructed in such a way as to reduce drag upon deployment and
retrieval. Two pieces of syntactic foam coated in epoxy were attached to the camera system to
attain neutral buoyancy with the GoPro’s field of view angled towards the seafloor. The camera
Fig 2. Deepwater Puerto Rico sampling sites. Map of all survey sites sampled in two years with a remote video camera and hook and line fishing along the western (A),
northeastern (B), and southeastern (C) coasts of Puerto Rico. The map layer used to generate this figure is from the NOAA National Centers for Environmental
Information and provided without restriction by the U.S. Government.
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rig was tethered to a vertical fishing line with two gangions approximately one meter above
two 9/0 Mustad Extra Strong1circle hooks and a bottom weight.
Environmental data
The targeted study area was limited by the availability of bathymetric data provided by
NOAA’s National Centers for Coastal Ocean Science (NCCOS) and encompassed three major
fishing regions off the coast of Puerto Rico: the west, northeast and southeast (Fig 4). Available
bathymetric data were collected in prioritized regions by NOAA NCCOS and the United
States Geological Society using multibeam echosounder systems (MBES). Due to limited map-
ping data and differing resolutions between regions (2 m, 4 m, 8 m, and 30 m), multibeam
bathymetry rasters were mosaicked in ArcGIS Pro (v10.1) to a spatial resolution of 8 m on the
northeast and southeast coasts, and to 30 m on the west coast (Fig 4). These two resolutions
showed the best compromise to explore the effect of differing spatial resolutions on capturing
Fig 3. Deepwater video camera system diagram. Deepwater video camera system, including a GoPro camera and Golem Gear housing with an attached LED light
and battery housing. Subsea buoyancy foam allows the system to achieve neutral buoyancy, oriented at 45 degrees to the seafloor. The camera system is attached to
the fisher’s vertical hook and line with two gangions. Below the camera system are two baited hooks, and a weight to keep the line stationary. Reprinted under a CC
BY license, with permission from Katherine Overly, original copyright 2019.
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queen snapper habitat preference and to produce the highest possible resolution multibeam
data, which is lacking in many regions in the U.S. Caribbean, particularly on the west coast.
As commonly performed in habitat mapping studies [4,36], multiple terrain attributes
were derived from the bathymetry data collected at each location. The terrain attributes were
selected based on several studies that determined an optimal selection of variables for species
distribution modeling in the marine environment [3,34,37]. The ArcGIS Pro Spatial Analyst
extension was used to derive the slope, slope of slope, general curvature, planform curvature,
and profile curvature. The Benthic Terrain Modeler (BTM) toolbox in ArcGIS was used to
compute the vector ruggedness measure (VRM), fine-scale benthic position index (BPI) using
and inner radius of 5 and an outer radius of 25, and broad-scale BPI using an inner radius of
25 and an outer radius of 250 [38]. The TASSE toolbox was used to derive relative distance
from mean value (i.e., a measure of relative position), standard deviation (i.e., a measure of
rugosity), and northerness and easterness (i.e., non-circular derivatives of aspect, the
Fig 4. Bathymetric map of Puerto Rico. Map depicting mosaicked bathymetric data used in this study for the west (A), northeast (B), and southeast (C) regions of Puerto
Rico. All depths are in meters (m). The map layers used to generate this figure are from NOAA National Centers for Environmental Information and provided without
restriction by the U.S. Government.
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orientation of the slope) [34,36,38]. Given the availability of bathymetric data in the sampling
universe (Fig 4), depth was limited to the 0 to 600 m range, which encompassed the queen
snappers’ observed depth range and slightly beyond. By delimiting depth, the potential for
there to be a misleading effect on model performance is reduced through the reduction of
large sections of depths with known zeros where pseudoabsence data would typically be
derived [39]. Correlation analyses were performed by way of a correlation matrix in R using
the ‘corrplot’ package to reduce the likelihood of model overfitting, uncorrelated variables
(Spearman’s correlation coefficient 2[-0.65, 0.65]) were retained for modeling.
Modeling
Individual ESDMs were developed and run using the statistical software ‘R’ and the package
‘SSDM’ in each region [40,41]. Algorithms in the SSDM package that were run included: gen-
eralized boosted regression models (GBM), multivariate adaptive regression splines (MARS),
classification tree analysis (CTA), random forest (RF), maximum entropy (MAXENT), artifi-
cial neural network (ANN) and support vector machine (SVM). Because the presence data in
the northeast (n = 20) was slightly less than in the west (n = 47) and southeast (n = 42), the
algorithms for generalized additive models (GAM) and generalized linear model (GLM) were
not included in the ensemble modeling due to more stringent sample size requirements. The
models were supplied with occurrence records and calibrated to pick pseudo-absence points
using the default strategy [42] incorporated within the SSDM package. The default strategy
included: 1) the averaging of several runs with fewer pseudo-absences with equal weighting for
presences and absences for MARS and discriminant analyses; 2) the use of the same number of
pseudo-absences as available presences for techniques such as GBM, CTA, and RF; 3) the ran-
dom selection of pseudo-absences when using regression techniques; and 4) the random selec-
tion of geographically and environmentally stratified pseudo-absences when utilizing
classification and machine-learning techniques [42]. To reduce the likelihood of spatial auto-
correlation, geographic resampling of data was incorporated into the model runs using the R
package for spatial thinning of species occurrences “spThin” [43]. This R package uses a ran-
domization approach to thin occurrence datasets, creating new subsets that meet a minimum
nearest neighbor distance constraint of two pixels. Individual SDMs were trained and tested
using the default parameters of the dependent R package of each statistical method (S1 Table).
The highest-performing algorithms (as determined by Cohen’s Kappa coefficient;
Kappa 0.70) for each region were retained in the ESDM. To ensure independence between
the training and evaluation sets for cross-validation and to combat issues with the metrics
influence on the selection of models, the “holdout” method was implemented in the modeling
workflow with ten iterations [41]. This method allowed for a subset of data independent from
the models to be used in the evaluation, e.g. a separate training and evaluation set, leaving out
30% of the presence records and pseudo-absences randomly, calibrating with 70%, and then
measuring the model performance with the independent points in each model.
The ESDM was created for each region from the highest performing SDM’s, capturing
components from each. To form a consensus among the highest performing SDM projections,
a simple average of the model outputs was taken [41], resulting in a consensus ESDM for each
region. ESDMs were verified using a ten-fold cross-validation procedure. The ESDMs gener-
ated a measure of uncertainty (between-methods variance), which was calculated for each
ensemble model, in addition to the AUC, sensitivity, specificity, omission rate, proportion of
correct predictions, Cohen’s Kappa coefficient.
There has been some debate on whether the AUC adequately assesses the accuracy of the
predictive distribution models, despite its tendency to be reported as a single measure of
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overall model performance [44–49]. The main cause for concern is that the AUC/ROC curve
itself does not reflect the true performance of the model [48], although it does provide infor-
mation regarding the degree to which a species is restricted to any particular part of a range of
predictor variables, i.e., presence/absence. AUC/ROC plots specifically require true absences
[50] to calculate the AUC metric. Identifying true absences is a very complex issue in a marine
environment and is made more difficult by species such as the queen snapper, which is mobile.
As a result, we cannot say for certain that areas where queen snapper were not caught or
observed on video are true absences. To address accuracy concerns, additional metrics were
utilized that take into account the importance of both commission and omission errors within
the models. The ESDM’s performance was additionally evaluated with the partial AUC/ROC
(pAUC/ROC), which leaves out the evaluation of absences and concentrates specifically on the
evaluation of presences [51]. For the pAUC/ROC, the proportion of error allowed was set to
0.05, and 500 iterations were used for the bootstrap. Variable relative importance was evalu-
ated based on a jackknife approach between a full model and a model with each environmental
variable omitted in turn [52]. The Pearson metric was utilized, which computed a simple Pear-
son’s correlation (r) between predictions of a full model and one omitting a variable, computed
as 1- r. The higher the return value, the more influence the variable has on the model. To
reduce the risk of model over-fitting, variables with a variable relative importance of 3.0%
were removed and the ESDM was re-run without the omitted variables.
Binary maps showing suitable and unsuitable locations were generated using the SSDM
package. The optimal threshold to split presences and absences on the basis of habitat suitability
probabilities was first set to the probability that maximizes the TSS, or the sum of the sensitivity
and specificity [52,53]. The results using the default TSS were evaluated and compared to what
we currently know of the species biology and ecology. Consequently, the models were rerun
using differing thresholds as were seen fit and re-evaluated using the standard protocol for
reporting SDMs called Overview, Data, Model, Assessment and Prediction (ODMAP) [44].
Results
A total of 471 sites were sampled over the course of the two-year project contributing queen
snapper presence data (Fig 2). Because queen snapper individuals appear to shy away from the
white LED light wavelength, and the bottom time for the video camera system was short, they
were not commonly seen on video over the two-year survey. Consequently, sites where queen
snappers were caught using hook and line methods were added to the sites where queen snap-
pers were positively identified on video to be utilized in the modeling (n = 109; west n = 47,
northeast n = 20, southeast n = 42). All occurrences were retained following the spatial thin-
ning process.
Variable selection
Of the 13 derived terrain attributes, bathymetry, slope, VRM, and fine-scale BPI, were retained
in the final models in all three regions (correlation coefficient <|0.65|; Table 1). Additionally,
broad-scale BPI and profile curvature were retained in the northeast and west, respectively,
and northerness was retained in the northeast and southeast. Curvature, standard deviation,
RDMV, and slope of slope were found to be highly correlated in all three regions and were not
included in the ESDMs; likewise, broad-scale BPI was found to be highly correlated in the west
and southeast and removed from analysis (correlation coefficient of >|0.65|; Table 1). Plan
curvature and easterness contributed minimally to variable relative importance (<3.0%) in
the three regions and were removed from the ensemble modeling; likewise, profile curvature
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in the northeast and southeast and northerness in the west were found to contribute minimally
to variable relative importance and were removed from modeling (Table 1).
Comparison of individual algorithm models
The Cohen’s Kappa coefficient, or Kappa value, which measures the extent to which the agree-
ment between observed and predicted values is higher than what would be expected by chance
alone, was used to select the highest performing algorithms for use in producing the ESDM’s
(Kappa value >0.70). Kappa values for the retained algorithms provided model predictions
ranging from almost perfect agreement (i.e., Kappa between 0.81 and 1.00) to substantial
agreement (i.e., Kappa between 0.61 and 0.80; Table 2) [45], depending on region (Table 2). In
the western region, GBM, RF and SVM algorithms resulted in Kappa values >0.70, or sub-
stantial agreement [45], and were retained for use in the ESDM (Kappa values = 0.71, 0.71 and
0.78, respectively). The northeast region results were more varied, with the five algorithms
obtaining Kappa values >0.70, including ANN, CTA, GBM, RF and SVM (Kappa values = 0.75,
Table 1. Summary of variables retained for final ensemble species distribution models.
Variable Region retained after correlation
analysis
a
Region retained after variable
importance analysis
b
Bathymetry West,
Northeast,
Southeast
West,
Northeast,
Southeast
Slope West,
Northeast,
Southeast
West,
Northeast,
Southeast
Slope of Slope None None
Curvature None None
Plan Curvature West,
Northeast,
Southeast
None
Profile Curvature West,
Northeast,
Southeast
West
Vector Ruggedness Measurement
(VRM)
West,
Northeast,
Southeast
West,
Northeast,
Southeast
Fine-scale BPI West,
Northeast,
Southeast
West,
Northeast,
Southeast
Broad-scale BPI Northeast Northeast
Standard Deviation None None
Relative Deviation from Mean Value
(RDMV)
None None
Easterness West,
Northeast,
Southeast
None
Northerness West,
Northeast,
Southeast
Northeast,
Southeast
a
Environmental variables derived from bathymetric data and retained for modeling in ESDM after correlation
analysis. Variables were not retained if Spearman’s coefficient was >|0.65|.
b
Variables retained following interpretation of variable relative importance. Variables with relative
importance 3.0% were removed from models.
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0.83, 0.77, 0.90, and 0.86, respectively; Table 2) ranging from substantial to almost perfect
agreement [45]. The highest performing algorithms selected in the southeast region were
GBM, RF, and SVM (Kappa values = 0.76, 0.76, and 0.80, respectively; Table 2).
Ensemble species distribution model
An ESDM was generated for each study region to analyze fish distribution and habitat suitabil-
ity at different spatial resolutions using the highest performing approaches for each region:
west—GBM, RF, and SVM; northeast–ANN, CTA, GBM, RF, and SVM; and southeast—
GBM, RF, SVM. The predictability of queen snapper presence was generally high across the
three ESDMs, with suitable habitat presence probabilities exceeding >95% in areas of all three
regions (Table 3 and Fig 5). The generated suitable habitat presence probability maps show
that on the west coast, queen snapper presence aligns with the presence of larger bathymetric
features. For example, the probability of occurrence around the seamounts Bajo de Sico, Mona
Island, Desecheo Island, and large ridge features throughout the Mona Passage reach up to
97%. On the northeast coast, queen snapper suitable habitat presence probability appeared to
be highest around the canyon-like features with some areas displaying probabilities as high as
97%. Suitable habitat presence probability in the southeast somewhat mirrored the west, with
the highest presence probability of 100% localized to areas near the extremely steep continental
slope and neighboring seamounts, Grappler, and Whiting.
Overall, we found the range in AUC values, coupled with the sensitivity and specificity met-
rics, and Cohen’s Kappa coefficient, provided evidence that the ESDM’s had excellent predic-
tive capabilities. AUC evaluation was conducted using the metric interpretations of Hosmer
and Lemeshow [46] where an AUC value equal to 0.5 is interpreted as ‘no discrimination’,
0.5 <AUC 0.7 as ‘poor’, 0.7 <AUC 0.8 as ‘acceptable’, 0.8 <AUC 0.9 as ‘excellent’,
and an AUC >0.9 as ‘outstanding’. The ensemble models for the three regions in Puerto Rico
provided ‘excellent’ to ‘outstanding’ (west, AUC = 0.87; northeast, AUC = 0.92; southeast,
AUC = 0.89) predictive capability [46] and highlight the models’ ability to correctly rank
occurrences above background locations. The sensitivity metric, which is the proportion of
true positives, or fish that are both predicted and observed to be present, was high for all
ESDMs (0.86–0.97) (Table 3). In addition to the sensitivity metric, the specificity metric,
which is the proportion of true negatives, or the fish that are both predicted and observed to be
Table 2. Species distribution models retained in the ensemble species distribution models.
Region MAXENT RF MARS GBM CTA ANN SVM
West - 0.71 - 0.71 - - 0.79
Northeast - 0.90 - 0.77 0.83 0.75 0.86
Southeast - 0.76 - 0.76 - - 0.80
Cohen’s Kappa Coefficient values for the retained SDMs for the three regions of Puerto Rico. NR = Kappa value <0.70
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Table 3. Results for region-specific ensemble species distribution models.
Region Threshold AUC Omission Rate Sensitivity Specificity Proportion Correct Kappa
West 0.32 0.87 0.14 0.86 0.88 0.87 0.74
Southeast 0.38 0.89 0.11 0.94 0.82 0.89 0.77
Northeast 0.66 0.92 0.09 0.97 0.86 0.91 0.82
Results of Ensemble Species Distribution Modeling for each study region in Puerto Rico.
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absent, was high for all ESDMs (0.82–0.88) (Table 3). The true positive rate tended to outper-
form the true negative rate in all three regions, showing a slight tendency for the model to mis-
classify fish absence between the predicted and observed. The Kappa value refers to how
representative the data collected are to the variables that were measured. The value can be
interpreted as Kappa 0 is ‘no agreement’, 0 >Kappa 0.2 is ‘slight’, 0.2 >Kappa 0.4 is
‘fair’, 0.4 >Kappa 0.6 is ‘moderate’, 0.6 >Kappa 0.8 is ‘substantial’, and
0.8 >Kappa 1.0 is ‘almost perfect’ [45]. The Kappa value for all three regions was >0.70,
indicating ‘substantial’ to ‘almost perfect’ model accuracy [45]. While these metrics indicate
that the models performed well, they are not necessarily rigorous indicators of model perfor-
mance due to issues with delineating true absence as described above [51]. By implementing
the holdout method, we were able to leave out the evaluation of absences entirely, concentrat-
ing solely on the evaluation of presences. This evaluation metric enabled us to accept an omis-
sion error level and test via hypothesis (Ho: pAUC 0.5) [51]. The mean value for the pAUC/
Fig 5. Region-specific presence probability maps. Queen snapper habitat suitability maps for the A) west, B) northeast, and C) southeast region of Puerto Rico. The
map layer used to generate this figure is from NOAA National Centers for Environmental Information and provided without restriction by the U.S. Government.
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ROC at random for all final ESDMs was <0.50, indicating that our resulting models are better
than chance alone.
Binary maps
Probabilities of suitable habitat were converted to a binary index of habitat suitability for the
three regions. An optimal threshold was first determined by the default parameters in the
SSDM package, as determined by maximizing the sum of the sensitivity and specificity metric
(Table 3), and used to convert the habitat suitability map into binary presence (i.e., a value of
one) and absence (i.e., a value of zero) maps. For the first iteration run, the TSS metric was
used to determine the threshold of probability separating probable presence from probable
absence. This threshold varied per ESDM from 32% in the west, 66% in the northeast, and
38% in the southeast. Using the aforementioned thresholds, critical evaluation of the prelimi-
nary results determined that they did not meet what we know of the species distribution from
field observations concerning bathymetry, meaning that predicted probable presence were
located in areas where true absence are known. Using the ODMAP protocol, varying thresh-
olds were explored and were manually adjusted to 70%, 80%, 85%, and 90% in the three
regions (Tables 4and 5) [44]. After critical evaluation, the thresholds of 85% and 90% were
determined to be the most representative of the species known range, and 85% was used to
determine the final habitat suitability maps. Our results suggest that care should be taken
when determining thresholds in ESDM, and highlight the difference between what statistical
tools provide, versus the critical evaluation of resulting maps and models. While the perfor-
mance metrics of the ESDMs in each region were high, model performance is different from
ecological realism. Queen snapper are known to inhabit depths of 100–534 m, with our models
predicting >85% suitable habitat presence probability that queen snapper resided in depths
ranging from 160–429 m island-wide in Puerto Rico [10]. It is critically important that model-
ers also consider the underpinning ecology, such as ground-truthing presence observations to
QA/QC model predictions to ensure the models are representative of the reality for the species.
In our case, this was accomplished by manually setting the threshold to explore the variables’
mean and range. In critically evaluating our resulting models, we can determine that while spa-
tial modeling tools provided in GIS or statistical packages have many easy-to-use tools, they
may not provide results that are completely representative of what we know of the species biol-
ogy and ecology.
The percentage of suitable habitat using the 85% threshold was calculated for each region’s
delimited (i.e., the 0–600 m depth range) multibeam mapping footprint. The western region
contained the largest amount of suitable habitat with 26.45 km
2
(0.8% of the delimited extent
of the area), followed by the northeast with 25.75 km
2
(3.3% of the delimited extent), and lastly
the southeast with 5.63 km
2
(2.3% of the delimited extent; Fig 6). Overall, when combined, we
identified 57.77 km
2
of suitable queen snapper habitat within the area covered by the delimited
bathymetric data (1.4%). While these percentages of suitable habitat appear to be small, it is
important to take into consideration that analysis examines the multibeam mapped data
slightly beyond the known depth range for the species. As a comparison, the percentage of
suitable habitat was calculated within the 85% threshold model predicted depth range (west:
187–429 m; northeast: 160–390 m; southeast: 253–428 m). This resulted in the largest amount
of suitable habitat in the northeast with 19.0% of the extent, followed by 6.6% in the southeast,
and 1.9% in the west (Fig 7); alternatively, the percentage of suitable habitat within the known
depth range of queen snapper in literature (100–534 m) was 9.4%, 3.2% and 1.1% respectively.
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Table 4. Region-specific ranges for the retained variables within suitable habitat.
Region Threshold Model Prediction
West Slope Bathymetry (m) VRM Fine-scale BPI Profile Curvature
32% 0.02–67.60 20–600 0.00000012–0.230 -208–188 -11.2–14.1
70% 5.00–67.60 21–459 0.00000520–0.230 -111–188 -11.2–12.9
80% 8.80–66.90 21–436 0.00015400–0.170 -85–188 -9.50–3.50
85% 9.00–54.70 187–429 0.00025600–0.069 -4–129 -4.10–1.45
90% 10.10–39.30 225–410 0.00067000–0.015 4–98 -1.64–0.76
Northeast Bathymetry (m) Broad-scale BPI Northerness Slope Fine-scale BPI VRM
66% 159–599 -244–161 -1- 1 0–81.7 -244–161 0–0.203
70% 160–592 -211–141 -1- 1 1.3–80.7 -211–141 0.000099–0.182
80% 160–400 -210–91 -1- 1 1.3–61.8 -210–91 0.000099–0.142
85% 160–390 -200–90 -1- 1 1.3–61.8 -200–90 0.000099–0.142
90% 168–390 -183–89 -1- 1 1.8–42.4 -183–89 0.000160–0.052
Southeast Bathymetry (m) Slope Northerness Fine-scale BPI VRM
38% 32–597 0.0–86.4 -1- 1 -104–242 -0.00000012–0.75
70% 240–441 15.8–86.4 -1- 1 -98–149 -0.00000012–0.52
80% 249–430 19.5–74.1 -1- 1 -55–66 -0.00000012–0.05
85% 253–428 19.5–62.9 -1- 1 -42–61 -0.00000012–0.04
90% 253–424 19.8–57.1 -1- 1 -29–47 0.00–0.03
Ranges of the contributing terrain attributes in order of importance for the trialed binary threshold values in the west, northeast and southeast region of Puerto Rico.
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Table 5. Region-specific mean values for the retained variables within suitable habitat.
Region Threshold Model Prediction
West Slope Bathymetry (m) VRM Fine-scale BPI Profile Curvature
32% 11.9 ±7.9 340 ±124 0.0027 ±0.0046 3.5 ±32.1 -0.015 ±0.460
70% 17.9 ±7.9 299 ±95 0.0046 ±0.0059 18.1 ±34.5 -0.103 ±0.610
80% 18.8 ±7.2 311 ±74 0.0047 ±0.0059 23.5 ±32.1 -0.214 ±0.580
85% 18.5 ±6.3 326 ±48 0.0040 ±0.0036 31.8 ±23.3 -0.223 ±0.450
90% 17.6 ±4.4 338 ±36 0.0025 ±0.0011 37.3 ±15.8 -0.103 ±0.306
Northeast Bathymetry (m) Broad-scale BPI Northerness Slope Fine-scale BPI VRM
66% 323 ±49 -35.5 ±53.3 0.54 ±0.47 12.3 ±7.7 -35.5 ±53.3 0.0016 ±0.0037
70% 327 ±43 -39.6 ±53.2 0.51 ±0.48 12.4 ±6.6 -39.6 ±53.2 0.0015 ±0.0031
80% 331 ±39 -46.7 ±52.7 0.41 ±0.50 13.8 ±6.0 -46.7 ±52.7 0.0019 ±0.0031
85% 333 ±37 -47.7 ±53.2 0.34 ±0.52 14.9 ±6.0 -47.7 ±53.2 0.0022 ±0.0034
90% 334 ±35 -45.4 ±52.1 0.24 ±0.51 16.0 ±5.5 -45.4 ±52.1 0.0026 ±0.0033
Southeast Bathymetry (m) Slope Northerness Fine-scale BPI VRM
38% 304 ±87 30.9 ±12.4 -0.62 ±0.57 -5.0 ±18.3 0.0056 ±0.0056
70% 323 ±44 33.7 ±9.8 -0.55 ±0.60 -8.6 ±17.5 0.0048 ±0.0095
80% 323 ±42 34.4 ±8.0 -0.47 ±0.66 -10.0 ±13.8 0.0039 ±0.0053
85% 327 ±41 35.5 ±6.5 -0.40 ±0.7 -10.8 ±12.4 0.0037 ±0.0044
90% 334 ±38 36.4 ±4.4 -0.25 ±0.8 -11.0 ±11.2 0.0032 ±0.0034
Mean values of the contributing terrain attributes, in order of importance, for the trialed binary threshold values in the west, northeast, and southeast regions of Puerto
Rico.
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Variable contribution
One of the most influential variables in predicting queen snapper probability of presence
across the three regions and both spatial resolutions was bathymetry, or depth (Table 6). The
second and third most important variables differed between region and resolution. In the
northeast and southeast bathymetry was the most important predictor of suitable habitat; how-
ever, in the west, slope contributed the greatest. Following bathymetry, the distribution in the
northeast was driven by broad-scale BPI and northerness, and in the southeast slope and
northerness. In the west, slope was followed by bathymetry and VRM.
Habitat associations
Queen snapper were positively correlated with increasing depth with a mean depth ranging
from 326–333 m, depending on region (Table 5). The depths at which probability occurrence
peaks are 366 ±7 m, 367 ±4 m, and 323 ±2 m in the west, northeast, and southeast regions,
Fig 6. Region-specific binary habitat maps. Binary index of habitat suitability for queen snapper on the A) west, B) northeast, and C) southeast region of Puerto
Rico. Total suitable habitat area (km
2
) out of total sampling frame area (km
2
), and percentage of total suitable habitat in lower left corner of each figure. The map
layer used to generate this figure is from NOAA National Centers for Environmental Information and provided without restriction by the U.S. Government.
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respectively (mean depth ±SD). Based on the variable relative importance, queen snapper
presence was positively associated with areas of moderate to high slope, with low to moderate
rugosity habitat (Tables 4and 5). Queen snapper suitable habitat was modeled adjacent to fea-
tures that are higher than the surrounding area (e.g., ridge-like features) and seamounts (Fig
7). The sign of the variables was seemingly affected by resolution, as fine-scale BPI was positive
in the west, and negative in the northeast and southeast (Tables 4and 5); additionally, broad-
scale BPI was negative in the northeast. In the southeast, mean northerness was negative;
whereas in the northeast, mean northerness was positive this indicates that north- and south-
facing slopes may influence currents and thus habitat suitability for queen snapper. Mean pro-
file curvature, which was found to contribute in the west, was negative although nearing zero.
Fig 7. Region-specific depth restricted within suitable habitat. Total delimited sampling frame area bathymetry restricted to the 85% threshold model predicted
depth range for the A) west, B) northeast, and C) southeast region of Puerto Rico (west: 187–429 m; northeast: 160–390 m; southeast: 253–428 m). The binary index
of habitat suitability for queen snapper within 85% threshold model predicted depth range is depicted in red. The map layer used to generate this figure is from
NOAA National Centers for Environmental Information and provided without restriction by the U.S. Government.
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Uncertainty
Uncertainty metrics were generated by the ESDM, which represent the between-model vari-
ance. In the three regions, areas with higher degrees of uncertainty were correlated with a
moderate to low probability of suitable habitat (Fig 8). Overall, uncertainty remained fairly
low in all regions with a maximum of 22% in the west, 27% in the northeast and 32% in the
southeast, and means of 3%, 6%, and 5%, respectively.
Discussion
Habitat suitability and environmental drivers
The habitat suitability modeling conducted in this study is the first effort made to map queen
snapper suitable habitat presence probability and associated uncertainty in the U.S. Caribbean.
Areas of suitable habitat were predicted to occur throughout all three of the study regions. Tak-
ing into account the depth distribution of queen snapper and delimiting the multibeam data
coverage across study regions the total area of suitable habitat increases to 3.6% when
restricted to the 85% threshold model predicted depth range (Fig 6).
Bathymetry was one of the most consistent and significant variables in the ESDMs, with a
relative model contribution between 30.2–49.2% (Table 6). The occurrence data collected for
this project was limited to 80–500 m depths, which encompassed the known queen snapper
depth distribution of 100–450 m at the time of sampling [17], whereas each sampling region
included environmental variables from 0–600 m depths. Therefore, it is not surprising that
bathymetry would be a driving factor in queen snapper habitat suitability models. There is a
growing body of literature that uses depth and terrain attributes derived from it as a predictor
of fish species and benthic species distribution [54,55]. Moderate to high slope was an impor-
tant variable in both the west and southeast ESDMs despite the difference in spatial resolution,
with a relative model contribution of 48% and 28% respectively (Table 6). Although slope was
not ranked as highly in the northeast ESDM, it did contribute 10%, highlighting a potential
Table 6. Region-specific variable importance.
Variable West Northeast Southeast
Bathymetry 30.2 45.5 49.2
Slope 48.3 10.0 28.3
Slope of Slope NR
a
NR
a
NR
a
Curvature NR
a
NR
a
NR
a
Plan Curvature NR
b
NR
b
NR
b
Profile Curvature 4.2 NR
b
NR
b
Vector Ruggedness Measurement (VRM) 9.0 5.0 3.1
Fine-scale BPI 8.3 6.1 9.3
Broad-scale BPI NR
a
22.5 NR
a
Standard Deviation NR
a
NR
a
NR
a
Relative Deviation from Mean Value (RDMV) NR
a
NR
a
NR
a
Easterness NR
b
NR
b
NR
b
Northerness NR
b
11.4 10.1
Environmental variable importance for each study region quantifying the relevance of any individual environmental variable that was used in ESDM. NR = not
reported.
a
Variables that were removed due to correlation with other variables (Spearman correlation coefficient >|0.65|).
b
Variables that were removed due to variable relative importance <3.0%.
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preference of queen snapper to areas of moderate slope. The differences in variable importance
could be due to several factors including the difference in resolutions [56], or regional differ-
ences in the relationship between the direct and indirect variables.
With little biological data currently available for queen snapper, using surrogates to assist
with determining management decisions is crucial [57]. Active acoustic data (i.e., bathymetry
and backscatter and their derivatives) provide a proxy to better understand the distribution
and complexity of marine benthic habitats and their relationship with direct and indirect sur-
rogate variables. Although queen snapper habitat suitability and EFH have not been previously
delineated, other species of Pacific Etelis, including Etelis carbunculus and Etelis coruscans,
have been linked to specific benthic features through modeling. Potential indirect environ-
mental drivers, specifically depth, have been shown to be the most important habitat predictor
for the genus [58–60]. Our models indicate that queen snappers prefer a mean depth of 326,
333, and 327 m in the west, northeast and southeast, respectively, with 85% suitable habitat
Fig 8. Region-specific probability of habitat suitability uncertainty maps. Uncertainty in ensemble projections of queen snapper occurrence in the A) west, B)
northeast, and C) southeast region of Puerto Rico. The map layer used to generate this figure is from NOAA National Centers for Environmental Information and
provided without restriction by the U.S. Government.
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presence probability in ranges between 187–429 m, and 160–390 m, and 253–428 m, respec-
tively. Misa et al., [58] also noted high-relief, hard-bottom areas as important habitat features
for Pacific Eteline species. This is likely because of large benthic features, such as seamounts,
pinnacles, and ridges retaining dense zooplankton populations due to upwelling from deeper
depths, which in turn attracts larger predators such as the queen snapper [58,61,62]. When
combined, terrain attributes can be linked to an environmental parameter such as food avail-
ability, which is difficult to calculate in situ. These observations of Pacific Eteline species are
concurrent with the queen snapper habitat suitability maps we estimated for the Caribbean.
The resulting models depict localized hot spots adjacent to Desecheo, Bajo de Sico, and Whit-
ing and Grappler seamount (Fig 5), all areas of moderate to steep slope and rugosity often asso-
ciated with harder substrates. Queen snappers were also predicted to be present in close
proximity to elevated ridge features throughout the west and northeast coast. The negative
mean fine-scale BPI in the northeast and southeast, and the negative mean broad-scale BPI
indicates that at a higher resolution, queen snapper suitable habitat consists of areas that are
lower than the surrounding area, such as depressions (fine-scale BPI) and valleys (broad-scale
BPI). In contrast, queen snapper suitable habitat was correlated with positive fine-scale BPI
and negative profile curvature in the west, although the layers were derived from 30 m resolu-
tion bathymetry as opposed to the 8 m resolution in the northeast and southeast.
The difference in variable relative importance highlights the idea that queen snapper could
respond to habitat at multiple spatial resolutions, which has been shown in other species of
marine fishes [63,64]. An example of this for our case study with queen snapper can be seen
with the slope variable. In the west, we measured slope at a 30 m resolution using a 3 x 3 win-
dow, overall characterizing slope over an area of 90 m by 90 m. In contrast, in the northeast
and southeast we measured slope at 8 m using a 3 x 3 window, thus quantifying slope over 24
m x 24 m. The lower resolution data may be missing variations in slope that could be found at
finer resolutions. Slope was found to be the highest contributing variable in the west (90 m)
which may indicate that regional currents drive habitat suitability in those areas and not more
localized currents that would be caught at higher resolutions. As this modeling approach was
conducted in three distinct regions, it is possible and quite likely that the three regional models
are showing differences in variable importance due to differences in the relationship between
the direct (environmental variables such as temperature, salinity, food availability, etc.) and
indirect surrogates (bathymetry, slope, rugosity, etc.) [54,55,57]. These relationships, while
hard to identify and measure given the lack of data in this region and empirical linkage among
variables, could potentially describe optimal food availability, refuge for juveniles, appropriate
temperatures, favorable currents or salinity levels.
Broader implications
The regional Fishery Management Councils are tasked with defining a species’ geographic
range and habitat requirements by life stage. A system to analyze habitat information was
developed under the Magnuson-Stevens Act Provisions (50 CFR Part 600) to better describe
and identify EFH. This framework consists of four levels: 1) distribution data are available for
some or all parts of the geographic range of the species; 2) habitat-related densities of the spe-
cies are available; 3) growth reproduction or survival rates within habitats are available; and 4)
production rates by habitat are available (50 CFR Part 600). Although queen snapper is
included in the CFMC’s Reef Fish Fishery Management Plan, data on queen snapper habitat
associations is scarce and the species does not have a well-defined EFH. The work and model-
based maps highlighted in this study are critical to begin investigating level 1 by delineating
the presence/absence of queen snapper and potential hotspots of occurrence for a portion of
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the species’ geographic range, in addition to investigating spatial scales (resolution and extent)
and other contributing factors that influence species-environment relationships. Additionally,
the predicted habitat suitability from queen snapper presence data does not take into consider-
ation the species’ abundance, as such, it will be necessary to obtain data on the relative abun-
dance to estimate potential yields.
Future research
While this study is filling gaps in our knowledge of a deepwater snapper species, future consid-
erations for modeling queen snapper and other deepwater snapper species in the U.S. Carib-
bean are important to note and could potentially bolster our understanding of habitat
suitability. Presence-background ESDMs were utilized in this methodology, however the pos-
sibilities are nearly endless, and additional strategies could be tested to see if results outperform
the strategy outlined in this study [65]. In addition, environmental data have been found to be
lacking in several areas and the region could benefit from directed studies collecting high-reso-
lution mapping and other benthic environmental data.
Backscatter data were available in areas that overlapped with our sampling universe; how-
ever, the data products were not standardized from the various sources. We attempted to uti-
lize the methods outlined in Misiuk et al., [66] to harmonize data products in post-processing,
but the backscatter files did not overlap to the degree needed to harmonize and create mosaics.
Future work could focus on subdividing the regions into the extent covered by various back-
scatter datasets, integrating backscatter as a variable for evaluation in addition to the previ-
ously derived terrain attributes in this study.
While depth is a commonly used indirect surrogate for environmental variables such as
water temperature, dissolved oxygen, and food abundance [57], our modeling approach could
be improved upon by the addition of environmental variables such as water temperature,
salinity, pH, and dissolved oxygen at depth. Due to the depths the target species occupies
(>100 m), temperate, salinity, and dissolved oxygen values that are consistent (from a period
that overlaps with our sampling efforts), accurate (at depth recordings, not surface values), and
spatially representative (within the spatial extent modeled) were not available for use. Addi-
tionally, surface-derived values for temperature and dissolved oxygen are not accurate repre-
sentations of benthic depths due to limited mixing at depths deeper than 200 m. Future
research should focus on collecting relevant environmental data for inclusion into habitat suit-
ability modeling in the U.S. Caribbean.
Additionally, sediment observations from archived underwater videos could be integrated
into a distribution model framework using a multiscale approach [e.g., 67]. The predicted sedi-
ment surfaces could then be incorporated as new variables in the queen snapper ESDM frame-
work and further explored to improve predictions. Similar work could be conducted using
deepwater coral species observations, as fishers around the island have noted entanglement
with deepwater coral and sponges at common fishing grounds for queen snapper. Preliminary
results from the fishery-independent data collected for use in this study suggest deepwater
snapper, including queen snapper, were found to co-occur with deep-sea coral ecosystems.
The survey recorded data at 471 survey sites, with 25% of sites documenting a minimum of
one deep-sea coral or sponge species. A total of 17% of survey sites documented both deep-sea
coral ecosystems and the presence of deepwater snappers [68].
Overall, the collection of high-resolution bathymetric and backscatter data and environ-
mental parameters island-wide would allow for not only additional direct and indirect surro-
gates to the modeling approach, but would also enable us to combine separate regions into one
large study region for comparison. As it stands currently, from an implementation standpoint
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combining the raster datasets from the three study regions where possible is too heavy to pro-
cess with standard computers, as the mosaicking process would add large quantities of “no
data” to each layer file where gaps in mapping exist. While the models may benefit slightly and
predicted areas of habitat suitability would likely be more narrowly focused, the computational
power needed is high and the process cumbersome. An additional concern with combining
regional datasets is the loss of variability in our results caused by the specificity of each study
area. As we do not understand all aspects that may drive queen snapper habitat suitability and
the species presence in an area, the indirect nature of the variables in our approach may mean
that the conditions driving the species’ habitat suitability in the three study regions are differ-
ent. On an island platform such as Puerto Rico, there is a directionality when dealing with the
data, specifically with regard to currents and food availability. Grouping the regions makes
assumptions that the same variables drive the distribution of species across the island platform;
specifically in the northeast and southeast which we know are likely different based on the geo-
morphology of the island platforms, dominant regional current directions, and the shallow
shelf between the regions [69–72]. If queen snapper’s range was restricted to the deep ocean,
assuming variable impacts are similar over a large extent is reasonable; however, with an island
landmass such as Puerto Rico, we believe smaller regional models are important in addition to
future comparison with a large mosaicked area to fully understand variable importance as data
becomes available.
Conclusions
This study used a state-of-the-art approach in the form of ensemble modeling to fill a gap in
the literature regarding habitat suitability for a commercially and ecologically important deep-
water snapper species. We took spatially-explicit seafloor variables derived from MBES
bathymetry datasets and queen snapper presence datasets collected from fishery-independent
methods to derive the probability of our target species inhabiting any particular area within
our study regions. From modeling, we developed habitat suitability and uncertainty maps for
each of the study regions. Our results demonstrate that seafloor characteristics function as
effective predictors for queen snapper distribution across mesophotic and deepwater habitats.
Our goal was to develop models and corresponding maps to be used as a tool to identify poten-
tial areas where queen snapper, a commercially and ecologically important species in the study
regions, may reside when intensive field sampling may be cost-prohibitive. Additionally, our
results highlight the potential effects of spatial variability in habitat suitability at multiple reso-
lutions and the importance of considering this when modeling the presence probability of suit-
able habitat for a species. Based on this case study utilizing queen snapper, depth, and the
orientation, arrangement and composition of benthic habitat features are key factors to inte-
grate in spatial modeling and delineation of habitat suitability. While our results complement
the limited knowledge that queen snapper can be found near oceanic islands and reefs on the
continental shelf and upper slope [12,58], they also serve to broaden our understanding of the
spatial extent of queen snapper and highlight hotspots for potential management concerns
such as EFH.
Supporting information
S1 Table. Algorithms used in species distribution modeling. Algorithms tested in each spe-
cies distribution model, the dependent package used in R software, the default parameters uti-
lized in each model, and the reference for each R package.
(DOCX)
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Modeling Queen Snapper Suitable Habitat in Puerto Rico
PLOS ONE | https://doi.org/10.1371/journal.pone.0298755 February 26, 2024 21 / 25
S1 File. Multibeam bathymetry files. National Centers for Coastal Ocean Science and United
States Geological Society collected multibeam echosounder systems data.
(XLSX)
Acknowledgments
We thank the many individuals who assisted with the collection of the queen snapper presence
data, including Andy David, Michelle Scha¨rer-Umpierre, Edwin Font, Jose Che Lopez,
Edgardo Agosto-Perez, Carlos Zayas-Santiago, Steve Smith and Ryan Caillouet. Thank you to
Chris Taylor and Jennifer Kraus for their assistance with acquiring bathymetric data. Thank
you to Matt Campbell for his comments and suggestions on this manuscript prior to
submission.
Author Contributions
Conceptualization: Vincent Lecours.
Data curation: Katherine E. Overly.
Formal analysis: Katherine E. Overly, Vincent Lecours.
Funding acquisition: Katherine E. Overly.
Investigation: Katherine E. Overly, Vincent Lecours.
Methodology: Katherine E. Overly, Vincent Lecours.
Resources: Katherine E. Overly.
Visualization: Katherine E. Overly.
Writing – original draft: Katherine E. Overly, Vincent Lecours.
Writing – review & editing: Katherine E. Overly, Vincent Lecours.
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