Abstract

INTRODUCTION

Invasive plant species may represent a threat for biodiversity hotspots, given their high dispersal capacity under different climatic and environmental conditions. Sometimes, these species may be even more adapted for surviving in an invaded environment than the native plants, resulting in the extinction of local species, followed by a drastic change in all trophic levels of this ecosystem (WeberWEBER E. 2017. Invasive plant species of the world: a reference guide to environmental weeds. 2nd ed., Walingford, CABI. 2017).

Schinus molle L. (Anacardiaceae) is a pioneer tree species well-known by its application in the popular medicine (DikshitDIKSHIT A, NAQVI AA and HUSAIN A. 1986. Schinus molle: a new source of natural fungitoxicant. App Environ Microb 51: 1085-1088. et al. 1986), by the pharmaceutical uses of its essential oils (MarongiuMARONGIU B, PORCEDDA APS, CASU R and PIERUCCI P. 2004. Chemical composition of the oil and supercritical CO2 extract of Schinus molle L. Flav Frag J 19: 554-558. et al. 2004), by the production of the spicy pink pepper (GoldsteinGOLDSTEIN DJ and COLEMAN RC. 2004. Schinus molle L. (Anacardiaceae) Chicha Production in The Central Andes. Econ Bot 58: 523-529. and Coleman 2004) and by its role in the ecological succession (LemosLEMOS RPM, D’OLIVEIRA CB, RODRIGUES CR, ROESCH LFW and STEFENON VM. 2014. Modeling distribution of Schinus molle L. in the Brazilian Pampa: insights on vegetation dynamic and conservation of the biome. An For Res 57: 205-214. et al. 2014). Native to South America, S. molle has been reported as an invasive species in Central and North America (HowardHOWARD LF and MINNICH RA. 1989. The introduction and Naturalization of Schinus molle (Pepper Tree) in Riverside, California. Lands Urban Plan 18: 77-95. and Minnich 1989, AsnerASNER GP, JONES MO, MARTIN RE, KNAPP DE and HUGHES RF. 2008. Remote sensing of native and invasive species in Hawaiian forests. Rem Sens Environ 112: 1912-1926. et al. 2008, Ramírez-AlboresRAMÍREZ-ALBORES JE, BUSTAMANTE RO and BADANO EI. 2016. Improved Predictions of the Geographic Distribution of Invasive Plants Using Climatic Niche Models. PLoS ONE 11: e0156029. et al. 2016), South Africa (IpongaIPONGA DM, MILTON SJ and RICHARDSON DM. 2009. Reproductive potential and seedling establishment of the invasive alien tree Schinus molle (Anacardiaceae) in South Africa. Aust Ecol 34: 678-687. et al. 2009) and Europe (Stinca et al. 2017RAMÍREZ-ALBORES JE, BUSTAMANTE RO and BADANO EI. 2016. Improved Predictions of the Geographic Distribution of Invasive Plants Using Climatic Niche Models. PLoS ONE 11: e0156029.) where it was introduced for ornamental purposes. In some areas in these regions, the self-establishment has been documented and S. molle is considered a naturalized species (Iponga et al. 2009, RichardsonRICHARDSON DM, IPONGA DM, ROURA-PASCUAL N, KRUG RM, MILTON SJ, HUGHES GO and THUILLER W. 2010. Accommodating scenarios of climate change and management in modelling the distribution of the invasive tree Schinus molle in South Africa. Ecography 33: 1049-1061. et al. 2010).

Within its natural range of occurrence in southern Brazil, the genetic analysis of nine natural populations of S. molle revealed low genetic diversity and estimations of gene dispersion distances larger than the current populations’ area (LemosLEMOS RPM, D’OLIVEIRA CB and STEFENON VM. 2015. Genetic structure and internal gene flow in populations of Schinus molle (Anacardiaceae) in the Brazilian Pampa. Tree Genet Genom 11: 75. et al. 2015). So, it is likely that the long-distance gene flow compensates the low genetic diversity of S. molle, and together with factors as high reproductive output and high resilience, allows the occupation of new geographic areas without further difficulties.

Modeling the ecological niche of Schinus molle within the Pampa biome in South America, a wide geographical area was identified as potential habitat for this species, with population expansion limited mainly by anthropic intervention (Lemos et al. 2014). Given the aptitude of this species for harboring forest expansion, these authors further pondered the importance of S. molle in the maintenance of the natural ecological dynamic of the Brazilian Pampa.

Considering the invasive ability of S. mole and the predicted scenarios of climatic changes for the next decades, the present study intended to expand the ecological niche modeling of this species over the world, in order to evaluate the potential risk of invasion for different biodiversity hotspots by S. molle, based on current climate data.

MATERIALS AND METHODS

For this study, geographical data on S. molle occurrence over the world was recovered from the SpeciesLink (http://splink.cria.org.br/) and the Global Biodiversity Information Facility (GBIF; https://www.gbif.org/) databases. The ecological niche modeling (ENM; see SoberónSOBERÓN J L and PETERSON T. 2005 Interpretation of models of fundamental ecological niches and species distributional areas. Biodiv Inf 2: 1-10. et al. 2017) was achieved with the maximum entropy distribution model algorithm, as performed in the software MaxEnt 3.4.1 (PhillipsPHILLIPS SJ, ANDERSON RP and SCHAPIRE RE. 2005. Maximum entropy modeling of species geographic distributions. Ecol Model 190: 231-259. et al. 2005), using the software’s standard parameters and the minimum of 1000 interactions with cumulative output.

Nineteen bioclimatic variables were extracted from the WorldClim database (http://www.worldclim.org; HijmansHIJMANS RJ, CAMERON SE, PARRA JL, JONES PG and JARVIS A. 2005. Very High Resolution Interpolated Climate Surfaces for Global Land Areas. Int J Climatol 25: 1965-1978. et al. 2005) and used for the niche characterization in the modeling analysis. A first run was performed with all 19 variables, using a Jackknife test to determine the prediction power of each variable and its percentage of contribution to the model (^{Supplementary Material - Table SI} SUPPLEMENTARY MATERIAL Table SI - Percent of contribution of the nineteen current climatic variables determining the occurrence areas of Schinus molle in the globe in the first MaxEnt run. ). With the Jackknife results, a correlation test was done on the ENMtools software (Warren et al. 2008WARREN DL, GLOR RE, AND TURELLI M. 2008. Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution 62:2868-2883.) and the strongly correlated (from r = -0.8 to r = 0.8) environmental variables were removed from the analysis. The MaxEnt algorithm was re-run twice keeping only the non-correlated variables presenting more than 1% of contribution to the model, as suggested by PetersonPETERSON T. 2011. Ecological niche conservatism: a time-structured review of evidence. J Biogeog 38: 817-827. (2011). Finally, the modeling was run using five bioclimatic variables (Table I). For correcting AUC parameter, we used the partial ROC analysis (pROC; Peterson et al. 2008PETERSON AT, PAPES M and SOBERÓN J. 2008. Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol Model 213: 63-72.) from Niche Analyst (NicheA) program (QiaoQIAO H, PETERSON AT, CAMPBELL LP, SOBERÓN J, JI L and ESCOBAR LE. 2016. NicheA: creating virtual species and ecological niches in multivariate environmental scenarios. Ecography 39(8): 805-813. et al. 2016). Employed parameters were setting as follow: convergence threshold = 10-⁵; maximum iterations = 1000; regularization multiplier = 1; duplicate presence records removal. The model training was performed using 80% of species records and 20% was used to test the model. The Geographic Information System Quantum-GIS version 2.18 was used to compile MaxEnt results and generate the probability distribution maps. Climatic surfaces for global land areas were interpolated in 2.5 arc minutes maps (about 4.5 km² resolution).

RESULTS AND DISCUSSION

A total of 7329 collection points of S. molle were compiled from SpeciesLink and GBIF databases (549 and 6780 respectively). Removing duplicate records, 1864 points remained for training. The distribution model revealed excellent quality (pROC = 0.983). The map for the predicted distribution of S. molle is the consensus for both databases (Figure 1).

Figure 1
Current ecological niche modeling map for Schinus molle. Highlighted areas are the 14 biodiversity hotspots (BH; Myers et al. 2000) that are susceptible to S. molle expansion. *Biodiversity Hotspots where S. molle is native. **Biodiversity Hotspots where S. molle invasion is already reported. BH1: Central Chile; BH2: Atlantic Forest; BH3: Chocó/Darlén/Western Ecuador; BH4: Caribbean Islands; BH5: Mesoamerica; BH6: California Floristic Province; BH7: Mediterranean Basin; BH8: Succulent Karoo; BH9: Cape Floristic Province; BH10: Madagascar; BH11: South-Central China; BH12: Sundaland; BH13: Southwest Australia; BH14: New Zealand.

The most important climatic variables determining the distribution of S. molle are related directly to temperature (Table I). Minimum temperature of coldest month and isothermality respond to 77.4% of the contribution to the model. Annual mean temperature, mean temperature of coldest quarter, and mean temperature of warmest quarter have a contribution of 22.6% to the model (Table I). The order of importance of the climatic variables in the present study slightly differs from the regional modeling performed for S. molle by Lemos et al. (2014) based in occurrence data of the species in the Brazilian Pampa. Differing from our global study, the modeling of Lemos et al. (2014) revealed precipitation seasonality and precipitation of the driest month as the two main variables contributing to the model (37.7% and 13.4% respectively), followed by four variables related to temperature. Schinus molle occurs in arid environments and the overall factors generating this dryness can marginally diverge among regions and are responsible by these differences (Iponga et al. 2009, Lemos et al. 2015). The much higher number of occurrence points with wider distribution employed in our global modeling covers a more complete representation of such overall factors, making the present study a better representation of the potential ecological niches of S. molle. The maximum entropy model performs confident analyses with information about presence-only point occurrences (Phillips et al. 2005) and can be improved by over-sizing the collection area (Ramírez-Albores et al. 2016).

The predicted occurrence areas with very high probability (near 100%) for S. molle occurrence in South America are the Brazilian Atlantic coast, the Chilean mountains, and the Pampean regions of Brazil, Argentina and Uruguay. The Central American islands and Mexico are predicted areas of occurrence, as are the Southernmost and the west coast regions of the United States of America (Figure 1). Further regions predicted for occurrence of S. molle are the West and Mediterranean regions of Europe, the South Balkans in Asia, the Madagascar Island, the Mediterranean coast, the Southernmost and the Northeast regions of the African continent, the Middle East region, and the southernmost Australia and surrounding islands (Figure 1).

According to the model, S. molle has the capacity of establishment (probability > 75%) in 14 out of 25 biodiversity hotspots (Figure 1) proposed by MyersMYERS N, MITTERMEIER RA, MITTERMEIER CG, FONSECA GAB and KENT J. 2000. Biodiversity hotspots for conservation priorities. Nature 403: 853-858. et al. (2000): Atlantic Forest, Chocó/Darlén/Western Ecuador, Central Chile, Mesoamerica, Caribbean Islands, California Floristic Province, Madagascar, Mediterranean Basin, South-Central China, Cape Floristic Province, Succulent Karoo, Sundaland, Southwest Australia and New Zealand.

Confident niche modeling is dependent not only on the bioclimatic factors, but also on biotic elements (ecological interactions) and on the species accessibility to environments, without barriers to movement and colonization (the M-area; Soberón and Peterson 2005SOBERÓN J, OSORIO-OLVERA L and PETERSON T. 2017. Diferencias conceptuales entre modelación de nichos y modelación de áreas de distribución. Revista Mexicana de Biodiversidad 88: 437-441., BarveBARVE N, BARVE V, JIMÉNEZ-VALVERDE A, LIRA-NORIEGA A, MAHER SP, PETERSON AT, SOBERÓN J and VILLALOBOS F. 2011. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecological Modelling 222: 1810-1819. et al. 2011). Even though the natural occurrence area of S. molle in South America presents geographic barriers to its dispersion throughout Europe, Africa, Asia and Oceania, the human-mediated introduction of the species in most of these regions (Figure 1) turns the invasion of such biodiversity hotspots a real threat.

The high dispersal and survival capacities of S. molle enable this species to invade about 56% of the world’s biodiversity hotspots, with higher risk of prompt invasion in regions where this species is already established, near such hotspots. The introduction of S. molle was already reported in California, where it is viewed as a pest in orange groves and other sites of irrigation (Howard and Minnich 1989); Israel, found along roadsides and in wastelands (DaninDANIN A. 2000. The inclusion of adventive plants in the second edition of Flora Palaestina. Willdenowia 30: 305-314. 2000); Hawaii, where it poses a threat to the rare endemic flora (Asner et al. 2008); South Africa, where a high density of young plants of S. molle is reported expanding over non-forested biomes as the savanna (Iponga et al. 2009, Richardson et al. 2010); Mexico, where it is widely naturalized (Ramírez-Albores et al. 2016); and Italy, where it escaped from cultivation and now is naturalized (Stinca et al. 2017).

This scenario may be even worse, because the area under consideration has become substantially drier since the 1980s, mainly as effect of the global warming induced by greenhouse gases (DaiDAI A and ZHAO T. 2017. Uncertainties in historical changes and future projections of drought. Part I: estimates of historical drought changes. Clim Change 144: 519-533. and Zhao 2017ZHAO T and DAI A. 2017. Uncertainties in historical changes and future projections of drought. Part II: model-simulated historical and future drought changes. Clim Change 144: 535-548.). Projections of climate changes for 21^th century suggest scenarios in which the warming increases over all latitudes and precipitation decreases over subtropical lands, while a continued intensification in global agricultural drought frequency and area is expected even under low-moderate gas emissions scenarios (Zhao and Dai 2017). Such forecasted scenario implies in increasing areas favorable for S. molle expansion, mainly in regions already predicted in our model (Figure 1) in South and Central Americas, Mexico, Southern USA, Mediterranean Europe, Africa and Australia (See Figure 4 in Zhao and Dai 2017).

As a general conclusion, we suggest that the risk of invasion over biodiversity hotspots should consider the map of predicted occurrence areas for S. molle presented in this study. This map was constructed based on current bioclimatic data, but climate change models have demonstrated increased variance of precipitation and seasonal changes worldwide, with wet zones becoming wetter, and dry zones becoming drier (DoreDORE MHI. 2005. Climate change and changes in global precipitation patterns: What do we know? Environ Inte 31: 1167-1181. 2005, MirandaMIRANDA JD, ARMAS C, PADILLA FM, PUGNAIRE FI. 2011. Climatic change and rainfall patterns: Effects on semi-arid plant communities of the Iberian Southeast. J Arid Environ 75: 1302e1309. et al. 2011, Zhao and Dai 2017). In addition, an increase in temperature variability in tropical countries over the next decades have being consistently projected. Temperature variability escalations by about 15% per degree of global warming in Amazonia and Southern Africa are expected (BathianyBATHIANY S, DAKOS V, SCHEFFER M and LENTON TM. 2018. Climate models predict increasing temperature variability in poor countries. Sci Adv 4: eaar5809. et al. 2018). Schinus molle commonly occurs in arid regions and so, the climatic oscillations tend to maintain or even increase the areas suitable for the expansion of this species. Therefore, special management strategies should be planned for the biodiversity hotspots in order to avoid negative impacts of S. molle dispersion, mainly over non-forested areas, where the species may reveal a particularly high dispersion capacity (Lemos et al. 2014). Such management strategies include: (1) prevention of expansion through educational initiatives, avoiding new introductions of S. molle near or within areas with risk of invasion; (2) early detection of the expansion and coordinated containment and eradication response, preventing large dissemination of the species; and (3) rehabilitation and restoration of degraded areas with fast-growing native species, diminishing potential areas for invasive species dispersion. In South America, where S. molle is a native species, three biodiversity hotspots are predicted areas for the species expansion (Atlantic Forest, Chocó/Darlén/Western Ecuador, and Central Chile) and the natural dynamic of the ecosystems should be studied and contemplated, as proposed for the Brazilian Pampa (Lemos et al. 2014).

ACKNOWLEGMENTS

Authors thank to Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Process 442995/2014-8), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES Finance Code 001), and Universidade Federal do Pampa (Unipampa) by the financial support.

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