Which features at home make a plant prone to become invasive?

Which features at home make a plant prone to become invasive? Javier Galán Díaz1,2, Enrique G. de la Riva3, Irene Martín-Forés4, Montserrat Vilà1,5 1Department of Plant Biology and Ecology, University of Sevilla, Profesor García González Street, 41012 Sevilla, Spain 2Department of Pharmacology, Pharmacognosy and Botany, Complutense University of Madrid, Ramón y Cajal Square, 28040 Madrid, Spain 3Department of Biodiversity and Environmental Management, Faculty of Biological and Environmental Sciences, University of León, Campus Vegazana, 24007 León, Spain 4School of Biological Sciences, The University of Adelaide, Adelaide, South Australia 5005, Australia 5Doñana Biological Station (EBD-CSIC) – Cartuja TA-10 Edificio I, Américo Vespucio Street, 41092 Sevilla, Spain Corresponding author: Javier Galán Díaz ([email protected]) Academic editor: José Hierro|Received 24 March 2023|Accepted 15 May 2023|Published 5 July 2023 Citation: Galán Díaz J, de la Riva EG, Martín-Forés I, Vilà M (2023) Which features at home make a plant prone to become invasive? NeoBiota 86: 1–20. https://doi.org/10.3897/neobiota.86.104039 Abstract Determining the factors that pre-adapt plant species to successfully establish and spread outside of their native ranges constitutes a powerful approach with great potential for management. While this source-area approach accounts for the bias associated with species’ regions of origin, it has been only implemented in pools of species known to be established elsewhere. We argue that, in regions with well-known introduction histories, such as the Mediterranean Biome, the consideration of co-dominant non-introduced species as a control group allows a better understanding of the invasion process. For this purpose, we used occurrence data from GBIF and trait data from previous studies to find predictors of establishment and invasion. We compare the frequency, climatic niche and functional traits of 149 co-dominant plant species in their native region in southern Spain, considering whether they have colonised other Mediterranean-climate regions or not and their level of invasion. We found that large native ranges and diverse climatic niches were the best predictors of species establishment abroad. Moreover, coloniser species had longer bloom periods, higher growth rates and greater resource acquisition, whereas coloniser species becoming invasive had also greater reproductive height and nitrogen use efficiency. This framework has the potential to improve prediction models and management practices to prevent the harmful impacts from species in invaded communities. Keywords Climatic niche, exotic plants, functional traits, Mediterranean-climate, source-area approach NeoBiota 86: 1–20 (2023) doi: 10.3897/neobiota.86.104039 https://neobiota.pensoft.net Copyright Javier Galán Díaz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. RESEARCH ARTICLE Advancing research on alien species and biological invasions A peer-reviewed open-access journal NeoBiota Javier Galán Díaz et al. / NeoBiota 86: 1–20 (2023) 2 Introduction Exotic plant species pose an increasing threat to native species and ecosystems conservation (Vilà et al. 2011; Bradley et al. 2019). Identifying predictors of invasion success constitutes a fundamental aim in invasion biology in order to support prevention measures and risk assessments (Pyšek et al. 2020). Key factors that drive successful invasion are high propagule pressure (Cassey et al. 2018) and having competitive traits or general-purpose phenotypes that confer ecological versatility and, therefore, high invasiveness (Drenovsky et al. 2012; Casado et al. 2018). Invasiveness also depends on the interaction with ecological and evolutionary processes occurring in the introduced range (Van Kleunen et al. 2010b); therefore, its determinants are context dependent (Novoa et al. 2020), which emphasises the necessity to identify variables that facilitate invasion under specific environmental constraints. Most studies interested in identifying factors promoting invasion success frequently focus on understanding the mismatch in functional trait performance between exotic species and their native competitors in the recipient communities (e.g. de la Riva et al. 2019; Galán Díaz et al. 2021a). An alternative approach is to compare functional strategies of species from the same source or geographic region (i.e. source-area approach) to identify whether species’ occurrence, traits and climatic niche in the native range allow us to predict their establishment and spread when introduced in other regions. This approach accounts for the bias associated with species region of origin and, potentially, allows us to focus on the role of traits alone (Martín-Forés et al. 2023). In this regard, it has been found that the climatic tolerance and ecological versatility of species in their native range are positively correlated with their capacity to establish and spread in other regions (Pyšek et al. 2009, 2015; Casado et al. 2018). This is probably associated with the display of certain trait attributes (Pyšek and Richardson 2007; Pyšek et al. 2009, 2015) and the fact that many exotic species are not only frequent and abundant in their invaded ranges, but also dominate in the communities of their native range (Firn et al. 2011; Galán Díaz et al. 2021b). Yet, most studies following the source-area approach have only looked at species known to be established in other regions, disregarding the role of co-dominant non-colonisers. We argue that, in regions with well-known introduction histories, the consideration of co-dominant non-introduced species as a control group allow us to broaden our understanding of the predictors of invasion success. Studies following the source-area approach have mainly explored the importance of life history traits as predictors of invasiveness (Pyšek et al. 2009; Arianoutsou et al. 2013; Casado et al. 2018), whereas the role of functional traits in species native ranges has been frequently overlooked (but see Schlaepfer et al. (2010); Pyšek et al. (2015)). Moreover, the combined consideration of above- and below-ground plant functional traits remains little explored in invasion studies (Fridley et al. 2022). Plant traits might be useful to predict invasion success because, under specific environmental constraints, traits that confer dominance in the native range might be the same that facilitate establishment and spread in the introduced range (Thompson et al. 1995). In this regard, it has been shown that exotic species in Mediterranean regions can be functionally different to other species in their native communities (Galán Díaz et al. 2023) Occurence, climatic niche and traits of exotic plants in their native range 3 and display phenotypic variation when compared with communities in the introduced range (Martín-Forés et al. 2017, 2018). This might suggest that invasive species could already have intrinsic attributes to establish and thrive in communities with similar abiotic constraints rather than only benefitting from extrinsic ecological and genetic factors (Schlaepfer et al. 2010; Colautti et al. 2014). Therefore, it is important to leverage the performance of plant functional traits within the native range as predictors of the establishment and future stage of the invasion of species in the introduced range. In addition, it is necessary to include measures of phylogenetic relatedness as shared evolutionary histories of species might lead to statistical non-independence of data (Felsenstein 1985; Schlaepfer et al. 2010; Vilà et al. 2015). Spain is home to many herbaceous species that are naturalised in other Mediterranean-climate regions of the world (Casado et al. 2018). The origin of these introductions can be tracked down to the arrival of the first Europeans settlers into these territories where species were introduced deliberately (i.e. crops, ornamental plants) or accidentally (i.e. weeds introduced with livestock, fodder, wool or cereals) (Barry et al. 2006; Martín‐Forés 2017). These species (henceforth coloniser species) co-existed long-term with anthropogenic activities in their native range (Schlaepfer et al. 2010; MacDougall et al. 2018) and benefitted from an initial high propagule pressure. These coloniser species brought novel traits into the recipient communities, such as annual life cycles and efficient resource-use strategies, highly beneficial in a context of farming, intense herbivory, long drought periods and high soil disturbance (Seabloom et al. 2003; Funk and Vitousek 2007; HilleRisLambers et al. 2010; Molinari and D’Antonio 2014). Therefore, Spain communities constitute good candidates to apply the source-area approach. Here, we compare the occurrence (i.e. frequency), climatic niche and functional traits of co-dominant plant species in their native region in southern Spain considering whether they have colonised other Mediterranean-climate regions or not. Our hypotheses are that: (1) Colonisers are more frequent and show greater climatic tolerances than co-occurring non-coloniser natives. This would reflect the importance of propagule pressure (high association with humans in the native range) and having great ecological versatility; (2) Colonisers are functionally different from non-coloniser species and show traits related to higher resource-acquisition rates and greater competitive ability. This would reflect that coloniser species benefit from niche opportunities or competitive advantages, even in Mediterranean-climate regions where the harsh environmental conditions frequently lead to functional convergence (Galán Díaz et al. 2021b, 2023); (3) Naturalised and invasive coloniser species are functionally distinct in their native range, i.e. certain plant attributes are related to the stage of invasion in other Mediterranean-climate regions. Material and methods Grassland surveys We used trait data from co-dominant grassland species in southern Spain (Andalucía) compiled by Galán Díaz et al. (2022) which is available from Dryad (Galán Díaz et Javier Galán Díaz et al. / NeoBiota 86: 1–20 (2023) 4 al. 2022). Dominant species were defined as those whose cumulative cover made up at least 90% of the total community cover (Garnier et al. 2004). We selected as a source species pool all species native to Spain (149 species in total) which were classified as coloniser (98 species) or non-coloniser (51 species), depending on whether they are known to be introduced in other Mediterranean-climate regions of the world or not. We further classified coloniser species according to their stage of invasion or performance in other Mediterranean-climate regions into naturalised (56) and invasive (42) (Arianoutsou et al. 2013; Calflora 2014; Henderson 2020; Pagad et al. 2022). Naturalised colonisers are those species that have established at least in another Mediterranean-climate region; whereas, invasive refers to naturalised species that reproduce and spread fast at least in another Mediterranean-climate region (Richardson et al. 2000; Blackburn et al. 2011). The list of species included in this study can be found in Suppl. material 1: appendix S1. Occurrence and climate data Occurrence data of the target species in southern Spain were downloaded via the Global Biodiversity Information Facility (GBIF) using the “rgbif” package (Chamberlain et al. 2022). Although the native range of some species extended beyond this area, we focused on the distribution patterns within the species’ native ranges where traits were measured (i.e. autonomous community of Andalusia) because intraspecific variation in species traits can be high across species distribution ranges (Umaña and Swenson 2019). To avoid artefacts related to collection bias and spatial clustering (Larridon et al. 2021), we filtered one observation per species and cell from a raster with a resolution of 30 seconds (0.86 km2 at the Equator) which represents species frequency or number of grid cells occupied by the species. For the filtered observations, climate data were obtained from WorldClim at a resolution of 30 seconds (Fick and Hijmans 2017). We used the variables Annual Mean Temperature, Maximum Temperature of Warmest Month, Temperature Annual Range, Annual Precipitation, Precipitation of Driest Month and Precipitation Seasonality which represent average, extreme and seasonal indexes of temperature and precipitation. Functional traits measurements We considered eight functional traits that reflect orthogonal axes of plant function related to plant investment in above- and belowground vegetative and reproductive structures and community assembly processes (Table 1; Garnier et al. 2016; Hulme and Bernard-verdier 2018). Traits were measured in 149 species (301 combinations of species × site). A detailed description of the methodology followed to measure each trait can be found in Galán Díaz et al. (2022). In addition, we retrieved another 13 traits from literature related to life and growth form, reproduction strategies, pollination vectors and dispersal vectors (Table 1; the list of references can be found in Suppl. material 1: appendix S2). Occurence, climatic niche and traits of exotic plants in their native range 5 Statistical analyses First, to estimate species’ climatic niches, we performed a Principal Component Analysis (PCA) with the six climatic variables and used the scores of the observations along the first three Principal Components (PCs) to calculate two indexes (Suppl. material 1: appendix S3): (i) climatic niche richness, calculated as the smallest convex hull that encloses all observations of a given species; and (ii) climatic niche diversity, calculated as the mean pairwise distance amongst observations of a given species. These indexes represent two independent facets of species climatic niches. While climatic richness represents the dispersion or range of species climatic niches, climatic diversity indicates how the observations are distributed within the convex hull. The mean pairwise distance is less sensitive to outliers and is better correlated to the number of different habitats occupied by the species in their native range, a known predictor of invasiveness (Pyšek et al. 2015). We used linear models to compare whether the species frequency and climatic niches differ between non-coloniser, naturalised and invasive species. Second, we compared trait differences between non-coloniser and coloniser species and differences between naturalised and invasive species within non-colonisers. For Table 1. Traits considered in this study. Traits marked with an asterisk were retrieved from literature (the list of references can be found in Suppl. material 1: appendix S2). Trait Abb. Units Significance Growth form * Bulbous/Erect/Graminoid/Prostrate/Rosette Life form * Therophyte/Geophyte//Hemicryptohpyte/Chamephyte Leaf Specific leaf area SLA cm2/g Resource acquisition rate and conservation, photosynthetic rate, relative growth rate Leaf dry matter content LDMC mg/g Leaf tissue density, resistance to physical hazards, stress tolerance Ratio C:N CN Resource allocation Isotopic carbon fraction δ13C ‰ Integrated water use efficiency Root Specific root length SRL cm/mg Resource acquisition rate and conservation, relative growth rate Root dry matter content RDMC mg/g Root tissue density, resistance to physical hazards, drought resistance Root diameter RD mm Mycorrhizal association Reproduction Reproductive height cm Dispersal capacity Seed mass * g Seedling survival and establishment Onset of flowering * OFL months Reproductive success Length of bloom * LB months Reproductive success Self-compatibility * 1/0 Pollination mechanism * Insects/Wind/Selfed Dispersal vector * Agochory 1/0 Humans Autochory 1/0 Self-dispersed Anemochory 1/0 Wind Hydrochory 1/0 Water Zoochory 1/0 Animals Number of dispersal vectors * numb_disp 1–5 Javier Galán Díaz et al. / NeoBiota 86: 1–20 (2023) 6 continuous traits, we used the median value per species. Reproductive height and seed mass were log‐transformed prior to analyses. We ran linear models to test for differences in continuous traits and chi-squared tests for categorical data. We ran Wilcoxon ranksum and Kruskal-Wallis tests for onset of flowering, length of the bloom period and number of dispersal mechanisms. To test for the effect of phylogenetic non‐independ- ence amongst species (i.e. whether the observed patterns reflect contrasting evolutionary histories), we ran a phylogenetic ANOVA using the aov.phylo function implemented in the “geiger” package (Pennell et al. 2014). For this, we used an existing megaphylogeny to obtain a phylogenetic inference of our study species (Qian and Jin 2016). Species which were missing in the original tree were substituted by congeneric species (de la Riva et al. 2019). The phylogenetic inference is available in Suppl. material 1: appendix S4. Third, we ran a supervised classification algorithm (random forest) to leverage the relative importance of species occurrence (i.e. frequency), climatic niches and traits as predictors of invasiveness. We removed qualitative traits with missing data and imputed continuous traits using the rfImpute function included in the “randomForest” package. We also included family as a predictor because of the importance of phylogenetic relationships inferred from the phylogenetic ANOVA. All statistical analyses were performed in R version 4.2.2. To ensure the results of this study are fully reproducible, codes are available from GitHub (https://github.com/ galanzse/colonizersathome) and data from the Dryad Digital Repository (Galán Díaz et al. 2022). Results Coloniser species were more frequent than non-coloniser species in their shared native range in southern Spain and had greater climatic niche richness and diversity (Fig. 1). When considering the stage of invasion in other Mediterranean-climate regions, naturalised species were more frequent than non-coloniser species; both coloniser groups (i.e. naturalised and invasive species) had similar climatic richness and greater climatic richness than non-colonisers; and invasive species had greater climatic diversity than non-coloniser and naturalised species (Fig. 1). We found significant functional differences between non-coloniser and coloniser species for four traits: specific leaf area (SLA), specific root length (SRL), length of bloom period and number of propagule dispersal vectors (Fig. 2 and Table 2). Coloniser species had on average 17.68% greater SLA and 15.23% greater SRL than noncoloniser species. The bloom period of coloniser species was one month longer than non-coloniser species. Coloniser species showed more propagule dispersal vectors than non-colonisers. We found significant differences between groups when considering the stage of invasion of colonisers in other Mediterranean-climate regions (Fig. 3). Naturalised colonisers had on average 23.20% greater SLA than non-colonisers and their bloom periods were one month longer. Invasive colonisers were 11.03% taller and had 20.78% Occurence, climatic niche and traits of exotic plants in their native range 7 greater C:N than naturalised colonisers. Both naturalised and invasive coloniser species showed more propagule dispersal vectors than non-colonisers. The phylogenetic ANOVAs revealed that evolutionary relatedness does not necessarily determine trait differences between non-coloniser and coloniser species, but plays a major role when considering the stage of invasion of colonisers. Functional Figure 1. Species frequency (i.e. number of cells occupied in the native region), climatic niche richness (i.e. smallest convex hull that encloses the observations) and climatic niche diversity (i.e. mean pairwise distance amongst occurrences) of non-coloniser and coloniser species, also considering the stage of invasion of coloniser species (i.e. naturalised or invasive) in other Mediterranean-climate regions. Javier Galán Díaz et al. / NeoBiota 86: 1–20 (2023) 8 differences between non-coloniser, naturalised and invasive species may reflect phylogenetic non-independence amongst groups, mostly due to the large proportion of invasive grasses (Suppl. material 1: appendix S5). Non-coloniser and coloniser species differed in many qualitative traits (Table 2). Naturalised coloniser were more frequently therophytes, whereas invasive colonisers were more frequently grasses and, therefore, pollinated by wind more than the other groups. More than fifty percent (51.02%) of coloniser species were reported to be selfcompatible in contrast to 15.68% of non-coloniser species. Overall, coloniser species presented more dispersal vectors than non-colonisers. The accuracy of the random forest model was 73.53% when predicting coloniser/ non-coloniser species (Table 3). The most important variables that contributed to this model were richness of the climatic niche, dispersal vectors, agochory, family and zoo- Figure 2. Functional differences between non-coloniser and coloniser species. * p-value < 0.05. Occurence, climatic niche and traits of exotic plants in their native range 9 chory (Fig. 4). The inclusion of the stage of invasion decreased the accuracy of the model to 58.82%. The most important variables that contributed to the model specifying the stage of invasion were richness of the climatic niche, dispersal vectors and family. 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Plant and Soil 199(2): 213–227. https://doi.org/10.1023/A:1004327224729 Zhang J, He N, Liu C, Xu L, Chen Z, Li Y, Wang R, Yu G, Sun W, Xiao C, Chen HYH, Reich PB (2020) Variation and evolution of C:N ratio among different organs enable plants to adapt to N-limited environments. Global Change Biology 26(4): 2534–2543. https://doi. org/10.1111/gcb.14973 Javier Galán Díaz et al. / NeoBiota 86: 1–20 (2023) 20 Supplementary material 1 Species list, references accessed during bibliographic research, phylogenetic inference used in the analyses, PCA of climatic variables, and results of linear regressions Authors: Javier Galán Díaz, Enrique G. de la Riva, Irene Martín-Forés, Montserrat Vilà Data type: species, references, phylogenetic, models Copyright notice: This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited. Link: https://doi.org/10.3897/neobiota.86.104039.suppl1