Kalanchoe × houghtonii (Crassulaceae) as an invasive plant: potential distribution by Ecological Niche Modelling (ENM)
Botanical Institute of Barcelona
MASTER’S DEGREE IN ECOLOGY, ENVIRONMENTAL MANAGEMENT AND RESTORATION
Author: Pilar Cachón
Supervisors: Jordi López-Pujol, Sergi Massó, Daniel Vitales
Tutor (UB): Emilia Gutiérrez
University of Barcelona October 2017
Kalanchoe × houghtonii (Crassulaceae) as an invasive plant:
potential distribution by Ecological Niche Modelling (ENM)
Botanical Institute of Barcelona
MASTER’S DEGREE IN ECOLOGY, ENVIRONMENTAL MANAGEMENT AND RESTORATION
Author: Pilar Cachón
Supervisors: Jordi López-Pujol, Sergi Massó, Daniel Vitales
Tutor (UB): Emilia Gutiérrez
University of Barcelona October 2017
Contents
ABSTRACT ......
1. INTRODUCTION ...... 1
1.1. Objectives ...... 3
2. MATERIAL AND METHODS...... 3
2.1. Study species ...... 3
2.2. Localities gathering and georeferencing ...... 4
2.3. Environmental variables and human footprint ...... 5
2.4. Niche modelling ...... 7
3. RESULTS ...... 9
3.1. Niche modelling and variable contribution ...... 9
3.2. Potential distribution at present ...... 10
3.3. Potential distribution under future climate (2070) ...... 11
4. DISCUSSION ...... 12
4.1. Current distribution and potential distribution under the present climate ...... 12
4.2. Potential effects of future climate change on distribution ...... 14
5. CONCLUSIONS ...... 17
ACKNOWLEDGEMENTS ...... 18
REFERENCES ...... 19
ABSTRACT
Alien species are currently considered one of the main direct threats to global biodiversity in our planet. Some alien plant species can become invasive and damage ecosystems, leading to negative effects on the local and global economy and society. Here, we have studied Kalanchoe × houghtonii D. B. Ward (Crassulaceae), a hybrid species artificially created by the horticulturist A. D. Houghton with gardening purposes in the 1930s. It is a result of the crossing between K. daigremontiana Raym - Hamet and H. Perrier and K. delagoensis Eckl. and Zeyh., two endemic species from Madagascar. Soon, the hybrid taxon showed large colonizing capacity, escaping outside the cultivation spots and rapidly expanding its distribution area. Now, K. × houghtonii is currently found in all continents except Antarctica. Therefore, considering the well-known background of the species, as well as its strong invading abilities, this plant represents an attractive model to investigate the expansion of alien species. The aims of the present study are evaluating the potential worldwide habitat of K. × houghtonii at different time frames, from present to future, employing various scenarios of climate change. To reach these goals, we first carried out a documentary research, involving the finding of localities where the species is present, through online databases, citizen science web portals, as well as many published academic papers. With the obtained occurrences, and using the maximum entropy algorithm implemented in MaxEnt, we modelled the potential habitat of the species to the present, which was later projected to the future under different scenarios. Results derived from this study would allow us to better understand the invading behavior of species with high colonizing potential such as K. × houghtonii, and, at the same time, inferring possible range contractions or expansions of the species across its whole distribution area under various scenarios of climate change.
1. INTRODUCTION
Introduction and subsequent invasion of non-native species is the second leading cause of global biodiversity loss (Pyšek et al., 2004), resulting in altered ecosystem functions, increased vectors of diseases, and reduced distribution and diversity of native populations, which also means economic and health damages (Vitousek et al., 1996).
A large proportion of invasive alien species worldwide were intentionally introduced to the areas where they are currently invasive, and many were widely disseminated once introduced to provide some value to humans (Ewel et al., 1999). A large proportion, if not the largest, of invasive plant species are introduced for horticultural use (Reichard and White, 2001; Kowarik, 2005). Ornamental plants are most likely to invade at the urban/wildland interface, where human habitation borders on natural vegetation. The cultivation and tending of plants in gardens produces high propagule pressure, so that many seeds and other propagules can spread into the surrounding natural vegetation. This then increases the likelihood and rate of any particular species invading natural and semi-natural ecosystems (Sullivan et al., 2004; Foxcroft et al., 2008).
An example of a plant introduced due to ornamental uses that have become a widespread invader is Kalanchoe × houghtonii D. B. Ward, an artificial hybrid obtained by experimental crosses by the eminent horticulturist A.D. Houghton in the 1930s in his California greenhouses (Guillot et al., 2014). This species comes from the crossing of two of the species of the genus most frequent in cultivation, Kalanchoe daigremontiana Raym - Hamet and H. Perrier and K. tubiflora (Harv.) Raym.Hamet (Guillot et al., 2014). The invasion capability K. × houghtonii is enhanced by vigorous clonal growth through pseudobulbils (Figure I) that arise from the margin of their leaves (hence the popular name ‘mother of millions’ or ‘mother of thousands’), and this feature has been acquired through their progenitors (Guerra-García et al. 2015).
Kalanchoe × houghtonii was firstly observed in the wild in Australia as early as 1965 (Guillot et al., 2014), and the plant has been reported throughout America (including the Caribbean islands), southern Europe, Asia (India, China), and Oceania (apart from Australia, in some Polynesian islands and in New Zealand) (Guillot et al., 2014; Wang et al., 2016). In some of these areas it has become a strong invader, such as in Queensland, Australia (Queensland Government, 2016) and Venezuela (erroneously identified as K. daigremontiana; Herrera et al. 2012). In southern Europe, such as in the Mediterranean coast of Spain, this hybrid is even more invasive than its parental species,
Page 1 of 30 and in some cities it has already become a common component of the urban landscape (Guillot et al. 2014).
From a management point of view, it is extremely important to identify areas which are not yet invaded but where early warning detection and control programs are essential to implement (Fang and Wan, 2009). Recent studies have developed niche- based models to assess the suitability of a region for a given species and its potential to spread throughout (Jimenez-Valverde et al., 2011). Ecological niche model using occurrence data and habitat environmental variables play important roles in predicting the potential distribution for alien plants (Thalmann et al., 2015). One of the most used methods of niche modelling is the maximum entropy algorithm as implemented in the software MaxEnt. It assesses the probability distribution of the study species by estimating the probability distribution of maximum entropy (Phillips and Dudík, 2008). The modelling is based on the combination of occurrence data with climatic layers and this method has the advantage that is based on only presence registers (Philips et al. 2006). Similarly, present-day distributions of species can be combined with environmental variables to enable projected distributions of species under future climate scenarios (Berry et al., 2002).
a b
Figure I. Kalanchoe × houghtonii. (Authors: a) S. Massó b) J. López-Pujol
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1.1. Objectives
The aims of this study are the following: (i) to explore the geography pattern of the distribution of Kalanchoe × houghtonii globally, gathering all the occurrences, both published in standard publications (e.g., articles and books) and available from other sources (e.g., databases including those based on citizen science, grey literature, personal communications, and even personal blogs), (ii) predicting the potential distribution of the species based on the gathered occurrences; such potential worldwide distribution will be predicted using the maximum entropy algorithm implemented in the software MaxEnt; and (iii) predicting how the distribution of this hybrid taxa is going to progress according to different scenarios of climate change.
2. MATERIAL AND METHODS
2.1. Study species
Kalanchoe × houghtonii is a monocarpic short perennial herb, morphologically similar to K. daigremontiana but distinguishable by the leaf base (usually neither auriculate nor cordate in K. × houghtonii). However, as leaves of K. × houghtonii may rarely present small auricles or can be weakly cordate, confusions with K. daigremontiana are relatively common. In fact, before the species was formally described in 2006 (Ward, 2006), many of its occurrences were erroneously assigned to K. daigremontiana (Moran, 2009). The species has leaves which are opposite, mottled, narrowly spaced and vastly sawn. The vegetative stem may reach 1 m and ends in one or several inflorescences of often more than one hundred, dark-red, tetrameric flowers (Guillot et al., 2014). According to Ward (2006), sexual reproduction is only occasional, with seeds rarely produced has not been observed. However, a series of experimental plantings were carried out in the Botanic Garden of Barcelona at the end of the last decade and it is known that the plant germinated (S. Pyke, pers. comm.).
Instead, Kalanchoe × houghtonii reproduces almost exclusively by plantlets borne in the notches along leaf margins (Moran, 2009). Every notch of every leaf seemingly produces a plantlet, but never does a second form after the first has dropped. In suitable open sandy locations these plantlets quickly form dense stands, and frequently become nuisances in flower beds and beneath greenhouse benches (Ward, 2008). This is the mechanism by which K. × houghtonii is been propagated rapidly. The species has been spread in cultivation in pots, by exchange of cuttings or propagules,
Page 3 of 30 and it easily escapes from gardens, quickly spreading in urban and semi-natural environments (Guillot et al., 2014) (Figure II).
Figure II. Kalanchoe × houghtonii (Author: S. Massó).
2.2. Localities gathering and georeferencing
To estimate the range of the species K. × houghtonii in all the planet, an extensive literature search was conducted, and included: (1) major regional taxonomic works, checklists, lists and catalogues of naturalized and invasive plants; (2) research articles; (3) grey literature (e.g. technical reports); (4) major databases and information systems, including citizen science projects and digitized herbaria, e.g. Global Biodiversity Information Facility (GBIF, www.gbif.org), iNaturalist (www.inaturalist.org), Missouri Botanical Garden (www.tropicos.org), Australasian Virtual Herbarium (https://avh.chah.org.au/), Chinese Virtual Herbarium (CVH, www.cvh.ac.cn), Chinese Field Herbarium (CFH, www.cfh.ac.cn), Plant Photo Bank of China (PPBC, www.plantphoto.cn), University of Florida Herbarium
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(https://www.floridamuseum.ufl.edu/herbarium/), or Auckland War Memorial Museum (http://www.aucklandmuseum.com/), among others; (5) personal communications and (6) personal blogs and other non-scientific websites. In cases where geographic coordinates were not available but a site description or map location was provided, the first were inferred, if possible, using Google Earth or a similar source (e.g. BaiduMaps for the case of China). All geographic references were recorded in the form of decimal coordinates, using coordinate converters in case of being in another nomenclature (http://www.coordenadas-gps.com/convertidor-de-coordenadas-gps) (see Appendix as electronic material).
All citations collected were thoroughly validated one by one, keeping only those in which there was an image (of the herbaria sheets or showing the individuals in the wild), with the exception of citations published by specialists in the genus, that would ensure that the species was correctly identified. The search and validation of localities was a very laborious task since, as has been said, we found many confusions between the hybrid and of its two parents (especially with K. daigremontiana). Therefore, in addition to searching for citations under the name K. × houghtonii (or any of its synonyms), it was necessary to check citations for the two parental species, and to verify that the plants of these citations were correctly identified or, on the contrary, were actually K. × houghtonii.
A total of 512 occurrences were gathered and validated in the first instance. After removing all the duplicates within each pixel (2.5 arc-min, ca. 5 km), 367 occurrences were employed for further analyses (see Appendix as electronic material). This resolution was considered the most suitable according to the nature of the study and the extension of the study area. Coarser resolution (e.g. 5 arc-min) would entail too much uncertainty in relation with the environmental conditions characterizing the localities of recording. Finer (e.g. 30 arc-sec) would involve uncertainty too, due to the inaccuracy of the georeferenced localities inferred from the site descriptions. Localities with inaccurate descriptions or with coordinates that offer very little precision (that is, they do not allow to discern with certainty the cell of 2.5 arc-min where the locality occurs) were not considered.
2.3. Environmental variables and human footprint
A set of 19 bioclimatic variables were downloaded from WorldClim (http://www.worldclim.org/) at 2.5 arc-min resolution covering the whole planet. Bioclimatic variables are derived from the monthly temperature and rainfall values in
Page 5 of 30 order to generate more biologically meaningful variables. They represent annual trends (e.g., mean annual temperature, annual precipitation) seasonality (e.g., annual range in temperature and precipitation) and extreme or limiting environmental factors (e.g., temperature of the coldest and warmest month, and precipitation of the wet and dry quarters) (Table I). In addition to these climatic variables, the Human footprint (Hfp) was added because the establishment and spread of K. × houghtonii is often facilitated by human disturbance (Guerra-García et al., 2015). The variable Hfp is based on the anthropogenic impacts on the environment (Sanderson et al., 2002), and it was created from nine global data layers covering human population pressure (population density), human land use and infrastructure (built-up areas, night-time lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers).
Table I. Bioclimatic variables obtained from Worldclim. Selected variables in bold.
Code Bioclimatic variables Bio1 Annual mean temperature Bio2 Mean diurnal range (Mean of monthly (max temp - min temp)) Bio3 Isothermality (Bio2/Bio7) (x 100) Bio4 Temperature seasonality (standard deviation x 100) Bio5 Maximum temperature of the warmest month Bio6 Mininmum temperature of the coldest month Bio7 Temperature annual range (Bio5 - Bio6) Bio8 Mean temperature of the wettest quarter Bio9 Mean temperature of the driest quarter Bio10 Mean temperature of warmest quarter Bio11 Mean temperature of coldest quarter Bio12 Annual precipitation Bio13 Precipitation of the wettest month Bio14 Precipitation of the driest month Bio15 Precipitation seasonality (Coefficient of variation) Bio16 Precipitation of the wettest quarter Bio17 Precipitation of the driest quarter Bio18 Precipitation of the warmest quarter Bio19 Precipitation of the coldest quarter
In order to select the most important uncorrelated variables which would be used in the environmental niche modelling (ENM) analyses afterwards, we performed both an
Page 6 of 30 analysis of variable contributions and a jackknife test of variable importance (e.g. jackknife of regularized gaining train) with the software MaxEnt v. 3.3.3k (Philips et al., 2006). The occurrences of K. × houghtonii across the planet and the 19 previously downloaded bioclimatic variables plus Human Footprint (Hfp) were used here as input. for this preliminary model with 10 replicates. In parallel, we did a correlation analysis with Excel with the 19 variables + Hfp. Pearson’s correlations were used to check for variable correlation. In cases of high correlation (r ≥ |0.85|) between pairs, the most explanatory variable was selected and the other discarded. Finally, we selected a smaller set of nine not highly correlated variables (Table I). The selection of variables from pairs or groups of highly correlated ones was done on the basis of their relative contribution to the model (percent contribution, jackknife tests of variable importance).
2.4. Niche modelling
The potential distribution of K. × houghtonii was estimated by using a maximum entropy algorithm in MaxEnt. This algorithm models the species’ ecological niche (a set of ecological conditions habitable for a species) by examining the relationship between the locations of known species’ presence and the environmental characteristics of that region and then extrapolating from this the regions where similar conditions take place in the study area (Vogler et al., 2013). The modelling with this software is based on the combination of presence-only data with climatic layers (Philips et al. 2006). The suitability of this method for the purposes of this study has been already reported, which discusses the characteristics of presence-only data, highlighting implications for modelling distributions (Elith et al. 2006).
Bootstrapping was selected as the method to get the replicates of the model because it ensures the best goodness of fit and stability (Kailihiwa, 2015). Twenty percent of localities were randomly selected to validate the model and the remaining 80% were used for the model training. For each model, 20 replicates were run to ensure reliable results. Since the potential distribution outputs in MaxEnt are delivered by a continuous cumulative probability ranging from 0 (not suitable at all) to 1 (maximum suitability), a threshold rule was applied to infer a binary presence/absence distribution for K. × houghtonii; maximum training sensitivity plus specifity (MSS) logistic threshold was chosen, which is considered to be very robust using only presence data (Liu et al., 2015).
The resulting model was evaluated using the area under the curve of the receiver operating characteristic curve (Fielding and Bell, 1997). The AUC is a threshold-
Page 7 of 30 independent measure commonly used to evaluate the predictive capacity of the model generated and ranges from 0.5 (no predictability) to 1 (perfect prediction; Vogler et al., 2013). According to Loo et al., (2007), values above 0.8 indicate a strong prediction. The AUC measures the probability that at a randomly selected point of presence a raster cell with a higher probability value is found for the presence of the species than at another point of randomly selected absence. The relative contribution of variables of the model was assessed by means of a jackknife test, and through the response curves obtained in the MaxEnt program as in Vogler et al. (2013) and Montecino et al. (2014).
Two sets of models were developed, one for current conditions and the other for future ones. In each group, we have distinguished between the projections that include the variable Hfp and those that do not include it. Thereby, for present conditions, two models have been obtained (one with Hfp and another without Hfp). For future projections, the General Circulation Model selected was MPI-ESM-LR (http://www.mpimet.mpg.de/en/science/models/mpi-esm/), which shows one of the best performance in the 5th Coupled Model Inter-Comparison Project experiment (McSweeney et al., 2015). In addition, projections of the future have been made with two different scenarios of Representative Concentration Pathway (RCP). The Representative Concentration Pathways selected were RCP 2.6 (i.e. the softest scenario) and RCP 8.5 (i.e. the hardest scenario), which refer two of the four RCPs used in the Fifth Assesment IPCC report. Therefore, either considering Hfp variable or not, a total of four future models have been obtained each one including the same input of occurrence points but different environmental layers corresponding to the two different scenarios forecasted for 2070.
The most explanatory variables for each ENM were firstly evaluated considering the jackknife test. Afterwards, the models were visualized and processed using the software ArcMap v. 10.2.2 (ESRI, Redlands, California, USA). Firstly, in order to be able to work with this software in the intending way, ASCII files reflecting the results of the projections for every model (i.e. average projection) were converted into Raster files by the package Conversion Tools. Then, the package Spatial Analysis Tools was used to convert the continuous value projections (Raster format) into category distribution projections applying the MSS threshold, through the option Reclass and finally Reclassify. In this way, eleven classes were created, classified as 0–threshold, threshold–0.1, 0.1–0.2, 0.2–0.3 … 0.9–1. The suitable area (i.e. sum of the areas with category > threshold) was calculated using the package Data Management Tools.
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3. RESULTS
The complete occurrence database of K. × houghtonii included 367 presence records from Oceania (53), America (147), Europe (115), Africa (30) and Asia (22), being Australia (44), United States (56) and Spain (89) countries with the highest number of presence records (Appendix as electronic material).
3.1. Niche modelling and variable contribution
All the models for both the present and the future scored high AUC values, all well above 0.9, which constitutes a signature of their strong predictive power (Elith et al. 2006). Also, in each model, a series of variables had more influence and contributed more importantly in the development of it, according to MaxEnt jackknife tests of variable importance (Table II). The human footprint variable was the one with the highest statistical weight in all models where it was considered. In addition, other climatic variables such as annual mean temperature (Bio1), temperature annual range (Bio7), mean temperature of the coldest quarter (Bio11) and annual precipitation (Bio12) were also recovered as highly explanatory for the models.
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Table II. AUC scores obtained for model validation of every ENM ± standard deviation (sd). Values higher than 0.9 are considered optimal by us to trust in the predictive power of the ENM. Most explanatory variables according Jackknife test
Model AUC ± (sd) Most explanatory variables
Present (with Hfp) 0.978 ± 0.001 Hfp
Present (without Hfp) 0.969 ± 0.001 Bio1 > Bio12
MPI 2.6 (with Hfp) 0.980 ± 0.001 Hfp > Bio1 ≈ Bio11
MPI 2.6 (without Hfp) 0.971 ± 0.001 Bio1 > Bio12
MPI 8.5 (with Hfp) 0.981 ± 0.001 Hfp
MPI 8.5 (without Hfp) 0.970 ± 0.002 Bio1 > Bio7 ≈ Bio11
3.2. Potential distribution at present
Despite that the two models for the present (using or not the human footprint) showed excellent performance, there were some differences between them, mostly derived from the fact that Hfp was the most important variable in the model that included it (Table II). As the suitable areas for the presence of Kalanchoe × houghtonii were considered those with an occurrence probability higher than 0.0414 (the maximum training sensitivity plus specificity logistic threshold) for the model without Hfp, and 0.0317 for the model with the variable. Although the model with Hfp predicted a smaller suitable area for K. x houghtonii than the model without Hfp (9,852,870 km2 vs. 13,404,208 km2), the general foci were the same (Fig. I). The maps depicted in Fig. III show how suitability of K. ×
Page 10 of 30 houghtonii was distributed across the planet. As far as the American continent was concerned, the State of Florida (USA) is the region with the highest level of suitability, as there were numerous populations recorded within this peninsula. Other regions of the continent also suitable were Mexico, the Caribbean, some areas along the Andes, the Atlantic part of Brazil, and Rio de la Plata, although some of them with low probability of occurrence. On the European continent, regions with a high likelihood of occurrence coincided with Mediterranean areas, notably the Iberian Peninsula, the Italian Peninsula, as well as some parts of North Africa (especially Morocco, Algeria and the Canary Islands). Also within Africa, the East African mountains, South Africa, and Madagascar appeared as suitable. In Asia there were also small areas with high probability, such as southern India, southern continental China, Taiwan, and some islands of South China Sea. The last region of high suitability was Oceania, particularly in southern and eastern Australia, but also in parts of northern New Zealand.
3.3. Potential distribution under future climate (2070)
The results regarding for the areas suitability for all future models are shown below in the following table (Table III). Projections under climate change conditions showed quite different results for suitable areas when comparing with the projection in the present. In all models for the year 2070, the areas of suitability decreased; such decreased was especially important MPI 8.5 model that does not consider the Human footprint variable (-38,9%), although maintaining the general distribution patterns (that is, Florida, Río de la Plata region, Mediterranean Europe, South Africa and Australia remained as the most suitable areas) (Figure III-V).
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Table III. Areas of suitability according to a threshold (MSS logistic threshold) and difference respect to present. Calculation of the difference is done by subtracting the area in present minus the corresponding model, and the percentage dividing that difference between the present area x 100.
Model MSS Logistic Total suitable Difference respect to Threshold predicted area Present (km2) (km2 and %)
Present
With Hfp 0.0317 9,852,870 - Without Hfp 0.0414 13,404,209 -
Future
MPI 8.5 (with Hfp) 0.0296 8,133,229 1,719,641; -17.45% MPI 8.5 (without Hfp) 0.0408 8,188,078 5216130; -38.91 %
MPI 2.6 (with Hfp) 0.0311 8,255,510 1,597,360; -16.21% MPI 2.6 (without Hfp) 0.0410 11,198,189 2,206,020; -16.45%
4. DISCUSSION
4.1. Current distribution and potential distribution under the present climate
The potential distribution area of K. × houghtonii is mostly focused on regions where the Mediterranean and subtropical climate prevails (i.e. from 25º to 40º of both north and south latitudes). In the coastal areas of south-western Europe and those of north-western Africa we can observe a high probability of occurrence (represented in red as indicated by the legend in the maps) that does not change significantly when introducing the human footprint variable, probably because the areas climatically suitable both in these regions are areas highly modified by the humans (De Montis et al., 2017). Therefore, these regions remain as main foci for the establishment of the plant, especially those areas where the species is still absent or, at least, not detected (e.g. Morocco and Corsica). Other areas with no major changes are Florida, the Atlantic coast of Brazil, the region of Rio de la Plata in South America, the East African Mountains, or South Africa
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(Figure III). Brazil and the East African Mountains, where there is a single occurrence of K. × houghtonii should be regarded as areas with a great danger for the species introduction. However, significant decrease in the habitat suitability in the model that includes the Hfp are clearly visible in other regions, as indicated by the loss of high values of suitability or by the total absence of suitable habitats even with low probabilities: the Gran Chaco region, the Yucatán Peninsula, the coasts of northern Kenya and southern Somalia, the Celebes Islands, New Guinea, and Australia (especially the Great Dividing Range) (Figure III). Please note that these areas are not deeply modified by the human presence (http://sedac.ciesin.columbia.edu/data/set/wildareas-v2-human-footprint-geographic).
A
B
Probability
Figure III. A. Map of the Present without the Hfp. B. Map of the Present with Hfp Maps obtained by ArcGis. Present models with suitability areas classified according to legend.
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How human activities could influence the distribution of species, that is, either positively or negatively, remains unclear. In general, the human footprint variable increases the areas suitable for alien plants when added to ecological niche modelling. For example, Abulizi et al., (2015) found that the land area of potential distribution of Acacia farnesiana increased when human footprint was taken into account. However, this seems not to be the case of Kalanchoe × houghtonii (Table III). Kalanchoe × houghtonii is a species that has been introduced as an ornamental plant, being mostly cultivated in pots in private and less-frequently in public gardens. In addition, it is not capable of long-distance dispersion, since the propagules germinate in situ, just beneath or very near the mother plant. This entails that their dispersal capacity is very limited to adjoining areas, although bioclimatically adequate areas extend to very large regions. So, this is a plant forcibly very attached to the human presence and the addition of the human footprint variable allow us the refine of the potential distribution models.
4.2. Potential effects of future climate change on distribution
Future projections from all models indicate that the climatically suitable areas for K. × houghtonii are smaller in all the models; area reduction is about 16 - 17 % in three of four models, with the exception of MPI RCP 8.5. without Hfp (which shows a loss of ca. 39% of the area). For the four 2070 models, despite of the area reduction, the main areas of high suitability (e.g. the Mediterranean Basin, Florida, Río de la Plata region, South Africa, southern China, southern and Western Australia) are more or less maintained, and such differences are even less important for the models using the Hfp (Figure II, III). Regarding the models without Hfp, there are two areas with a considerable loss of suitable habitat in the RCP 2.6 model: the Gran Chaco region and the East African Mountains; for the RCP 8.5 model, additional areas are lost: central Mexico, Yucatán, southern India, and New Guinea (Figure IV, V). These future projections suggest that the plant, despite having invasive character, would be affected by weather conditions due to the fact that climatic variables are decisive factors in distribution of K. × houghtonii. This could be explained because it is a hybrid descendant of two parental species (endemic to Madagascar) that prefer mainly the tropical climates where there is a hardly seasonality; climate change is forecasted to affect dramatically global mean temperature and precipitation worldwide, increasing extreme temperatures and droughts (IPCC 2014). Finally, we must acknowledge that the 2070 projections are affected by the uncertainty derived from lack of information on how Hfp will evolve in the future; in fact, we kept it constant (as there is no Hfp layer for any
Page 14 of 30 future scenario available), probably generating smaller areas than those putatively recovered making made projections with Hfp foreseen in 2070 (as the tendency is that human impacts will significantly increase during the following decades).
A
B
Probability
Figure IV. A. Map of the MPI RCP 8.5 ENM without Hfp B. Map of the MPI RCP 8.5 ENM with Hfp. Maps obtained by ArcGis. Suitable areas are classified according to legend
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A
B
Probability
Figure V. A. Map of the MPI RCP 2.6 ENM without Hfp B. Map of the MPI RCP 2.6 ENM with Hfp. Maps obtained by ArcGis. Suitable areas are classified according to legend.
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5. CONCLUSIONS
(i) Under current climate scenarios, our ecological niche modelling infers new potential distribution areas –on the five continents– where K. × houghtonii has not been cited so far.
(ii) The human footprint variable has a large contribution when included in the model, resulting in a more precise reconstruction of the potential distribution of the species, essentially on highly anthropised areas.
(iii) The potential distribution area of K. × houghtonii is located around Mediterranean and subtropical regions, as expected regarding the notable importance in the model of climatic variables such as the annual temperature range or the temperature of the coldest quarter.
(iv) Under future climate scenarios our models infer a reduction in the potential distribution area of the species, bearing in mind that these future projections do not take into account forthcoming changes in the human footprint on the planet.
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ACKNOWLEDGEMENTS
To all the Botanical Institute for their support in carrying out this study and for making me participate in the project. Thank you for offering me not only a place of work, but also a second home where I have felt very comfortable from the first day.
Thanks to Jordi López-Pujol for the warmth with which he has welcomed me in his Asian office; for all his help, effort and hours he has dedicated to making things easier for me; for receiving me every day in a good mood and for transmitting me some of his botanical knowledge and also knowledge about life. Thank you for teaching me so much.
Thanks to Sergi Massó and Daniel Vitales for also helping me with the work with their advice and all the attention they have had on me despite all the things they have to do every day.
Thanks to Sonia Herrando for investing with me her time and for all the things she has explained to me without losing her smile.
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