Abstract

Therriault, T. W., and Herborg, L-M. 2008. Predicting the potential distribution of the vase tunicate Ciona intestinalis in Canadian waters: informing a risk assessment. – ICES Journal of Marine Science, 65: 788–794.

A crucial step in characterizing the potential risk posed by non-native species is determining whether a potential invader can establish in the introduced range and what its potential distribution could be. To this end, various environmental models ranging from simple to complex have been applied to predict the potential distribution of an invader, with varying levels of success. Recently, in marine waters, tunicates have received much attention, largely because of their negative impacts on shellfish aquaculture. One of these species is the vase tunicate Ciona intestinalis, which recently has had a negative impact on aquaculture operations in Atlantic Canada and could pose a risk in Pacific Canada. To inform the risk assessment of this species, we evaluated two different types of environmental model. Simple models based on reported temperature or salinity tolerances were relatively uninformative, because almost all waters were deemed suitable. In contrast, a more complex genetic algorithm for rule-set prediction (GARP) environmental niche model, based on documented Canadian occurrence points, provided informative projections of the potential distribution in Canadian waters. In addition to informing risk assessments, these predictions can be used to focus monitoring activities, particularly towards vectors that could transport C. intestinalis to these favourable environments.

Introduction

Non-indigenous species (NIS) pose an enormous risk to native biodiversity and can compromise ecosystem function (Sala et al., 2000). For marine ecosystems, non-indigenous tunicates have become a global concern because they can displace native species and foul aquaculture gear (Mazouni et al., 2001; Lambert and Lambert, 2003; Blum et al., 2007). To characterize the potential risk posed by a new invader or the spread of an existing invader to additional waters, typically a formal risk assessment is conducted where the potential for arrival, survival, reproduction, and spread beyond the area of introduction is characterized (Mandrak and Cudmore, 2004). The probability of arrival is a measure of a species’ introduction potential and requires the presence of suitable vectors and pathways (Colautti and MacIsaac, 2004). In contrast, the probability of survival and reproduction is primarily associated with the physiological tolerances of the species and characteristics of the receiving environment (Guisan and Thuiller, 2005), or secondarily associated with community interactions (Shea and Chesson, 2002). Environmental modelling is one approach used to characterize locations where a species could survive, relative to those where it could not, provided vectors and pathways are available. Therefore, predicting the potential distribution of a NIS is a critical component of the risk assessment.

Predicting the potential distribution of organisms, based on biological traits, habitat, and environmental and distributional data, has been attempted for a variety of taxa (e.g. Goodwin et al., 1999; Ruesink, 2005; Kaschner et al., 2006). However, most studies of this type have suggested data limitations that may compromise model performance. Therefore, more robust approaches are sought, such as the development of environmental niche models to predict suitable locations (Drake and Bossenbroek, 2004; Iguchi et al., 2004; Guinotte et al., 2006; Herborg et al., 2007). Despite the increasing availability of oceanographic data, surprisingly few marine taxa have been modelled, notably sessile organisms such as tunicates. The vase tunicate Ciona intestinalis is a tunicate species that could pose a risk to Canadian waters. It is a large, solitary tunicate that is found in dense aggregations, often as a dominant member of the epibenthic community, especially in enclosed or semi-protected marine embayments such as harbours or marinas (Monniot and Monniot, 1994; Lambert and Lambert, 2003; Ramsay et al., in press). Although often considered a member of the subtidal deep-water fauna (Brunel et al., 1998), many populations have been reported in shallow coastal waters, most notably fouling artificial structures (Cohen et al., 2000; Lambert and Lambert, 2003) or aquaculture gear (Mazouni et al., 2001; Carver et al., 2003).

Ciona intestinalis is tolerant of a wide range of environmental conditions, partially evidenced by its cosmopolitan distribution. Reported temperature tolerance varies among populations, but C. intestinalis is generally considered a cold-water or temperate species, although temporary or transient populations have been reported from tropical harbours (Monniot and Monniot, 1994). Dybern (1965) identified 30°C as an upper threshold for the species, but Petersen and Riisgård (1992) noted that filtration rates decreased >21°C, suggesting thermal stress at higher temperatures. Generally, adult mortality increases <10°C, but in Atlantic Canada, populations have survived for several months at temperatures around −1°C (Carver et al., 2003). As a euryhaline species, C. intestinalis is tolerant of a wide range of salinities (12–40), although most populations are found in water of <30 salinity, because this species cannot withstand extended periods in waters with salinity <11 (Dybern, 1967), although it may be able to withstand short-term fluctuations in salinity.

For NIS, it is beneficial if the native range is known, especially for understanding the potential risks posed by an invader, including its potential distribution. However, the native range for C. intestinalis has not been resolved and is the focus of continuing debate. The species ranges from the Mediterranean Sea to Scandinavia and Greenland (Millar, 1966), causing some to suggest that the species should be classified as cryptogenic (NIMPIS, 2002; J. T. Carlton, pers. comm.), at least until the native range can be resolved. Huntsman (1912) listed C. intestinalis as a member of the benthic invertebrate community on the west coast of Canada, but it is probable that specimens of Ciona savignyi, an Asian native, were misidentified at that time (Lambert, 2003). Ramsay et al. (in press) suggest that C. intestinalis is non-indigenous to bays around Prince Edward Island, but the species has been reported at other locations in eastern Canada (Carver et al., 2003). Until further taxonomic and/or genetic studies can confirm its native range, we consider C. intestinalis to be cryptogenic in Atlantic Canadian waters and non-indigenous in Pacific Canadian waters. The species has been widely introduced to parts of South America, Africa, Oceania, and Asia (Millar, 1966; NIMPIS, 2002; Figure 1), primarily by shipping as a hull-fouling organism (Monniot and Monniot, 1994).

Figure 1.

Global distribution of Ciona intestinalis, showing countries where the species should be considered cryptogenic (grey) and countries where the species has invaded (black).

Figure 1.

Global distribution of Ciona intestinalis, showing countries where the species should be considered cryptogenic (grey) and countries where the species has invaded (black).

We evaluate different environmental models that could be used to inform risk assessments (among other activities) by predicting the potential distribution of C. intestinalis in Canadian waters. Specifically, we compare simple models based on reported environmental tolerances (temperature and salinity, see below) and a genetic algorithm for rule-set prediction (GARP) environmental niche model, based on the known invaded distribution in Canadian waters.

Material and methods

Environmental variables

Data on ocean temperature, salinity, and dissolved oxygen were extracted from the National Oceanographic and Atmospheric Administration’s World Ocean Database (www.nodc.noaa.gov/OC5/indprod.html), to a depth of 50 m. Temperature and salinity data were divided into four seasons based on sampling months (January–March, April–June, July–September, and October–December) using Ocean Data View 3.2.0. Seasonal temperature and salinity, and annual oxygen data were converted into a continuous data layer by calculating the mean value of each variable over the 50 m using inverse distance weighting (Geostatistical Wizard within ArcMAP 9.1). Annual surface chlorophyll a data were obtained from the “Integrating Multiple Demands on Coastal Zones with Emphasis on Aquatic Ecosystems and Fisheries” project database (www.incofish.org). The resulting ten environmental layers, January–March temperature, April–June temperature, July–September temperature, October–December temperature, January–March salinity, April–June salinity, July–September salinity, October–December salinity, annual oxygen, and annual chlorophyll a, all at 0.01° resolution, were then mapped for the simple models or compared with occurrence data for C. intestinalis in the environmental niche model. For both coasts, predictions were made from 45°N to 60°N for Canadian coastal waters, where depth was bounded at the 200 m contour.

Predicting suitable environments

The potential range or distribution of C. intestinalis was predicted using two approaches: simple models based on reported environmental tolerances and an environmental niche (GARP) model. We used seasonal temperature and salinity data to identify potential suitable habitats based on literature reports of temperature and salinity tolerances: temperature between −1°C and 30°C, and salinity between 12 and 40 (see above). These reported temperature and salinity tolerances represent the maximum range from populations around the globe, including cryptogenic and invaded distributions. Also, we combined the simple temperature and salinity models to create a combined model, for which any area deemed unsuitable in either the temperature or salinity model for each season was deemed unsuitable in the combined model.

In addition to the simple models, we explored the use of a more complex environmental niche model, a GARP model, to predict suitable environments based on the current Canadian distribution. GARP models were constructed using species presence data from the current Canadian range, and georeferenced environmental data. As C. intestinalis has not been reported from Canadian Pacific coastal waters, Canadian east coast data were used to predict the potential west coast distribution, in addition to the potential east coast distribution. Recent georeferenced occurrence points (n = 53) were available from 2004, based on continuing monitoring and research activities conducted by Fisheries and Oceans Canada (D. Sephton, B. Vercaemer, J. Martin, and R. Bernier, pers. comm.).

All ten environmental layers were tested separately for their contribution to the GARP models using multiple linear regression following Drake and Bossenbroek (2004). The number of presence points correctly predicted by GARP provided a measure of model accuracy (Peterson and Cohoon, 1999). Variables positively correlated with omission errors (false negatives) were rejected. Environmental variables that contributed significantly to model prediction accuracy were created following the best subset procedure described by Anderson et al. (2003). GARP was set for 100 predictions using a 0.001 convergence limit and a maximum of 3000 iterations (per simulation) with a 5% “hard” omission and a 50% commission threshold. Resulting predictions were converted into a map of percentage environmental suitability using the raster calculator in ArcMAP 9.1.

Evaluating model performance

Herborg et al. (2007) identified additional tests to validate GARP model predictions further. Hierarchical partitioning measures the relative contribution of each environmental layer, using all possible combinations of environmental variables included in the final GARP prediction, as well as its associated accuracy (Peterson and Cohoon, 1999). In addition, we used the evaluation strip method to determine the actual range of environmental conditions deemed suitable by the model, following the procedure described by Elith et al. (2005). This approach is based on the insertion of columns containing the full range of values for each environmental parameter into each environmental layer. The number of models that predict each value for the environmental parameters deemed suitable can then be used to identify suitable environmental ranges (Elith et al., 2005).

As an independent measure of model performance, we calculated the area under the receiver operating characteristic curve (AUC), a widely used measure of the ability of a model to discriminate between sites where a species is present or absent (Hanley and McNeil, 1982; Wiley et al., 2003; Elith et al., 2006). The AUC measures predictive accuracy on a scale between 0 and 1, where 1 represents perfect prediction for both presence and absence points, and 0.5 represents a no-better-than-random predictive ability. The AUC was based on occurrence points used for the GARP prediction and an equal number of randomly selected absence points obtained from the area within the analysis mask. The GARP score from the 100 best subsets used in the final prediction were extracted for each of these points, and the AUC was calculated using the “verification” package in R 2.5.0 software (R Development Core Team, 2007). As C. intestinalis has been reported only for the east coast of Canada, this test only applies to east coast predictions. Further, we applied the AUC to the temperature and salinity simple models and the combined simple model based on reported temperature and salinity tolerances to compare GARP model performance with these models.

Results

Potential distribution in Canada

Based on literature accounts of environmental tolerances, the potential distribution for C. intestinalis in Canadian waters was extremely broad. Winter temperatures (January–March) in Atlantic Canada determined the most unsuitable habitat in the combined model. No additional unsuitable locations in eastern Canadian waters were identified, based on the remaining three seasonal temperature models or the four seasonal salinity models. For Atlantic Canada, the combined model identified unsuitable environments northwest of the Magdalen Islands to the New Brunswick coast and through the northwest part of Northumberland Strait between New Brunswick and Prince Edward Island (Figure 2a). Additional pockets of unsuitable environment existed in the northern Gulf of St Lawrence (around Anticosti Island), the north coast of Labrador, waters between Labrador and Newfoundland, and waters off the northeast and southeast coasts of Newfoundland (Figure 2a). On the west coast of Canada, there were no unsuitable environments identified in Canadian waters, based on temperature or salinity, for any of the four seasons considered. The combined model identified all coastal British Columbian waters as suitable (Figure 2b).

Figure 2.

Simple ecological niche models based on combined seasonal temperature and salinity data used to identify suitable (dark grey) and unsuitable (light grey) environments, based on reported tolerances for (a) the Atlantic coast, and (b) the Pacific coast.

Figure 2.

Simple ecological niche models based on combined seasonal temperature and salinity data used to identify suitable (dark grey) and unsuitable (light grey) environments, based on reported tolerances for (a) the Atlantic coast, and (b) the Pacific coast.

Canadian sites in which C. intestinalis is currently found along the Atlantic coast were used to generate GARP model predictions. On the Atlantic coast, waters along the Atlantic coast of Nova Scotia extending around Cape Breton Island and into the southern Gulf of St Lawrence to the Gaspé Peninsula, including the Magdalen Islands, are predicted to have high environmental suitability, as are the waters around the Bay of Fundy between Nova Scotia and New Brunswick (Figure 3a). Favourable conditions also exist on the north and west coasts of Prince Edward Island and around the southwest corner of Newfoundland (Figure 3a). A slightly lower environmental suitability exists for deeper waters off the coast of Nova Scotia and the south coast of Prince Edward Island (Figure 3a). On the Pacific coast, the highest environmental suitability exists along the outer coast of British Columbia’s central and north coasts, almost continuous from Johnstone Strait to the border with Alaska (Figure 3b). Moderate to high environmental suitability also exists along the north and northeast coasts of the Queen Charlotte Islands (Figure 3b). Ciona intestinalis has a very low environmental match throughout the Strait of Georgia (Figure 3b).

Figure 3.

Environmental suitability predicted by GARP ecological niche models for (a) the Atlantic coast, and (b) the Pacific coast. Environmental suitability is displayed as the number (out of 100) of models that predicted a particular location to be suitable.

Figure 3.

Environmental suitability predicted by GARP ecological niche models for (a) the Atlantic coast, and (b) the Pacific coast. Environmental suitability is displayed as the number (out of 100) of models that predicted a particular location to be suitable.

Model performance

The AUC test for the simple combined temperature and salinity model demonstrated that predictive capability was better than random (score 0.7738; p < 0.001). The unsuitable environments determined by the combined model were largely influenced by the winter temperature layer for the east coast because, when the AUC test was applied to the temperature-only model, results were similar (score 0.7738; p < 0.001), whereas the salinity-only model was not significant (score 0.7371; p = 0.0951). The GARP model had significantly greater predictive capacity than the simple models based on reported tolerances, because the AUC test for the east coast GARP model revealed a significant increase in predictive capability (score 0.9694; p < 0.0001).

All environmental variables contributed significantly to the overall GARP model accuracy. Except for the spring salinity layer, the remaining nine environmental layers contributed relatively uniformly to the overall model, with contributions between 8% and 16% (Table 1). Although there were slight seasonal differences, the values determined using the environmental strips were consistent with reported literature tolerances, but did not approach the maximum values reported (Table 1).

Table 1.

Environmental variables used to predict potential east and west coast distributions of Ciona intestinalis.

Variable Hierarchical partitioning (%) Environmental suitability 
January–March temperature (°C) 12.9 −0.6–3.7 
April–June temperature (°C) 9.2 3.3–8.2 
July–September temperature (°C) 8.8 9.3–17.4 
October–December temperature (°C) 15.4 7.2–12.7 
January–March salinity 8.1 30.8–32.4 
April–June salinity 0.9 27.4–31.3 
July–September salinity 8.2 24.4–32.1 
October–December salinity 13.5 28.6–31.8 
Annual oxygen (mg l–111.2 4.6–6.5 
Annual chlorophyll a (mg l–111.9 1 057–1 957 
Variable Hierarchical partitioning (%) Environmental suitability 
January–March temperature (°C) 12.9 −0.6–3.7 
April–June temperature (°C) 9.2 3.3–8.2 
July–September temperature (°C) 8.8 9.3–17.4 
October–December temperature (°C) 15.4 7.2–12.7 
January–March salinity 8.1 30.8–32.4 
April–June salinity 0.9 27.4–31.3 
July–September salinity 8.2 24.4–32.1 
October–December salinity 13.5 28.6–31.8 
Annual oxygen (mg l–111.2 4.6–6.5 
Annual chlorophyll a (mg l–111.9 1 057–1 957 

Their relative contribution to prediction accuracy was determined by hierarchical partitioning. The environmental suitability range for each variable where model accuracy increased is also shown. Reported literature tolerances were −1°C to 30°C, and salinity 12–40.

Discussion

We applied simple and complex environmental niche models to predict the potential distribution of C. intestinalis in Canadian waters. The advantage of these models is that they can be used to guide management options for NIS (Drake and Bossenbroek, 2004; Roura-Pascual et al., 2004; Herborg et al., 2007). Further, identifying potentially suitable habitat is a crucial component of most risk assessment methodologies for NIS (Pheloung et al., 1999). However, the choice of environmental model is critical, especially when resources are limited. The extremely broad potential ranges that were projected, based on reported temperature and salinity tolerances for C. intestinalis, were relatively uninformative. These simple models depend on field observations or experiments that do not necessarily mimic the environment in which the species actually lives. Although we considered seasonal temperature and salinity, the simplistic approach does not capture other environmental or biotic interactions that could be taking place. In contrast, the GARP model develops its own environmental niche definition based on occurrence points and the environmental conditions encountered there. This allows prediction of environmental ranges, even for species whose physiological tolerances are not fully understood. Further, perhaps biotic interactions at these distribution points are influenced by the environmental variables included in the GARP model. Therefore, the GARP approach provides focus for potential management options by identifying areas where monitoring efforts should be directed and providing a basis for evaluating potential vectors and pathways.

Environmental niche modelling in the marine environment is relatively rare. This could be the result, at least in part, of limited georeferenced occurrence data. Mapping large-scale distributions of marine mammals, Kaschner et al. (2006) noted that a lack of sightings data precluded application of standard approaches, whereas a relative environmental suitability model performed well. Similarly, when anemonefish data were limiting, Guinotte et al. (2006) used environmental data for the host anemone to map the potential distribution of the symbiont. Using occurrence data for C. intestinalis here, we were able to identify potentially suitable environments in both Atlantic Canada, where the species is already present, and Pacific Canada, where it has yet to invade. The advantage of combining occurrence and environmental data is the ability to predict the presence (or absence) of a species (Wiley et al., 2003), which is important not only for biological invasions but also for the conservation of marine organisms.

The simple environmental models, based on reported temperature and salinity tolerances, likely represent multiple genetic strains of C. intestinalis (if they exist), because such data are derived from a number of populations. The more complex environmental niche model based on Canadian occurrence points may or may not represent a common genetic strain. However, given the available sample size and the spatial distribution of the occurrence points, it is likely that we have captured the realized niche of this species, at least sufficiently to be useful for making predictions. There are a number of examples where different distributions have been noted based on different genetic strains. For example, it is believed that European green crab (Carcinus maenas) introductions in North America represent two genetically different strains (Roman, 2006). The initial introduction was a warmer-water strain, and the second appears to be more cold-water tolerant and has spread farther north in Atlantic Canada. Similarly, Therriault et al. (2004) identified a fresh-water and brackish water race of Dreissena rostriformis. If the reported tolerances encompass more than one strain or race, then the potential distribution will represent the combined tolerances of all genetic strains or races, so potentially overestimating the tolerances associated with a single strain or race. However, this may represent a worst case scenario from a prediction perspective, because all strains or races have contributed to the realized niche for the species and would have utility for risk assessments because the genetic strain(s) of an invader often is (are) unknown. Simple models based on environmental tolerances within C. intestinalis’ global distribution would likely over-predict the potential distribution in Canada. Alternatively, GARP models based on the current invaded range in Canada identify suitable environments for the introduced strain(s), but predictions will need updating as the species spreads so as not to under-predict the potential distribution. It is unclear how many potential genetic varieties or races of C. intestinalis exist, but literature accounts mention at least three forms (forma typica, forma gelatinosa, and forma longissima), which could have different environmental tolerances or habitat preferences, because forma gelatinosa and forma longissima are reported as Arctic to Subarctic forms (Millar, 1966).

Although C. intestinalis is considered to be cryptogenic in Atlantic Canada, there is considerable potentially suitable habitat along the Atlantic coast that is not currently known to support populations. Much of this potential habitat exists along the Atlantic coast of Nova Scotia, but it also includes additional areas around Prince Edward Island and the southeastern corner of Newfoundland. Ciona intestinalis has already been reported as an aquaculture pest in Nova Scotia (Carver et al., 2003), and it is a species of concern for the shellfish aquaculture industry in Prince Edward Island (Ramsay et al., in press). Additional spread of C. intestinalis in eastern Canada should be expected, with potentially suitable environments being a priority for monitoring, especially areas with increased densities of artificial structures that can facilitate population establishment (Carver et al., 2003; Lambert and Lambert, 2003). On the Pacific coast of Canada, where C. intestinalis has not invaded, much of the suitable environment exists between Johnstone Strait and the British Columbia–Alaska border, consistent with the characterization of C. intestinalis as a colder-water species (Brunel et al., 1998). The Strait of Georgia, one of the most invaded habitats in British Columbia (Levings et al., 2002), appears largely unsuitable for C. intestinalis.

Distributional models of NIS help inform risk assessments that can serve as a basis for decision-makers, managers, and policy-makers, who have to manage, control, or mitigate the potential impact of NIS (for the C. intestinalis risk assessment, see Therriault and Herborg, 2008). Based on limited occurrence data, we were able to apply an environmental niche model that identified potentially suitable environments for C. intestinalis on both coasts of Canada, with increased resolution over simple tolerance models. The refined predictive ability of this model should make it easier for managers to allocate resources. It should be expected that risk assessments will need to be revisited from time to time as more information is gained and identified data gaps are filled. For example, inclusion of additional data points from its cryptogenic or invaded range might refine the actual environmental niche of C. intestinalis. Nonetheless, it is unlikely that the actual habitat suitable for C. intestinalis would be as broad as that predicted by the simple environmental models based on reported temperature or salinity tolerances.

Acknowledgements

D. Sephton, B. Vercaemer, J. Martin, and R. Bernier each graciously provided C. intestinalis occurrence points that allowed us to conduct our GARP modelling. The work was supported by funds from DFO’s AIS programme and funds from the Centre of Expertise for Aquatic Risk Assessment (CEARA). We thank the participants from the Charlottetown, Prince Edward Island, tunicate meeting for helpful advice, comments, and discussion, and the reviewers for their input, which greatly improved the manuscript.

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