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Article

How Will the Distributions of Native and Invasive Species Be Affected by Climate Change? Insights from Giant South American Land Snails

by
Wanderson Siqueira Teles
1,2,
Daniel de Paiva Silva
2,
Bruno Vilela
3,
Dilermando Pereira Lima-Junior
4,
João Carlos Pires-Oliveira
5 and
Marcel Sabino Miranda
6,*
1
Programa de Pós-Graduação em Ecologia e Evolução, Universidade Federal de Goiás, Goiania 74001-970, GO, Brazil
2
Conservation Biogeography and Macroecology Laboratory (COBIMA LAB), Departamento de Ciências Biológicas, Instituto Federal Goiano, Urutai 75790-000, GO, Brazil
3
Instituto de Biologia, Universidade Federal da Bahia, Salvador 40170-115, BA, Brazil
4
Laboratório de Ecologia e Conservação de Ecossistemas Aquáticos, Universidade Federal de Mato Grosso, Campus Universitário do Araguaia, Pontal do Araguaia 78698-000, MT, Brazil
5
Programa de Pós-Graduação em Ecologia e Conservação, Universidade do Estado do Mato Grosso, Nova Xavantina 78690-000, MT, Brazil
6
Programa de Pós-Graduação em Biologia Animal, Instituto de Biologia, Universidade Estadual de Campinas, Campinas 13083-970, SP, Brazil
*
Author to whom correspondence should be addressed.
Diversity 2022, 14(6), 467; https://doi.org/10.3390/d14060467
Submission received: 22 April 2022 / Revised: 27 May 2022 / Accepted: 8 June 2022 / Published: 11 June 2022
(This article belongs to the Special Issue Continental Mollusca under Global Change)
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Abstract

:
Climate change and invasive species are critical factors affecting native land snail diversity. In South America, the introduced Giant African Snail (Lissachatina fulica) has spread significantly in recent decades into the habitat of the threatened native giant snails of the genus Megalobulimus. We applied species distribution modeling (SDM), using the maximum entropy method (Maxent) and environmental niche analysis, to understand the ecological relationships between these species in a climate change scenario. We compiled a dataset of occurrences of L. fulica and 10 Megalobulimus species in South America and predicted the distribution of the species in current and future scenarios (2040–2060). We found that L. fulica has a broader environmental niche and potential distribution than the South American Megalobulimus species. The distribution of six Megalobulimus species will have their suitable areas decreased, whereas the distribution of the invasive species L. fulica will not change significantly in the near future. A correlation between the spread of L. fulica and the decline of native Megalobulimus species in South America was found due to habitat alteration from climate change, but this relationship does not seem to be related to a robust competitive interaction between the invasive and native species.

1. Introduction

Climate change is one of the main drivers of global biodiversity change, along with habitat destruction and anthropogenic pressures. Some effects on biodiversity include changes in distribution, abundance, and phenology, increasing the extinction risk for some species [1]. A rapid and remarkable change is predicted for this century, which will affect all levels of biodiversity, from individuals to biomes [2,3]. It is now well established from various studies that climate change will likely be one of the main factors affecting species extinction in the 21st century [4,5].
Along with climate change, invasive species are also a significant problem affecting biodiversity changes. Invasive species usually compete for resources with native species [6] and can modify communities [7]. The interactions between native and invasive species are varied [8], whereas niche overlap might be high, and competition may lead to a decline, or even extinction, of native populations [6,9,10]. Invasive species can also negatively impact local and national economies and might cause health and social problems [11,12,13]. Climate change can also further intensify these effects by facilitating the spread and establishment of invasive species [14].
The Giant African Snail, Lissachatina fulica (Bowdich, 1822), is considered one of the world’s most invasive species, with considerable known negative impacts. This species is native to East Africa [15], and it was introduced in South America in the 1980s as a substitute for the escargot Cornu aspersum (Müller, 1774) as a human food item [16]. Since then, the species has rapidly spread across the continent, possibly still currently invading some areas [17,18,19]. This species can act as an intermediate host of several parasites, such as Angiostrongylus cantonensis (Chen, 1935), which causes eosinophilic meningitis in humans [20,21]. Lissachatina fulica has a generalist diet [22] and an outstanding reproductive potential; one snail can lay clutches of up to 400 eggs, with an annual production of 1200 eggs [23]. Predation by L. fulica on invasive veronicellid slugs has also been reported in Hawaii [24], indicating that it can also directly affect the native fauna, combined with its other biological characteristics, aside from the possible indirect effects of competition for resources.
One of the main characteristic groups of land snails in South America is the giant snail of the genus Megalobulimus (Miller, 1878). The genus has more than 80 described species [25,26,27], one of the region’s most speciose genera. It is endemic to South America, with some introductions to the Caribbean Islands [25,28,29]. They are nocturnal, burying themselves in the litter during the day, or during periods of hibernation or aestivation [25,30,31]. Megalobulimus activity is very seasonal [32,33], occurring in low densities [34], and they also have low reproductive potential, with an annual production of up to nine eggs [33]. Due to life history traits, some species are considered threatened [34,35,36].
More recently, with the increasing availability of climatological and species occurrences data, it became possible to evaluate the potential effect of invasive species on native varieties on macroecological scales—such as the case of L. fulica and the Megalobulimus snails. At the same time, further developments of niche and species distribution modeling (SDM) allow researchers to test ecological processes on large spatial scales [37,38]. SDMs can be used to test hypotheses regarding the conservation of native species, the management of invasive species [39], or the detection of areas that can be potentially invaded [39,40]. SDM analyses can also show how species distributions change over time [41].
In this context, we evaluated the potential effect of L. fulica on the distribution of the native species of the genus Megalobulimus in South America using a climate change scenario. For this, we assembled a comprehensive compilation of occurrences records for the invasive species and 10 native Megalobulimus species and explored these using SDM and ecological niche analyses. We expected that climate change would increase the potential effects of L. fulica on the future distributional ranges for the native Megalobulimus species.

2. Materials and Methods

2.1. Occurrence Datasets

The records of L. fulica were obtained through access to online databases and literature searches. We used the following online databases to search for occurrences of the target species: Species Link [42], Portal da Biodiversidade Reflora [43], GBIF [44], and iNaturalist [45]. The literature search was made in the primary collection of the Web of Science [46] using “Achatina fulica”, “A. fulica”, “Lissachatina fulica”, “L. fulica”, “Caramujo-Gigante-Africano”, and “Giant African Snail” as keywords. We obtained 2493 occurrences of the Giant African Snail in South America (Figure 1; Table S1).
For the genus Megalobulimus, the records were obtained from the selected literature and validated by specialists, as records from the online databases have doubtful identifications [47]. Throughout the literature, we used occurrences from species with at least 10 occurrence records of the following 10 species: Megalobulimus dryades (Fontenelle, Simone, and Cavallari, 2021); Megalobulimus elongatus (Bequaert, 1948); Megalobulimus granulosus (Rang, 1831); Megalobulimus haemastomus (Scopoli, 1786); Megalobulimus intertextus (Pilsbry, 1895); Megalobulimus lorentzianus (Doering, 1876); Megalobulimus musculus (Bequaert, 1948); Megalobulimus paranaguensis (Pilsbry and Ihering, 1900); Megalobulimus yporanganus (Ihering and Pilsbry, 1901); and Megalobulimus sanctipauli (Ihering and Pilsbry, 1900) (Figure 1; Table S2). Across these 10 species, we obtained 250 records from five countries (Brazil, Argentina, Uruguay, Paraguay, and Bolivia) in 4 different South American biomes (Atlantic Rainforest, Cerrado, Pampas, and Chaco) (Figure 1; Table S2).
We created a dataset containing geographical coordinate information (in decimal degrees) and species names. After completing the datasets, we reduced spatial sampling bias by thinning occurrence records to a minimum distance of at least 4 km between them [48].

2.2. Environmental Data

Environmental data for current and future climatic conditions were obtained from WorldClim 2.0 [49] at a spatial resolution of 4 km. We used the scenario CSM2-MR with shared socioeconomic pathway 126 (ssp126) for 2040–2060. This scenario predicts average global warming of 2 °C until 2100 [50]. The 19 raw bioclimatic variables were used in our modeling experiment (Table 1). The variables of the current scenario were standardized and submitted to principal component analysis (PCA). Then, we used the first six principal components responsible for approximately 95% of the variations as environmental predictors (Table 1). This approach was used to prevent multicollinearity between the bioclimatic variables [51,52]. For the 2040–2060 scenario, we projected the linear coefficients of the current principal components into the future to generate a correspondence between scenarios, and then we applied PCA to generate PCs for that scenario.

2.3. Modeling Procedures

Validating the absences of the Megalobulimus species is complex because they have low-density populations, and because the individuals remain buried in the soil [25,30]. Nonetheless, true absences are a general issue affecting the prediction of species ranges. Moreover, most species analyzed here have a minimum number of occurrences. For these reasons, the potential distribution of species was estimated using the maximum entropy algorithm (Maxent), with the automatic linear and quadratic resources activated and all other parameters set as default [53]. Maxent is relatively robust for species with few occurrences [54]. We used the receiver operating characteristic curve (ROC) threshold to transform species suitability into binary maps balancing omission and commission errors. We used the Jaccard index to evaluate our models, using the models produced with observed data as a base [55]. This index varies between 0 and 1, with values close to 1 indicating better goodness of fit and a correct preview [55]. Jaccard values around 0.5 indicate a random distribution of data.
We used a Student t-test for dependent samples to evaluate the overlap between the L. fulica and Megalobulimus species in current and future scenarios. We also used a Student t-test to assess whether there was a difference in the geographical distribution in both scenarios. We overlapped the resulting maps to evaluate the changes between the current and 2040–2060 scenarios. In this approach, the grid was divided into (1) areas with no prediction; (2) areas suitable only in the current scenario; (3) areas suitable only in the 2040–2060 scenario; (4) areas suitable in both scenarios (stable areas). Additionally, we calculated the expected species richness projected in the current and future scenarios, summing the number of species in a grid cell in the current and future scenarios. The analyses were run in the R 3.6.6 environment [56], using the script of the ENMTML package [57] (File S3). Differences between treatments were considered when p < 0.05.

2.4. Bioclimatic Niche Analysis

We used Broennimann et al.’s [38] method to evaluate the environmental niche of each species. We used the PCA-env approach to consider all the areas occupied by the species. We compared the environmental conditions available for native species with the environmental conditions of invasive species. With these comparisons, we calculated the niche overlap using D metrics [58], with values ranging from 0 (no overlap between niches) to 1 (complete overlap between niches). We then created a randomization routine to test the hypothesis of niche overlap based on niche similarity, following the method of Warren et al. [59]. The niche similarity test compares whether the niche overlap of the range of the native species randomly distributed over its background keeps the range of invasive species unchanged (1→2), and then the reciprocal comparison is made (1←2). Moreover, niche unfilling and stability metrics were also calculated. Stability is the proportion of kernel densities in one species distribution overlapping with the other species distribution, whereas unfilling is the proportion of kernel distribution of one distribution located in conditions other than the further distribution. These variables ranged from 0 to 1 [60]. The script of the environmental niche analysis can be found in File S4.

3. Results

The Jaccard value for L. fulica was 0.755, and for Megalobulimus species, the values varied between 0.444 and 0.933, with a mean value of 0.725 (S.D. = 0.158). Eight species of Megalobulimus had Jaccard values higher than 0.7 (Table 2). We observed in the current scenario that most of South America is suitable for L. fulica, except for the majority of Argentina, and Uruguay, where few areas are suitable in the north of both countries, and the northern Chilean regions (Figure 2). An island of suitability was found in western Patagonia (Figure 2). Megalobulimus species had smaller suitable areas when compared with L. fulica, and most of them had significant overlapping distribution with the invasive species, except for M. lorentzianus (Figure 2).
For future scenarios, our results indicate a decrease in suitable areas for L. fulica and six Megalobulimus species (M. dryades, M. haemastomus, M. paranaguensis, M. sanctipauli, M. yporanganus, and M. intertextus), while we observed an increase in suitable areas for four species (M. elongatus, M. granulosus, M. lorentzianus, and M. musculus) (Table 2; Figure 3). There were no significant differences found between present and future scenarios in distribution areas (t = 0.489; d.f. = 10; p = 0.635) or between overlapping distribution areas between L. fulica and Megalobulimus species (t = 1.932; d.f. = 9; p = 0.085).
With the overlap between current and future distributions for each species, we observed that the most suitable predicted areas were climatically stable (Figure 4). Lissachatina fulica showed large stable areas where distribution remained unchanged between current and future predictions (Figure 4). Megalobulimus elongatus, M. granulosus, and M. lorentzianus showed large suitable areas predicted for the future, indicating an increase in their distribution, whereas M. dyades, M. haemastomus, M. paranaguensis, M. sanctipauli, and M. yporanganus showed larger suitable areas in the current scenario, indicating a trend to decrease their distribution in the future (Table 2; Figure 4).
The modeled species richness of Megalobulimus in the current scenario was high in the Brazilian southern and southeastern coastal plain and the frontier between Brazil, Argentina, and Paraguay, with a small connection among the areas in these countries (Figure 5). There is a trend to reduce the richness and a loss of connection between both regions in the future (Figure 5). The expected richness reduces in the interior of Brazil, without migration to the south of the continent (Figure 5). These richest areas continue to overlap with the L. fulica distribution in both scenarios (Figure 5).
Lissachatina fulica and the native species had distinct niches (Figure 6; Table 3) and slight overlaps (D < 0.05; Table 3). Niche similarity tests indicated that overlap values between L. fulica and M. lorentzianus, and L. fulica and M. intertextus, were no different from a null expectation (Table 3). The invasive species occupies a more prominent and extensive climatic space than the Megalobulimus species (Figure 6). A great unfilling (<0.56) of Megalobulimus species can be detected in all cases, whereas L. fulica had small unfilling values (>0.05), except for M. lorentzianus (=0.558). Lissachatina fulica had great stability values (<0.95) for almost all Megalobulimus species, except for M. lorentzianus (=0.442).

4. Discussion

Here, we showed that L. fulica has a broader environmental niche and potential distribution than the South American native Megalobulimus species, whose environmental niches are narrower than the invasive species niche. Our results agree with previous studies suggesting that L. fulica has high environmental tolerance and prefers warmer environments [19,23,34,61,62], making their niches wider in tropical regions. Although L. fulica has small genetic variability where it was introduced, as in South America [63], its generalist behavior allows the species to occupy a wide distribution. On the other hand, Megalobulimus species occur in more restricted geographical distributions [25,35,36,64,65] and have more specific habits [31,32,33,34,63,66], which makes their niche breadth more specialized.
Our models in the current scenario for L. fulica matched previous predictions made for South America [19,67,68], with susceptible areas identified in Guyana, French Guiana, Suriname, Peru, Venezuela, Ecuador, and Colombia. At the same time, regions in Chile and Uruguay appear least vulnerable to invasion, except for the area of southern Chile. The suitable areas found in different SDMs for L. fulica in southern Chile seem to be an artifact of methodology caused by the occurrence of this species in other northern Andes areas, such as Ecuador and Peru. The main difference is that most Amazon Rainforests are considered suitable for L. fulica. Although the models cannot be directly compared [53], parallel interpretation of the output can help identify areas susceptible to L. fulica invasion [19]. This can indicate that the current distribution of L. fulica is still underestimated, and that new occurrences may still be revealed in many other areas.
In South America, few countries have specific legislation and control policies for the Giant African Snail [17,19]. We suggest that control and management policies should be created and implemented, especially in the northern countries of the continent. Besides the wide distribution in South America, our results show that the distribution of L. fulica did not significantly differ between the current and future scenarios tested, indicating that the spread of the species could stabilize in the near future.
Native land snails are among the most threatened groups globally, with the highest extinction rates reported [69]. Furthermore, this number is undoubtedly underestimated [70]. In South America, this scenario is similar to other regions, with the conservation status of most land snails inadequate [35,71,72]. The distribution of land snails in South America suffers from knowledge gaps [26,73,74], especially concerning taxonomic identity (Linnean shortfall) and spatial distribution (Wallacean shortfall). Nonetheless, SDMs are essential tools to access the distribution of these “data deficient” groups [36,65], allowing for inclusion in broad-scale conservation studies. Integrating information on the basic biology and ecology of land snails is essential to better inform such conservation efforts and the interpretation of the SDMs. Given this scenario, future studies in the areas highlighted in our analysis may add important information about species distribution and their natural history.
The genus Megalobulimus is probably one of the groups most highly threatened by many factors, such as anthropogenic pressures and climate change [34,35,36]. In this respect, our results reinforce that this group is of great concern, as six species have their suitable areas reduced. Moreover, an expected distributional shift to higher latitudes due to climate change [36,75,76] was not detected, with a loss of connection between populations (Figure 5), especially in south and southeastern Brazil and the frontiers between Brazil, Argentina, and Paraguay. This situation is expected, since land snails, in general, are a group with low dispersal abilities [34,77]. Thus, our results show a decline in Megalobulimus species that needs to be monitored in more detail to prevent the loss of future local populations and species extinctions.
Our results indicate that the suitable areas for the Megalobulimus species, which occur in areas that overlap with L. fulica (e.g., M. dryades, M. paranaguensis, M. yporanganus), will decrease. At the same time, species with smaller or almost no overlap (e.g., M. elongatus and M. lorentzianus) will see an increase in their suitable areas in South America. Moreover, there were no changes in overlapping areas between L. fulica and Megalobulimus species in current and future scenarios. In general, invasive species become established, and native species usually decline due to habitat modifications by humans [12]. Although the presence in protected areas is reported in some cases [78,79,80], the abundance of L. fulica tends to decline from disturbed habitats at the forest margin toward the interior of the natural forest [81,82]. Thus, the occurrence of invasive species can be used as an indicator of environmental conditions becoming unsuitable for native species.
It is speculated in the literature that the decline in some native populations might be caused by invasive achatinid species [83,84,85]. However, no clear cases in which competition between native and invasive achatinids has been demonstrated [34,85,86], and there is little evidence of competitive exclusion in land snail communities [87,88]. Invasive land snails occur preferentially in strongly modified environments and are abundant in deforested habitats [82], and changes in community composition are not primarily caused by invasive species [12,89]. Some behavioral changes of L. fulica in the presence of other snails can be detected [24,86], but no negative direct quantitative effect of this invasive species on native species is reported. Therefore, as far as we know, there is no negative immediate direct quantitative impact of this invasive species. Although the possibility of competition between invasive and native land snails cannot be excluded, it is speculated that it might be limited, since most land snails are generalists, but adverse effects may lead to decline or extinction in the long term [12,80,81]. The lack of data regarding the interaction effects of the invasive L. fulica on the native species requires long-term field studies to better understand these relationships.

5. Conclusions

We observed that L. fulica has a broader niche and potential distribution compared with the native Megalobulimus species, where niches are narrower than for invasive species. We found that the distribution of L. fulica will probably not change significantly in the near future, whereas some Megalobulimus species will decrease their suitable areas. Our results indicate a correlation between the spread of L. fulica and the decline of native Megalobulimus species due to climate change, but this relationship does not seem to have a strong competitive interaction. Thus, the occurrence of invasive species can indicate environmental conditions becoming unsuitable for native species. Through SDMs and environmental niche analysis, we identified areas of potential distribution of invasive and native species and the niche relationship between them. This application provides essential and promising information for non-model and lesser-known organisms, such as giant land snails. This knowledge is beneficial to governmental authorities for the development of actions to control L. fulica populations, and at the same time, establish baseline data for integrated conservation actions to preserve the threatened native malacofauna diversity, focusing on the preservation of their natural habitats.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/d14060467/s1, Table S1: Occurrence points of L. fulica; Table S2: Occurrence points of Megalobulimus species; File S3: Script of SDM; File S4: Environmental niche analysis of L. fulica and Megalobulimus species.

Author Contributions

Conceptualization, D.d.P.S. and M.S.M.; methodology, D.d.P.S., D.P.L.-J., B.V. and J.C.P.-O.; formal analysis, W.S.T., D.d.P.S., B.V. and M.S.M.; data curation, W.S.T. and M.S.M.; writing—original draft preparation, W.S.T., D.d.P.S. and M.S.M.; writing—review and editing, W.S.T., D.d.P.S., B.V., D.P.L.-J., J.C.P.-O. and M.S.M.; supervision, D.d.P.S. All authors have read and agreed to the published version of the manuscript.

Funding

M.S.M. received a grant from the São Paulo Research Foundation (FAPESP), processes 2011/20917-8, 2013/00670-6 and 2017/01081-5. D.d.P.S. was supported by productivity grants from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq-Proc. Number: 304494/2019-4). D.P.L.-J. was supported by productivity grants from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq-Proc. Number: 305923/2020-0).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

Thanks are due to Mark Stevens (South Australian Museum), for the English review and improvement of this paper.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Occurrence records and spatial distribution maps of the ten species of Megalobulimus and Lissachatina fulica in South America. The gray minimum convex polygon of L. fulica is represented in the maps of the native species.
Figure 1. Occurrence records and spatial distribution maps of the ten species of Megalobulimus and Lissachatina fulica in South America. The gray minimum convex polygon of L. fulica is represented in the maps of the native species.
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Figure 2. Predicted distribution maps for the current scenario of the ten species of Megalobulimus and Lissachatina fulica. The native species are represented by the following colors: M. sanctipauli (black), M. dryades (yellow), M. elongatus (green), M. granulosus (blue), M. haemastomus (red), M. intertextus (brown), M. lorentzianus (orange), M. musculus (beige), M. paranaguensis (pink), and M. yporanganus (purple). The lines on the maps for each species correspond to the distribution of L. fulica.
Figure 2. Predicted distribution maps for the current scenario of the ten species of Megalobulimus and Lissachatina fulica. The native species are represented by the following colors: M. sanctipauli (black), M. dryades (yellow), M. elongatus (green), M. granulosus (blue), M. haemastomus (red), M. intertextus (brown), M. lorentzianus (orange), M. musculus (beige), M. paranaguensis (pink), and M. yporanganus (purple). The lines on the maps for each species correspond to the distribution of L. fulica.
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Figure 3. Predicted distribution maps for the future scenario (2040–2060) of the ten species of Megalobulimus and Lissachatina fulica. The native species are represented by the following colors: M. sanctipauli (black), M. dryades (yellow), M. elongatus (green), M. granulosus (blue), M. haemastomus (red), M. intertextus (brown), M. lorentzianus (orange), M. musculus (beige), M. paranaguensis (pink), and M. yporanganus (purple). The lines on the maps for each species correspond to the distribution of L. fulica.
Figure 3. Predicted distribution maps for the future scenario (2040–2060) of the ten species of Megalobulimus and Lissachatina fulica. The native species are represented by the following colors: M. sanctipauli (black), M. dryades (yellow), M. elongatus (green), M. granulosus (blue), M. haemastomus (red), M. intertextus (brown), M. lorentzianus (orange), M. musculus (beige), M. paranaguensis (pink), and M. yporanganus (purple). The lines on the maps for each species correspond to the distribution of L. fulica.
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Figure 4. Maps of overlapping potential distribution between current and future (2040–2060) scenarios indicate stable areas (purple), predicted areas under the current scenario (blue), and predicted areas only in future scenarios (red). White regions were predicted to be unsuitable.
Figure 4. Maps of overlapping potential distribution between current and future (2040–2060) scenarios indicate stable areas (purple), predicted areas under the current scenario (blue), and predicted areas only in future scenarios (red). White regions were predicted to be unsuitable.
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Figure 5. Potential species richness distribution of Megalobulimus species in the current and future scenarios. Locations with low species richness are shown in blue, whereas locations with high species richness are shown in red. The potential distribution of Lissachatina fulica in both scenarios is hatched.
Figure 5. Potential species richness distribution of Megalobulimus species in the current and future scenarios. Locations with low species richness are shown in blue, whereas locations with high species richness are shown in red. The potential distribution of Lissachatina fulica in both scenarios is hatched.
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Figure 6. Broennimann et al. [38] PCA-env environmental niche analysis between Lissachatina fulica and 10 Megalobulimus species. The solid and dashed lines represent 50% and 100% of the available environmental conditions for each species, respectively. The following colors represent the species: L. fulica (purple), M. sanctipauli (black), M. dryades (yellow), M. elongatus (green), M. granulosus (blue), M. haemastomus (red), M. intertextus (brown), M. lorentzianus (orange), M. musculus (beige), M. paranaguensis (pink), and M. yporanganus (purple). In the niche overlap analysis, the stability is pink, the unfilling of Megalobulimus species in the L. fulica environmental niche is purple, and the unfilling of L. fulica in the Megalobulimus environmental niche is green.
Figure 6. Broennimann et al. [38] PCA-env environmental niche analysis between Lissachatina fulica and 10 Megalobulimus species. The solid and dashed lines represent 50% and 100% of the available environmental conditions for each species, respectively. The following colors represent the species: L. fulica (purple), M. sanctipauli (black), M. dryades (yellow), M. elongatus (green), M. granulosus (blue), M. haemastomus (red), M. intertextus (brown), M. lorentzianus (orange), M. musculus (beige), M. paranaguensis (pink), and M. yporanganus (purple). In the niche overlap analysis, the stability is pink, the unfilling of Megalobulimus species in the L. fulica environmental niche is purple, and the unfilling of L. fulica in the Megalobulimus environmental niche is green.
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Table
Table 1. Summary of the PCA from which the principal components (PC) used as new environmental layers were generated. Each cell value represents the individual loadings of each variable in each of the PCs. The PCs, along with their individual and accumulated proportions, are also shown.
Table 1. Summary of the PCA from which the principal components (PC) used as new environmental layers were generated. Each cell value represents the individual loadings of each variable in each of the PCs. The PCs, along with their individual and accumulated proportions, are also shown.
Environmental VariablesPC1PC2PC3PC4PC5PC6
Annual mean temperature (bio1)0.272−0.223−0.132−0.0500.060−0.001
Mean diurnal range (bio2)−0.173−0.242−0.1280.495−0.122−0.524
Isothermality (bio3)0.239−0.0170.3120.1050.225−0.529
Temperature seasonality (bio4)−0.2500.008−0.386−0.063−0.2280.101
Maximum temperature of warmest period (bio5)0.193−0.316−0.343−0.036−0.134−0.065
Minimum temperature of warmest period (bio6)0.295−0.1180.005−0.1600.0850.017
Annual temperature range (bio7)−0.249−0.125−0.3290.200−0.250−0.086
Mean temperature of wettest quarter (bio8)0.235−0.251−0.2350.0560.1160.010
Mean temperature of driest quarter (bio9)0.277−0.1540.016−0.172−0.001−0.029
Mean temperature of warmest quarter (bio10)0.231−0.260−0.306−0.103−0.0340.031
Mean temperature of coldest quarter (bio11)0.287−0.181−0.005−0.0330.101−0.029
Annual precipitation (bio12)0.2620.224−0.0400.187−0.1930.065
Precipitation of wettest period (bio13)0.2700.0810.1140.242−0.3240.215
Precipitation of driest period (bio14)0.1410.397−0.2410.0290.113−0.296
Precipitation seasonality (bio15)−0.029−0.3380.4010.340−0.1340.015
Precipitation of wettest quarter (bio16)0.2700.0910.1020.247−0.3210.212
Precipitation of driest quarter (bio17)0.1520.396−0.2320.0310.084−0.269
Precipitation of warmest quarter (bio18)0.1600.193−0.2050.5620.3420.328
Precipitation of coldest quarter (bio19)0.1980.2090.073−0.195−0.605−0.241
Individual proportion0.5530.1980.0930.0560.0420.027
Cumulative proportion0.5530.7510.8430.8990.9410.968
Table
Table 2. Descriptive data of the species distribution models for each species.
Table 2. Descriptive data of the species distribution models for each species.
SpeciesJaccardSize at Present (n. Cells)Size at Future (n. Cells)Percentage of Change
M. dryades0.74117,90415,599−12.874
M. elongatus0.444 36,31747,879+31.836
M. granulosus0.88136,57941,984+14,776
M. haemastomus0.666114,933104,166−9680
M. lorentzianus0.75754077983+47.641
M. musculus0.48317,80418,597+4.454
M. paranaguensis0.729 85,60578,483 −8319
M. sanctipauli 0.80579866394−19.934
M. yporanganus0.81824441924−21.276
M. intertextus0.93399,41096,305−3.123
L. fulica0.755156,294151,516−3.057
Table
Table 3. Results of niche overlap comparison between Lissachatina fulica and Megalobulimus species. Significant p-values of the similarity test are shown in bold.
Table 3. Results of niche overlap comparison between Lissachatina fulica and Megalobulimus species. Significant p-values of the similarity test are shown in bold.
Species SimilarityUnfillingStability
D1→21←2MegalobulimusL. fulicaMegalobulimusL. fulica
M. dryades0.0390.0100.0100.7280.0000.2721.000
M. elongatus0.0210.0200.0300.8010.0000.1991.000
M. granulosus0.0330.0100.0100.7830.0190.2170.981
M. haemastomus0.0330.0400.0400.6010.0420.3990.958
M. lorentzianus0.0020.1880.2180.9830.5580.0170.442
M. musculus0.0350.0200.0100.6460.0460.3540.954
M. paranaguensis0.0460.0100.0100.7360.0000.2641.000
M. sanctipauli0.0240.0400.0200.7900.0130.2100.987
M. yporanganus0.0240.0100.0200.7720.0000.2281.000
M. intertextus0.0140.0400.0590.5640.0330.4360.967
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Teles, W.S.; Silva, D.d.P.; Vilela, B.; Lima-Junior, D.P.; Pires-Oliveira, J.C.; Miranda, M.S. How Will the Distributions of Native and Invasive Species Be Affected by Climate Change? Insights from Giant South American Land Snails. Diversity 2022, 14, 467. https://doi.org/10.3390/d14060467

AMA Style

Teles WS, Silva DdP, Vilela B, Lima-Junior DP, Pires-Oliveira JC, Miranda MS. How Will the Distributions of Native and Invasive Species Be Affected by Climate Change? Insights from Giant South American Land Snails. Diversity. 2022; 14(6):467. https://doi.org/10.3390/d14060467

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Teles, Wanderson Siqueira, Daniel de Paiva Silva, Bruno Vilela, Dilermando Pereira Lima-Junior, João Carlos Pires-Oliveira, and Marcel Sabino Miranda. 2022. "How Will the Distributions of Native and Invasive Species Be Affected by Climate Change? Insights from Giant South American Land Snails" Diversity 14, no. 6: 467. https://doi.org/10.3390/d14060467

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