Next Article in Journal
Numerical Experiments Applying Simple Kriging to Intermittent and Log-Normal Data
Next Article in Special Issue
Colonization Dynamics of Potential Stowaways Inhabiting Marinas: Lessons from Caprellid Crustaceans
Previous Article in Journal
Low-Carbon Tour Route Algorithm of Urban Scenic Water Spots Based on an Improved DIANA Clustering Model
Previous Article in Special Issue
Rapid Spread of the Invasive Brown Alga Rugulopteryx okamurae in a National Park in Provence (France, Mediterranean Sea)
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Water
Volume 14
Issue 9
10.3390/w14091365
water-logo
Submit to this Journal Review for this Journal Edit a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Importance of Anthropogenic Determinants of Tubastraea coccinea Invasion in the Northern Gulf of Mexico

by
Emily E. Brockinton
1,†,
Miranda R. Peterson
2,†,
Hsiao-Hsuan Wang
2,* and
William E. Grant
2
1
Department of Marine and Coastal Environmental Science, Texas A&M University at Galveston, Galveston, TX 77554, USA
2
Ecological Systems Laboratory, Department of Ecology and Conservation Biology, Texas A&M University, College Station, TX 77843, USA
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Water 2022, 14(9), 1365; https://doi.org/10.3390/w14091365
Received: 23 February 2022 / Revised: 15 April 2022 / Accepted: 18 April 2022 / Published: 22 April 2022
(This article belongs to the Special Issue Biological Invasions in the Marine Environment)
Browse Figures
Versions Notes

Abstract

:
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.

1. Introduction

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.
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.

2. Materials and Methods

2.1. Focal Species

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].

2.2. Study Area

The GoM occupies 1.5 million km2 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].

2.3. Data Collection

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).

2.4. Data Processing and Analysis

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].

3. Results

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 final species distribution model, with two benthic variables contributing 54.7%, one anthropogenic variable contributing 23.5%, and one surface variable contributing 21.8% to the overall model (Figure 2). 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. Partial dependence plots indicated that T. coccinea occurrences were associated with benthic conditions characterized by maximum salinity between 36.21 and 36.61 PPS (Figure 3a) and minimum nitrate between 0.000053 and 1.17 mol m−3 (Figure 3c), anthropogenic factors characterized by a distance to platform between 380 and 9958 m (Figure 3b), and surface conditions characterized by mean pH less than 8.06 (Figure 3d). These results suggest the potential for range expansion of T. coccinea in the northern GoM is the highest along the Texas and Louisiana coasts between 90° W and 95° W, where the estimated probabilities of occurrence (P) were relatively high (0.7 < P ≤ 0.8) (Figure 4). Considering our entire study area, approximately 93.51%, 6.12%, 0.29%, and 0.08% of the cells fell within the P ≤ 0.5, 0.5 < P ≤ 0.6, 0.6 < P ≤ 0.7, and P > 0.7 categories, respectively.

4. Discussion

Species distribution models and ecological niche models have been increasingly used to address a variety of ecological issues involving endangered species, invasive species, and emergent infectious diseases [35,36,37]. Many approaches, ranging from statistical methods to machine learning methods, have been developed and applied. Moreover, data-related issues (including data availability and limitation, inclusion of independent variables, and scaling data) remain a fruitful area of debate [38,39]. Indeed, providing reliable predictions of a potential habitat for T. coccinea now and in the future remains a challenge. Variables affecting T. coccinea spread operate at different spatial scales, resulting in data limitations and modelling challenges [40,41]. Coral range projections have been made for more than a decade [42], including four recent studies focused on T. coccinea [14,43,44,45]. Even though these four studies all focused mainly on the impacts of benthic and surface variables on the distributions of T. coccinea, their results differed somewhat due to the limited availability of occurrence data, the different research regions from which the data were collected, and, hence, the inclusion of different independent variables in the different models [46,47,48]. The relative importance of independent variables may also vary with the stage of invasion [40,49]. The selection of specific independent variables may have markedly changed the model development and likelihood estimates [41], which is a general problem related to structural uncertainty in the quantitative representation of natural systems [50,51]. In our study, the possibility always remains that we failed to include some important independent variables, and that the relative importance of those variables we included depends on the current state of the system [35,40]. The availability of additional data on the biological process (e.g., coexistence and competition) and environmental variables (e.g., suspended sediments and current direction), as well as the functional relationships between the variables and the stage of invasion, would likely enhance the prediction precision [52,53,54].
Our results highlight the importance of considering the effects of anthropogenic factors such as oil/gas platforms and shipping fairways as potential determinants of range expansion by invasion corals. We found distance to oil/gas platforms to be among the most important determinants of T. coccinea invasion in the northern GoM, contributing on the same level as more commonly considered benthic and surface variables in terms of relative importance in our species distribution model. Below, we first compare our results with those of Derouen et al. [14] and comment briefly on the environmental factors included in our final model. We then conclude by offering some thoughts on the inclusion of anthropogenic factors in species distribution models of invasive corals.
Our estimated probabilities of T. coccinea occurrence indicated a potential distribution that differs noticeably from that reported by Derouen et al. [14] in that it did not include areas along the Gulf coast of Florida (Figure 4). Our highest probabilities of occurrence (p > 0.7) were clustered in two areas along the Texas and Louisiana coasts, between approximately 90° W and 94° W. Derouen et al. [14] estimated that such high probabilities (p > 0.7) extended farther along the Texas and Louisiana coasts, from 88° W to 97° W. Derouen et al. [14] identified three benthic variables (maximum current velocity, minimum iron, and minimum dissolved oxygen) and two surface variables (mean pH and mean calcite) as the most important determinants of T. coccinea invasion (collectively contributing >99% to their overall model). Our results concur that both benthic and surface environmental conditions are important determinants of invasion. However, consideration of anthropogenic factors, which led to the inclusion of distance to gas/oil platforms in our model, changed the identity and relative importance of the environmental factors included in the final model. Our model included none of the environmental variables included in the model of Derouen et al. [14], except for mean pH, and reduced the overall contribution of environmental variables by almost one-quarter (to ≈76%). Statistically, it is not surprising that the addition of a new variable changed the relative influence of variables included in the original set. Nor, from an ecological perspective, is it surprising that the relative importance of specific environmental variables shifted, given the complex interactions among environmental factors and coral physiology. Derouen et al. [14] comment on the physiological/ecological relevance of each variable included in their model, and we do the same in the following paragraphs. What, perhaps, is surprising is the omission of anthropogenic factors from species distribution models focused on coastal marine environments—a point to which we will return in our concluding remarks.
Maximum salinity was the most influential variable in our model, contributing 31.9%. Throughout the study area, maximum salinity ranged from 30.86 PSS to 36.79 PSS (Table 1), while T. coccinea was associated with maximum salinity at the higher end of that range (between 36.21 and 36.61 PPS). As a structuring factor in marine ecosystems, salinity affects the distribution of corals [55]. In the models of Derouen et al. [14] and Riul et al. [43], both of which included only environmental variables, salinity was found to be less important. The model of Santos et al. [56], which included a variable representing distance to oil/gas platforms, found salinity to be associated with Tubastraea spp. occurrence, although the presence in their study was associated with lower rather than higher salinity. Corals have a low tolerance to large changes in salinity, although studies have shown that some corals are able to withstand moderate salinity fluctuations [57]. Low salinity may cause reduced photosynthesis and bleaching [57,58], and stony corals, such as T. coccinea, are assumed to be quite sensitive to freshwater compared to other marine invertebrates [59]. High salinity may lead to increased thermotolerance [60], but T. coccinea has not yet been subjected to such testing, and more studies investigating the effect of salinity fluctuation on coral fitness are needed [57].
Minimum nitrate was the second most influential environmental variable in our model, contributing 22.8% to the overall model. Minimum nitrate throughout the study area ranged from 0.00 to 27.57 mol m−3. Tubastraea coccinea was associated with minimum nitrate levels between 0.000053 and 1.17 mol m−3. Nitrate is a limiting nutrient for marine organisms, and the input of fixed nitrogen into reef ecosystems sustains primary production and overall net productivity [61]. Corals may increase nitrogen fixation in response to increased temperatures and dissolved organic carbon availability [62], which could provide corals with a potential mechanism to persist in variable environments [61]. Corals prefer oligotrophic water, and an excessive nitrogen supply can result in phosphate starvation, which can increase the vulnerability of corals to heat and light stress [63].
Mean pH was the third most influential environmental variable in our model, contributing 21.8% to the overall model. Mean pH throughout the study area ranged from 7.97 to 8.21. Tubastraea coccinea was associated with a mean pH less than 8.06. Mean pH was the most influential variable in the model of Derouen et al. [14], and those authors found that T. coccinea was associated with a somewhat similar range of mean pH (less than 8.12). pH is an important physicochemical in marine ecosystems [64], directly affecting calcification rates [65]. Tubastraea coccinea has shown little response to changes in pH in previous studies [66]. When under heat stress, invasive T. coccinea in the GoM are more stress-resistant to changes in pH than their endemic Indo-Pacific counterparts [67], which may give T. coccinea a competitive advantage [66].
The anthropogenic factor included in our model, distance of oil/gas platforms to the nearest centroid, contributed almost one-quarter (23.5%) of the explanatory power of the model. The distances to oil/gas platforms throughout the study area ranged from 25 to 782,115 m. Tubastraea coccinea was associated with a distance to platform between 380 and 9958 m (Figure 3b). There are ≈1862 oil/gas platforms in the GoM [28], which provide hard substrates for benthic community colonization and development [68]. Within the northern GoM, T. coccinea was first discovered on seven oil/gas platforms, and these platforms likely served as stepping stones that facilitated further spread [12]. Coral larvae can disperse hundreds to thousands of kilometers to settle [69], and T. coccinea populations have been utilizing oil/gas platforms to expand their geographic range in the northern GoM [6,12].
Our model and the model of Derouen et al. [14] both estimated the highest probabilities of T. coccinea occurrence to be along the coasts of Louisiana and Texas. Derouen et al. [14] suggested that this distribution may be explained by gulf currents transporting planulae, and they identified maximum current velocity as the main contributor to their model. With the addition of distance to oil/gas platforms as a variable, we found maximum current velocity to be unimportant in our model. Derouen et al. [14] also suggested heavy shipping traffic could be a possible influential variable, although they did not investigate its potential effect on their model. We explicitly included distance to shipping fairways as a potential determinant of invasion, but it was not an important contributor to our model.
The take-home message from the comparison of these two models is not that one model is superior to the other in any absolute sense. Certainly, the roles of both current flows and the distribution of oil/gas platforms in the range expansion of T. coccinea are ecologically interpretable, as are the roles of the other variables included in each model. Rather, the take-home message is that the inclusion of anthropogenic variables as potential determinants of occurrence can alter the relative importance of environmental variables and can enrich the ecological interpretation of species distribution models. Other studies have also emphasized the importance of including anthropogenic factors in species distribution models of corals [11].
Regarding the management implications of our results, we concur that the installation of new oil/gas platforms will require a priori risk assessments and that the decommissioning of retired platforms should take into consideration non-native species dispersal, especially that of T. coccinea [70]. In the United States, federal legislation requires that all offshore platforms be removed within a year after production has ended [71]. The Bureau of Ocean Energy Management (BOEM) has created the “rigs-to-reefs” program, which encourages owners to donate oil/gas platforms to the program. The donated platforms are either left in place or toppled and towed to an artificial reef site located in the U.S. Exclusive Economic Zone (EEZ) [19]. Tubastraea coccinea thrives on artificial substrates [12], and significantly higher densities of T. coccinea have been found on toppled rigs-to-reefs platforms than on standing platforms [19]. Careful consideration is essential both prior to and after the creation of an artificial reef regarding its possible facilitation of the invasion of non-native species such as T. coccinea [5].

5. Conclusions

Our results highlight the importance of considering the effects of anthropogenic factors as potential determinants of range expansion by invasive corals. Considering the invasive nature of T. coccinea and its impact on natural and artificial reefs, the documentation of its present distribution and the identification of likely areas of potential range expansion are of paramount importance. Oil/gas platforms in particular can act as a vector for the range expansion of T. coccinea within the northern GoM. The installation of new oil/gas platforms will require a priori risk assessments, and the decommissioning of retired platforms should take into consideration non-native species dispersal, especially that of T. coccinea. Such considerations are imperative within programs such as the “rigs-to-reefs” program in the United States.

Author Contributions

Conceptualization, M.R.P. and H.-H.W.; methodology, E.E.B., M.R.P. and H.-H.W.; data curation, E.E.B. and M.R.P.; software, E.E.B., M.R.P. and H.-H.W.; validation, M.R.P. and H.-H.W.; formal analysis, H.-H.W.; writing—original draft preparation, E.E.B. and M.R.P.; writing—review and editing, H.-H.W. and W.E.G.; visualization, E.E.B., M.R.P. and H.-H.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Acknowledgments

We would like to thank the Handling Editor, María Pilar Cabezas Rodríguez, Three anonymous reviewers for their time and effort. The manuscript is greatly improved as a result of their comments.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Brander, L.; van Beukering, P. The Total Economic Value of US Coral Reefs: A Review of the Literature; National Oceanic and Atmospheric Administration: Washington, DC, USA, 2013; p. 32.
  2. Katsanevakis, S.; Wallentinus, I.; Zenetos, A.; Leppäkoski, E.; Çinar, M.E.; Oztürk, B.; Grabowski, M.; Golani, D.; Cardoso, A.C. Impacts of invasive alien marine species on ecosystem services and biodiversity: A pan-European review. Aquat. Invasions 2014, 9, 391–423. [Google Scholar] [CrossRef]
  3. Mack, R.N.; Simberloff, D.; Mark Lonsdale, W.; Evans, H.; Clout, M.; Bazzaz, F.A. Biotic invasions: Causes, epidemiology, global consequences, and control. Ecol. Appl. 2000, 10, 689–710. [Google Scholar] [CrossRef]
  4. Wang, H.-H.; Grant, W.E.; Gan, J.; Rogers, W.E.; Swannack, T.M.; Koralewski, T.E.; Miller, J.H.; Taylor, J.W. Integrating spread dynamics and economics of timber production to manage Chinese tallow invasions in southern US forestlands. PLoS ONE 2012, 7, e33877. [Google Scholar]
  5. Hill, C.E.; Lymperaki, M.M.; Hoeksema, B.W. A centuries-old manmade reef in the Caribbean does not substitute natural reefs in terms of species assemblages and interspecific competition. Mar. Pollut. Bull. 2021, 169, 112576. [Google Scholar] [CrossRef]
  6. Sammarco, P.W.; Atchison, A.D.; Boland, G.S. Expansion of coral communities within the Northern Gulf of Mexico via offshore oil and gas platforms. Mar. Ecol. Prog. Ser. 2004, 280, 129–143. [Google Scholar] [CrossRef]
  7. López, C.; Clemente, S.; Moreno, S.; Ocaña, O.; Herrera, R.; Moro, L.; Monterroso, O.; Rodríguez, A.; Brito, A. Invasive Tubastraea spp. and Oculina patagonica and other introduced scleractinians corals in the Santa Cruz de Tenerife (Canary Islands) harbor: Ecology and potential risks. Reg. Stud. Mar. Sci. 2019, 29, 100713. [Google Scholar] [CrossRef]
  8. Cairns, S.D. A revision of the shallow-water azooxanthellate Scleractinia of the Western Atlantic. Stud. Fauna Curacao Other Caribb. Isl. 2000, 75, 1–34. [Google Scholar]
  9. Hoeksema, B.W.; Hiemstra, A.F.; Vermeij, M.J. The rise of a native sun coral species on southern Caribbean coral reefs. Ecosphere 2019, 10, e02942. [Google Scholar] [CrossRef]
  10. Hoeksema, B.W.; ten Hove, H.A. The invasive sun coral Tubastraea coccinea hosting a native Christmas tree worm at Curaçao, Dutch Caribbean. Mar. Biodivers. 2017, 47, 59–65. [Google Scholar] [CrossRef][Green Version]
  11. Couto, T.D.; Omena, E.P.; Oigman-Pszczol, S.S.; Junqueira, A.O. A method to assess the risk of sun coral invasion in marine protected areas. An. Da Acad. Bras. De Ciências 2021, 93, e20200583. [Google Scholar] [CrossRef]
  12. Fenner, D. Biogeography of three Caribbean corals (Scleractinia) and the invasion of Tubastraea coccinea into the Gulf of Mexico. Bull. Mar. Sci. 2001, 69, 1175–1189. [Google Scholar]
  13. Fenner, D.; Banks, K. Orange cup coral Tubastraea coccinea invades Florida and the Flower Garden Banks, northwestern Gulf of Mexico. Coral Reefs 2004, 23, 505–507. [Google Scholar] [CrossRef]
  14. Derouen, Z.C.; Peterson, M.R.; Wang, H.-H.; Grant, W.E. Determinants of Tubastraea coccinea invasion and likelihood of further expansion in the northern Gulf of Mexico. Mar. Biodivers. 2020, 50, 101. [Google Scholar] [CrossRef]
  15. Assis, J.; Tyberghein, L.; Bosch, S.; Verbruggen, H.; Serrão, E.A.; De Clerck, O. Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Glob. Ecol. Biogeogr. 2018, 27, 277–284. [Google Scholar] [CrossRef]
  16. Lesson, R.P. Voyage Autour Du Monde, Execute Par Ordre Du Roi, Sur la Corvettede Sa Majeste, la Coquille, Pendant Les Annees 1822, 1823, 1824, Et 1825. Par LI Duperrey; Arthus Bertrand: Paris, France, 1830. [Google Scholar]
  17. de Paula, A.F.; Creed, J.C. Two species of the coral Tubastraea (Cnidaria, Scleractinia) in Brazil: A case of accidental introduction. Bull. Mar. Sci. 2004, 74, 175–183. [Google Scholar]
  18. Fofonoff, P.; Ruiz, G.; Steves, B.; Simkanin, C.; Carlton, J. National Exotic Marine and Estuarine Species Information System. Available online: http://invasions.si.edu/nemesis/ (accessed on 6 September 2019).
  19. Sammarco, P.; Lirette, A.; Tung, Y.; Boland, G.; Genazzio, M.; Sinclair, J. Coral communities on artificial reefs in the Gulf of Mexico: Standing vs. toppled oil platforms. ICES J. Mar. Sci. 2014, 71, 417–426. [Google Scholar] [CrossRef][Green Version]
  20. Castro, C.B.; Pires, D.O. Brazilian coral reefs: What we already know and what is still missing. Bull. Mar. Sci. 2001, 69, 357–371. [Google Scholar]
  21. Felder, D.; Camp, D. Gulf of Mexico Origin, Waters, and Biota: Biodiversity; Texas A&M University Press: College Station, TX, USA, 2009. [Google Scholar]
  22. Shepard, A.N.; Valentine, J.F.; D’Elia, C.F.; Yoskowitz, D.; Dismukes, D.E. Economic impact of Gulf of Mexico ecosystem goods and services and integration into restoration decision-making. Gulf Mex. Sci. 2013, 31, 10–27. [Google Scholar] [CrossRef]
  23. OBIS. Tubastraea Coccinea Lesson. 1830. Available online: https://obis.org/taxon/291251 (accessed on 6 January 2019).
  24. GBIF. Occurrence Download. Available online: https://www.gbif.org/occurrence/download/0022600-181108115102211 (accessed on 6 September 2019).
  25. Clarivate. Web of Science. Available online: www.webofknowledge.com (accessed on 6 September 2019).
  26. Tyberghein, L.; Verbruggen, H.; Pauly, K.; Troupin, C.; Mineur, F.; De Clerck, O. Bio-ORACLE: A global environmental dataset for marine species distribution modelling. Glob. Ecol. Biogeogr. 2012, 21, 272–281. [Google Scholar] [CrossRef]
  27. FMI. The Intersect of the Exclusive Economic Zones and IHO Sea Areas, Version 3. Available online: http://www.vliz.be/en/imis?dasid=6042&doiid=324 (accessed on 6 January 2019).
  28. BSEE. Bureau of Safety and Environmental Enforcement. Available online: https://www.data.bsee.gov/ (accessed on 16 September 2021).
  29. ESRI. Global Shipping Routes. Available online: https://bobson.maps.arcgis.com/apps/webappviewer/index.html?id=7ba696c12aa34f2f8c19c96c4a70091f (accessed on 12 November 2021).
  30. Elith, J.; Leathwick, J.R.; Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 2008, 77, 802–813. [Google Scholar] [CrossRef]
  31. Wang, H.-H.; Koralewski, T.E.; McGrew, E.K.; Grant, W.E.; Byram, T.D. Species distribution model for management of an invasive vine in forestlands of eastern Texas. Forests 2015, 6, 4374–4390. [Google Scholar] [CrossRef][Green Version]
  32. R Foundation for Statistical Computing. R 3.6.0. 2017. Available online: http://www.R-project.org/ (accessed on 22 February 2022).
  33. Ridgeway, G. The gbm Package, Version 1.6-3. Available online: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.456.9958&rep=rep1&type=pdf (accessed on 24 February 2019).
  34. ESRI. ArcGIS Pro 2.8.3; Environmental Systems Research Institute, Inc.: Redlands, CA, USA, 2021. [Google Scholar]
  35. Villamizar-Gomez, A.; Wang, H.H.; Peterson, M.R.; Grant, W.E.; Forstner, M.R.J. Environmental determinants of Batrachochytrium dendrobatidis and the likelihood of further dispersion in the face of climate change in Texas, USA. Dis. Aquat. Org. 2021, 146, 29–39. [Google Scholar] [CrossRef] [PubMed]
  36. Culpepper, L.Z.; Wang, H.-H.; Koralewski, T.E.; Grant, W.E.; Rogers, W.E. Understory upheaval: Factors influencing Japanese stiltgrass invasion in forestlands of Tennessee, United States. Bot. Stud. 2018, 59, 20. [Google Scholar] [CrossRef] [PubMed]
  37. Randklev, C.R.; Wang, H.-H.; Groce, J.E.; Grant, W.E.; Robertson, S.; Wilkins, N. Land use relationships for a rare freshwater mussel species endemic to central Texas. J. Fish Wildl. Manag. 2015, 6, 327–337. [Google Scholar] [CrossRef][Green Version]
  38. Friedman, J.; Hastie, T.; Tibshirani, R. Response to Mease and Wyner, Evidence Contrary to the Statistical View of Boosting, JMLR 9: 131–156, 2008. J. Mach. Learn. Res. 2008, 9, 175–180. [Google Scholar]
  39. Mease, D.; Wyner, A. Evidence contrary to the statistical view of boosting. J. Mach. Learn. Res. 2008, 9, 131–156. [Google Scholar]
  40. Wang, H.-H.; Grant, W.E.; Swannack, T.M.; Gan, J.; Rogers, W.E.; Koralewski, T.E.; Miller, J.H.; Taylor, J.W., Jr. Predicted range expansion of Chinese tallow tree (Triadica sebifera) in forestlands of the southern United States. Divers. Distrib. 2011, 17, 552–565. [Google Scholar] [CrossRef]
  41. Koo, H.; Iwanaga, T.; Croke, B.F.W.; Jakeman, A.J.; Yang, J.; Wang, H.-H.; Sun, X.; Lü, G.; Li, X.; Yue, T.; et al. Position paper: Sensitivity analysis of spatially distributed environmental models- a pragmatic framework for the exploration of uncertainty sources. Environ. Model. Softw. 2020, 134, 104857. [Google Scholar] [CrossRef]
  42. Storlazzi, C.; Brown, E.; Field, M.; Rodgers, K.; Jokiel, P. A model for wave control on coral breakage and species distribution in the Hawaiian Islands. Coral Reefs 2005, 24, 43–55. [Google Scholar] [CrossRef]
  43. Riul, P.; Targino, C.H.; Júnior, L.A.; Creed, J.C.; Horta, P.A.; Costa, G.C. Invasive potential of the coral Tubastraea coccinea in the southwest Atlantic. Mar. Ecol. Prog. Ser. 2013, 480, 73–81. [Google Scholar] [CrossRef][Green Version]
  44. Carlos-Júnior, L.; Barbosa, N.; Moulton, T.; Creed, J. Ecological Niche Model used to examine the distribution of an invasive, non-indigenous coral. Mar. Environ. Res. 2015, 103, 115–124. [Google Scholar] [CrossRef]
  45. Carlos-Júnior, L.A.; Neves, D.M.; Barbosa, N.P.U.; Moulton, T.P.; Creed, J.C. Occurrence of an invasive coral in the southwest Atlantic and comparison with a congener suggest potential niche expansion. Ecol. Evol. 2015, 5, 2162–2171. [Google Scholar] [CrossRef] [PubMed]
  46. Wang, H.-H.; Wonkka, C.L.; Treglia, M.L.; Grant, W.E.; Smeins, F.E.; Rogers, W.E. Incorporating local-scale variables into distribution models enhances predictability for rare plant species with biological dependencies. Biodivers. Conserv. 2019, 28, 171–182. [Google Scholar] [CrossRef]
  47. Wang, H.-H.; Wonkka, C.L.; Treglia, M.L.; Grant, W.E.; Smeins, F.E.; Rogers, W.E. Species distribution modelling for conservation of an endangered endemic orchid. AoB Plants 2015, 7, plv039. [Google Scholar] [CrossRef] [PubMed][Green Version]
  48. Wang, H.-H.; Wonkka, C.L.; Grant, W.E.; Rogers, W.E. Range expansion of invasive shrubs: Implication for crown fire risk in forestlands of the southern USA. AoB Plants 2016, 8, plw012. [Google Scholar] [CrossRef] [PubMed][Green Version]
  49. Smith, A.B.; Godsoe, W.; Rodríguez-Sánchez, F.; Wang, H.-H.; Warren, D. Niche estimation above and below the species level. Trends Ecol. Evol. 2019, 34, 260–273. [Google Scholar] [CrossRef]
  50. Iwanaga, T.; Wang, H.-H.; Hamilton, S.H.; Grimm, V.; Koralewski, T.E.; Salado, A.; Elsawah, S.; Razavi, S.; Yang, J.; Glynn, P.; et al. Socio-technical scales in socio-environmental modeling: Managing a system-of-systems modeling approach. Environ. Model. Softw. 2021, 135, 104885. [Google Scholar] [CrossRef]
  51. Iwanaga, T.; Wang, H.-H.; Koralewski, T.E.; Grant, W.E.; Jakeman, A.J.; Little, J.C. Toward a complete interdisciplinary treatment of scale: Reflexive lessons from socioenvironmental systems modeling. Elem. Sci. Anthr. 2021, 9, 00182. [Google Scholar] [CrossRef]
  52. Fern, R.R.; Morrison, M.L.; Wang, H.-H.; Grant, W.E.; Campbell, T.A. Incorporating biotic relationships improves species distribution models: Modeling the temporal influence of competition in conspecific nesting birds. Ecol. Model. 2019, 408, 108743. [Google Scholar] [CrossRef]
  53. Moustaka, M.; Langlois, T.J.; McLean, D.; Bond, T.; Fisher, R.; Fearns, P.; Dorji, P.; Evans, R.D. The effects of suspended sediment on coral reef fish assemblages and feeding guilds of north-west Australia. Coral Reefs 2018, 37, 659–673. [Google Scholar] [CrossRef]
  54. Fern, R.R.; Morrison, M.L.; Grant, W.E.; Wang, H.; Campbell, T.A. Modeling the influence of livestock grazing pressure on grassland bird distributions. Ecol. Processes 2020, 9, 42. [Google Scholar] [CrossRef]
  55. Kleypas, J.A.; McManus, J.W.; Meñez, L.A. Environmental limits to coral reef development: Where do we draw the line? Am. Zool. 1999, 39, 146–159. [Google Scholar] [CrossRef]
  56. Santos, H.; Silva, F.; Masi, B.; Fleury, B.; Creed, J. Environmental matching used to predict range expansion of two invasive corals (Tubastraea spp.). Mar. Pollut. Bull. 2019, 145, 587–594. [Google Scholar] [CrossRef] [PubMed]
  57. Coles, S.L.; Jokiel, P.L. Effects of salinity on coral reefs. In Pollution in Tropical Aquatic Systems; CRC Press: Boca Raton, FL, USA, 2018; pp. 147–166. [Google Scholar]
  58. Kerswell, A.P.; Jones, R.J. Effects of hypo-osmosis on the coral Stylophora pistillata: Nature and cause of low-salinity bleaching. Mar. Ecol. Prog. Ser. 2003, 253, 145–154. [Google Scholar] [CrossRef][Green Version]
  59. Röthig, T.; Ochsenkühn, M.A.; Roik, A.; Van Der Merwe, R.; Voolstra, C.R. Long-term salinity tolerance is accompanied by major restructuring of the coral bacterial microbiome. Mol. Ecol. 2016, 25, 1308–1323. [Google Scholar] [CrossRef] [PubMed][Green Version]
  60. Gegner, H.M.; Ziegler, M.; Rädecker, N.; Buitrago-López, C.; Aranda, M.; Voolstra, C.R. High salinity conveys thermotolerance in the coral model Aiptasia. Biol. Open 2017, 6, 1943–1948. [Google Scholar] [CrossRef][Green Version]
  61. Rädecker, N.; Pogoreutz, C.; Voolstra, C.R.; Wiedenmann, J.; Wild, C. Nitrogen cycling in corals: The key to understanding holobiont functioning? Trends Microbiol. 2015, 23, 490–497. [Google Scholar] [CrossRef][Green Version]
  62. Santos, H.F.; Carmo, F.L.; Duarte, G.; Dini-Andreote, F.; Castro, C.B.; Rosado, A.S.; Van Elsas, J.D.; Peixoto, R.S. Climate change affects key nitrogen-fixing bacterial populations on coral reefs. ISME J. 2014, 8, 2272–2279. [Google Scholar] [CrossRef][Green Version]
  63. Wiedenmann, J.; D’Angelo, C.; Smith, E.G.; Hunt, A.N.; Legiret, F.-E.; Postle, A.D.; Achterberg, E.P. Nutrient enrichment can increase the susceptibility of reef corals to bleaching. Nat. Clim. Change 2013, 3, 160–164. [Google Scholar] [CrossRef]
  64. Sheppard, C.; Davy, S.; Pilling, G.; Graham, N. The Biology of Coral Reefs; Oxford University Press: New York, NY, USA, 2017. [Google Scholar]
  65. Marubini, F.; Atkinson, M. Effects of lowered pH and elevated nitrate on coral calcification. Mar. Ecol. Prog. Ser. 1999, 188, 117–121. [Google Scholar] [CrossRef]
  66. Margolin, C.L. Interactive Effects of Water Flow and Light Levels with Decreasing pH on the Growth and Survival of Tropical Cnidarians; University of Miami: Coral Gables, FL, USA, 2012. [Google Scholar]
  67. Strychar, K.B.; Hauff-Salas, B.; Haslun, J.A.; DeBoer, J.; Cryer, K.; Keith, S.; Wooten, S. Stress resistance and adaptation of the aquatic invasive species Tubastraea coccinea (Lesson, 1829) to climate change and ocean acidification. Water 2021, 13, 3645. [Google Scholar] [CrossRef]
  68. Kolian, S.R.; Sammarco, P.W.; Porter, S.A. Abundance of corals on offshore oil and gas platforms in the Gulf of Mexico. Environ. Manag. 2017, 60, 357–366. [Google Scholar] [CrossRef] [PubMed]
  69. Richmond, R.H.; Tisthammer, K.H.; Spies, N.P. The effects of anthropogenic stressors on reproduction and recruitment of corals and reef organisms. Front. Mar. Sci. 2018, 5, 226. [Google Scholar] [CrossRef][Green Version]
  70. Braga, M.D.A.; Paiva, S.V.; de Gurjão, L.M.; Teixeira, C.E.P.; Gurgel, A.L.A.R.; Pereira, P.H.C.; de Oliveira Soares, M. Retirement risks: Invasive coral on old oil platform on the Brazilian equatorial continental shelf. Mar. Pollut. Bull. 2021, 165, 112156. [Google Scholar] [CrossRef] [PubMed]
  71. BOEM. Bureau of Ocean Energy Management Governing Statutes. Available online: http://www.boem.gov/Regulations/BOEM-Governing-Statutes.aspx (accessed on 9 November 2021).
Water 14 01365 g001 550
Figure 1. Maps of (a) the study area, which includes the exclusive economic zone of the United States within the northern portion of the Gulf of Mexico, and (b) the records of Tubastraea coccinea, oil/gas platforms, and lanes of heavy shipping fairways within the northern Gulf of Mexico.
Figure 1. Maps of (a) the study area, which includes the exclusive economic zone of the United States within the northern portion of the Gulf of Mexico, and (b) the records of Tubastraea coccinea, oil/gas platforms, and lanes of heavy shipping fairways within the northern Gulf of Mexico.
Water 14 01365 g001
Water 14 01365 g002 550
Figure 2. Relative contribution (%) of the four variables used in our final model.
Figure 2. Relative contribution (%) of the four variables used in our final model.
Water 14 01365 g002
Water 14 01365 g003 550
Figure 3. Partial dependence plots for the four variables, (a) maximum salinity, (b) distance to nearest oil platform, (c) minimum nitrate, and (d) mean pH, included in the final model. The y-axis represents the logit scale used for the indicated variable; hash marks at the top of the plot indicate deciles of each variable.
Figure 3. Partial dependence plots for the four variables, (a) maximum salinity, (b) distance to nearest oil platform, (c) minimum nitrate, and (d) mean pH, included in the final model. The y-axis represents the logit scale used for the indicated variable; hash marks at the top of the plot indicate deciles of each variable.
Water 14 01365 g003
Water 14 01365 g004 550
Figure 4. Estimated probabilities of Tubastraea coccinea occurrence in the northern Gulf of Mexico.
Figure 4. Estimated probabilities of Tubastraea coccinea occurrence in the northern Gulf of Mexico.
Water 14 01365 g004
Table
Table 1. Units and descriptive statistics for the nine environmental variables and the two anthropogenic variables.
Table 1. Units and descriptive statistics for the nine environmental variables and the two anthropogenic variables.
VariableUnitMaximumMeanMinimum
Benthic
 Maximum current velocitym−11.29800.19150.0319
 Minimum dissolved oxygenmol m−3210.0137181.9234119.4268
 Minimum light at bottom-19.00541.49780
 Maximum salinityPSS36.794735.628530.8558
 Minimum ironμmol m−30.00730.00110.0004
 Minimum nitratemol m−327.570912.44560.0000
 Maximum primary productivityg m−3 day−10.13130.01140
Surface
 Mean calcitemol m−30.05600.00260.0001
 Mean pH-8.21398.11957.9690
Anthropogenic
 Distance to nearest oil platformm782,115167.22025
 Distance to nearest shipping fairwaym309.94981.06710
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Brockinton, E.E.; Peterson, M.R.; Wang, H.-H.; Grant, W.E. Importance of Anthropogenic Determinants of Tubastraea coccinea Invasion in the Northern Gulf of Mexico. Water 2022, 14, 1365. https://doi.org/10.3390/w14091365

AMA Style

Brockinton EE, Peterson MR, Wang H-H, Grant WE. Importance of Anthropogenic Determinants of Tubastraea coccinea Invasion in the Northern Gulf of Mexico. Water. 2022; 14(9):1365. https://doi.org/10.3390/w14091365

Chicago/Turabian Style

Brockinton, Emily E., Miranda R. Peterson, Hsiao-Hsuan Wang, and William E. Grant. 2022. "Importance of Anthropogenic Determinants of Tubastraea coccinea Invasion in the Northern Gulf of Mexico" Water 14, no. 9: 1365. https://doi.org/10.3390/w14091365

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop