What does the future hold for a thermophilus and widely introduced gecko, Tarentola mauritanica (Squamata: Phyllodactylidae)?

doi:10.21203/rs.3.rs-2696040/v1

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What does the future hold for a thermophilus and widely introduced gecko, Tarentola mauritanica (Squamata: Phyllodactylidae)?

https://doi.org/10.21203/rs.3.rs-2696040/v1

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published 26 Dec, 2023

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The continuous growing and movement of the human population is increasing the frequency in translocating species from their native ranges to novel environments. However, biological invasions offer a rare opportunity to investigate how species are able to colonise and adapt to new conditions. In that sense, Ecological Niche Models (ENMs) can be a powerful tool to predict where invasive species will spread over the next decades, although they depend heavily on climatic niche conservatism between native and exotic ranges. To reduce these uncertainties, ENMs can be refined by accounting for dispersal constraints. The common wall gecko, Tarentola mauritanica is a native and widespread Mediterranean lizard that has been introduced across different tropical and sub-tropical regions. In this study, we aim to predict the potential and future distribution of T. mauritanica geckos using correlative models, its potential colonization regions under a dispersal model, and the niche overlap between native and introduced populations. The correlative models predict that the most suitable geographic areas for this gecko correspond to Mediterranean-type ecosystems, such as California, central Chile, the Cape Region of South Africa, around the Caspian Sea, south-eastern Asia, and south-western and southern Australia. The species distribution models projected to 2061–2080, forecast that the range of T. mauritanica is likely to shift towards northern latitudes but, surprisingly, not to expand. According to the dispersal models, T. mauritanica will be able to colonise a similar geographic range compared to the one obtained with the correlative models for the future. Finally, the niche overlap results demonstrate that T. mauritanica’s realised niche has not been conserved over space, as the naturalised climatic niche of the introduced populations differs significantly from its native one. The latter results suggest that there has been no climatic niche conservatism during the several introductions of T. mauritanica, and that this species seems to be able to cope with novel and more humid environments, typical from the tropics.

ENMs

Tarentola mauritanica

niche overlap

species dispersal models

climate change

New species populations are established by the accidental and intentional transportation of animals and plants outside their native ranges, facilitated by the movement of the growing human population (Wilson et al. 2009). Part of these introduced species have become naturalised in their recipient ecosystems; many of them are a major cause of various environmental problems ranging from biodiversity loss to disrupted ecosystem services (Butchart et al. 2010; Gurevitch, Padilla 2004). Invasive herpetofauna are an important cause of the global decline of biodiversity. The recent meta-analysis by Capinha et al. (2017) revealed that (1) nearly 200 reptile species became established in at least one of the 359 world regions considered in their study, and (2) reptile invasions are accelerating. This is of particular concern when considering that the species richness of these invasive species is much higher in biodiversity hotspots (Li et al. 2016). Furthermore, climate change may intensify these impacts since it affects both the establishment of new invasive species and the distribution of the existing ones (Hellmann et al. 2008). Nevertheless, these effects are species-dependent: where some invasive species may thrive in a changing climate, others may not. In that sense, species distribution models (SDMs) – also known as environmental or ecological niche models (ENMs) – are widely used in invasion biology (Srivastava et al. 2019), especially to predict invasion risk (e.g., Bradley et al. 2010; Jiménez-Valverde et al. 2011; Kramer et al. 2017; Lecocq et al. 2016; Tingley et al. 2018) and optimise control strategies (e.g., Giljohann et al. 2011; Tulloch et al. 2014). ENMs are also commonly used in other areas of conservation science, such as predicting the potential effects of climate change on genetic (e.g., Alsos et al. 2012; Pauls et al. 2013), taxonomic (e.g., Domisch et al. 2013; Garcia et al. 2012; Maiorano et al. 2011; Srinivasulu et al. 2021), and functional diversity (e.g., Barbet-Massin et al. 2012; Thuiller et al. 2015; Weterings et al. 2019), or in the selection of protected areas (e.g., Guisan et al. 2013; Hannah et al. 2007; Marini et al. 2009).

Numerous ENM studies have shown that climatic niches are similar between populations of species in their native and introduced ranges (e.g., Peterson 2003; Petitpierre et al. 2012; Zhu et al. 2020), and that the niche models based on the species’ native distribution can potentially predict their spread in the introduced range (e.g., Thuiller et al. 2005). In general terms, the concordance between the realised climatic niches of a species’ native and introduced distributions may provide evidence of niche conservatism (e.g., Petitpierre et al. 2012; Wiens, Graham 2005). Hence, this pattern of niche conservatism assumes that niche divergence will be negligible over the timeframe of prediction, being used for predicting the impacts of anthropogenic climate change on species distributions and persistence (e.g., Petitpierre et al. 2012). Nevertheless, many other studies have repeatedly shown frequent shifts between climatic niches of species in their native and introduced ranges (e.g., Atwater et al. 2018; Broennimann et al. 2007; Colautti, Barrett 2013; Early, Sax 2014; Fernández, Hamilton 2015; Gallagher et al. 2010; Lauzeral et al. 2011; Li et al. 2014; Parravicini et al. 2015). Moreover, recent studies have also proposed that shifts in introduced populations might possibly shed light on how species can respond to climate change (e.g., Fernández, Hamilton 2015; Guisan et al. 2014; Monahan, Tingley 2012; Moran, Alexander 2014).

Gecko lizards are among the most successful terrestrial vertebrates in undertaking effective – natural and/or anthropogenic – long-distance dispersal events into new areas (e.g., Agarwal et al. 2021; Perella, Behm 2020; Rocha et al. 2007; Rocha et al. 2022). This can be attributed to factors such as their commensal existence with humans, small body size, largely nocturnal habitats, calcareous eggs, and high population densities (Locey, Stone 2006; Rödder, Lötters 2009; Weterings, Vetter 2018). In fact, human translocation, and posterior colonisation of new areas are so common that some invasive geckos now have an almost cosmopolitan distribution in tropical and temperate regions (Baldo et al. 2008). In that sense, the cosmopolitan genus Hemidactylus stands out among geckos. Although the majority of the more than 165 species (Agarwal et al. 2021) are restricted to small-ranging habitats in South Asia, approximately 10 species of Hemidactylus have achieved intercontinental distributions (Capinha et al. 2017; Kluge 1969; Kraus 2009; Weterings, Vetter 2018).

The common wall gecko Tarentola mauritanica (Linnaeus, 1758), a species complex encompassing six putative distinct taxa (Rato et al. 2016), is native and widespread throughout the entire west coastal Mediterranean Basin (Vogrin et al. 2017). However, due to its frequent association to human settlements and capacity for long-distance dispersal (Carranza et al. 2000), it has been introduced in multiple mainland and island regions across the Mediterranean (Cabana 2008; Cabana, López-Bao 2007; Deso et al. 2020; Hódar et al. 2009; Mačát et al. 2014; Mizerakis, Strachinis 2017; Mizsei et al. 2017; Mori, Plebani 2012; Paramos, Ayres 2007; Strachinis, Artavanis 2017; Strachinis, Pafilis 2018; Strachinis et al. 2020; Tejado, Potes 2011), Macaronesia (Báez, Biscoito 1993; Barreiros et al. 2010; Jesus et al. 2008; Rato et al. 2015b), as well as North (Mahrdt 1998; Ortiz-Medina et al. 2019), and South America (Arredondo, Núñez 2014; Baldo et al. 2008; Díaz-Fernández et al. 2019; Huerta-Vera 2016).

The potential environmental impacts of T. mauritanica in these newly colonised habitats are largely unknown (Baldo et al. 2008; Deso et al. 2020). However, T. mauritanica populations in Croatia compete with those of Hemidactylus turcicus through spatial displacement (Lisičić et al. 2012). In Madeira Island, they may have some impact on introduced arthropod communities (Martins et al. 2022).

Tarentola mauritanica is highly adapted to living near people, provided it has proper refugia to hide (Martínez-Rica 1974; Mellado et al. 1975; Salvador 1978; Valverde 1967). They frequently feed on insects that are attracted to artificial light sources (Valverde 1967). These highly opportunistic behaviours have contributed to their successful establishment in many regions, and because of their anthropophilic character, they get accidentally stowed away in boxes which are placed on boats, cars or trucks, allowing them to quickly disperse over large distances (e.g., Arredondo, Núñez 2014; Díaz-Fernández et al. 2019; Ortiz-Medina et al. 2019). Surely, individuals of the common wall gecko arrive in many parts of the world, but not always manage to establish viable populations since the climatic conditions could be a limiting factor. However, under a changing climate scenario, these conditions can become more suitable and certain populations may be able to thrive. As a response to global warming, there is evidence of a northward shift of populations of T. mauritanica in the Iberian Peninsula (Moreno-Rueda et al. 2011) and France (Geniez, Cheylan 2012). Likewise, this species is predicted to expand in Madeira Island while selecting Mediterranean-like environmental conditions (Silva-Rocha et al. 2022). The climatic variables related to humidity and temperature seasonality have proven to be key determinants of the niche shift and genetic diversification of native T. mauritanica (Rato et al. 2015a). They are also associated with the successful establishment of the common wall gecko in Madeira Island (Silva-Rocha et al. 2022). Furthermore, a previous ecophysiological study highlighted the species’ plasticity regarding water loss (Rato, Carretero 2015), which is likely a key trait for the success of introduced populations worldwide.

In this study, we aimed to 1) predict the potential and future distribution of T. mauritanica geckos, using correlative models; 2) predict their potential invasive range under future climatic scenarios, using species-specific dispersal models; and 3) assess the niche overlap between native and introduced populations using ellipsoid envelope models. Considering the worldwide geographic distribution of the model species, we anticipate that the predicted range of habitat suitability will be larger than the one currently known. Also, the affinity of this thermophilus species with Mediterranean climates, foresees that future climate change will be beneficial, increasing their suitable habitat. Finally, taking into account the geographic extent of the species distribution, we hypothesise that environmental niches of native and introduced populations will be divergent.

Species occurrence

Species occurrence records of Tarentola mauritanica from native and introduced ranges were compiled from the Global Biodiversity Information Facility (GBIF) database (November 2022, www.gbif.org), personal databases (Catarina Rato and Ricardo Rocha), and recently published records (Arredondo, Núñez 2014; Baldo et al. 2008; Díaz-Fernández et al. 2019; Huerta-Vera 2016; Mačát et al. 2014; Mizerakis, Strachinis 2017; Mizsei et al. 2017; Ortiz-Medina et al. 2019; Strachinis, Artavanis 2017; Strachinis, Pafilis 2018). From GBIF, we only included records containing animal pictures to be sure about the taxonomic assignment. The combination of all these different record sources allowed covering the entire known distribution (native and introduced) of T. mauritanica. Species occurrences were cleaned and filtered for duplicates (i.e., records from the same location) and erroneous geo-⁠referenced records, and later removed from the final dataset. Additionally, to reduce the effects of sampling bias, we performed a spatial thinning on the native and introduced records which were less than 20- and 10 km apart respectively, with the spThin R package (Aiello-Lammens et al. 2015). In the end, a total of 723 records comprising the entire distribution (native and introduced), were used to perform all further analyses.

Environmental data

Using the function worldclim_global from the geodata R package (Hijmans et al. 2022), a set of 19 bioclimatic variables was downloaded from WorldClim v2.1 (http://www.worldclim.org) at a resolution of 2.5 arc minutes, used to calculate the realised ecological niche (sensu Sillero 2011) of the species across the globe. The bioclimatic variables were masked to a hypothesis of accessible area for T. mauritanica

(Anderson, Raza 2010; Barve et al. 2011; Soberón, Peterson 2005). The mask was a 0.5-degree buffered spatial polygon using the occurrence points (Phillips et al. 2009). To test for multicollinearity among environmental variables, we used the function vif from the usdm R package (Naimi et al. 2014). To reduce the number of highly correlated variables, we applied a Pearson correlation with the function layerStats from the package raster (Hijmans 2022), and a hierarchical cluster analysis to select the most relevant variables among collinear variables with a correlation < |0.75|. In the end, the following variables were selected: Mean Diurnal Range (bio2), Temperature Seasonality (bio4), Max Temperature of Warmest Month (bio5), Min Temperature of Coldest Month (bio6), Mean Temperature of Wettest Quarter (bio8), Annual Precipitation (bio12), Precipitation Seasonality (bio15), Precipitation of Driest Quarter (bio17), Precipitation of Warmest Quarter (bio18), and Precipitation of Coldest Quarter (bio19).

Ecological Niche Models (ENMs)

Potential changes in the spatial distribution of the target species were implemented using six different algorithms/models in the biomod2 R package (Thuiller et al. 2009), a computer-based platform for ensemble forecasting of species distributions, enabling the treatment of a range of methodological uncertainties in models, and the examination of species–environment relationships (Araújo, New 2007; Thuiller et al. 2009). The algorithms were, the GLM (generalised linear models), GBM (generalised boosted regression models), GAM (generalised additive models), CTA (classification tree analysis), RF (random forest for classification and regression), and the MAXENT.Phillips (maximum-entropy model).

Standard procedures were followed to compute the realised niche models (Sillero et al. 2021; Sillero, Barbosa 2021). Species occurrence data were coupled with 10,000 randomly generated pseudo-absences, following the recommendations of Barbet-Massin et al. (2012). Ten replicate models were built using 70% of the native and introduced occurrences and pseudo-absence data, and the remaining 30% were withheld for evaluating predictions. Hence, we evaluated a total of 60 models: 1 pseudo-absence set × 10 iterations × 6 models. Models’ performance was evaluated using two discrimination measures: the true skill statistic (TSS) (Allouche et al. 2006) and the area under the receiver operating characteristic (ROC) curve (AUC) (Fielding, Bell 1997). For the ensemble modelling, only those models with a TSS and ROC scores above 0.8, and 0.9, respectively (i.e., with minimum ‘good’ rating) were considered (Allouche et al. 2006; Shabani et al. 2018).

A preliminary screening was carried out to investigate which variables and algorithms would be more suitable for predicting the target species’ ENMs, based on model evaluation (i.e., TSS, and ROC), and projections to the current conditions (See Supplementary Material). One of the main features of the biomod2 software is the ability to combine predictions made in single models in an ensemble.

For the best models, we projected the current potential distribution of T. mauritanica, using the BIOMOD_Projection function from biomod2, and generated an ensemble model as the mean of all models.

To project the potential species distribution under climate change (2061–2080), we used nine CMIP6 climate models (BCC-CSM2-MR, CanESM5, CNRM-CM6-1, CNRM-ESM2-1, CMCC-ESM2, IPSL-CM6A-LR, MIROC6, MIROC-ES2L, MRI-ESM2-0), each within the four shared socio-economic pathways (SSP126, SSP245, SSP370 and SSP585), downloaded from WorldClim v2.1, using the function cmip6_world from the geodata R package (Hijmans et al. 2022). The final future projections were performed using the BIOMOD_Projection function from biomod2, and an ensemble model was generated by averaging all climatic models for each SSP.

Species dispersion model

A cellular automaton model was implemented using the the MigClim R package (Engler, Guisan 2009; Engler et al. 2012) to incorporate dispersal constraints of T. mauritanica and predict the potential dispersal movements towards future climatic scenarios. Datasets to simulate dispersal (i.e., the biomod2 models) had a resolution of 2.5 arcmin per pixel (~ 3.5 km). MigClim enables the implementation of species-specific dispersal constraints and demographic parameters into projections of species distribution models under environmental change and/or landscape fragmentation scenarios (Engler et al. 2012). It can equally well be used to simulate dispersal in stable environments and undisturbed landscapes (e.g., for modelling the potential spread of invasive species) (Engler et al. 2012). MigClim calculates the probability of each grid cell being colonised as a function of its distance to the source cells, its future habitat suitability, the dispersal ability of the species, and the propagule production potential of source cells over time.

We used MigClim to model the dispersal of T. mauritanica under a no-barrier dispersal scenario, that is, the species can disperse to any suitable cell (following Carranza et al. 2000). To do that, we provided MigClim with the following inputs: a map defining the species’ initial distribution, the species’ dispersal parameters, and maps of the current and future species’ habitat suitability obtained previously from biomod2. We chose a reclassification threshold of 0 (i.e., continuous mode) along with a dispersal kernel of 0.1. Long distance dispersal frequency was set to 0.1 and min-max distance range as 25 cells and 2,857 cells, respectively, since the minimum recorded dispersal event is 89 km from the Iberian Peninsula to Alborán islet (Rato et al. 2021a), and the maximum from France to Chile (10.000 km in Arredondo, Núñez 2014). Propagule production potential was set to 1. We had 2 environmental change steps (present, and future 2061–2080) with 30 dispersal steps in each environmental change step. Accordingly, the total number of dispersal steps simulated was equal to [envChgSteps] × [dispSteps], here 60, which corresponds to 60 years from 2023 to 2080. Simulations were repeated 100 times.

Ecological niche overlap

To characterise and compare the niches of both native and introduced populations of T. mauritanica, we created ellipsoidal envelope ecological niche models for each group of interest.

Ellipsoidal envelopes were built using the first three PCAs of the variables previously used in the species distribution modelling (see Environmental data). Then, we retrieved the environmental information of the occurrence points for each group and fit three-dimensional minimum volume ellipsoids (Van Aelst, Rousseeuw 2009). PCAs were obtained using the kuenm_rpca function from the kuenm R package (Cobos et al. 2019).

To measure overlap using these ellipsoids, we used two different approaches. First, the full overlap, which considered the background data that overlap between the two ellipsoids. To this end, a cloud of points uniformly distributed in the multi-dimensional environmental space that covers the two ellipsoids is used to detect how those envelopes overlap (Qiao et al. 2016). Secondly, we measured the background overlap, which considers only the subset of the full overlap corresponding to environments that are accessible to the groups of interest (which may or may not fill all the volume of the two ellipsoids) (Nuñez-Penichet et al. 2021). The overlap is measured using the Jaccard Index (Mammola 2019), where the value of overlap is the ratio between the number of points within the intersection of two ellipsoids (E1 ∩ E2) and the total number of points contained by two ellipsoids (E1 ∪ E2): J = E1 ∩ E2/E1 ∪ E2. To perform the analyses of overlap using the environmental combinations accessible to each group, we used the values of the PCs for each group. To test the statistical significance of our observed values of overlap, we compared the observed results to a null distribution of 1,000 overlapped values derived from comparing ellipsoid envelopes created from points randomly sampled from each group’s accessible environment. The null hypothesis is that the two ellipsoids fitted to actual observations overlap at least as much as random-data ellipsoids. If the observed niche overlap value falls inside the upper 95% of the null distribution, the null hypothesis cannot be rejected. As the full overlap analysis uses points that are uniformly distributed, significance tests (which rely on available conditions) were performed only for analyses of background overlap. Analyses of niche overlap were carried out in R using the package ellipsenm (Cobos et al. 2020).

All six algorithms (GLM, CTA, RF, GBM, GAM, and MAXENT.Phillips) produced species distribution models that can be considered good to excellent, with TSS and ROC evaluation scores over 0.8 and 0.9, respectively (Fig. S1). However, both RF and CTA algorithms were removed from the modelling and projection analyses; the CTA exhibited a low mean ROC score (< 0.75; Fig. S2), and they both delivered unrealistic ENMs for T. mauritanica, such as Scandinavia (in CTA), Alaska, and Siberia (in RF) (Fig. S5). Moreover, most of the environmental variables contributed very little to the RF model (Fig. S3), which might explain its poor capacity to estimate accurately suitable habitats for T. mauritanica. As for the initially considered environmental variables, they were all kept for further analyses.

In general, the most suitable areas for T. mauritanica correspond to regions with low to moderate precipitation, moderate to high temperatures, low-temperature seasonality, and low to moderate mean diurnal range (Fig. S4).

Tarentola mauritanica’s ensembled potential distribution is predicted to be wider than the range it is known today (Figs. 1 and 2). For instance, the regions around the Black and Caspian Seas present high habitat suitability, as well as south-eastern Asia, the south-western coast of South Africa and the southern coast of Australia. In Europe, suitable regions also include the northern latitudes of this continent.

As for the habitat suitability to 2061–2080 (Fig. 3), basically, all SSP scenarios support very similar models for the future distribution of T. mauritanica. In Europe, a shift to northern latitudes is forecasted, reaching the southern UK and the southern Scandinavian Peninsula. In North America, the north-eastern region of the USA will become suitable in the future. Moreover, south-eastern Spain and North Africa are predicted to become less suitable for this gecko species. The same is observed for south-eastern Asia, Argentina, and Uruguay.

Again, the species dispersal models among the different future scenarios are very similar (Fig. 4), and they also predict that over the next 60 years, T. mauritanica will be able to colonise a similar geographic range compared to the one obtained with the correlative models for the future (Fig. 3).

The full niche overlap between native and introduced ellipsoids was 0.43, and the overlap using the available conditions was 0.58 (Fig. 5). The null hypothesis of niche overlap (using the background) was rejected (p-value = 0.00), with the observed values of niche overlap being smaller than expected, falling below the 5% confidence limit.

The common wall gecko, Tarentola mauritanica is a native and widespread Mediterranean lizard (Vogrin et al. 2017). Therefore, in its native range the species is subject to mild wet winters, and warm and dry summers (Lionello et al. 2006). These specific climatic conditions coincide with the obtained response curves for the several temperature and precipitation variables used in this study. Not surprisingly, the correlative models predicted California, central Chile, the Cape Region of South Africa, around the Caspian Sea, south-eastern Asia, and south-western and southern Australia, as some of the most suitable geographic areas for this gecko to occur, which also correspond to the areas of the world with Mediterranean-type ecosystems (Esler et al. 2018; Sayre et al. 2020). Nevertheless, the cool temperate climate we find in Northern Europe is also predicted as moderately suitable for T. mauritanica.

Although this gecko species has been successfully introduced across several sub-tropical and tropical regions of the world (e.g., Madeira, Azores, Yucatán, Florida, Uruguay and Argentina in Arredondo, Núñez 2014; Báez, Biscoito 1993; Baldo et al. 2008; Barreiros et al. 2010; Díaz-Fernández et al. 2019; Huerta-Vera 2016; Jesus et al. 2008; Rato et al. 2015b), our ENMs have identified only one (not yet colonised) highly suitable tropical region located in south-western Iran, facing the United Arab Emirates Peninsula.

Despite some known limitations (Araújo, Peterson 2012), ENMs can be a powerful tool to predict where invasive species will spread next. However, until the last stage of invasion, an introduced species is not yet at equilibrium with its environment, as demonstrated in previous studies focusing on other invasive species (e.g., Barbet-Massin et al. 2018; Gallien et al. 2012; Václavík, Meentemeyer 2012). The equilibrium hypothesis is an important assumption, and its violation has to be acknowledged when interpreting ENMs predictions (Garcia et al. 2012). Indeed, violating the equilibrium hypothesis has some consequences when modelling species distribution, such as the underestimation of the potential climatic niche of a species, which can in turn underestimate the geographical area the species can invade (Václavík, Meentemeyer 2012). Considering that the introduction of T. mauritanica into tropical ecosystems has taken place only in the last 20 to 30 years, it is reasonable to assume that these populations have not yet reached an equilibrium with the novel environmental conditions, and that the habitat suitability models might be underestimating the probabilities of species occurrences. Actually, over the last three decades, the occurrence area of T. mauritanica in Madeira Island has increased by more than 20 km (Silva-Rocha et al. 2022). Moreover, the same study demonstrates that despite preferring Mediterranean-like climate areas, more humid regimes seem also suitable for the introduced populations of the common wall gecko in sub-tropical Madeira Island.

Furthermore, our niche overlap results demonstrate that T. mauritanica’s realised niche has not been conserved over space, as the naturalised climatic niche of the introduced populations differs significantly from its native one (e.g., Early, Sax 2014; Medley 2010; Parravicini et al. 2015). These results highlight two important aspects: first, there has been no climatic niche conservatism during the several introductions of T. mauritanica; and second, this species seems to be able to cope with novel and more humid environments.

Undoubtedly, biological invasions offer a rare opportunity to investigate how species colonise new environments (Kueffer et al. 2013; Richardson, Pyšek 2008; Sax et al. 2007), and whether they preserve their climatic niche in a new range (Pearman et al. 2008). Addressing this question has proven important in recent years as a test for ecological niche models, which depend heavily on climatic niche conservatism between native and exotic ranges (Colwell, Rangel 2009; Pearman et al. 2008; Peterson 2011). Evidence exists both for (e.g., Peterson 2011; Petitpierre et al. 2012; Strubbe et al. 2013), and against (e.g., Broennimann et al. 2007; Fitzpatrick et al. 2007; Lauzeral et al. 2011; Li et al. 2014; Medley 2010; Rödder, Lötters 2009) climatic niche conservatism during invasions, which is most likely related to the different types of niche change, biological and/methodological study contexts, data types, species characteristics, or methods being used (see references in Guisan et al. 2014).

The introduction and expansion of the common wall gecko populations across different tropical ranges (Arredondo, Núñez 2014; Baldo et al. 2008; Silva-Rocha et al. 2022), suggests that the species can cope with more humid environments. However, as the response curves demonstrate, when humidity is too high, the habitat becomes unsuitable for T. mauritanica. This is most likely due to the limited favourable conditions viable for gekkotan reproduction, particularly concerning humid environments. Like most gecko lizards, T. mauritanica produces rigid-shelled eggs (a pre-adaptation to arid environments) that in conditions of high humidity can limit embryo development (Pike et al. 2012). Moreover, evidence from the gecko Chondrodactylus turneri suggests that under high moisture conditions, fungal infections can decrease the viability of hard-shelled gekkotan eggs (Andrews 2015). Therefore, unless the individuals inhabiting the tropics can select suitable micro-habitats for egg laying, the future of these populations might be compromised under current climatic conditions. Yet, the climate is changing at a global scale and at such a fast pace that species are more likely to change their distributions as a response than to adapt in situ (Bradshaw, Holzapfel 2006).

Indeed, the species distribution models projected to 2061–2080, forecast that the range of T. mauritanica is likely to shift towards northern latitudes but, surprisingly, not to expand. South-eastern Spain and parts of North Africa will become unsuitable, while the south of the UK and the Scandinavian Peninsula seem to offer more relevant environmental conditions for this gecko species. Furthermore, the climte in the tropics will become unsuitable, while parts of the north-eastern USA seem to favour the establishment of T. mauritanica. At least in the Iberian Peninsula and France, a northward shift in the range of the common wall gecko’s populations has already been documented as a response to global warming (Geniez, Cheylan 2012; Moreno-Rueda et al. 2011). According to Sumner et al. (2003), by the late twenty-first century, the far south of Spain will undergo a continued spread of aridity of the climate, extending westwards. This is already the hottest and one of the aridests parts of Spain, and the predicted environmental changes will be disastrous to several taxa of the region (e.g., Algyroides marchii in Rato et al. 2021b), including a thermophilus reptile such T. mauritanica.

Contrary to what we obtained with the species distribution models, the dispersal models forecast that colonization by the common wall gecko will still take place across southern Spain and North Africa. Indeed, the largest colonisation will take place in the European continent, with T. mauritanica expanding its territory towards the northern and eastern regions. In South America, the dispersal models also predicted a potential expansion to the south, and towards the north following the Atlantic coast of Brazil. The population from California is also expected to colonise more northern territories.

The common wall gecko occurs naturally in typical Mediterranean climate and has mostly a crepuscular activity. It thermoregulates during the first 2–3 h of the day near the refugium, to where it returns once their optimal temperature is achieved (Martínez-Rica 1974). In comparison to other sympatric similar-sized lacertid lizards, this gecko has a higher resistance to dehydration (García-Muñoz, Carretero 2013; Osojnik et al. 2013), with great plasticity among populations for this trait (Rato, Carretero 2015). Indeed, this feature conveys the common wall gecko with a great capacity to withstand long periods without water, and able to survive during transmarine trips. Moreover, future global warming would not be a limiting factor for this species, as already documented in previous studies (Geniez, Cheylan 2012; Moreno-Rueda et al. 2011). In fact, Moreno-Rueda et al. (2011) saw that the southern range of T. mauritanica has not been affected by global warming during the last 70 years. Therefore, both the predicted future range and colonization models are in line with the ecophysiological requirements and dispersal capacity of this species.

Overall, globalisation is increasing the frequency in translocating species from their native ranges to novel transmarine environments. Over the last 30 years, the native Mediterranean common wall gecko, Tarentola mauritanica, has been introduced across several tropical and subtropical regions of the globe, where it has found novel environmental conditions. Our results confirm that the realised niche occupied by these introduced populations is significantly different from their native one, highlighting the capacity of this species to cope with novel ecosystems, which is key to a successful colonisation. Over the next 60 years, the geographic range of this gecko is likely to shift, benefiting from global climate change. The lack of knowledge on the effects it might have on local species and ecosystems reinforces the need for strong and serious monitoring actions.

Data availability

The datasets generated and/or analysed, and R scripts used during the current study are available in the Figshare repository [link will be made available upon acceptance of the manuscript].

Funding

CR is supported by a postdoctoral contract from Fundação para a Ciência e Tecnologia (FCT), Portugal (DL57/2016/CP1440/CT0005). ISR was supported by FCT through a post-doc grant within project PTDC/BIA-EVL/27958/2017.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Catarina Rato and Iolanda Silva-Rocha. The first draft of the manuscript was written by Catarina Rato and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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What does the future hold for a thermophilus and widely introduced gecko, Tarentola mauritanica (Squamata: Phyllodactylidae)?

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