Research Article |
Corresponding author: Javier Galán Díaz ( javiergalandiaz@gmail.com ) Academic editor: José Hierro
© 2023 Javier Galán Díaz, Enrique G. de la Riva, Irene Martín-Forés, Montserrat Vilà.
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.
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
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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.
Climatic niche, exotic plants, functional traits, Mediterranean-climate, source-area approach
Exotic plant species pose an increasing threat to native species and ecosystems conservation (
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.
Studies following the source-area approach have mainly explored the importance of life history traits as predictors of invasiveness (
Spain is home to many herbaceous species that are naturalised in other Mediterranean-climate regions of the world (
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 (
We used trait data from co-dominant grassland species in southern Spain (Andalucía) compiled by
Occurrence data of the target species in southern Spain were downloaded via the Global Biodiversity Information Facility (GBIF) using the “rgbif” package (
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
Traits considered in this study. Traits marked with an asterisk were retrieved from literature (the list of references can be found in Suppl. material
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 |
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
Second, we compared trait differences between non-coloniser and coloniser species and differences between naturalised and invasive species within non-colonisers. For 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 rank-sum 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‐independence 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 (
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 (
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.
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.
We found significant differences between groups when considering the stage of invasion of colonisers in other Mediterranean-climate regions (Fig.
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 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
Non-coloniser and coloniser species differed in many qualitative traits (Table
The accuracy of the random forest model was 73.53% when predicting coloniser/non-coloniser species (Table
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.
Functional differences between non-coloniser, naturalised and invasive species. Letters denote statistical differences in post-hoc comparison (p-value < 0.05).
Contingency table of qualitative traits of non-coloniser and coloniser species. Coloniser species are separated considering their level of invasion in other Mediterranean-climate regions. * p-value < 0.05.
trait | non-coloniser (n = 51) | coloniser (n = 98) | ||
---|---|---|---|---|
naturalised (n = 56) | invasive (n = 42) | |||
Life form | therophyte | 32 | 51 | 28* |
geophyte | 2 | 1 | 0 | |
hemicryptophyte | 10 | 4 | 13 | |
chamephyte | 3 | 0 | 1 | |
Growth form | bulbous | 3 | 1 | 0 |
erect | 26 | 28 | 17* | |
graminoid | 4 | 6 | 17* | |
prostrate | 13 | 18 | 4* | |
rosette | 2 | 3 | 4 | |
Pollination | insects | 32 | 38 | 18* |
wind | 6 | 9 | 21* | |
self-compatible | 8 | 33 | 17* | |
Dispersion | agochory | 2 | 22 | 23* |
anemochory | 12 | 19 | 23* | |
autochory | 4 | 24 | 6* | |
hydrochory | 1 | 12 | 11* | |
zoochory | 13 | 36 | 28* |
Confusion matrices of random forest models. Rows indicate the actual (true) values for each category and columns indicate predicted values. The classification error corresponds to the proportion of wrongly classified cases, i.e. for a given category, the classification equals to the number false negative predictions divided by the total number of actual cases.
predicted | |||||
---|---|---|---|---|---|
actual | Model 1 | coloniser | non-coloniser | classification error | |
coloniser | 79 | 16 | 0.17 | ||
non-coloniser | 20 | 21 | 0.49 | ||
Model 2 | invasive | naturalised | non-coloniser | ||
invasive | 18 | 13 | 10 | 0.56 | |
naturalised | 11 | 36 | 7 | 0.33 | |
non-coloniser | 6 | 9 | 26 | 0.37 |
Variable importance plot of random forest classification models. Origin indicates whether the species are introduced in other Mediterranean-climate regions of the world (i.e. non-coloniser and coloniser). Invasiveness refers to species’ stage of invasion in other Mediterranean-climate regions (i.e. non-coloniser, naturalised and invasive).
Discerning general invasion syndromes across ecosystems can facilitate the identification of species with greater risks of establishment and support management actions at different stages of the invasion process (
We found that coloniser species are more widespread (i.e. frequent) in their native region than co-dominant non-coloniser species. This result matches the
There were functional differences between non-coloniser and coloniser species. Overall, coloniser species achieve a combination of traits that facilitate rapid growth, regeneration and spread compared to non-coloniser species. That is, colonisers species displayed greater SLA and SRL, which indicates high resource-use efficiency and relative growth-rates (i.e. high C gain and leaf production when resources are abundant;
As we pointed out, maximising resource uptake and high relative growth rates has been observed as a successful strategy for coloniser species. However, when considering invasion status, the patterns were more nuanced: we did not find significant differences in SRL associated with invasion status and the species displaying the highest SLA and longer bloom periods were naturalised instead of invasive colonisers. Invasive species, in turn, displayed higher values of reproductive height, which is closely correlated to plant stature in grassland species and C:N concentration. Therefore, different traits might be relevant along different stages of the invasion process (
The phylogenetic regressions suggest that some observed differences across stages of invasion may be masked by evolutionary relatedness amongst groups: naturalised species were more frequently forbs, whereas invasive colonisers were more frequently grasses. For instance, invasive species not displaying significantly greater SLA than non-colonisers, but showing greater C:N, might reflect greater carbon allocation to leaves in grasses than in forbs (
The two most important predictors of the random forest models were climatic richness and number of dispersal vectors. Family was an important variable reflecting the importance of considering evolutionary relatedness in biological invasions to account for unmeasured trait diversity and to correctly interpret the observed differences (
We have shown that coloniser species are already pre-adapted to broader climatic niche conditions in their native range, which predisposes them to occupy greater diverse conditions once they are introduced in a new area. In a similar manner, certain traits, especially indicating aided dispersal, high relative growth rate and resource efficiency, are related to successful colonisation; whereas, invasion processes in grasslands are more associated with plants displaying higher reproductive height and nitrogen use efficiency. The source-area approach can be especially useful when comparing regions with shared histories of colonisation and trade where plant introduction histories have been mostly unidirectional as is the case of the Mediterranean Biome. The knowledge derived from such studies may allow us to improve prediction models, identifying key species to monitor; this could, therefore, prevent potential harmful impacts from coloniser species in invaded communities and reduce the investment necessary to target eradication measures.
JGD: Conceptualisation, Methodology, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review and editing. EGR: Methodology, Supervision, Writing - review and editing. IMF: Investigation, Writing - review and editing. MV: Conceptualisation, Supervision, Funding acquisition, Writing - review and editing.
The data and codes used in this study are archived in Dryad and Github.
Javier Galán Díaz and Enrique G. de la Riva are respectively supported by Margarita Salas and María Zambrano fellowships funded by the Ministry of Universities and European Union-Next Generation Plan. This research has received financial support through “la Caixa” INPhINIT Fellowship Grant for Doctoral studies at Spanish Research Centres of Excellence (LCF/BQ/DI17/11620012) and from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement No. 713673. This research was partially funded by the Ministerio de Ciencia e Innovación project PREABROAD (EUR2022-134026).
Species list, references accessed during bibliographic research, phylogenetic inference used in the analyses, PCA of climatic variables, and results of linear regressions
Data type: species, references, phylogenetic, models