1 INTRODUCTION

Non-indigenous species (NIS) are one of the biggest threats to global biodiversity (Bax et al., 2003; Molnar et al., 2008), and understanding their potential distributions in the near future is a priority for biodiversity managers (Guisan et al., 2013; Sinclair et al., 2010). Preventing the establishment of NIS is recognized as the most efficient way of reducing their impact on ecosystems (Simberloff et al., 2013), and analyses predicting habitat suitability of NIS are fundamental for preventing NIS introductions and controlling their spread (Jiménez-Valverde et al., 2011; Leidenberger et al., 2015; Therriault & Herborg, 2008). Ecological niche models (ENMs) are popular mathematical models used to increase our understanding of suitable habitats and predicting areas that may be at risk of invasion (Mainali et al., 2015; Thuiller et al., 2005; Václavík & Meentemeyer, 2012). In addition, ENMs forecast range expansions and contractions under contemporary climate change (CCC) conditions (Elith et al., 2010; Elith & Leathwick, 2009) such as poleward range shifts (Cheung et al., 2009; Jones & Cheung, 2015; Jueterbock et al., 2016; Saupe et al., 2014). In the marine environment, range shifts are an order of magnitude faster than terrestrial ecosystems (Sorte et al., 2010), and thus investigating potential marine NIS distributions and areas at risk of invasion with CCC is of high importance. By the end of the century, the oceans will experience environmental conditions without modern analogue due to CCC (Williams & Jackson, 2007) and this is expected to lead to the creation of novel ecological communities (Pecl et al., 2017). Although an increasing number of studies using ENMs suggest that NIS will expand their ranges and/or shift their ranges poleward as a result of ocean warming (Goldsmit et al., 2018; de Rivera et al., 2011; Saupe et al., 2014), less is known about the stability of their niche space over time.

One of the main assumptions of correlative ENMs is that a species niche is stable in space and time and that the species is in equilibrium with the environment (i.e. they live in all suitable areas and are absent from unsuitable ones) (Elith et al., 2010; Hutchinson, 1957). When modelling NIS, these assumptions are unlikely to be valid due to the actual nature of species introductions. NIS are transported into a new location where the introduced species do not yet inhabit all suitable areas and are far from equilibrium (Gallien et al., 2012). This raises the question of whether to model with occurrence data from only their native range in ENMs (where the species is likely to be in equilibrium) or the entire range (Early & Sax, 2014; Guisan & Thuiller, 2005; Hudson et al., 2021). Studies using only the native range have poorly predicted habitat suitability in introduced ranges (Beaumont et al., 2009; Verbruggen et al., 2013) and thus an increasing number of ENM studies are considering both the native and introduced ranges. In addition, a growing number of studies suggest that modelling efforts can be affected by the so-called “niche shifts” (Broennimann et al., 2007; Parravicini et al., 2015; Reiss et al., 2014). Niche shifts describe the divergence in the physical and environmental requirements of a species over time and geographical space (Figure 1) due to ecological and evolutionary changes (Broennimann et al., 2007). CCC has the potential to alter species niches and identifying if NIS can undergo rapid niche shifts may help understand how the study species responds to novel climates (Guisan et al., 2014; Moran & Alexander, 2014). Coupling these investigations with ENM studies improves our forecasting accuracy and increases our understanding of the potential spread of NIS under CCC (Parravicini et al., 2015; Tingley et al., 2014). For example, ENMs that account for niche changes (by combining data from both native and introduced ranges) have led to models with higher predictive power across both ranges and under future CCC scenarios (Beaumont et al., 2009; Broennimann & Guisan, 2008; Pili et al., 2020). Quantifying niche changes, such as the degree of overlap or expansion, has recently become a rapidly expanding field of research with the development of several frameworks (Blonder et al., 2014; Broennimann et al., 2012). However, studies directly comparing different frameworks are rare (Pili et al., 2020) and further work is required to understand how these frameworks may affect the overall evaluation of niche shifts.

Quantifying niche dynamics across different geographic ranges requires a distinction between analogue and non-analogue environments (Figure 1) (Guisan et al., 2014). If the introduced niche shifts into non-analogue climates (i.e. where environmental conditions are not found in the native range), it cannot be directly assumed that the niche has shifted as it could simply be a factor of these environmental conditions being unavailable in the native range (Guisan et al., 2014; Mandle et al., 2011). In analogue climates (i.e. where the same environmental conditions are available in both native and introduced ranges) niche changes demonstrate “true niche shifts” as the introduced individuals inhabit environments that are available in both ranges, but the native do not inhabit (Figure 1). Nonetheless, colonization of non-analogue climates from the introduced range still provides crucial information on the potential tolerance of NIS to novel climates and has important implications for future management strategies (Early & Sax, 2014). The majority of studies modelling niche shifts do not distinguish between non-analogue and analogue climates and, therefore, may just be identifying environmental conditions that are unavailable in one range (Guisan et al., 2014).

Here, we first assessed the potential for a highly successful NIS, the Pacific oyster Magallana gigas (Thunberg, 1793), to shift its niche in total environmental (non-analogue and analogue) and analogue climates using different frameworks for quantifying niche shifts. M. gigas has wild populations in more than 17 countries mainly due to the species artificial introductions for aquaculture purposes (Herbert et al., 2016). Field observations have shown that warming over the last few decades has facilitated the spread of M. gigas along the coastlines of Europe and North America (Diederich et al., 2005; Valdez & Ruesink, 2017; Wrange et al., 2010). Laboratory experiments have also shown that M. gigas can tolerate a wide range of environmental conditions (Pack et al., 2021). Therefore, studying whether M. gigas has undergone rapid niche shifts during its invasion may further unravel its ability to thrive under novel CCC conditions. In this study we used ENMS with data from both native and introduced ranges to forecast if, under continued warming scenarios predicted by the end of the century, M. gigas distributions will continue to extend poleward as new locations become suitable. We hypothesized that: (1) quantifying the degree of niche overlap in non-analogue and analogue environmental space would show the introduced niche of M. gigas has expanded compared to the native niche, (2) different niche dynamic frameworks would show similar results in terms of overlap and expansion of M. gigas niches, and (3) ensemble ENMs would forecast a poleward expansion of suitable geographic areas for M. gigas by the end of the century.

2 METHODS

2.4 Habitat suitability modelling

As a broad geographical extent allows for a closer approximation of the fundamental niche (Broennimann & Guisan, 2008; Broennimann et al., 2007; Phillips et al., 2006), ENMs were calibrated using occurrence records from both the native and introduced ranges. We then used these records to predict the current and future habitat suitability of M. gigas around the globe.

An ensemble model of four widely used presence-only models, BIOCLIM, Mahalanobis distance, Maxent and Boosted Regression Trees (BRT), was used to estimate habitat suitability of M. gigas globally. Each individual model was run using a bootstrap with replacement method, replicated 100 times and the median model determined. Habitat suitability in the ensemble model was calculated as the mean probability per grid cell weighted by their performance (area under the curve, see Section 2.4.1). Habitat suitability was modelled for the present-day using all five present-day environmental variables and for two end-of-the-century scenarios based on RCP4.5 and RCP8.5 projections. The end-of-the-century models were trained on environmental variables and occurrence records in the present-day then projected into conditions for each scenario.

2.4.1 Model validation and variable importance

To evaluate model performance, the area under the curve (AUC) of the receiver operating characteristic (ROC) and true skill statistic (TSS) were calculated based on 20 model runs with a randomly sampled training and test data set of 75% and 25% of the presence records, respectively.

The importance of each of the five variables in the present-day model was examined using a leave-one-out technique. BIOCLIM, Mahalanobis distance, Maxent and BRT were run and evaluated based on models with only four of the five environmental variables and the mean AUC and TSS for each leave-one-out model was calculated based on 20 randomly sampled training and test datasets. The changes in the AUC and TSS between the models were then compared.

To assess the robustness of the habitat suitability model results, the ensemble models were rerun using different levels of presence records by applying a set of thresholds. For M. gigas to be considered present in a given 1° × 1° grid cell, either 3, 5, or 7 records were required. These three threshold levels resulted in three different presence datasets, each of which was used to predict habitat suitability under the present-day and end-of-the-century RCP8.5 scenario. Each model was run 100 times using a bootstrapping with replacement method and the ensemble models calculated using the same method as the original model. The model predictions were then compared to the original model.

2.4.2 Latitudinal shift in habitat suitability

The average poleward shift of suitable habitat for M. gigas was predicted between the present-day ensemble model and the two end-of-the-century ensemble models. Firstly, both ensemble models were split geographically into northern and southern hemispheres to account for the differences in the direction of movement above and below the equator. Then, the latitudinal centroids , weighted by habitat suitability, were calculated using

where is the latitude at the centre of the spatial cell , is the habitat suitability in the cell, and is the total number of cells (Cheung et al., 2009; Jones & Cheung, 2014). The difference between the latitudinal centroid was then calculated and converted to kilometres using:

where and are the latitudinal centroids for present day and future, respectively, and 6378.2 is the approximate diameter of the Earth in kilometres (Cheung et al., 2009; Jones & Cheung, 2014). Habitat suitability was averaged per 1° latitude for the present day and end-of-the-century models and the difference was calculated.

3 RESULTS

3.1 Niche overlap

3.1.1 COUE and NDH niche shifts

The univariate density curves showed that the biggest difference between the climatic niches occurred for temperature where the optimum temperature in the native niche was 19.6°C and the optimum in the introduced was 12.1°C (Figure 2a). The introduced niche occupied a similar but broader set of conditions than the native niche for salinity, pH and calcite (Figure 2b,c,e). The introduced and native niches occupied a similar range of chlorophyll concentrations (Figure 2d).

The COUE framework suggested that the introduced niche covered a broader set of environmental conditions than the native niche (Figure 3a). Schoener's D metric indicated that niche overlap was moderately low (D = 0.12) and the climatic niche of introduced M. gigas has expanded by approximately 30% compared to the native (Figure 3a) with niche stability at 70% and unfilling niche at 0%. The null hypothesis of niche equivalency was accepted (p = 0.21, Figure 3b) implying the niches were the equivalent. The environments occupied by the introduced niche were more similar to the native niche than expected by chance (p = 0.032) (Figure 3c). These results were expected as the native niche was completely overlapped by the introduced niche in Figure 3a. The environments occupied by the native niche were not more similar than expected by chance to the introduced niche (p = 0.10) (Figure 3d), reflecting the expansion in the introduced niche.

Jaccard and Sørensen-Dice indices in two-dimensional space were moderately high in two-dimensional space at 0.34 and 0.51, respectively (Table 1), reflecting the overlap observed from the COUE framework. Only 14% of the native niche was unique and 64% of the introduced niche was unique. These results imply that M. gigas in the introduced range have colonized nearly all the available climate conditions occupied by the native niche and has expanded into novel climates.

TABLE 1. The metrics for determining the proportion of the niches that are unique, and the Jaccard and Sørensen-Dice indices for the union between the native and introduced niches of Magallana gigas in environmental space

	Total environment		Analogue environment
	2 dimensions	5 dimensions	2 dimensions	5 dimensions
Fraction of unique niche outside of the overlap (native/introduced) (%)	14% / 64%	66% / 83%	50% / 75%	86% / 87%
Jaccard index	0.34	0.13	0.20	0.07
Sørensen-Dice index	0.51	0.23	0.33	0.13

Note

• The metrics were quantified in two-dimensional and five-dimensional principal components space for the total environment (combined non-analogue and analogue environments) and the analogue space.

Similar to the COUE framework, the five-dimensional NDH showed that the introduced niche covers a broader range of environmental conditions than the native niche (Figure 4a). The first plot of the hypervolume (PC1 vs PC2, Figure 4a) is synonymous to the COUE plot (Figure 3a). There was low overlap at the 95% percentile probability boundary of the hypervolumes with 0.23 and 0.13 from the Sørensen-Dice and Jaccard indices, respectively (Table 1). Contrary to the two-dimensional metrics, 66% of the native hypervolume was unique and 83% of the introduced hypervolume was unique (Table 1). The null hypothesis of niche equivalency was rejected (Sørensen-Dice: p = 0.003, Jaccard: p = 0.003, Figure 4b,c), thus the niches were not more equivalent to each other than expected by chance in a five-dimensional space.

3.1.2 Niche shifts in analogue environments

Isolating the analogue environment showed that approximately 61.7% of the introduced occurrences (376 of the total 609) exist in areas with environments analogous to the native range, with the remaining 38.3% living in environments deemed non-analogue. From the COUE framework, the Schoener index implied moderate overlap, D = 0.31, with an expansion in the introduced niche of 17.8%, niche stability of 82.2% and niche unfilling of 24.1% (Figure 3e). The null hypothesis of niche equivalency was accepted (p = 0.39, Figure 3f) implying the niches were equivalent. The environments were also more similar than expected by chance for both niches (Figure 3g,h). The two-dimensional Jaccard and Sørensen-Dice indices for the analogue space were 0.20 and 0.33, respectively with 50% of the native niche and 75% of the introduced niche being unique (Table 1).

In analogue space, a clear separation in the native and introduced hypervolumes can be observed between the niches between PC2 and PC3 (Figure 5a). The Sørensen-Dice and Jaccard indices for the five-dimensional hypervolume indicated very low intersection at 0.07 and 0.13, respectively, and a high proportion of the native and introduced niches were unique, 86% and 87%, respectively (Table 1). The niches were considered not equivalent as the observed Sørensen-Dice and Jaccard indices were less than 95% of simulated values in the null distribution (Sørensen-Dice: p < 0.001, Jaccard: p < 0.001, Figure 5b,c).

3.1.3 Partial least squares

Partial least squares were consistent with the hypervolume statistics, showing a clearer separation between the native and introduced niches within the first two PLS factor (Figure S1.2). The first PLS was predominately based on temperature in contrast with calcite, hence temperature was largely responsible for the separation between the native and introduced niches (Table S1.2). The pairwise PC plots misleadingly suggested a high level of overlap with the COUE framework implying a complete overlap of the native by the introduced niches (Figure 3). This was reflected in the eigenvectors that showed that none of the variables had a strong influence on the axes (Table S1.2), thus not representing the difference between the native and introduced niches.

3.2 Ecological niche modelling

Moderate to high habitat suitability in temperate regions of the northwest and northeast Pacific, north Atlantic and Australasia were predicted for the present day, with the northern European coast and UK predicted to have the most suitable habitat for M. gigas, highlighting areas where dense populations can currently exist (Figure 6a). The model predicted areas of moderate habitat suitability in areas where few presence records have been recorded, such as the coasts of South America and South Africa and east coast of North America. The final ensemble model had high predictive ability with an AUC of 0.92 and the TSS of 0.74. Standard deviations implied a low level of variation of habitat suitability (Figure S1.3).

Overall, habitat suitability decreased for both scenarios predicted by the end of the century compared to the present-day ensemble model (Figure 6b,c). Habitat suitability for M. gigas was predicted to increase at higher latitudes such as the west coast of Canada and Alaska, northwest Europe and northeast Asia by the end of the century in both scenarios (Figure 6b,c). A decline in habitat suitability was predicted in some regions, such as along the coasts of Australasia, southern Africa and south America, particularly under the ‘business as usual’ scenario (RCP8.5) (Figure 6c).

Under both RCP scenarios, the two envelope models, BIOCLIM and Mahalanobis distance, predicted low to no suitable habitat by the end of the century (Figure S1.4). Maxent and BRT models were, therefore, responsible for the predicted habitat suitability in the ensemble models. All four models had good accuracy (Figure S1.5). The leave-one-out method for variable importance showed consistent results across all four models. The removal of temperature had the greatest effect on the AUC and TSS. Temperature was, therefore, the most important predictor of habitat suitability for M. gigas. The other four variables had minimal impact on the model accuracy when omitted (Figure S1.5).

Both the northern and southern hemisphere latitudinal centroids of habitat suitability shifted polewards by the end of the century in both IPCC scenarios (Table 2). As expected, the predicted shift in the latitudinal centroids was more extreme for RCP8.5 (924.4 and 785.4 km) than for RCP4.5 (501.7 and 203.5 km). The latitudinal differences between the present-day and future scenarios showed that habitat suitability decreased in lower temperate latitudes but increased in the higher latitudes by approximately 10%–25% (Figure S1.6).

Thresholding of the occurrence data showed consensus with the original models (see Appendix S2). All the models predicted similar habitat suitability globally, however suitability and predicted geographical extent marginally decreased where fewer presence locations were included at the range edges, as might be expected (Figure S2.1 for comparisons). In addition, suitable geographic regions were predicted even when the thresholds removed presence data in these regions, such as the coastline of South Africa and South America (Figure S2.1, S2.2). Predicted latitudinal shift between the present-day and RCP8.5 model was also similar across the models (Table S2.1). Overall, the similarities between these model predictions suggested that the original model results remained robust.

TABLE 2. The predicted latitudinal shift in Magallana gigas under RCP4.5 and RCP8.5 climate change scenarios

Hemisphere	Present-day centroid (decimal degrees)		Future centroid (decimal degrees)		Difference (decimal degrees)		Distance (km)
	N	S	N	S	N	S	N	S
RCP4.5	45.5	34.6	50.0	36.6	4.5	2.0	501.7	203.5
RCP8.5			53.8	41.7	8.3	7.1	924.4	785.4

Note

• The latitudinal centroids for both the present-day and end of the century ensemble models were determined for the northern (N) and southern (S) hemispheres. The predicted future latitudinal shifts were then calculated as the difference between the present and future centroids and expressed in kilometres.

4 DISCUSSION

In environmental space, we found that M. gigas in the introduced range has shifted their niche relative to the native niche in both analogue and non-analogue environments. These results indicated that M. gigas can thrive in novel environments, suggesting the presence of mechanisms that may facilitate present-day and future spread. We found partially inconsistent results from examining niche shifts using two popular frameworks, as the five-dimensional NDH framework showed shifts in the introduced niche and the two-dimensional COUE framework implied niche conservatism. However, the first two dimensions used in the COUE framework only accounted for a small proportion of the overall environmental variability. Thus, this study highlights the need for careful interpretation of the results when comparing niches in two-dimensional space, particularly where the two PCs explain a low amount of the variability in the environmental data. Areas of suitable climates for M. gigas were generated for both the present day and end of the century, with results predicting an increase in suitable habitat towards the poles and a contraction towards the tropics under two forecasted CCC scenarios by 2100. This agrees with previous ENM studies that showed that towards the end of the century, several NIS are predicted to undergo poleward range shifts (Goldsmit et al., 2018; Lowen & DiBacco, 2017; de Rivera et al., 2011) due to CCC.

The results from this study support previous findings showing that NIS across a range of habitats and taxa are undergoing rapid niche shifts in their introduced ranges (Li et al., 2014; Parravicini et al., 2015; Pili et al., 2020; Tingley et al., 2014; Torres et al., 2018; Zhang et al., 2020). Parravicini et al. (2015) demonstrated that 33% of the invasive tropical fishes studied in the Mediterranean Sea showed expansions in their introduced niches and concluded that ENMs that do not account for niche shifts may underestimate their potential spread. In freshwater ecosystems, up to 90% of the 22 invertebrate species exhibited significant changes in their introduced niche compared to their native niche (Torres et al., 2018). Identifying niche shifts gives an indication of the potential for a species to adapt and spread into novel climates both currently and in the future, which may ultimately increase NIS success with CCC.

Our modelling in analogue climates indicated that a “true” niche shift has occurred in the introduced range of M. gigas over a relatively short time since its global introduction for aquaculture purposes. Several factors may contribute to an observed shift in the realized niche of the introduced range (Alexander & Edwards, 2010; Pearman et al., 2008): expansion in the introduced niche or unfilling in the native niche may be a result of rapid evolution, a change in ecological interactions (for example, competition and predation), or changes in dispersal capabilities (Alexander & Edwards, 2010; Atwater et al., 2018; Rödder & Lötters, 2009; Sotka et al., 2018). Rapid evolutionary changes, such as adaptation during early life-history stages to novel climates (Hoffmann & Sgrò, 2011; Whitney & Gabler, 2008), hybridization of previously isolated native genotypes (Rius & Turon, 2020), and broadening environmental tolerances (Davidson et al., 2011; Tepolt & Somero, 2014) are commonly observed in studies of NIS. M. gigas has been shown to be genetically diverse in both native and introduced populations (Boom et al., 1994; English et al., 2000) and exhibit high phenotypic plasticity in terms of growth and survival across a wide range of environmental conditions (Hamdoun et al., 2003; Li et al., 2018; Taris et al., 2006). For example, growth in adult oysters occurs in temperatures between 10–40°C and salinities of 10–30, and can spawn across a wide range of conditions (Troost, 2010). This broad environmental tolerance suggests that a niche shift in analogue climates could be due to an increased availability of suitable habitats due to, for example, reduced levels of competition or predation within the introduced range. This is further supported by the high number of M. gigas inhabiting non-analogue climates which could also be a result of habitat availability or rapid evolution. Future studies should couple their investigations with experiments investigating variability in physiological tolerances between native and introduced ranges, and to gain insights into the underlying causes behind niche shifts in NIS.

Habitat suitability models can be useful for guiding NIS monitoring programmes that can aid conservation and management efforts (Crall et al., 2013). In this work, the present-day models suggest there are climatically suitable areas of the globe where M. gigas have not yet been recorded (e.g. South America and South Africa). Similarly, modelling in environmental space highlighted areas of unfilling in the native niche. These results are likely a factor of either M. gigas not having been introduced, not yet spread to these locations from their points of introduction, or a lack of survey effort in these areas. Occurrence records for marine invasive species tend to be opportunistic or incidental (Guillera-Arroita et al., 2015; Phillips et al., 2009), a widespread problem is the lack of true presence and absence records, which may directly affect our perception of species niches (Elith et al., 2006). The present habitat suitability maps for M. gigas can, however, be used to highlight areas at potential risk to invasion and guide targeted surveys, particularly in under-represented areas, which would ultimately increase the number of records and make for more robust model predictions (Feeley & Silman, 2011; Ruiz & Hewitt, 2002).

The present and end-of-the-century ENMs predicted suitable geographic areas vulnerable to future invasion. However, this relies on whether the species could be artificially introduced or successfully spread to these areas. Factors such as natural (propagules/larvae) or artificial (shipping and aquaculture) dispersal and species interactions are rarely incorporated into ENMs (Wisz et al., 2013) but are important factors to understand NIS’ absence from areas of its fundamental niche (Araújo & Guisan, 2006; Briscoe et al., 2019; Hastings et al., 2005). There have been advances in hybridizing ENMs that incorporate dispersal models that have shown an increased accuracy in predicting NIS distributions (Václavík & Meentemeyer, 2009; Wisz et al., 2013). However, whilst incorporating biological interactions has made recent progress (Araújo et al., 2014; Araújo & Luoto, 2007), challenges remain in determining which interactions are important for structuring communities and having sufficient data on geographical scales. Predictions of M. gigas distributions would highly benefit from inclusion of these biotic factors to determine their realized niche at regional and local scales.

The COUE and NDH frameworks used for examining native and introduced niches showed contrasting results. The hypervolume for both the total and analogue environmental space supported the likelihood that introduced M. gigas have undergone a niche shift. These results were inconsistent with the COUE framework, which found niches to be equivalent and similar, implying niche conservatism. The five-dimensional hypervolumes showed that there were large unique fractions of both niches occurring outside their overlap (Figures 4a, 5a). It is likely, however, that the COUE framework is limited by comparing niches in only two dimensions. Other studies comparing both of these popular frameworks have similarly shown differences in the results of niche shifts (Pili et al., 2020). Conversely, Tingley et al. (2014) found both methods provided similar results; however, the first two PCs in their study accounted for 88.9% of the variability in the data. It is therefore unlikely that modelling in higher dimensions would have changed their conclusions. Studies investigating niche shifts commonly find low correlations among the studied environmental variables. The first two PCs do not necessarily contain information relevant to the differences between niches, as PC analysis only attempts to explain variability in the environmental data. The use of the NDH framework allowed us to account for 100% of the variability in the environmental data, therefore providing a more complete understanding of the differences between the native and introduced niches.

Sørensen-Dice and Jaccard indices predicted low niche overlap in M. gigas niches; however, the two-dimensional PC plots (Figure 3a,e) did not represent this difference. The pairwise PC plots misleadingly suggested a high level of overlap. This provided evidence that the PCs did not in themselves contain the relevant information to enable a visualization of the niche differences, thus niche overlap analyses using only the first two PCs should be interpreted with caution. For visualizing the data, the PLS approach found new factors (analogous to PCs) specifically to explain species differences using the first two PLS factors and provided an informative 2D projection. Two PCs allowed for a simpler interpretation of high-dimensional data; however, in cases where the first two PCs accounted for a low amount of the variability in the dataset, higher-dimensional techniques or PLS should be explored.

5 CONCLUSION

This study revealed rapid niche shifts in a highly successful marine NIS in its introduced range. Niche divergence and the broad environmental tolerances shown by M. gigas highlighted its ability to establish in novel climates and may facilitate its spread under CCC conditions. From comparing the popular COUE and NDH frameworks, we highlighted that modelling niches in more than two dimensions in environmental space should be considered for analysing niche overlap, especially when low dimensions do not describe a high amount of the variability in environmental variables. In addition, we predicted a poleward expansion and tropical contraction of suitable habitat by the year 2100 under two RCP scenarios. Finally, exploring niche shifts of highly successful NIS advances our understanding on the establishment and spread of particular NIS, but the underlying causes of niche shifts require further investigation before they can be fully integrated into ecological niche models.

CONFLICT OF INTEREST

The Authors declare that there is no conflict of interest.

BIOSKETCH

Kathryn E. Pack is a marine ecologist primarily investigating the effects of contemporary climate change and ocean acidification on non-indigenous species physiology and distribution.

Nova Mieszkowska is a physiological ecologist who runs the MarClim intertidal time series and links omics to ecosystem-level research on the impacts of climate change and ocean acidification on marine species.

Marc Rius uses cutting-edge analytical and high-throughput sequencing techniques to conduct studies on biogeography, community ecology, population genetics and biodiversity conservation, with a special focus on assessing how human activities reshape species ranges.

Author contributions: Kathryn E. Pack, Conceptualization, Methodology, Formal analysis, Investigation, Visualization, Writing - original draft, Writing - review & editing. Nova Mieszkowska, Funding acquisition, Writing - review & editing. Marc Rius: Funding acquisition, Writing - review & editing.