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Tubastraea coccinea is an invasive coral that has had ecological, economic, and social impacts in the Atlantic Ocean, the Caribbean Sea, and the Gulf of Mexico (GoM). Tubastraea coccinea is considered a major threat to marine biodiversity, whose occurrence in its non-native range has been associated with artificial structures such as oil/gas platforms and shipwrecks. A recent species distribution model identified important determinants of T. coccinea invasion in the northern GoM and projected its potential range expansion. However, the potential effects of anthropogenic factors were not considered. We used boosted regression trees to develop a species distribution model investigating the importance of oil/gas platforms and shipping fairways as determinants of T. coccinea invasion in the northern GoM. Our results indicate that maximum salinity, distance to platform, minimum nitrate, and mean pH were the first to fourth most influential variables, contributing 31.9%, 23.5%, 22.8%, and 21.8%, respectively, to the model. These findings highlight the importance of considering the effects of anthropogenic factors such as oil/gas platforms as potential determinants of range expansion by invasive corals. Such consideration is imperative when installing new platforms and when decommissioning retired platforms.
Coral reefs provide USD 3.4 billion per year in ecosystem services in the U.S. [1], and invasive marine species have negatively impacted these ecosystem services [2]. Biological invasions often result in a reduction in biodiversity, loss of native and commercial species, and changes in the structure and function of communities and ecosystems [3, 4]. Rising globalization has increased the number of anthropogenic structures in marine environments, while coral species colonize manmade reefs [5, 6]. In the Gulf of Mexico (GoM), the “rigs-to-reefs” program, conducted under the auspices of the Bureau of Safety and Environmental Enforcement, converts decommissioned offshore oil and gas rigs into artificial reefs [5]. Oil/gas platforms facilitate the dispersal of coral larvae and may be accelerating the range expansion of invasive corals in the northern GoM [6].
Tubastraea coccinea is an invasive coral that has had ecological, economic, and social impacts in the Atlantic Ocean, the Caribbean Sea, and the GoM [7]. It is commonly known as orange cup coral or sun coral [8, 9, 10]. Tubastraea coccinea, whose occurrence in its non-native range is mainly associated with artificial structures such as oil/gas platforms and shipwrecks, is considered a major threat to marine biodiversity [6, 11, 12, 13]. A recently developed species distribution model identified important determinants of T. coccinea invasion in the northern GoM and projected its potential range expansion [14]. Five environmental factors, including two variables from the top surface layer of the ocean (mean pH and mean calcite) and three variables from benthic layers adjacent to the seabed (maximum current velocity, minimum iron, and minimum dissolved oxygen), contributed >99% to the overall model [14, 15]. However, the potential effects of anthropogenic factors were not considered.
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In this paper, we use boosted regression trees to develop a species distribution model investigating the importance of anthropogenic determinants of T. coccinea invasion in the northern GoM. Specifically, we focus on the potential effects of oil/gas platforms and shipping fairways as determinants of the invasion.
Tubastraea coccinea is an azooxanthellate coral native to the Indo-Pacific reefs, where it was first described near Bora Bora Island in French Polynesia [16]. It was first reported on offshore oil platforms in Brazil in the 1980s [17]. It is easily identified by its red-to-orange body and orange-to-yellow tentacles, although colonies of T. coccinea can vary in size and color [18]. Tubastraea coccinea is not considered a reef-building coral [8]. Colonies are composed of a spongy calcareous base with protruding calcareous cups known as corallites [17]. Each corallite contains a single polyp [14], which can be up to 11 mm in diameter and can extend up to 4 cm from the spongy calcareous base [8]. In the GoM, T. coccinea rarely occurs at depths >78 m [19]. Tubastraea coccinea have been reported as fouling organisms on oil/gas platforms [20].
And contains thousands of species from over 40 phyla [21]. Our research focuses on the northern GoM along the coasts of Texas, Louisiana, Mississippi, Alabama, and Florida (Figure 1a). The gulf coastal waters adjacent to these five states contribute greatly to ecosystem goods and services. On average, they contribute over USD 2 trillion per year to the gross domestic product, excluding additional income produced by non-market regulating, cultural, and supporting services [22].
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We obtained T. coccinea occurrence data from Derouen et al. [14] (Figure 1b). Derouen et al. [14] collected data from the Ocean Biogeographic Information System [23], the Global Biodiversity Information Facility [24], and the Web of Science [25]. They identified nine environmental variables as being physiologically and/or ecologically relevant for marine organisms in general [15, 26] and for T. coccinea in particular [14]. Seven were benthic variables (maximum current velocity, minimum dissolved oxygen, minimum light at the bottom, maximum salinity, minimum iron, minimum nitrate, and maximum primary productivity) and two were surface variables (mean calcite and mean pH) (Table 1). These variables were determined to be independent using Pearson’s correlation coefficient (electronic supplementary material 2 of Derouen et al. [14]). We downloaded data on these nine variables as TIFF raster files from Bio-ORACLE. We downloaded a map of the GoM study area as a marine region shapefile, which included the exclusive economic zone and International Hydrographic Organization sea area, from a website (marineregions.org) managed by the Flanders Marine Institute [27]. Derouen et al. [14] provide further details on the collection and processing of these environmental data. In addition to the environmental data, we downloaded a CSV containing georeferenced locations of oil/gas platforms in the GoM from the Bureau of Safety and Environmental Enforcement Data Center [28], and obtained the routes of associated shipping fairways from ESRI [29] (Figure 1b).
We overlaid the occurrence data on the map of the study area to produce a T. coccinea occurrence map. Each of the nine environmental variables were joined with the occurrence map in QGIS 3.20 (Odense). We overlaid a georeferenced grid containing 0.083° × 0.083° cells on the joined (T. coccinea occurrence plus nine environmental variables) map to calculate the potential predictor variables at the centroids in each cell. We overlaid the oil/gas platform points and shipping fairways on the study area map. We then used the GRASS plugin tool, v.distance, to calculate the nearest cell centroid (in m) to each platform and the nearest cell centroid to each shipping fairway (Table 1). We conducted our analysis using boosted regression trees following the procedure described by Derouen et al. [14]. Specifically, the optimal model was determined (1) by altering the learning rate and tree complexity until the predictive deviance was minimized without over-fitting, and (2) by limiting our choice of the final model to those that contained at least 1000 trees, following the recommendations of Elith et al. [30]. Once the optimal combination of learning rate and tree complexity was found, model performance was evaluated using a tenfold cross-validation procedure with re-substitution. For each cross-validation trial, 60% of the dataset was randomly selected for model fitting, and the excluded 40% was used for testing, following the recommendation of Wang et al. [31]. We derived our optimal model in R 3.6.0 [32] using the gbm package version 1.5–7 [33]. We calculated the relative influence of each potential determinant variable in the model and constructed partial dependence plots for the most influential variables. We used the optimal model to calculate the probabilities of T. coccinea occurrence in the northern GoM and superimposed these probabilities on the map of the northern GoM using ArcGIS Pro 2.8.3 [34].
Analyses of 500 combinations of tree complexity (ranging from 3 to 7) and learning rates (ranging from 0.001 to 0.01) produced models with between 450 and 3100 trees. The optimal model had a tree complexity of 5, a learning rate of 0.003, and a total of 1050 trees. The AUC score was 0.920 ± 0.022 (“very good” ability to discriminate between species presence and absence). Four variables were included in our
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We obtained T. coccinea occurrence data from Derouen et al. [14] (Figure 1b). Derouen et al. [14] collected data from the Ocean Biogeographic Information System [23], the Global Biodiversity Information Facility [24], and the Web of Science [25]. They identified nine environmental variables as being physiologically and/or ecologically relevant for marine organisms in general [15, 26] and for T. coccinea in particular [14]. Seven were benthic variables (maximum current velocity, minimum dissolved oxygen, minimum light at the bottom, maximum salinity, minimum iron, minimum nitrate, and maximum primary productivity) and two were surface variables (mean calcite and mean pH) (Table 1). These variables were determined to be independent using Pearson’s correlation coefficient (electronic supplementary material 2 of Derouen et al. [14]). We downloaded data on these nine variables as TIFF raster files from Bio-ORACLE. We downloaded a map of the GoM study area as a marine region shapefile, which included the exclusive economic zone and International Hydrographic Organization sea area, from a website (marineregions.org) managed by the Flanders Marine Institute [27]. Derouen et al. [14] provide further details on the collection and processing of these environmental data. In addition to the environmental data, we downloaded a CSV containing georeferenced locations of oil/gas platforms in the GoM from the Bureau of Safety and Environmental Enforcement Data Center [28], and obtained the routes of associated shipping fairways from ESRI [29] (Figure 1b).
We overlaid the occurrence data on the map of the study area to produce a T. coccinea occurrence map. Each of the nine environmental variables were joined with the occurrence map in QGIS 3.20 (Odense). We overlaid a georeferenced grid containing 0.083° × 0.083° cells on the joined (T. coccinea occurrence plus nine environmental variables) map to calculate the potential predictor variables at the centroids in each cell. We overlaid the oil/gas platform points and shipping fairways on the study area map. We then used the GRASS plugin tool, v.distance, to calculate the nearest cell centroid (in m) to each platform and the nearest cell centroid to each shipping fairway (Table 1). We conducted our analysis using boosted regression trees following the procedure described by Derouen et al. [14]. Specifically, the optimal model was determined (1) by altering the learning rate and tree complexity until the predictive deviance was minimized without over-fitting, and (2) by limiting our choice of the final model to those that contained at least 1000 trees, following the recommendations of Elith et al. [30]. Once the optimal combination of learning rate and tree complexity was found, model performance was evaluated using a tenfold cross-validation procedure with re-substitution. For each cross-validation trial, 60% of the dataset was randomly selected for model fitting, and the excluded 40% was used for testing, following the recommendation of Wang et al. [31]. We derived our optimal model in R 3.6.0 [32] using the gbm package version 1.5–7 [33]. We calculated the relative influence of each potential determinant variable in the model and constructed partial dependence plots for the most influential variables. We used the optimal model to calculate the probabilities of T. coccinea occurrence in the northern GoM and superimposed these probabilities on the map of the northern GoM using ArcGIS Pro 2.8.3 [34].
Analyses of 500 combinations of tree complexity (ranging from 3 to 7) and learning rates (ranging from 0.001 to 0.01) produced models with between 450 and 3100 trees. The optimal model had a tree complexity of 5, a learning rate of 0.003, and a total of 1050 trees. The AUC score was 0.920 ± 0.022 (“very good” ability to discriminate between species presence and absence). Four variables were included in our
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