Journal Search Engine
Search Advanced Search Adode Reader(link)
Download PDF Export Citaion korean bibliography PMC previewer
ISSN : 1226-9999(Print)
ISSN : 2287-7851(Online)
Korean J. Environ. Biol. Vol.38 No.1 pp.179-188
DOI : https://doi.org/10.11626/KJEB.2020.38.1.179

Prediction of potential habitats and distribution of the marine invasive sea squirt, Herdmania momus

Ju-Un Park1,3,*, Taekjun Lee1, Dong Gun Kim2, Sook Shin1,3,*
1Institute of Marine Life Resources, Sahmyook University, Seoul 01795, Republic of Korea
2Smith Liberal Arts College, Sahmyook University, Seoul 01795, Republic of Korea
3Department of Animal Biotechnology & Resource, College of Science and Technology, Sahmyook University, Seoul 01795, Republic of Korea
*Corresponding author Sook Shin Tel. 02-3399-1717 E-mail. shins@syu.ac.kr
06/03/2020 17/03/2020 18/03/2020

Abstract


The influx of marine exotic and alien species is disrupting marine ecosystems and aquaculture. Herdmania momus, reported as an invasive species, is distributed all along the coast of Jeju Island and has been confirmed to be distributed and spread to Busan. The potential habitats and distribution of H. momus were estimated using the maximum entropy (MaxEnt) model, quantum geographic information system (QGIS), and Bio-ocean rasters for analysis of climate and environment (Bio-ORACLE), which can predict the distribution and spread based only on species occurrence data using species distribution model (SDM). Temperature and salinity were selected as environmental variables based on previous literature. Additionally, two different representative concentration pathway (RCP) scenarios (RCP 4.5 and RCP 8.5) were set up to estimate future and potential habitats owing to climate change. The prediction of potential habitats and distribution for H. momus using MaxEnt confirmed maximum temperature as the highest contributor (77.1%), and mean salinity, the lowest (0%). And the potential habitats and distribution of H. momus were the highest on Jeju Island, and no potential habitat or distribution was seen in the Yellow Sea. Different RCP scenarios showed that at RCP 4.5, H. momus would be distributed along the coast of Jeju Island in the year 2050 and that the distribution would expand to parts of the Korea Strait by the year 2100. RCP 8.5, the distribution in 2050 is predicted to be similar to that at RCP 4.5; however, by 2100, the distribution is predicted to expand to parts of the Korea Strait and the East Sea. This study can be utilized as basic data to effectively control the ecological injuries by H. momus by predicting its spread and distribution both at present and in the future.



초록


    Ministry of Oceans and Fisheries
    20190518

    INTRODUCTION

    Marine invertebrates have shown high invasion rates in recent times (Lee et al. 2008;Robinson et al. 2011;Capinha et al. 2012), and have been increasing exponentially owing to human activities such as trade and aquaculture, thereby disrupting native ecosystems (Kerckhof et al. 2007;Meyerson and Mooney 2007;Vander Zanden et al. 2010). However, accurate field surveys are not available because of the wide range of oceanic characteristics and depths in the ocean, and it is difficult to accurately determine their distribution through direct field surveys (Kaschner et al. 2006;Valavanis et al. 2008;Franklin 2010). Also, owing to limited data availability and gathering of new collection data, characterization of the distribution and spread of these invasive species is problematic (Valavanis et al. 2008;Franklin 2010). These short-comings have been overcome with the development of tools such as GIS (Geographic Information System), SDMs (Species Distribution Model) that would provide information on marine ecosystems. These tools have enabled several studies on the distribution and prediction of various marine species such as fish, jellyfish, coral, crabs, and algae, including marine invasive species of tunicates (Maravelias and Reid 1997;Guinotte et al. 2006;Graham et al. 2007;Tittensor et al. 2009;Comptom et al. 2010;de Rivera et al. 2011;Tyberghein et al. 2012;Januario et al. 2015;Assis et al. 2018). SDMs are currently used to predict geographic distributions (Elith and Leathwick 2009). They are also used for biodiversity assessment, habitat restoration, and risk assessment of invasive species on land, freshwater, and marine ecosystems (Elith et al. 2006;Kwon et al. 2012;Lee et al. 2016). In addition, the development of GIS has led to the further expansion of species distribution prediction methods (Foody 2008;Swenson 2008).

    Recently, the maximum entropy (MaxEnt) model using presence only data of species has been used frequently. With the ability to predict the distribution of wild animals, plants and marine organisms even in the absence of sufficient data, this is presently a commonly used model not only in South Korea (Seo 2008; Park 2018) but also globally (Elith et al. 2006;Hernandez et al. 2006;Phillips et al. 2006;Wisz et al. 2008;Elith et al. 2011;Merow et al. 2013). Many recent studies have utilized the MaxEnt model (Radosavljevic and Anderson 2014) and more than 1,000 studies have been published since 2006, because of the advantages of accurate prediction of species distribution and easy programing features (Merow et al. 2013). Research on the distribution and spread of invasive species in Korea and abroad is being actively conducted on terrestrial organisms (Cho et al. 2015;Park et al. 2017); however, marine foreign species are not being investigated to the same extent (Robinson et al. 2011;Park et al. 2018).

    One of the marine invasive sea squirts, Herdmania momus, belonging to the class Ascidiacea and phylum Chordata, is a solitary ascidian and is distributed in tropical and temperate regions with the exception of the Eastern Pacific and Eastern Atlantic oceans (Kott 2005). Herdmania momus is known as an invasive species that migrated from the Red Sea to the Mediterranean Sea when the Suez Canal was opened (Shenkar and Loya 2008). In Korea, this alien species was first reported from Seogwipo in 1969 (Rho 1971), and was expected to compete with seashells and abalone that feed on algae and damage the aquaculture industry on Jeju Island (Yi and Kim 2016). Furthermore, it has been confirmed recently that H. momus was usually distributed in depth of 9-10 m, spread all over Jeju Island and distributed in Busan (Shin et al. 2013;Park 2019). Owing to rapid development and growth rates, H. momus is expected to increase the pace and widen the range of distribution. In Korea, studies on the spread and distribution of H. momus are insufficient, and there are no studies to predict its spread of distribution (Park 2019). This study is based on the MaxEnt model, which can predict the distribution of species using only presence information (Phillips et al. 2006, 2017), and QGIS was used to prepare the main map of H. momus and predict its potential habitats and distribution (QGIS Development Team 2018). Marine invasive species were known to be affected and distributed by rising temperatures due to climate change (Raitsos et al. 2010). Therefore, two different RCP scenarios, RCP 4.5 and RCP 8.5, were set to predict the potential habitats and distribution of H. momus for the future. Thus, this study was conducted to provide basic data on the distribution of H. momus and control of domestic marine ecosystems by predicting the spread and distribution of H. momus owing to climate change caused by a rise in temperatures.

    MATERIALS AND METHODS

    Collections of H. momus were done from 2010 to 2018 to check its occurrence status from a total 81 harbors: 33 harbors in the East Sea, 18 harbors in the South Sea, 14 harbors in the Yellow Sea and 16 harbors in the Jeju Island. The list of the harbors with geographical coordination was shown in appendix 1. However, H. momus samples were collected only from several harbors on Jeju Island and at Busan (Fig. 1, Table 1).

    Ascidians belonging to the phylum Chordata are known to be affected by temperature and salinity (Thiyagarajan and Qian 2003;Tyberghein et al. 2012;Januario et al. 2015;Kim et al. 2019). Based on the experimental results of egg development of H. momus (Park 2019), salinity (psu) and temperature (°C) were selected as environmental variables (Table 2). The data for environmental variables such as maximum temperature, average temperature, lowest temperature, highest salinity, average salinity, and lowest salinity were obtained from the Bio-ORACLE database (Tyberghein et al. 2012). Remotely sensed data were taken from various ocean-observing satellite sensors. The monthly satellite data was used (Aqua-MODIS, http://oceancolor.gsfc.nasa.gov/ for sea surface temperature; WOD09, Boyer et al. 2009 for Salinity) from 2010 to 2018 at a 5 arcmin (c. 9.2 km) spatial resolution (Tyberghein et al. 2012). 10-fold cross-validation was performed to increase the reliability of the model, and the Jackknife technique was used to identify the importance of environmental variables. All analyses used linear and quadratic features (other MaxEnt settings at their default value). The number of iterations was increased to 5,000 for model accuracy. Distribution maps were created using QGIS (version 2.18.16; QGIS Development Team 2018), and the MaxEnt program (version 3.4.1; Phillips et al. 2006, 2017) was used. Furthermore, two different RCP scenarios, RCP 4.5 (if most of greenhouse gas reduction policies were implemented) and RCP 8.5 (Extreme emissions scenario of greenhouse gases) were established for 2050 and 2100 (Stocker et al. 2013) for the prediction of potential future habitats and distribution of H. momus.

    RESULTS

    The results of the analysis using MaxEnt model show that area under curve (AUC) of the receiver operating characteristics (ROC) curve was 0.961 (Fig. 2), indicating that this predictive model shows high performance (Franklin 2010). Maximum temperature contributed the highest (77.1%) to potential habitats and distribution of H. momus at with a permutation importance index of 0.3%, and the lowest contribution (0.0%) was from the average salinity. Contributions of average and minimum temperatures were 9.7 and 9.2%, respectively, with permutation importance indexes of 21.3 and 75.2%, respectively. The maximum and minimum salinities contributed 1.6 and 2.4%, respectively, to the model with permutation importance indexes of 1.0 and 2.2%, respectively (Table 3). The permutation importance Jackknife analysis showed that the minimum, average, and maximum temperatures had considerably affected on distribution, and the highest salinity was also considered to be an important factor of distribution (Fig. 3). In addition, habitat distribution increased with increasing temperature, and the distribution was estimated to decrease when the salinity was beyond the optimum range in the response curve analysis (Fig. 4). According to the results of the prediction, the spread and distribution of the domestic H. momus are predicted to be most affected by temperature and salinity, which is consistent with the results reported for a sea squirt, Ciona robusta (Park 2019). Experimental results of the distribution and spread of H. momus, belonging to the same phylum as C. robusta, also showed temperature as the most influential environmental factor, and salinity was also reported as an important factor (Januario et al. 2015), which is consistent with the results of this study. The results of the present study indicate that the spread and distribution of H. momus were limited by temperature and salinity. Considering the annual salinity fluctuations of the East Sea (32-34 psu), the South Sea (31-34 psu) and the West Sea (28-32 psu), the salinity conditions of the Yellow Sea are not suitable for the distribution and spread of H. momus.

    DISCUSSION

    The results of this study showed that the spread of H. momus was influenced by temperature and salinity, and it was especially affected by temperature. In particular, the change of max temperature by climate change showed major influence of their spread and distribution, and the change of the average and minimum temperatures is considered as the sensitive factors. According to the results of the model, the distribution of H. momus in the RCP 4.5 and 8.5 scenarios by 2050 and in the RCP 4.5 scenario by 2100 along the entire coasts of Jeju Island and the South Sea was shown to be similar to that observed at present. However, in the RCP 8.5 scenario, the distribution would be further extended to the entire coastal areas of Jeju Island and the South Sea in 2100, and would spread further, especially to the coast of the East Sea. Since the spread of H. momus is expected to increase economic damage including that to aquaculture and marine industries, long-term monitoring of H. momus in the South Sea and East Sea, centered on the Busan area, is a priority.

    As climate change continues, the distribution and spread of H. momus is expected to increase economic damage, including damages to aquaculture and marine industries. The environmental variables used in this study were limited to temperature and salinity based on experiments on egg development and settlement of H. momus (Park 2019). However, chemical and physical variables such as pH, dissolved oxygen, nitrate, phosphate, chlorophyll, cloud cover etc. are considered as major environmental factors (Tyberghein et al. 2012). Therefore, to predict potential habitats and distribution more accurately, modeling with salinity and temperature, as well as other environmental variables should be performed. The results of this study showed the potential of spread of the H. momus by climate change, and are expected to be used as basic data for predicting the distribution of H. momus. To achieve effective management and prevention of damages, the continuous field survey and monitoring are required.

    ACKNOWLEDGEMENTS

    This research was a part of the project titled ‘Improvement of management strategies on marine disturbing and harmful organisms’ funded by the Ministry of Oceans and Fisheries, Korea (No. 20190518).

    Figure

    KJEB-38-1-179_F1.gif

    A map showing the presence sites of Herdmania momus in Korea. 1. Jeju, 2. Jocheon, 3. Gimnyeong, 4. Jongdal, 5. Seongsanpo, 6. Pyoseon, 7. Wimi, 8. Seogwipo, 9. Munseom, 10. Mosulpo, 11. Hanrim, 12. Ongpo, 13. Aewol, 14. Dodu. 15. Passenger terminal, Busan, 16. Sinseondae, Busan.

    KJEB-38-1-179_F2.gif

    Area under curve (AUC) obtained in the receiver operating characteristic (ROC) curve with the species distribution model of Herdmania momus.

    KJEB-38-1-179_F3.gif

    Jackknife test of Herdmania momus in this study.

    KJEB-38-1-179_F4.gif

    Response curves of Herdmania momus for selected environmental variables.

    KJEB-38-1-179_F5.gif

    Potential habitats and distribution of Herdmania momus predicted by RCP scenarios.

    Table

    The list of the 81 harbors with geographical coordination selected for this study

    Collected sites of Herdmania momus in Korea

    Environmental variables used in this study

    Analysis of percent contribution and permutation importance of environmental variables for the prediction of potential habitats of Herdmania momus

    Reference

    1. Assis J, L Tyberghein, S Bosch, H Verbruggen, EA Serrão and O de Clerck. 2018. Bio -ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Glob. Ecol. Biogeogr. 27:277-284.
    2. Beaugrand G, M Edwards, K Brander, C Luczak and F Ibanez. 2008. Causes and projections of abrupt climate-driven ecosystem shifts in the North Atlantic. Ecol. Lett. 11:1157-1168.
    3. Capinha C, P Anastácio and JA Tenedório. 2012. Predicting the impact of climate change on the invasive decapods of the Iberian inland waters: an assessment of reliability. Biol. Invasions 14:1737-1751.
    4. Cho KH and SH Lee. 2015. Prediction of changes in the potential distribution of a waterfront alien plant, Paspalum distichum var. indutum, under climate change in the Korean Peninsula. Ecol. Resil. Infrastruct. 2:206-215.
    5. Compton TJ, JR Leathwick and GJ Inglis. 2010. Thermogeography predicts the potential global range of the invasive European green crab (Carcinus maenas). Divers. Distrib. 16:243-255.
    6. Davies AJ, M Wisshak, JC Orr and JM Roberts. 2008. Predicting suitable habitat for the cold-water coral Lophelia pertusa (Scleractinia). Deep-Sea Res. PT I. 55:1048-1062.
    7. de Rivera CE, BP Steves, PW Fofonoff, AH Hines and GM Ruiz. 2011. Potential for high-latitude marine invasions along western North America. Divers. Distrib. 17:1198-1209.
    8. Dukes JS and HA Mooney. 1999. Does global change increase the success of biological invaders? Trends Ecol. Evol. 14:135-139.
    9. Elith J, CH Graham, RP Anderson, M Dudík, S Ferrier, A Guisan and J Li. 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecography 29:129-151.
    10. Elith J and JR Leathwick. 2009. Species distribution models: ecological explanation and prediction across space and time. Ann. Rev. Ecol. Evol. Syst. 40:677-697.
    11. Elith J, SJ Phillips, T Hastie, M Dudík, YE Chee and CJ Yates. 2011. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17:43-57.
    12. Foody GM. 2008. GIS: biodiversity applications. Prog. Phys. Geog. 32:223-235.
    13. Franklin J. 2010. Mapping Species Distributions: Spatial Inference and Prediction. Cambridge University Press, Cambridge, UK.
    14. Graham MH, BP Kinlan, LD Druehl, LE Garske and S Banks. 2007. Deepwater kelp refugia as potential hotspots of tropical marine diversity and productivity. Proc. Natl. Acad. Sci. USA 104:16576-16580.
    15. Guinotte JM, JD Bartley, A Iqbal, DG Fautin and RW Buddemeier. 2006. Modeling habitat distribution from organism occurrences and environmental data: case study using anemonefishes and their sea anemone hosts. Mar. Ecol. Prog. Ser. 316:269-283.
    16. Hernandez PA, CH Graham, LL Master and DL Albert. 2006. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29:773-785.
    17. Januario SM, SA Estay, FA Labra and M Lima. 2015. Combining environmental suitability and population abundances to evaluate the invasive potential of the tunicate Ciona intestinalis along the temperate South American coast. Peer J. 3:e1357.
    18. Kaschner K, R Watson, AW Trites and D Pauly. 2006. Mapping world-wide distributions of marine mammal species using a relative environmental suitability (RES) model. Mar. Ecol. Prog. Ser. 316:285-310.
    19. Kerckhof F, J Haelters and S Gollasch. 2007. Alien species in the marine and brackish ecosystem: the situation in Belgian waters. Aquat. Invasions 2:243-247.
    20. Kim MK, DH Kim, JU Park, DH Kim, TJ Yoon, DG Kim and S Shin. 2019. Effects of temperature and salinity on the egg development and larval settlement of Ciona robusta (Ascidiacea, Phlebobranchia, Cionidae). Ocean Sci. J. 54:97-106.
    21. Kott P. 2005. The genus Herdmania Lahille, 1888 (Tunicata, Ascidiacea) in Australian waters. Zool. J. Linn. Soc.-Lond. 134:359-374.
    22. Kwon HK, JE Ryu, CG Seo, JY Kim, DO Lim and MH Suh. 2012. A study on distribution characteristics of Corylopsis coreana using SDM. J. Environ. Impact Assess. 21:735-743.
    23. Lee H, DA Reusser, JD Olden, SS Smith, J Graham, V Burkett, JS Dukes, RJ Piorkowski and J McPhedran. 2008. Integrated monitoring and information systems for managing aquatic invasive species in a changing climate. Conserv. Biol. 22:575-584.
    24. Lee SH, HK Cho and WJ Lee. 2016. Prediction of potential distributions of two invasive alien plants, Paspalum distichum and Ambrosia artemisiifolia, using species distribution model in Korean Peninsula. Ecol. Resil. Infrastruct. 3:189-200.
    25. Maravelias CD and DG Reid. 1997. Identifying the effects of oceanographic features and zooplankton on prespawning herring abundance using generalized additive models. Mar. Ecol. Prog. Ser. 147:1-9.
    26. Merow C, MJ Smith and JA Silander Jr. 2013. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36:1058-1069.
    27. Meyerson LA and HA Mooney. 2007. Invasive alien species in an era of globalization. Front. Ecol. Environ. 5:199-208.
    28. Park HC, JC Lim, JH Lee and GG Lee. 2017. Predicting the potential distributions of invasive species using the Landsat imagery and Maxent: Focused on “Ambrosia trifida L. var. trifida” in Korean demilitarized zone. J. Korean Env. Res. Tech. 20:1-12.
    29. Park JU, J Hong, DG Kim, TJ Yoon and S Shin. 2018. Prediction of the suitable habitats of marine invasive species, Ciona robusta based on RCP scenarios. Korean J. Environ. Biol. 36: 687-693.
    30. Park JU. 2019. Effects of temperature and salinity on the egg development and the prediction of potential habit of Herdmania momus. MS thesis. Sahmyook University, Seoul. pp. 1-80.
    31. Phillips SJ, RP Anderson and RE Schapire. 2006. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190:231-259.
    32. Phillips SJ, M Dudk and RE Schapire. 2017. Maxent software for modeling species niches and distributions (Version 3.4.1). Available from: http://biodiversityinformatics.amnh.org/open_source/maxent/
    33. QGIS Development Team. 2018. QGIS Geographic Information System. Open Source Geospatial Foundation Project. Available at https://www.qgis.org/en/site/
    34. Radosavljevic A and RP Anderson. 2014. Making better Maxent models of species distributions: complexity, overfitting and evaluation. J. Biogeogr. 41:629-643.
    35. Raitsos DE, G Beaugrand, D Georgopoulos, A Zenetos, AM Pancucci -Papadopoulou, A Theocharis and E Papathanassiou. 2010. Global climate change amplifies the entry of tropical species into the Eastern Mediterranean Sea. Limnol. Oceanogr. 55:1478-1484.
    36. Rho BJ. 1971. A study on the classification and the distribution of the Korean ascidians. J. Korean Res. Inst. Bet. Liv. 6:103-166.
    37. Robinson LM, J Elith, AJ Hobday, RG Pearson, BE Kendall, HP Possingham and AJ Richardson. 2011. Pushing the limits in marine species distribution modelling: lessons from the land present challenges and opportunities. Glob. Ecol. Biogeogr. 20:789-802.
    38. Shenkar N and Y Loya. 2008. The solitary ascidian Herdmania momus: native (Red Sea) versus non-indigenous (Mediterranean) populations. Biol. Invasions 10:1431-1439.
    39. Shin S, JH Park, JS Lee, IH Kim, JE Seo, HS Kim, GS Min and SH Kim. 2013. Marine Introduced Benthos of Korea. Ministry of Oceans and Fisheries. Sejong, Korea. pp. 1-102.
    40. Stocker TF, D Qin, GK Plattner, MMB Tignor, SK Allen, J Boschung, A Nauels, Y Xia, V Bex and PM Midgley. 2013. The physical Science Basis Working Group I. Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK. pp. 1-115.
    41. Swenson NG. 2008. The past and future influence of geographic information systems on hybrid zone, phylogeographic and speciation research. J. Evol. Biol. 21:421-434.
    42. Thiyagarajan V and PY Qian. 2003. Effect of temperature, salinity and delayed attachment on development of the solitary ascidian Styela plicata (Lesueur). J. Exp. Mar. Biol. Ecol. 290:133-146.
    43. Tittensor DP, AR Baco, PE Brewin, MR Clark, M Consalvey, J Hall -Spencer and AD Rogers. 2009. Predicting global habitat suitability for stony corals on seamounts. J. Biogeogr. 36:1111-1128.
    44. Tyberghein L, H Verbruggen, K Pauly, C Troupin, F Mineur and O De Clerck. 2012. Bio-Orcale: a global environmental dataset for marine species distribution modelling. Glob. Ecol. Biogeogr. 21:272-281.
    45. Valavanis VD, GJ Pierce, AF Zuur, A Palialexis, A Saveliev, I Katara and J Wang. 2008. Modelling of essential fish habitat based on remote sensing, spatial analysis and GIS. pp. 5-20. In: Essential Fish Habitat Mapping in the Mediterranean. Springer, Dordrecht, Netherlands.
    46. Vander Zanden MJ, GJ Hansen, SN Higgins and MS Kornis. 2010. A pound of prevention, plus a pound of cure: early detection and eradication of invasive species in the Laurentian Great Lakes. J. Great Lakes Res. 36:199-205.
    47. Wisz MS, RJ Hijmans, J Li, AT Peterson, CH Graham, A Guisan and NCEAS Predicting Species Distributions Working Group. 2008. Effects of sample size on the performance of species distribution models. Divers. Distrib. 14:763-773.
    48. Yi CH and JH Kim. 2016. Population dynamics of the solitary Ascidian Herdmania momus (Savignyi, 1816) in Jeju Island, Korea. Ocean Sci. J. 51:363-371.