Heterogeneity in our data was mainly concentrated at the study level, indicating much higher similarity of effect sizes within than between studies (Fig. 2.4). This
heterogeneity remained largely unexplained since none of the predictors at the study level showed a consistent effect. The use of a common control for calculating effect sizes was the second most important source of heterogeneity, followed by species-specific effects (Fig. 2.4). At the species level, we showed that N-fixers were less tolerant to competition, but not necessarily less suppressive of their neighbors than non-N-fixers. The latter, however, should not be considered in conflict with the extensive evidence on the
facilitative role of N-fixers (Gómez-Aparicio et al. 2004). The spatial and temporal scale captured by our data was too small for detecting facilitation (Bruno et al. 2003).
Moreover, soils used in competition experiments might have been initially rich in nitrogen, which could negatively affect competitive ability of N-fixers or mitigate their positive effect on neighbors (Harpole & Tilman 2007). Phylogenetic relatedness among neighbor and target species explained only little heterogeneity in the data and correcting for it did not affect the overall results (Figs. 2.2, 2.3). Furthermore, in agreement with previous studies (Cahill et al. 2008; Dostál 2011; Fritschie et al. 2014; Godoy et al. 2014;
46
Feng & van Kleunen 2016), we were unable to provide support for the relatedness-
competition hypothesis. As previously suggested, lack of the relationship between
phylogenetic relatedness and plant competition and coexistence might be because phylogeny does not precisely capture average fitness differences and stabilizing niche differences among species (Godoy et al. 2014). Notably, the amount of heterogeneity remaining after accounting for all the known correlative structures (i.e. residual heterogeneity) was negligible, or put another way, variation in the data occurred at the levels higher than individual observation. The presence of multiple crossed dependencies within our data once again highlights the high context-dependent nature of the process of plant competition. We therefore highly encourage the use of a multilevel approach in future syntheses on plant competition and invasion.
2.5.3. Publication bias
We detected a significant negative asymmetry in funnel plots, suggesting that our inference could be potentially biased towards negative effect sizes (Fig. S2.3). We were not surprised by this as the bias in reporting and interpretation of the results towards significant negative effects has long been recognized among studies on species
competition and invasion (Connell 1983; Warren et al. 2017). However, in contrast to the high sensitivity to extreme values of the methods used for detecting publication bias (the significance of the Egger’s regression intercept was driven by roughly 6% and 3% of the ANNE and RNNE data, respectively), our analyses were robust to potential outliers (Fig. S2.1). Moreover, our data were not systematically biased towards one origin group, which suggests that origin-level comparisons are largely unaffected by publication bias.
47
More importantly, we stress that the results of this study are representative of the short- term outcomes of competition in two-species systems under not necessarily realistic conditions and therefore might not scale up to community-level patterns (see Duralia & Reader 1993; Dormann & Roxburgh 2005; Engel & Weltzin 2008; Martorell &
Freckleton 2014 for further discussion). Moreover, we used individual plant biomass as a proxy for fitness, yet biomass and fitness are not always positively correlated (e.g. Colautti & Barrett 2013). When possible, future studies on the role of origin in plant competition should incorporate indirect effects that arise in systems with three and more species and assess the outcome of competition directly in terms of fitness.
2.5.4. Concluding remarks
Our study demonstrated that non-native plants are competitively different from their heterospecific native counterparts in several ways. Specifically, we showed that, contrary to what is commonly believed, neighbor tolerance and not neighbor suppression allows non-natives to outperform native plants in a two-species setting. Interestingly, even though non-natives were less affected by heterospecific natives than vice versa, their overall performance when surrounded by natives was often not enhanced in comparison to that in the presence of conspecifics. We also showed that both inter- and intraspecific competition tended to be more intense among non-natives that among natives. Collectively, our results suggest that reduced intensity of interspecific
competition in the introduced compared to native range plays an important role in the success of non-native plant species.
48
It remains unknown to what degree competitive tolerance of non-natives is driven by their distinct eco-evolutionary history and to what degree it is a product of rapid evolution in the introduced range. If competitive tolerance has high adaptive potential in the novel range, we might expect to see its increase in non-native plants over time (e.g. Huang et al. 2018). Similarly, we are limited in our knowledge on how native plants evolve in response to competition from non-native neighbors (Strauss et al. 2006). A handful of studies showed than native plants can develop tolerance towards their novel counterparts (e.g. Callaway et al. 2005; Lau 2006), and this could to a certain extent explain why native plants responded similarly to native and non-native neighbors in our study. Future studies that experimentally manipulate both competition and selection would greatly improve our understanding of the role of competition in plant invasions.
2.6. Acknowledgements
We thank Drs. Carol Adair, Nicholas Gotelli, Anthony D’Amato, Aimee
Classen, and the three anonymous reviewers for valuable comments on earlier versions of the manuscript, and Hannah Epstein, Suma Lashof, and Ryan Colarusso for assistance with data collection. We also thank Elizabeth Boughton, Larry Burrows, Ellen Cieraad, Hall Cushman, Pablo Homet Gutierrez, Andy Kleinhesselink, Ian Pearse, José María Gómez Reyes, Maya Svriz, and Stephanie Yelenik for sharing their data. Funding was obtained from USDA Forest Service (grant no. 16-CR-11242303-061) and The
49
2.7. Authorship
MG and KFW conceived and designed the study. MG collected data, performed statistical analyses, and wrote the first draft of the manuscript. KFW obtained funding and contributed to revisions.
2.8. Data Accessibility
Data available from the Figshare digital repository (DOI: 10.6084/m9.figshare.5868699).
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