Climate Change Impacts on Marine Invaders: Analyzing Future Distribution and Ecological Niche Shifts of Pterois miles and Pterois volitans

Abstract

Invasive lionfish species, Pterois miles and Pterois volitans, are poised for significant range expansion due to environmental changes and their intrinsic adaptability. Utilizing Species Distribution Models (SDMs), this study predicts a marked increase in the future spread of these species, particularly as non-suitable areas transform into highly suitable habitats under projected climate change scenarios. Our findings indicate that both species are expanding their ecological niches in invaded territories, where they increasingly occupy more dominant ecological positions. This shift is likely facilitated by rising sea temperatures and alterations in marine ecosystems, which enable these invasive species to exploit new areas and outcompete native fauna. The projected movement towards higher latitudes represents a significant ecological threat, with potential severe impacts on biodiversity and the functionality of marine ecosystems. This study highlights the urgent need for proactive strategies to monitor and manage the spread of lionfish, aiming to mitigate the substantial risks associated with their future expansion and dominance in new marine environments.

Full text

Climate Change Impacts on Marine Invaders: Analyzing
Future Distribution and Ecological Niche Shifts of Pterois
Miles and Pterois Volitans
Yiyi Lu1,a,*
1School of Design, Jiangnan University, Wuxi, 214122, China
a. yiyilu@163.com
*corresponding author
Abstract: Invasive lionfish species, Pterois miles and Pterois volitans, are poised for
significant range expansion due to environmental changes and their intrinsic adaptability.
Utilizing Species Distribution Models (SDMs), this study predicts a marked increase in the
future spread of these species, particularly as non-suitable areas transform into highly suitable
habitats under projected climate change scenarios. Our findings indicate that both species are
expanding their ecological niches in invaded territories, where they increasingly occupy more
dominant ecological positions. This shift is likely facilitated by rising sea temperatures and
alterations in marine ecosystems, which enable these invasive species to exploit new areas
and outcompete native fauna. The projected movement towards higher latitudes represents a
significant ecological threat, with potential severe impacts on biodiversity and the
functionality of marine ecosystems. This study highlights the urgent need for proactive
strategies to monitor and manage the spread of lionfish, aiming to mitigate the substantial
risks associated with their future expansion and dominance in new marine environments.
Keywords: climate change, invasive alien species, ecological niche shift, ensemble model,
Pterois spp.
1. Introduction
In the era of globalization, human activities, including international trade, tourism, transportation and
infrastructure construction, increase the risk of biological invasion. The Intergovernmental Platform
on Biodiversity and Ecosystem Services (IPBES) Assessment Report found that more than 37,000
established alien species have been introduced by human activities across all regions and biomes,
with new alien species presently being recorded at an unprecedented rate of approximately 200
annually [1]. Among these alien species, about 3,500 species are categorized as invasive alien species
(IAS), which have negative impacts on nature and human society [1]. Consequently, the
establishment and spread of non-indigenous species are important drivers for biodiversity loss and
disorders of ecosystem structure and function. Besides the ecological impacts, IAS pose a significant
threat to production in many sectors, such as agriculture, forestry, animal husbandry, and fisheries.
Although this issue has attracted increasing attention from both scholars and the general public,
policies and their implementation toward the prevention and control of IAS are still insufficient in
most countries [2]. In addition, the number of IAS and their negative impacts are likely to increase
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due to the amplification of multiple drivers including but not limited to demographic, economic and
land- and sea-use change [1, 3]. Climate change can cause shifts in species distribution in some areas
leading to a reduction in species diversity and the exposure of vacant ecological niches, making the
ecosystem more vulnerable to IAS and enhancing the success rate of invasive species population
establishment.
Alien marine fish are mainly introduced through maritime transportation and canal construction.
However, few worldwide fish invasions of similar magnitude are documented as the introduction of
lionfish (Pterois volitans Linnaeus, 1758; Pterois miles Bennett, 1828) in the western Atlantic Ocean,
which is one of the fastest and most ecologically harmful marine fish invasions to date [4]. P. volitans
is a Pacific Ocean species while P. miles is native to the Indian Ocean from the Red Sea to eastern
South Africa, Arabian Sea, Persian Gulf, Andaman Sea and Sumatra, the eastern extreme of its
distribution, where populations of P. miles and P. volitans overlap [5]. The invasive lionfish was first
documented along Florida coasts in 1985 due to either intentional or accidental aquarium release. In
less than 30 years, they have expanded their distribution range to the Caribbean Sea, the Gulf of
Mexico and recently there are new records of lionfish for the Brazilian coast, in the southwestern
Atlantic [6]. Studies about invasive lionfish in the western Atlantic indicated that they are
opportunistic generalist carnivores, consuming at least 167 vertebrate and invertebrate prey species
across multiple trophic guilds [7]. Within their invaded range, lionfish have reached densities up to
ten times that of their native range [8]; on the individual level, they have larger average body lengths
with growth rate of 1.25 to 2.25 times faster than lionfish in the Pacific [9]. As the dominant predator
on coral reefs with a great impact on native coral reef fishes, lionfish have reduced the abundance of
small native reef fishes by up to 95% at some invaded site [10]. Female lionfish are capable of
spawning every 3.6-4.1 days throughout the year after getting matured at about 1 year of age, and
produce around two million buoyant gelatinous eggs per year [11, 12]. During the pelagic larval phase,
which is estimated to be 25-40 days, lionfish larvae can float with the ocean currents and be dispersed
across great distances [13]. These reproductive characteristics (i.e. rapid growth, early sexual maturity,
and frequent spawning ability) of lionfish would contribute to their rapid expansion and sustained
population levels in invaded ranges [12, 14-15]. In addition, the successful invasion of lionfish also
results from other biological traits, including their generalist feeding habits, antipredator defense and
wide ecophysiological tolerance [4]. In recent years, lionfish has spread rapidly through the eastern
and central Mediterranean. Following the typical pattern of Lessepsian migration, it progressively
moved toward the northwest and spread in the Ionian Sea [16] and the Adriatic Sea [17]. With ongoing
climate change, sea warming is favoring the spread of such tropical fish [18], enabling them to survive
and reproduce in previously thermically inhospitable ecosystems, and therefore further expand
westwards and northwards.
To the best of our knowledge, most research on the lionfish invasion focus on its biological and
ecological traits, and tracking the invasion history to date, with little attention given to the correlation
between its distribution patterns and environmental parameters. Therefore, it is necessary to
strengthen our understanding of how climate change will influence lionfish distribution in order to
mitigate the negative impacts of this species. Predictions of the effects of rising temperatures on
invasive alien species (IAS) typically include observational studies of population dynamics under
climate change, mechanistic studies in controlled settings [19-21], and climate modeling [22], often
integrating these methods [23]. Recently, species distribution models (SDMs) have emerged as a
powerful tool for analyzing the effects of climate change on invasive alien species populations [24,
25]. SDMs are commonly used to predict species’ potential geographic distributions and migration
trends under varying climate conditions. Popular models include MAXENT, CLIMEX, GLMs,
GARP, and Biomod2 [26-28]. Among these, Biomod2 is an R-based platform that supports various
modeling methods like ANN, GAM, GLM, MAXENT, and XGBOOST, which can be combined into
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a single integrated model [29]. Compared to single models, ensemble models (EMs) enhance
accuracy by reducing noise through multi-model integration, which is vital for accurate species
prediction [30-32], thus are more reliable than single models [33-35].
To effectively investigate the temporal and spatial distribution of P. volitans and P. miles, we
applied the Biomod2 integrated model. This approach enables integrated analysis of environmental
factors, enhancing our understanding and management of P. volitans and P. miles invasions. Our
strategy prioritizes identifying key environmental factors for prevention and control in areas
climatically similar to those invaded. Considering the significant impacts of P. miles and P. volitans
on marine ecology, this study focuses on exploring their potential distribution and ecological niche
changes across the global ocean. We will conduct an in-depth analysis of their environmental
adaptations to reveal the dynamics and drivers of their invasion, providing a scientific basis for
effective biosecurity and management strategies. The approach includes: (1) selecting top single
models for Biomod2 integration to find the most effective model; (2) using the best integrated model
to predict suitable and overlapping distribution areas of P. volitans and P. miles under SSP1-2.6 and
SSP5-8.5 scenarios for 2050 and 2090; (3) examining the environmental adaptability and ecological
niche dynamics; (4) analyzing environmental similarities in suitable areas using multivariate
environmental similarity surfaces (MESS) and ANOVA.
2. Materials and Methods
2.1. Species geographical distribution data
We obtained occurrence records for P. miles and P. volitans from the Global Biodiversity Information
Facility (GBIF, https://www.gbif.org/) and the Ocean Biodiversity Information System (OBIS,
https://obis.org/) [36]. To align with the resolution of our environmental variables, we optimized our
occurrence data using the ENMtools [37]. We filtered the data to maintain only one distribution point
per 10 × 10 arcminute grid, significantly reducing spatial autocorrelation. If unaddressed, spatial
autocorrelation could skew results by clustering data points [38, 39]. Following this screening, we
compiled 1,077 and 3,543 valid distribution points for P. miles and P. volitans respectively. This
dataset forms the foundation for our predictive modeling, enabling a robust analysis of the
relationship between species distributions and environmental factors.
2.2. Environmental variables
Based on previous studies, we selected 36 ocean climate variables for both current and future periods
(2050s: 2050-2060; 2090s: 2090-2100) [40, 41]. We obtained climate data for both periods from the
Bio-Oracle database (accessed on June 5, 2024, https://bio-oracle.org/), selecting future projections
from the BCCCSM2-MR model of the CMIP6 program. This selection represents a shift from the
Representative Concentration Pathways (RCPs) of CMIP5, which integrated socio-economic
pathways (SSPs) with RCPs to model future socio-economic scenarios [42]. To ensure accurate
predictions, these diverse variables included temperature, pH, velocity, oxygen, primary productivity
and bathymetry, whose data were at various resolutions. However, previous research has indicated
that higher spatial resolution does not necessarily enhance SDM predictive performance [43].
Therefore, we standardized the spatial resolution of all environmental variables across the models to
minimize prediction uncertainty. We analyzed these bioclimatic factors using Pearson correlation and
assessed their intercorrelations and significance with the variance inflation factor (VIF) [44].
Employing the R programming language, we performed Spearman’s correlation and multicollinear
VIF assessments on the interpolated data from our occurrence points. Our selection criteria for
relevant factors were a correlation coefficient <0.8 and VIF values <10 [45]. The variance inflation
factor (VIF), the inverse of tolerance, quantifies the extent of multicollinearity among variables.
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Specifically, a VIF value below 10 suggests no multicollinearity, values between 10 and 100 suggest
multicollinearity, and values above 100 indicate severe multicollinearity [46]. This methodology aids
in identifying the most relevant and independent bioclimatic variables, ensuring our ecological niche
model's robustness. The filtered modeling factors are presented in Table 1.
Table 1: Environment variables involved in modeling.
Parameter code
Description
Parameter code
Description
ben_b_max
Maximum benthic
bathymetry
surf_ph
Mean surface pH
ben_b_mean
Mean benthic bathymetry
surf_pp
Mean surface
primary
productivity
ben_o
Mean benthic oxygen
surf_s_range
Surface salinity
range
ben_pp
Mean benthic primary
productivity
surf_t_max
Maximum surface
temperature
ben_s_range
Benthic salinity range
surf_t_range
Surface temperature
range
ben_t_range
Benthic temperature range
surf_v
Surface velocity
2.3. Model accuracy assessment
We modeled the current and future geographic distributions of P. miles and P. volitans using global
occurrence data and environmental variables, utilizing the Biomod2 package in RStudio (version
3.1.0, 2014) [35]. By integrating multiple models, we leverage their unique sensitivities and
explanatory capabilities to enhance the diversity, robustness, and accuracy of our predictions. This
method balances model biases, reduces errors, and merges outputs to improve forecast accuracy and
assess uncertainty more effectively [30]. Research shows that integrating multiple models typically
yields higher prediction accuracy compared to using a single model, thereby enhancing the reliability
and practicality of the scientific research [33]. Within Biomod2, we utilized twelve diverse modeling
algorithms: artificial neural networks (ANN), classification tree analysis (CTA), flexible discriminant
analysis (FDA), generalized additive models (GAM), generalized boosting models (GBM),
generalized linear models (GLM), multivariate adaptive regression splines (MARS), maximum
entropy (MAXENT), MAXNET, random forest (RF), XGBOOST, and species range envelope (SRE).
To refine the models, we employed a BioModel tuning command to optimize the parameters. We
designated 75% of the data as the training set, ensuring equal weighting for both distribution and
pseudo-absence data to maintain model balance. We iterated the modeling process five times to
enhance reliability, producing 60 simulation models. From these, we selected single models with a
True Skill Statistic (TSS) greater than 0.8 and Area Under the Curve (AUC) greater than 0.9 for
further consideration, indicative of high predictive accuracy and reliability [47]. We constructed an
Ensemble Model (EM) to simulate the potential distribution areas of P. miles and P. volitans using
several methods, including EMmean, EMcv, EMci, EMmedin, EMca, and EMwmean. EM
demonstrated superior performance, with TSS and AUC values exceeding 0.85 and 0.95, respectively.
This comprehensive approach allowed for thorough evaluation of the models, ensuring selection of
the best integrated model for predicting and analyzing the potential distribution of P. miles and P.
volitans.
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2.4. Ecological niche quantification
The ecological niche of P. miles and P. volitans was analyzed using the ecospat package in RStudio
4.2.3, integrating PCA-env and COUE schemes [48, 49]. This analysis encompassed gathering
species distribution and climatic data, defining geospatial and environmental parameters on a two-
dimensional grid, and applying kernel smoothing to estimate climatic densities. Statistical methods,
including stochastic tests, were used to compare the climatic ecological niches of the original and
invasion areas [50]. Additionally, a stochastic test was used to quantify the differences in niche
dynamics, specifically vacancy, stability, and shifts, between these areas [51].
2.5. Analysis of MESS and MoD
Using environmental variables from the current potential distribution area of P. miles and P. volitans
as a reference layer, we employed MESS to assess climatic anomalies in suitable habitats for P. miles
and P. volitans under the projected climate change scenarios. The most divergent variable was then
analyzed to identify the key factors influencing shifts in P. miles and P. volitans potential geographic
distribution.
The MESS calculation quantifies the similarity between the predictive variables (V1, V2, Vi...)
and reference points. For any environmental variable, Vi, in the reference layer, mini and maxi
represent the minimum and maximum values, respectively. The variable  denotes the Vi value at a
specific point P during a defined period and  is the proportion of points in the study area where Vi
< Π [52]. The MESS calculation varies based on the percentage () of points where the environmental
variable  is less than its value  at point P. Specifically: when  = 0, MESS = 100 (p − mini)/(maxi
− mini); when 0 <  ≤ 50, MESS = 2; when 50 <  < 100, MESS = 2(100 − ); when  = 100,
MESS = 100 (maxi − p)/(maxi − mini). The MESS at point P represents the minimum similarity
across variables, which is termed the MoD [53]. A negative MESS indicates that one or more variable
values fall outside the environmental range of the reference points, thereby identifying a climate
anomaly point. Conversely, a MESS of 100 suggests complete consistency with the reference climate,
indicating normal climatic conditions. The variable with the greatest anomaly, or the MoD, is critical
for understanding potential geographic shifts in species distribution. The density.tool.novel tool was
incorporated into the MaxEnt model for this analysis [52, 54].
3. Results
3.1. Current geographical distributions and further migration of P. miles and P. volitans
The current (1979-2013) geographical distributions of P. miles and P. volitans are shown in Figure 1.
The current distribution area of P. miles include the Arafura Sea, Timor Sea, Flores Sea, East China
Sea, and Gulf of Mexico; for P. volitans, these are in the Coral Sea, Indian Ocean, South Atlantic
Ocean, and Gulf of Mexico. The overlapping areas for both species currently include the Flores Sea,
South China Sea, East China Sea, Caribbean Sea, and Gulf of Mexico. At present, P. miles occupies
a total habitat area of 1,590.02 × 104 km², P. volitans occupies 2,359.23 × 104 km², with an
overlapping area of 311.74 × 104 km². (Table 2)
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Figure 1: Current geographic distribution of P. miles and P. volitans.
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Table 2: Current global area (×104 km2) of suitable habitat of P. miles and P. volitans.
Habitat type
Species
Unsuitable
Low
Moderate
High
P. miles
34409.98
767.71
613.54
208.75
P. volitans
33640.76
1277.64
840.52
241.06
The optimal prediction of the Biomod2 model, Emwmean, was transformed into raster data to
calculate the suitable area for P. miles and P. volitans under different climate scenarios. The results
indicated a substantial increase in suitable areas compared to the current climate scenario (Fig2, Tab3).
For P. miles, the total area of suitable habitats is projected to expand under most future scenarios.
Under the SSP585 climate scenario, the expansion of the high-fitness zone is the most significant,
with the area of the high-fitness zone growing from 213.31 × 104 km2 in 2050 to 382.50 × 104 km2 in
2090, mainly in the Mediterranean Sea, the Red Sea, the Timor Sea, the Coral Sea and other seas.
The non-fitness zones will gradually transform into the fitness zones, and the low and medium-fitness
zones in the current period will gradually transform into the high-fitness zones. For P. volitans, the
total area of suitable zone reaches the maximum under SSP126 scenario in 2050, which is about
2,668.49 × 104 km2, while it seems to shrink from then. In 2090, the area of high suitable zone under
SSP585 scenario expands to the maximum, which is mainly distributed in the Gulf of Mexico,
Sargasso Sea, South China Sea, East China Sea, Timor Sea, and Coral Sea, etc., and the high suitable
zone is gradually expanding under each future scenario.
Table 3: Area of habitat suitability class under future climate scenarios (×104 km2).
Species
Climate scenario
Unsuitable
Low
Moderate
High
All
P. miles
2050s_SSP126
34314.15
758.33
634.33
293.18
1685.84
2050s_SSP585
34441.84
699.83
645.00
213.31
1558.15
2090s_SSP126
34304.30
764.27
663.34
268.07
1695.69
2090s_SSP585
34349.90
633.55
641.69
382.50
1657.75
P. volitans
2050s_SSP126
33331.50
1305.97
618.08
744.43
2668.49
2050s_SSP585
33495.25
1244.87
698.23
561.63
2504.74
2090s_SSP126
33405.30
1239.81
615.12
739.75
2594.69
2090s_SSP585
33660.91
1044.88
445.04
856.84
2346.77
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Figure 2: Future potential geographic distribution of P. miles and P. volitans.
3.2. Differentiation and overlapping of their ecological niches
Visualizing the ecological niches of P. miles and P. volitans offers intuitive insights into their
adaptability to future climate change scenarios. Figure 3 demonstrates the significant ecological niche
drift for P. miles and P. volitans under future climate scenarios. For P. miles, ecological niche
differences between future periods were marked, with maximum and minimum overlaps of 0.77
(2050s_SSP585) and 0.69 (2090s_SSP585) respectively. For P. volitans, the maximum overlap is
0.74 (2050s_SSP126), and the minimum is 0.24 (2090s_SSP585). The ecological niche overlap of
the two species varies over time and climate scenarios, with niche drift intensifying and overlap
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decreasing as radiation intensity increased. The significant ecological niche overlaps suggest that P.
miles and P. volitans are highly resilient to future climate changes. Additionally, the ecological niche
overlap under the 2090 climate scenario was notably lower than under the 2050 scenario, indicating
that the risk of dispersal and invasion by P. miles and P. volitans may increase over time.
Figure 3: Niche differences of P. miles and P. volitans in different climatic backgrounds in the future.
D: ecological niche overlap value. A: P. miles B: P. volitans
As depicted in Figure 4 and Table 4, the future overlapping geographic distribution of these two
invasive species generally exhibits an expansion trend. Under the 2050s_SSP585 scenario, a
reduction in overlap, approximately 100.83 × 104 km2, will occur primarily in the Gulf of Mexico,
Sargasso Sea, Caribbean Sea, South China Sea, Java Sea, East China Sea, and Arafura Sea. For other
periods and climate scenarios, the largest overlapping area occurs under the 2050s_SSP126 scenario,
approximately 1,035.50 × 104 km². The smallest increase is observed in the 2090s_SSP126 scenario,
with an expansion of about 214.18 × 104 km² compared with current scenario. Expansion
predominantly occurs in the nearshore areas of the Gulf of Mexico, Sargasso Sea, Caribbean Sea,
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South Atlantic Ocean, Mediterranean Sea, Red Sea, Arabian Sea, East China Sea, South China Sea,
Java Sea, Timor Sea, Arafura Sea, and Coral Sea. This trend mirrors the expansion of the habitable
zones of the two species under future scenarios.
Figure 4: Predicted overlapping geographic ranges of P. miles and P. volitans in the 2050s and 2090s
under SSP126 and SSP585.
Table 4: Area of overlap between P. miles and P. volitans (×104 km2).
Climate
scenario
Only A
Only B
A and B overlap
2050s_SSP126
16.34
976.70
1035.50
2050s_SSP585
8.55
1395.74
210.91
2090s_SSP126
3.85
1421.93
525.92
2090s_SSP585
0.54
1064.35
835.77
3.3. MESS and MoD variable analysis
Under the SSP126 and SSP585 scenarios, the mean environmental similarity at 719 effective
distribution points of P. miles is 2.59 and 0.78 in 2050, and 2.54 and -0.10 in 2090, respectively (see
Fig.5). The scenarios with significant variability and decreasing anomalies in climate similarity are
SSP585 and SSP126 in both 2050 and 2090. For P. volitans, the mean environmental variable
similarity at 2887 effective distribution sites under the SSP126 and SSP585 scenarios is 1.22 and 0.42
in 2050, and 1.47 and 0.04 in 2090, respectively. The scenarios with the most climate variability and
decreasing anomalies are SSP126 in 2090, SSP585 in 2050, and SSP585 in 2090. The dominant
variables remain ben_pp (mean benthic primary productivity) and surf_t_max (maximum surface
temperature), which exhibit significant variability in future periods. The expansion of both species
may vary due to differences in changes in surf_t_max.
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Figure 5: MESS and MoD analysis of P. miles and P. volitans under future climate scenarios.
3.4. Analysis of dominant environmental factors
From Figure 6, the First Principal Component (Dim1) accounts for 55.8% of the variance and is
closely associated with factors such as ben_b_max (maximum benthic bathymetry), ben_b_mean
(mean benthic bathymetry), ben_t_range (benthic temperature range), and ben_s_range (benthic
salinity range). The closeness of these factor directions suggests a positive correlation, significantly
impacting the model's explanatory power. The Second Principal Component (Dim2) accounts for
36.5% of the variance and is significantly influenced by factors such as surf_t_max (maximum
surface temperature) and surf_pp (mean surface primary productivity), highlighting their impact on
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species distribution along a dimension distinct from major chemical or physical factors. This PCA
analysis reveals that ben_b_max (maximum benthic bathymetry) is one of the most influential
environmental factors affecting the distribution of these species, suggesting that bathymetry might be
critical limiting factors. Additionally, the temperature and salinity ranges play a crucial role,
potentially related to the species’ adaptation to environmental stability.
Figure 6: Dominant environmental factors. A: P. miles B: P. volitans
4. Discussion
4.1. The ecological niche dynamics of P. miles and P. volitans
Our study has employed Species Distribution Models (SDMs) to project the global potential
geographical distribution and analyze the ecological niches of two invasive lionfish species, Pterois
miles and Pterois volitans. The results of this analysis strongly support the ecological niche shift
hypothesis, suggesting that these species have undergone significant changes in their ecological
preferences and tolerances after establishing populations in non-native regions. In comparing the
ecological niches of these species in their native versus invaded ranges, our findings reveal that both
P. miles and P. volitans have not only expanded their habitat preferences but also exhibit distinct
patterns of resource utilization and environmental resistance. Such shifts are indicative of an
evolutionary response to novel selection pressures in invaded ecosystems, potentially including
changes in prey availability, absence of natural predators, and new competitive dynamics [11, 55-56].
The potential for further invasion and spread remains high, which suggest that vast areas of the world's
oceans are climatically suitable but not yet colonized by these lionfish. This ongoing threat
underscores the need for continued monitoring and development of management strategies aimed at
mitigating the impact of lionfish on local marine biodiversity and fisheries [4, 10, 57].
4.2. Potential geographical distribution shifts of the P. miles and P. volitans under climate
change
The impact of global climate change on IAS such as Pterois miles and Pterois volitans is of critical
concern, as shifts in temperature and sea conditions can significantly alter their potential geographical
distributions. Our research supports the hypothesis that global warming is driving the latitudinal
migration of these invasive lionfish species, potentially enhancing their range expansion into
previously unsuitable areas. Consistent with prior studies [40, 58], our findings suggest that rising sea
temperatures and altered ocean currents are facilitating the northward and southward expansion of P.
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miles and P. volitans. This trend towards higher latitudes corresponds with the predicted shifts in
suitable habitats caused by warming waters, which extend the thermal niche boundaries that
previously constrained the distribution of these tropical and subtropical species. Furthermore, models
developed by Poursanidis D [36, 59] and other researchers have projected significant range
expansions for numerous marine species, including invasive ones like lionfish, under various climate
change scenarios. These models underline that P. miles and P. volitans may exploit new regions as
their thermal habitats shift poleward, potentially leading to new ecological challenges for the native
marine communities in these higher latitudes. Our study adds to the evidence that climate change acts
as a catalyst for the spread of IAS by altering their geographical range limits. The predicted expansion
of suitable habitats for P. miles and P. volitans into higher latitudes not only confirms the adaptive
flexibility of these species but also highlights the urgency for enhanced monitoring and management
strategies in these new potential invasion fronts. In summary, the shifting geographical distribution
of P. miles and P. volitans under climate change scenarios emphasizes the dynamic nature of
ecological niches and the profound implications of environmental changes on invasive species
distribution. This knowledge is crucial for developing predictive models and proactive strategies to
mitigate the ecological impacts of these expanding invasive populations.
5. Conclusions
This study utilized Species Distribution Models (SDMs) to evaluate the global potential distribution
and ecological niche dynamics of two invasive lionfish species, Pterois miles and Pterois volitans.
The analysis provided significant insights into the adaptability and potential spread of these species
under current and projected environmental conditions. Our findings reveal that both P. miles and P.
volitans have successfully expanded their ecological niches beyond their native ranges, exhibiting
considerable adaptability to varying marine environments. This adaptability underpins their invasive
success and poses a substantial threat to biodiversity and local ecosystems across their potential
distribution zones. The study supports the hypothesis that global warming could further facilitate the
expansion of these invasive species by making higher latitude marine environments more hospitable.
This potential shift in geographical distribution due to climate change highlights the urgent need for
robust monitoring and management strategies. Proactive measures are essential to mitigate the
ecological and economic impacts of these invasions, which threaten marine ecosystems globally. In
conclusion, our research underscores the critical importance of utilizing advanced modeling
techniques to predict and prepare for the spread of invasive species. Effective management and
conservation efforts must be informed by continuous scientific research to adapt to the dynamic
challenges posed by species like P. miles and P. volitans in a rapidly changing world.
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