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# Ecological Niche Modeling of Three Species of *Stenella* Dolphins in the Caribbean Basin, With Application to the Seaflower Biosphere Reserve

**OPEN ACCESS**

**Edited by:**
Santiago Herrera,
Lehigh University, United States

**Reviewed by:**
Lindsay Porter,
University of St Andrews,
United Kingdom
Samuel Georgian,
Marine Conservation Institute,
United States

*Correspondence:
Dalia C. Barragán-Barrera
daliac.barraganbarrera@gmail.com
Karina Bohrer do Amaral
karinabohrerdoamaral@gmail.com
Daniel M. Palacios
daniel.palacios@oregonstate.edu

**Specialty section:**
This article was submitted to Marine Conservation and Sustainability, a section of the journal *Frontiers in Marine Science*

**Received:** 31 July 2018
**Accepted:** 11 January 2019
**Published:** 12 February 2019

**Citation:** Barragán-Barrera DC, do Amaral KB, Chávez-Carreño PA, Farías-Curtidor N, Lancheros-Neva R, Botero-Acosta N, Bueno P, Moreno IB, Bolaños-Jiménez J, Bouveret L, Castelblanco-Martínez DN, Luksenburg JA, Mellinger J, Mesa-Gutiérrez R, de Montgolfier B, Ramos EA, Ridoux V and Palacios DM (2019) Ecological Niche Modeling of Three Species of *Stenella* Dolphins in the Caribbean Basin, With Application to the Seaflower Biosphere Reserve. *Front. Mar. Sci.* 6:10. doi: 10.3389/fmars.2019.00010

Dalia C. Barragán-Barrera¹,²*, Karina Bohrer do Amaral³*, Paula Alejandra Chávez-Carreño⁴, Nohelia Farías-Curtidor¹, Rocio Lancheros-Neva¹,⁵, Natalia Botero-Acosta¹, Paula Bueno⁶, Ignacio Benites Moreno³,⁷, Jaime Bolaños-Jiménez⁸,⁹, Laurent Bouveret¹⁰, Delma Nataly Castelblanco-Martínez¹¹,¹², Jolanda A. Luksenburg¹³,¹⁴, Julie Mellinger¹⁰, Roosevelt Mesa-Gutiérrez¹⁵, Benjamin de Montgolfier¹⁶, Eric A. Ramos¹²,¹⁷, Vincent Ridoux¹⁸ and Daniel M. Palacios¹⁹*

¹ Fundación Macuáticos Colombia, Medellín, Colombia, ² Laboratorio de Ecología Molecular de Vertebrados Acuáticos, Department of Biological Sciences, Universidad de los Andes, Bogotá, Colombia, ³ Laboratório de Sistemática e Ecologia de Aves e Mamíferos Marinhos, Instituto de Biociências, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil, ⁴ Independent Researcher, La Paz, Mexico, ⁵ Parques Nacionales Naturales de Colombia, Bogotá, Colombia, ⁶ WWF Colombia, Bogotá, Colombia, ⁷ Centro de Estudos Costeiros, Limnológicos e Marinhos, Universidade Federal do Rio Grande do Sul, Imbé, Brazil, ⁸ Asociación Civil Sea Vida, Aragua, Venezuela, ⁹ Instituto de Ciencias Marinas y Pesquerías, Universidad Veracruzana, Veracruz, Mexico, ¹⁰ Observatoire des Mammifères Marins de l'Archipel Guadeloupéen, Archipelago's Guadeloupe, France, ¹¹ Consejo Nacional de Ciencia y Tecnología, University of Quintana Roo, Chetumal, Mexico, ¹² Fundación Internacional para la Naturaleza y la Sostenibilidad, Chetumal, Mexico, ¹³ Institute of Environmental Sciences, Leiden University, Leiden, Netherlands, ¹⁴ Department of Environmental Science and Policy, George Mason University, Fairfax, VA, United States, ¹⁵ Duke Marine Laboratory, Nicholas School of the Environment, Duke University, Durham, NC, United States, ¹⁶ Aquasearch, Sainte-Luce, France, ¹⁷ The Graduate Center, City University of New York, New York, NY, United States, ¹⁸ Observatoire PELAGIS, Université de La Rochelle, La Rochelle, France, ¹⁹ Marine Mammal Institute and Department of Fisheries and Wildlife, Oregon State University, Newport, OR, United States

Dolphins of the genus *Stenella* occur in pelagic waters of both tropical and warm-
temperate oceans. Three species, the Atlantic spotted dolphin (*Stenella frontalis*), the
pantropical spotted dolphin (*S. attenuata*), and the spinner dolphin (*S. longirostris*) are
abundant worldwide, but in the Caribbean Basin they have been poorly studied and
information on their distribution patterns is scarce. Specifically, in Colombia's remote
Seaflower Biosphere Reserve (SFBR) *S. attenuata* has been reported occasionally,
but *S. frontalis* and *S. longirostris* have never been recorded before. To address this
information gap, an ecological niche modeling approach was used to determine the
potential distribution patterns of these three dolphin species in the region. Records
of these species for the Caribbean Basin were compiled, including both published
and unpublished data. Environmental information, including bathymetry, bathymetric
slope, distance to shore, sea surface temperature, sea surface salinity, and chlorophyll-
a concentration was gathered from public databases (MARSPEC and Bio-ORACLE)
in raster format. The maximum entropy algorithm (Maxent) for modeling species'
geographic distributions with presence-only data was used. After filtering the data,
210 records of *S. attenuata*, 204 of *S. frontalis*, and 80 of *S. longirostris* were used
to run models. The best configuration for each model was chosen based on the
ΔAICc criterion. For all three species, the final ecological niche models returned AUC
test values higher than 0.8, indicating satisfactory model performance. The resulting potential distribution maps suggested that areas closest to continental shorelines of the Caribbean Basin and surrounding islands had the highest environmental suitability for all species (>70%). All models reported high environmental suitability for *S. attenuata* and *S. longirostris* in the SFBR, mainly in the southernmost part surrounding San Andrés and Providence Archipelago. Assessment of niche overlap from the predictions of species distributions using the similarity statistic and pairwise map overlap indicated that *S. frontalis* and *S. longirostris* had niches slightly more similar in comparison to *S. attenuata*. As this was a first effort to fill a gap in our understanding of the distribution of species in the genus *Stenella* in the Caribbean Basin, further studies are necessary using both niche modeling and biological/ecological approaches.

**Keywords: Stenella, potential distribution area, Caribbean Basin, Seaflower Biosphere Reserve, Maxent, ecological niche modeling, niche overlap**

INTRODUCTION

The ocean comprises 70% of the planet and offers a wide range of habitats to marine life (e.g., Spalding et al., 2012; Kelley et al., 2016). These different habitats are characterized by gradients in sea surface temperature (SST), sea surface salinity (SSS), productivity, and topography at both broad and local scales (Redfern et al., 2006), playing an important role to explain distribution patterns of marine top predators such as cetaceans (e.g., Baumgartner et al., 2001; Palacios et al., 2013b). Dolphins of the genus *Stenella* (Gray, 1866) are common in tropical, subtropical, and temperate waters worldwide. Because of such wide distribution patterns, they are often considered “umbrella species,” since any conservation efforts made on their behalf benefit many other species within their habitat (Jefferson et al., 2008).

Although the taxonomy of *Stenella* remained unclear until the 1980s, the five species that make up the genus are now well characterized. The genus is considered a non-monophyletic assemblage (see Perrin et al., 2013), with some species being more related to *Tursiops* (Leduc et al., 1999). While the *Stenella* species have great dispersal capacity, they exhibit significant distribution changes in response to seasonal variation in oceanographic conditions (Reilly, 1990; Moreno et al., 2005). Additionally, some species within the genus are considered sympatric throughout their distribution, although at an individual level they appear to have distinct environmental requirements at regional scales (e.g., Baumgartner et al., 2001; Davis et al., 2002; do Amaral et al., 2015).

The Atlantic spotted dolphin (*S. frontalis*), endemic to the Atlantic Ocean, is distributed mainly in areas closer to continental shelves and surrounding islands (Perrin et al., 1987; Davis et al., 1998; do Amaral et al., 2015). The pantropical spotted dolphin (*S. attenuata*) and the spinner dolphin (*S. longirostris*) are more widely distributed in the Atlantic, Indian, and Pacific Oceans (Jefferson et al., 2008), and appear to have similar patterns of habitat use that favor their presence over deeper waters (Davis et al., 1998, 2002; Baumgartner et al., 2001; do Amaral et al., 2015). Although these three species have been previously reported in the Caribbean Basin (e.g.,

Caldwell et al., 1971; Mignucci-Giannoni, 1998; Pardo et al., 2009; Palacios et al., 2013b; Niño-Torres et al., 2015; Ramos et al., 2016), little is known about their habitat preferences and spatial distribution in coastal or oceanic areas. Hence, it is important to identify areas where these species are likely to be found in order to direct research and improve management measures.

Particularly in Colombia's Caribbean Sea, cetacean research has been limited, with most studies focusing on specific coastal mainland locations, including Santa Marta (Pardo and Palacios, 2006; Fraija et al., 2009; Pardo et al., 2009), Bay of Cispatá (García and Trujillo, 2004), Gulf of Morrosquillo (Palacios et al., 2013b), and La Guajira (Combatt and González, 2007; Palacios et al., 2012; Farías-Curtidor et al., 2017). To date, almost no research effort has been conducted in the remote oceanic region surrounding the Archipelago of San Andrés, Providencia and Santa Catalina (Pardo et al., 2009; Palacios et al., 2013b). Located in this region, the Seaflower Biosphere Reserve (SFBR) is one of the largest marine reserves in the western hemisphere, containing the most extensive open-ocean coral reefs in the Caribbean Sea (Mow et al., 2007; Coralina-Invemar, 2012). This unique marine ecosystem is highly biodiverse (Taylor et al., 2013) and may be of importance for cetaceans. However, given the remoteness of the region and the prevalence of windy conditions, the development of systematic visual surveys necessary to assess cetacean abundance and distribution has been logistically and financially challenging.

To date, at least 22 species of marine mammals have been confirmed to occur in the Colombian Caribbean, including 18 odontocetes (Palacios et al., 2013b). Within the SFBR, pantropical spotted dolphins, bottlenose dolphins (*Tursiops truncatus*), false killer whales (*Pseudorca crassidens*), and sperm whales (*Physeter macrocephalus*) are all confirmed species (Pardo et al., 2009; Barragán-Barrera et al., 2017). Stranding reports in the San Andrés Archipelago also indicate the presence of pygmy or dwarf sperm whales (*Kogia* sp.) (Palacios et al., 2013b) and Cuvier's beaked whales (*Ziphius cavirostris*) (Prieto-Rodríguez, 1988; Vidal, 1990). However, the area can potentially host several other species, including Atlantic spotted dolphins, spinner dolphins, striped dolphins (*S. coeruleoalba*), short-finned pilot whales
(Globicephala macrorhynchus), killer whales (Orcinus orca), and even humpback whales (Megaptera novaeangliae) (Pardo et al., 2009; Palacios et al., 2013b; Bolaños-Jiménez et al., 2014).

Ecological niche modeling is a widely used tool to predict
cetaceans' distribution and understand the ecological precursors
(Redfern et al., 2006; Gregr et al., 2013; Palacios et al., 2013a).
These models can also be implemented to assess areas of
high conservation priority and identify key areas for research
on interactions between humans and wildlife (Bailey and
Thompson, 2009). For the Caribbean Basin, the use of advanced
geospatial analysis tools has the potential to improve the
current comprehension of cetacean distribution patterns and the
environmental factors influencing them. However, these analyses
have never been conducted in the region, despite some studies
being developed in the adjacent Gulf of Mexico (e.g., Davis
et al., 1998, 2002; Baumgartner et al., 2001) and the Western
South Atlantic Ocean (e.g., do Amaral et al., 2015). Since 2014,
Colombia has been carrying out scientific cruises in the SFBR
to assess the local biodiversity of the islands (Murillo, 2014).
Despite cetaceans having been taken into account in these efforts,
in practice the Colombian Program of Marine Fauna Observers
has been the main source of this type of information in recent
times (Palacios et al., 2013b). Research efforts could be optimized
if uncertainty regarding cetacean occurrence and distribution in
the Reserve is overcome.

With the aim of determining the potential distributions of
spotted and spinner dolphins throughout the Caribbean Basin,
and more specifically within the SFBR, we adopted an ecological
niche modeling approach. Because absence data are generally not
available for oceanic dolphins and the offshore limits of their
distributions are unknown, we used a presence-only method
to predict the potential distribution of these species (Robinson
et al., 2017). The chosen method was maximum entropy (Phillips
et al., 2006), which has been applied successfully on species with
limited data (Wisz et al., 2008; Elith et al., 2011). Furthermore,
this method has been successfully used with cetaceans (Derville
et al., 2018), and has already been used to predict the potential
distribution of *Stenella* species in the Western South Atlantic
(do Amaral et al., 2015). We hypothesized that these species
have different distribution patterns as a consequence of distinct
environmental requirements, as has been found in adjacent
regions such as the Gulf of Mexico and the Western South
Atlantic. In addition to overcoming the prevailing information
gaps, our results could contribute to the understanding of the
effectiveness of the SFBR in protecting top predators such as
dolphins, and to identify other areas that deserve protection
throughout the Caribbean Basin.

MATERIALS AND METHODS

Study Area

The geographical extent of the models and the background
sampling was restricted to the Caribbean Basin (59°24′W–
89°21′W and 7°42′N–22°41′N). The Caribbean is a semi-
enclosed tropical sea adjacent to the Atlantic Ocean (Müller-
Karger et al., 1989; Aguirre, 2014). Although the area is influenced

by the discharge of the Amazon and Orinoco rivers, it is generally
considered oligotrophic and homogeneous despite seasonal
upwelling events (Müller-Karger et al., 1989; Ciales-Hernández
et al., 2006; Arévalo-Martínez and Franco-Herrera, 2008; Chollett
et al., 2012; Rueda-Roa and Muller-Karger, 2013), and the
influence of the warm Caribbean Current (Aguirre, 2014).

Within the Caribbean Basin, the SFBR is located in
oceanic waters of the Colombian Exclusive Economic Zone
(80°39′W–82°17′W and 11°56′N–13°55′N) (Coralina-Invemar,
2012) **(Figure 1)**. The Reserve was created as the first Colombian
Marine Protected Area and was declared as a UNESCO Biosphere
Reserve in November 2000 (Taylor et al., 2013). It has an
area of around 65,000 km² and encompasses the San Andrés,
Providencia and Santa Catalina Archipelago **(Supplementary
Figure S1)** (Taylor et al., 2013). The SFBR is a highly biodiverse
region, containing more than 77% of the shallow coral reefs of
Colombia, as well as other coastal/marine ecosystems such as
atolls, beaches, sandy bottoms, mangroves, and seagrass beds
(Coralina-Invemar, 2012; Taylor et al., 2013).

Environmental Data

Environmental layers used in the modeling were selected after
reviewing previous cetacean niche modeling studies (e.g.,
Baumgartner et al., 2001; Cañadas et al., 2002; Davis et al.,
2002; do Amaral et al., 2015; Tobeña et al., 2016; Gomez
et al., 2017). The selected layers included both static and
dynamic variables for all three species (Table 1). Dynamic
variables used in this study included SST, chlorophyll-a
concentration (Chl-a), diffuse attenuation (Da), and SSS.
Static layers included bathymetry, slope and distance to
shore (Table 1). Environmental layers representing SST,
SSS and Chl-a were included as two different metrics:
the annual mean and the annual range (the differences
between annual maximum and minimum). For Da, only
the annual mean was included because annual range was
not available.

Raster layers of dynamic and static information data
were gathered from the public database Bio-Oracle (Oceans
Rasters for Analysis of Climate and Environment) (Tyberghein
et al., 2012) and MARSPEC (Ocean Climate Layers from
Marine Spatial Ecology) (Sbrocco and Barber, 2013). While
the Bio-Oracle public database carried raster layers of Chl-a
and Da at the 9.2 km resolution, the remaining layers
were available in the MARSPEC public database at 1 km
resolution. All layers were cropped to encompass the study
area and adjusted to a common resolution of 9.2 km per
grid cell using the raster package (Hijmans, 2017) in R
3.4.2 (R Core Team, 2018). The correlation among layers
was investigated using the function pairs in the raster
package. Only layers with correlation coefficient below 0.7
were included in the ecological niche models (Dormann et al.,
2013) (Supplementary Figure S2). A principal component
analysis (PCA) was performed to visualize the environmental
heterogeneity across the study area using the rasterPCA
function from Rstoolbox package (Leutner and Horning, 2016)
(Supplementary Figure S3).
<figure><img src="image_2.png" /></figure>

Occurrence Data

Georeferenced sighting data for Atlantic spotted dolphins, pantropical spotted dolphins, and spinner dolphins occurring between 1981 and 2016 in the Caribbean Basin, were compiled from several sources: published records, unpublished data, and the Sistema de Información Ambiental Marina (SIAM<fnref n="1" />) operated by the Instituto de Investigaciones Marinas y Costeras (INVEMAR, Colombia's Marine and Coastal Research Institute). These occurrence data were collected through different seasons

and years, during both systematic and opportunistic marine
wildlife observations. Only confirmed sightings correctly
identified at the species level as *Stenella* were included.

After scrutinizing, filtering, cleaning duplicate records and
removing some of the bias by subsampling records (see Hijmans
and Elith, 2017), a total of 494 records were included: 210
sightings of pantropical spotted dolphins, 204 Atlantic spotted
dolphins, and 80 spinner dolphins (Table 2 and Figure 2).
The dataset used for this study is presence-only, meaning
that absences (such as survey track lines) were not considered
(Franklin, 2009). A subset of records (20%) was used as testing

<footnote n="1">¹http://siam invemar.org.co</footnote>
<table><caption>**TABLE 1** | List of environmental variables used in this study and their respective source, original grid resolution, and unit of measurement.</caption><thead><tr><th>Environmental variables</th><th>Source</th><th>Unit</th><th>Original resolution</th></tr></thead><tbody><tr><td>Bathymetry (depth of the seafloor)</td><td>MARSPEC</td><td>m</td><td>1 km</td></tr><tr><td>Distance to shore</td><td>MARSPEC</td><td>km</td><td>1 km</td></tr><tr><td>Bathymetric slope</td><td>MARSPEC</td><td>degrees</td><td>1 km</td></tr><tr><td>Mean annual chlorophyll-a concentration</td><td>Bio-Oracle</td><td>mg/m<sup>3</sup></td><td>9 km</td></tr><tr><td>Annual range in chlorophyll-a concentration</td><td>Bio-Oracle</td><td>mg/m<sup>3</sup></td><td>9 km</td></tr><tr><td>Mean annual diffuse attenuation</td><td>Bio-Oracle</td><td>m<sup>-1</sup></td><td>9 km</td></tr><tr><td>Mean annual sea surface salinity</td><td>MARSPEC</td><td>psu</td><td>1 km</td></tr><tr><td>Annual range in sea surface salinity</td><td>MARSPEC</td><td>psu</td><td>1 km</td></tr><tr><td>Mean annual sea surface temperature</td><td>MARSPEC</td><td>°C</td><td>1 km</td></tr><tr><td>Annual range in sea surface temperature</td><td>MARSPEC</td><td>°C</td><td>1 km</td></tr></tbody></table>

data as explained below, and the remaining was used as training data.

## Ecological Niche Modeling

We used a presence-only approach to model the distribution of *Stenella* species in the Caribbean Basin. Specifically, we used the maximum entropy (Maxent) algorithm (Phillips et al., 2017) to model the potential distribution of the three species. Maxent estimates the geographic range of a species by finding the distribution that has the maximum entropy constrained by the environmental conditions at recorded occurrence locations (Phillips et al., 2017). Maxent predicts environmental suitability, with better conditions represented by higher values (Phillips et al., 2006). This method tends to predict the largest possible suitable area consistent with the data (Merow et al., 2013). While careful consideration of the limitations and biases in the data must be taken, Maxent performs well when compared to presence-absence models (Derville et al., 2018).

In order to build the Maxent species distribution models, we used the Maxent function available within the dismo version 1.1-4 package in R (Hijmans et al., 2017). Maxent model settings were defined through the ENMevaluate function of the ENMeval package (Muscarella et al., 2014), which provided species-specific settings to generate models, such as feature classes and standardized multiplier values. The model with the lowest value of the Akaike's Information Criterion corrected

for small samples sizes (*AICc*) reflects both model's goodness-of-fit and complexity (Muscarella et al., 2014). We used the block as data partitioning method, which splits the data into four bins based on lines of latitude and longitude to divide occurrence localities as equally as possible (Muscarella et al., 2014). For model validation, we adopted the values of the area under the receiver-operator (ROC) curve (*AUC*) as indicators of the predictive skill of the model (Phillips et al., 2006), where *AUC* closest to a value of 1 would be a perfect model and *AUC* = 0.5 would indicate that the model performed no better than random.

Twenty percent of the sightings for each species were randomly selected as test data sets to evaluate the fit of the model and its predictive power through the evaluate function in the dismo package. Maps were exported in raster format with continuous values and were interpreted from a percentage of environmental suitability ranging from unsuitable (0%) to highly suitable (100%). For visualization purposes of the potential distributions of these three species across the study area in a binary map of presence/absence, these were built based on the threshold of equal test sensitivity and specificity.

## Statistical Comparisons of Distribution Patterns

We tested the null hypothesis that each species had similar distributions of the environmental variables using the non-parametric Kruskal-Wallis test followed by the Dunn test available in the dunn package (Dinno, 2017). The range and distributional properties of the environmental layers where species were recorded were represented as boxplots.

Comparative similarity measures among *Stenella* species and the niche identity test implemented in the dismo package were computed. Specifically, we estimated the “D” and “I” similarity statistics of Warren et al. (2008) using the functions niche overlap from predictions of species’ distributions, where the resulting values ranges from 0 (no overlap) to 1 (the distributions are identical). We tested the null hypothesis that the ecological niche models of each species pair were identical using the function nicheEquivalency based on 499 randomizations. To visualize the degree of spatial overlap between the *Stenella* species distributions, we overlapped binary maps for the three pairwise combinations of species using the crop function in the raster package.

<table><caption>**TABLE 2** | The ecological niche models with the best configuration for each species, the number of training and testing data, the *AUC* testing values and the relative contributions of the major environmental variables to the Maxent models with dynamic and static variables.</caption><thead><tr><th>Species</th><th>Feature classes<br>and regularization<br>multipliers values</th><th>Training<br>data</th><th>Testing<br>data</th><th>AUC<br>testing</th><th>Variable contribution (%)</th></tr></thead><tbody><tr><td>Atlantic spotted dolphin</td><td>LQHP RM 3.5</td><td>163</td><td>41</td><td>0.852</td><td>Distance (30.49%) Mean Chl-a (22.88%) Bathymetry (22.68%)</td></tr><tr><td>Pantropical spotted dolphin</td><td>LQH RM 2.5</td><td>168</td><td>42</td><td>0.856</td><td>Distance (29.63%) Mean Chl-a (19.66%) Range SST (16.27%)</td></tr><tr><td>Spinner dolphin</td><td>H RM 2.5</td><td>64</td><td>16</td><td>0.854</td><td>Distance (57.94%) Mean Chl-a (26.84%) Slope (10.63%)</td></tr></tbody></table>

L, linear; Q, quadratic; H, hinge; P, product; RM, regularization multiplier.
<figure><img src="image_3.png" /><figcaption>**FIGURE 2** | Species records in the Caribbean Basin used as both training and testing data in the ecological niche models for Atlantic spotted dolphin (green, *n* = 204), pantropical spotted dolphin (red, *n* = 210), three of which occurred inside the Seaflower Biosphere Reserve, and spinner dolphin (yellow, *n* = 80).</figcaption></figure>

# RESULTS

## Ecological Niche Modeling

Ecological niche models were built based on seven uncorrelated environmental layers: bathymetry, distance to shore, slope, Mean SSS, Mean SST and Mean Chl-a, as well as Range SST. In total, 887 records of *Stenella* species were compiled (**Supplementary Table S1**), and after data cleaning 494 were kept (**Table 2** and **Figure 2**).

The model with the best configuration for each species is presented in **Table 2**. Ecological niche models incorporating dynamic and static variables returned AUC test values higher than 0.85 for all three species, indicating that these models had

a good performance. In general, distance to shore and Mean Chl-a were the environmental variables that contributed the most within all models (**Table 2**). The potential geographic range of distribution for each species, as predicted by the best models, is shown in **Figures 3–5**.

## Atlantic Spotted Dolphin Ecological Niche Model

The predicted potential distribution map for the Atlantic spotted dolphin indicated that the areas closest to continental shorelines had the highest environmental suitability (>70%). Specifically a high suitability area was found mainly in the coastal region off mainland Colombia and more broadly, along the southeastern Caribbean, with oceanic areas and
<figure><img src="image_4.png" /><figcaption>**FIGURE 3** | Predicted potential distribution for the Atlantic spotted dolphin in the Caribbean Basin from the best ecological niche model. Red colors indicate highest habitat suitability and blue lowest suitability.</figcaption></figure>

particularly the SFBR presenting a low environmental suitability (Figure 3). High environmental suitability (higher than 80%) was observed in areas with bathymetry ranging from 9 m to 1,067 m (median = 77 m); distance to shore between 1 km and 12 km (median = 4 km); slope ranging from 1° to 113° (median = 24°); Mean SST between 25 and 27°C (median = 26.9°C); Range SST between 1.89 and 3.34°C (median = 2.55°C); Mean SSS ranging from 34.3 to 36.1 psu (median = 34.7 psu); and Mean Chl-a between 0.12 and 1.68 mg/m³ (median = 0.64 mg/m³).

## Pantropical Spotted Dolphin Ecological Niche Model

An environmental suitability higher than 60% was predicted for the pantropical spotted dolphin across most of the

Caribbean Basin, but mainly closer to the continent and nearby islands, including the SFBR (Figure 4). Records with suitability higher than 80% ranged in depth from 58 m to 2,215 m (median = 813 m); distance to shore between 3 km and 72 km (median = 42 km); slope ranging from 2° to 120° (median = 26°); Mean SST between 25.6 and 28.3°C; Range SST was between 1.1 and 4.1°C (median = 1.3°C); Mean SSS around 35 psu; and Mean Chl-a ranging from 0.12 to 1.12 mg/m³ (median = 0.21). In the SFBR, environmental suitability around 60% was detected within the southern portion of the Reserve.

## Spinner Dolphin Ecological Niche Model

The highest suitability (>70%) for spinner dolphins was predicted in waters surrounding islands or seamounts, and with
<figure><img src="image_5.png" /><figcaption>**FIGURE 4** | Predicted potential distribution for the pantropical spotted dolphin in the Caribbean Basin from the best ecological niche model. Red colors indicate highest habitat suitability and blue lowest suitability.</figcaption></figure>

a steeper slope (Figure 5), in almost all coastal areas in the Caribbean including surrounding islands. High environmental suitability (over 80%) corresponded to areas with bathymetry ranging from 30 m to 1,599 m (median = 620 m); distance to shore between 1 km and 9 km (median = 5 km); slope ranging from 1° to 165° (median = 106.5°); Mean SST between 25.4 and 27.7°C (median = 27.2°C); Range SST between 2.14 to 4°C (median = 2.5°C); Mean SSS ranging from 34.6 to 35.9 psu (median = 35.1 psu); and Mean Chl-a between 0.11 and 1.7 mg/m³ (median = 0.38 mg/m³). The SFBR exhibited a high habitat suitability for spinner dolphins, primarily around the insular region, where environmental suitability reached 72% (e.g., around San Andrés Island).

## Niche Overlap and Identity Test

Pairwise overlap for the three *Stenella* species is presented in Table 3. We found high “I” and “D” similarity statistic scores for all pairwise comparisons, indicating that ecological niches were broadly similar among Atlantic spotted dolphins, pantropical spotted dolphins, and spinner dolphins in the Caribbean Basin. Niche identity tests did not reject the null hypotheses that assumed the niches were identical between pairwise *Stenella* species comparisons (Table 3).

However, our results indicated that while the distribution of Atlantic spotted dolphins and spinner dolphins showed the highest statistical similarity, Atlantic and pantropical spotted dolphins shared the lowest similarity values. In general, all three
<figure><img src="image_6.png" /><figcaption>**FIGURE 5** | Predicted potential distribution for the spinner dolphin in the Caribbean Basin from the best ecological niche model. Red colors indicate highest habitat suitability and blue lowest suitability.</figcaption></figure>

<table><caption>**TABLE 3** | Metrics obtained in niche overlap comparison and niche identity tests conducted pairwise between *Stenella* species.</caption><thead><tr><th rowspan="2">Pairwise combinations</th><th colspan="2">Niche overlap</th><th colspan="2">Niche identity</th></tr><tr><th>D similarity statistics</th><th>I similarity statistics</th><th>D similarity statistics</th><th>I similarity statistics</th></tr></thead><tbody><tr><td>Atlantic spotted dolphin versus pantropical spotted dolphin</td><td>0.73</td><td>0.94</td><td>0.71 (p-value = 1)</td><td>0.92 (p-value = 1)</td></tr><tr><td>Atlantic spotted dolphin versus spinner dolphin</td><td>0.80</td><td>0.96</td><td>0.76 (p-value = 0.99)</td><td>0.95 (p-value = 0.98)</td></tr><tr><td>Pantropical spotted dolphin versus spinner dolphin</td><td>0.78</td><td>0.96</td><td>0.67 (p-value = 0.99)</td><td>0.90 (p-value = 1)</td></tr></tbody></table>

species coincided in some areas, according to the overlap map based on the threshold of equal test sensitivity and specificity (Table 4 and Figures 6–8). All species overlapped in their

distributions in coastal areas of the southern and southeastern Caribbean off Colombia, Venezuela, and surrounding areas in the Antilles, as well as in the SFBR. Nonetheless, while
<table><caption>**TABLE 4** | Threshold of equal test sensitivity and specificity obtained for each model generated among three *Stenella* species in the Caribbean Basin.</caption><thead><tr><th>Species</th><th>Threshold of equal test sensitivity and specificity</th></tr></thead><tbody><tr><td>Atlantic spotted dolphin</td><td>0.432</td></tr><tr><td>Pantropical spotted dolphin</td><td>0.516</td></tr><tr><td>Spinner dolphin</td><td>0.365</td></tr></tbody></table>

the distribution of pantropical spotted dolphins included more oceanic waters around 1,000 m deep, spinner dolphins appeared to occupy areas closer to island chains, and Atlantic spotted dolphins appeared to be more restricted to coastal/neritic waters.

## Statistical Comparisons of Environmental Variables

The median values of the environmental variables for each species indicated significant interspecific differences (Table 5 and Figure 9). The null hypothesis of equal medians for bathymetry, distance to shore, slope, Mean Chl-a, Mean SST and Range SST was rejected (Kruskal-Wallis test, *p* < 0.05).

Pantropical spotted dolphins had the highest median bathymetry, distance to shore, Mean SST, and the lowest median Mean Chl-a. In other words, pantropical spotted dolphins occupied areas of low phytoplankton biomass, and deeper (up to about 1,000 m) and warmer waters. Atlantic spotted dolphins and pantropical spotted dolphins differed in relation to bathymetry,

<figure><img src="image_7.png" /><figcaption>**FIGURE 6** | Pairwise overlap map of Atlantic spotted dolphin (ASD) and pantropical spotted dolphin (PSD) based on the threshold of equal test sensitivity and specificity.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**FIGURE 7** | Pairwise overlap map of Atlantic spotted dolphin (ASD) and spinner dolphin (SD) based on the threshold of equal test sensitivity and specificity.</figcaption></figure>

slope, distance to shore, Range SST and Mean Chl-a, with the former occurring in shallow waters closer to the shore, with lower slope values and Mean SST but higher productivity. Likewise, spinner dolphins occurred in areas surrounding the coast, with more phytoplankton biomass, and in shallower waters, although in regions characterized by a steeper slope.

DISCUSSION

This study established a series of potential “baseline” ecological
niche models for Atlantic spotted dolphins, pantropical spotted
dolphins and spinner dolphins. These models were used for
assessing their distribution patterns in the Caribbean Basin,

particularly in the SFBR. In this study, we provide the
first characterization of the environmental niches of three
*Stenella* species and their potential distributional patterns in the
Caribbean Basin. Our findings show that distance to coastline and
Mean Chl-a play an important role that influences the potential
distribution of these three species in the region.

Species of *Stenella* are widely distributed in the Caribbean
Basin, yet their patterns have been poorly studied. Small
delphinids of the genus *Stenella* carry out long-range movements
of up to 2,500 km (Reilly, 1990), presumably associated
with prey availability dynamics (Davis et al., 2002). In fact,
ecological niches for dolphins appear to be defined by variables
that affect distribution and abundance of resources they
rely upon (Baumgartner et al., 2001; MacLeod et al., 2008).
<figure><img src="image_9.png" /><figcaption>**FIGURE 8** | Pairwise overlap map of pantropical spotted dolphin (PSD) and spinner dolphin (SD) based on the threshold of equal test sensitivity and specificity.</figcaption></figure>

The availability of dolphin prey depends mainly on primary productivity, which is highly variable in the oligotrophic waters of the Caribbean Basin (Corredor, 1979; Müller-Karger and

Aparicio, 1994; Aguirre, 2014). In the first part of the year, primary productivity concentrates along the continental coasts of Colombia and Venezuela, while shifting to Caribbean islands such as Puerto Rico, the Virgin Islands and the Antilles in the second part (Müller-Karger et al., 1989; Müller-Karger and Aparicio, 1994). This variability enables *Stenella* species, such as pantropical spotted dolphins and spinner dolphins, to exploit oceanic and deeper-water environments given their long-distance movement capabilities.

Coastal-marine ecosystems such as coral reefs and seagrass beds host a high biodiversity and productivity that provide food resources for local and oceanic biological communities. The SFBR appears to be an important area for dolphins in the Caribbean Basin not only because it contains the largest open-ocean coral reefs in the Caribbean, but because it also holds extensive and healthy seagrass beds (Taylor et al., 2013). Consequently, the overlap in the ecological niche of pantropical spotted dolphins

<table><caption>**TABLE 5** | Results of the Kruskal-Wallis test for comparing environmental variables between Atlantic spotted dolphins, pantropical spotted dolphins and spinner dolphins.</caption><thead><tr><td>Environmental variable</td><td>Kruskal-Wallis (X<sup>2</sup>)</td><td>p-value</td></tr></thead><tbody><tr><td>Bathymetry</td><td>40.567</td><td>1.552e-09</td></tr><tr><td>Distance to shore</td><td>18.999</td><td>7.487e-05</td></tr><tr><td>Bathymetric slope</td><td>8.1875</td><td>0.01668</td></tr><tr><td>Mean annual sea surface temperature</td><td>17.65</td><td>0.000147</td></tr><tr><td>Annual range in sea surface temperature</td><td>14.7705</td><td>0.0006203</td></tr><tr><td>Mean annual sea surface salinity</td><td>4.84</td><td>0.08892</td></tr><tr><td>Mean annual chlorophyll-a concentration</td><td>35.297</td><td>2.165e-08</td></tr></tbody></table>
<figure><img src="image_10.png" /><figcaption>**FIGURE 9** | Box plots of values extracted from environmental layers for Atlantic spotted dolphins (ASD), pantropical spotted dolphins (PSD) and spinner dolphins (SD) in the Caribbean Basin. The thick horizontal line inside each box represents the median; the upper and lower borders of the box are the 25th and 75th percentiles; the 5th and 95th percentiles are represented by the error bars; small circles are outliers.</figcaption></figure>

and spinner dolphins in the area is not surprising. In contrast, Atlantic spotted dolphins primarily inhabit coastal environments throughout most of their range, a distribution pattern similar to the one previously described in the Western South Atlantic Ocean by do Amaral et al. (2015). Productive coral reefs and seagrass beds are also found along the Colombian mainland coast, notably in Morrosquillo Gulf, the Rosario and San Bernardo Archipelago, Santa Marta, and the Tayrona region (Garzón-Ferreira et al., 2001).

Water temperature has been shown to be an important attribute of cetaceans' ecological niches (MacLeod, 2009). In the Caribbean Basin, the southern region along the coast of Colombia and Venezuela exhibits low SST associated with coastal upwelling, mainly off La Guajira region (Colombia) in the central Caribbean and around Margarita Island (Venezuela) in the eastern Caribbean (Gordon, 1967; Müller-Karger et al., 1989; Andrade et al., 2003; Andrade and Barton, 2005; Lonin et al., 2010; Paramo et al., 2011). This upwelling generates a high primary productivity (Corredor, 1979; Andrade et al., 2003; Andrade and Barton, 2005; Criales-Hernández et al., 2006; Arévalo-Martínez and Franco-Herrera, 2008; Lonin et al., 2010) that has been related to the occurrence of other dolphin species in the area (Farias-Curtidor et al., 2017). Considering that low

SST was an important descriptor of habitat suitability for Atlantic spotted dolphins, it is not surprising that they were preferentially found in Caribbean environments defined by shallow waters with lower temperatures and elevated primary productivity.

Bathymetry is another important predictor of Atlantic spotted dolphin habitats in the Caribbean Basin. Similar to what has been reported for the Gulf of México and the Western South Atlantic Ocean, this species showed a preference for shallower waters along the Colombian Caribbean (Davis et al., 1998; do Amaral et al., 2015). Atlantic spotted dolphins feed preferentially on coastal prey (Perrin et al., 1994), so their presence in shallow waters is frequent (Davis et al., 1998, 2002). These coastal habits could suggest that this species is not very cosmopolitan in the Atlantic Ocean, showing restricted movements across northern and southern portions of this ocean. Genetic studies have shown that individuals from the Colombian Caribbean are closely related to those population units from locations as far as west and east north Atlantic (Mesa-Gutiérrez et al., 2015); while populations from Azores and Madeira, which show high connectivity between them (Quérouil et al., 2010), resulted closely related to those from the Western North Atlantic and Brazil (Caballero et al., 2013; Mesa-Gutiérrez et al., 2015). Additionally, Atlantic spotted dolphins along the Brazilian coast
also show genetic (Caballero et al., 2013) and morphological
divergence (Moreno et al., 2005) associated with differences
in environmental conditions between southern and northern
Brazilian populations (do Amaral et al., 2015). Furthermore,
Atlantic spotted dolphin populations along the east coast
of the United States and Gulf of Mexico show significant
population structure due mainly to an extensive continental
shelf that promotes differential habitat structuring and hence,
the aggregation of resources in specific zones to be exploited
(Adams and Rosel, 2006). All these evidences suggest that
environmental conditions associated with coastal habitats could
drive differentiation between Atlantic spotted dolphins across
the Atlantic Ocean although more studies are needed to support
this assumption.

Similar to Atlantic spotted dolphins, spinner dolphins occur
in productive regions surrounding coastlines but with steeper
slopes, mainly near islands (Figure 7). Indeed, the niche for
these two species overlapped in much of coastal areas around
the Caribbean Basin, except in waters surrounding islands such
as in the SFBR, which is characterized by a pronounced slope
(mean = 4.5°). The slope had a considerable contribution for
the spinner dolphin model, preferred by this species due to the
vertical and horizontal migration of mesopelagic fish considered
an important part of their diet (Benoit-Bird and Au, 2003;
Thorne et al., 2012).

Pantropical spotted dolphins also appeared to prefer waters
around islands, but are present in deeper waters, as reported
in the Gulf of Mexico (Davis et al., 1998) and in the Western
South Atlantic (do Amaral et al., 2015). SST had a considerable
contribution to the species' model, indicating that offshore
pantropical spotted dolphins are usually found in warm tropical
waters, as reported previously in the Western South Atlantic
(do Amaral et al., 2015).

Although the *Stenella* species analyzed here showed
differences in their environmental requirements in the Caribbean
Basin, in general both spotted and spinner dolphins occupied
similar habitats in the region. It has been suggested that Atlantic
spotted dolphins and pantropical spotted dolphins are sympatric
in the Atlantic Ocean (Perrin and Hohn, 1994). Likewise,
associations between pantropical spotted dolphins and spinner
dolphins have been documented in the Atlantic (Silva et al.,
2005). However, do Amaral et al. (2015) suggested that *Stenella*
dolphins, particularly the three species considered for the current
study, could exhibit niche partitioning and spatial segregation
in the Western South Atlantic. Davis et al. (1998) also reported
differences in distribution of these three *Stenella* species along
the Gulf of Mexico. These differences have been related not
only to oceanographic conditions (do Amaral et al., 2015),
but also to prey distribution and feeding preferences among
*Stenella* species (Davis et al., 2002). However, in areas where
nutrients are scarce, feeding preferences facilitate that small
cetaceans share their habitat (Davis et al., 2002). In the Caribbean
Basin, Atlantic spotted, pantropical and spinner dolphins seem
to overlap much more in their distributions, particularly in
high-productivity areas.

These findings generate special concern regarding
climate change due to the effects on marine and coastal

productive ecosystems such as coral reefs. Particularly, these
ecosystems have been heavily affected in the Caribbean
Basin (Connell, 1997; Pandolfi et al., 2003), allowing the
increase in macroalgae coverage (Mumby et al., 2007) and
the consequent decreases in local biodiversity. Similarly,
prey distribution could be affected due to increases in water
temperature (Learmonth et al., 2006; MacLeod, 2009). These
shifts in prey availability can affect distributional patterns
on dolphins in the Caribbean Basin (Learmonth et al., 2006;
MacLeod, 2009; Ramos et al., 2016), which could force them
to spend more time traveling in oceanic waters looking
for resources.

While oceanic areas in the Caribbean Basin appear to
be potentially important for *Stenella* dolphins, most studies
have taken place in coastal waters. Therefore, the distribution
patterns suggested in this study, based only on sightings, do
not predict suitable habitat beyond the known records of
*Stenella* in the Caribbean Basin. Although efforts have increased
in the region to advance marine mammal research, there is
still a large knowledge gap to improve the management of
such species. Areas of very low coverage (like the center of
Western Caribbean where no research has been done) could
contribute to the habitat close to shore and of specific depths
being deemed highly suitable. This is the case for the SFBR,
which is an oceanic area poorly studied with regarding marine
mammals. This Reserve holds high local biodiversity related
to coastal marine ecosystems that are potentially important
for dolphins' distribution as foraging hotspots. The SFBR
appears to be more suitable in its southern portion for
pantropical and spinner dolphins, mainly around the islands,
but their ecological and oceanographic conditions could allow
the occurrence of other small cetacean species. Future research
should focus on this protected area, to assess if it should
be considered as a priority area for cetacean species in the
Caribbean Basin.

AUTHOR CONTRIBUTIONS

DB-B, PC-C, NF-C, RL-N, PB, JB-J, LB, DC-M, JL, JM, RM-G, BdM, ER, VR, and DP contributed to the acquisition of field data. DB-B, KBA, and NF-C organized the data and compiled the literature. DB-B and KBA conceived and designed the experiments, analyzed the data, and wrote the manuscript. KBA performed the experiments. DB-B, KBA, PC-C, NB-A, IM, JB-J, DC-M, JL, RM-G, BdM, ER, and DP contributed to the manuscript writing. All authors have approved the final version of the manuscript.

ACKNOWLEDGMENTS

We thank the Colombian Program of Marine Fauna Observers,
and acknowledge the collaboration of all the observers, captains,
and crew members of seismic vessels. Special thanks to Comisión
Colombiana del Océano, Armada Nacional de Colombia, ARC
Providencia crew, and Universidad de los Andes for their
logistic support during the Scientific Seaflower Expedition
Cayo Serrana Island 2016. DB-B received a small grant
provided by Cetacean Society International to participate
in this expedition (2015). Fieldwork in Colombia was also
supported by the Society for Marine Mammalogy (DB-B, 2014),
the "Proyecto Semilla - 2013-2 Call for Funding of Research
Category: Master and Doctoral students, project 'Genetic
structure and diversity of bottlenose dolphins *Tursiops truncatus*
(Montagu, 1821) (Cetacea: Delphinidae) in La Guajira,
Colombian Caribbean" from Universidad de los Andes (DB-B,
2014), the "Proyecto Semilla - 2015-1 Call for Funding of
Research Category: Master and Doctoral students, project
'Occurrence, distribution and preliminary genetic status
of delphinids in La Guajira, Colombian Caribbean" from

Universidad de los Andes (DB-B, 2015), and the Rufford Small
Grant Foundation (NF-C, 2016). KBA received a Ph.D. degree
scholarships from Coordenação de Aperfeiçoamento de Pessoal
de Nível Superior (CAPES) (process 1395438). Finally, we really
appreciate the comments that reviewers have provided to the
manuscript, which have been valuable and useful to improve the
final version.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fmars.
2019.00010/full#supplementary-material

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**Conflict of Interest Statement:** The 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.

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