Figure 5. Species networks indicating the net strength of interactions (sum of direct interactions and higher-order interactions per species pair) in the most parsimonious models (ΔAIC < 2) at zero (a) and at maximum (b) drought index. The color of the network links indicates whether the net interaction is positive (blue) or negative (red). The color of the species relates to its functional group including grasses (green), non-N-fixing-forbs (brown) and N-fixers (purple). When multiple interactions were found between the same species pair in different models, their strengths were averaged. The thickness of the connections indicates net interaction strength. Non-significant interactions were excluded. For full species names, see methods section 3.1.
Discussion
Inclusion of species interactions (including HOIs) increased the accuracy of plant performance models along the drought gradient compared to models only including the drought effect. Direct interactions improved the explanatory power of models more on average compared to HOIs, though the latter were present in a larger number of the most parsimonious models (Table S4). This finding affirms the necessity to include at least some HOIs to improve net species interaction estimates in complex and diverse ecological networks (Li et al., 2020; Li et al., 2021; Martyn et al., 2021; Mayfield & Stouffer, 2017). Particularly in plant communities subjected to drought, where species interactions can change drastically (Grant et al., 2014; Van den Berge et al., 2014), facilitation and/or competition may be overestimated without inclusion of HOIs, limiting our ability to predict ecosystem trajectories (Brooker et al., 2008). Incorporating HOI approaches in more studies could thus improve the predictability of species performance under climate change.
Contrasting with the stress gradient hypothesis (Bertness & Callaway, 1994), more net interactions became negative on average along the drought gradient. This occurred because direct interactions predominantly shifted to more negative with longer drought, while HOIs were doing the opposite, albeit without fully neutralizing intensified competition (Fig. 4). In line with Xiao et al. (2020) and Li et al. (2021), these findings indicate that HOIs may influence community stability by partially counteracting increased direct competitive effects under increasing precipitation variability. While Xiao et al. (2020) argue that especially intraspecific HOIs play a stabilizing role, our experimental findings suggest that interspecific HOIs may also contribute to stabilizing community dynamics (Bairey et al., 2016; Grilli et al., 2017; Singh & Baruah, 2021). Hence, persistence of highly diverse communities under increasing drought stress could be promoted by interspecies HOIs counteracting stronger direct competitive effects, thus stabilizing community dynamics, and preventing dominance of one single species group (Brooker et al., 2008). Since such stabilizing HOIs are more likely to be generated in multi-species assemblages (Grilli et al., 2017), this observation may also point at an additional mechanism through which more diverse communities could be better equipped to withstand climate extremes (Brooker et al., 2008; De Boeck et al., 2018).
In terms of directionality, the majority of HOIs showed a shift towards stronger facilitation (or weaker competition) along the drought gradient on average (positive values in Fig. 3d), in line with the stress gradient and dominance hypotheses (Bertness & Callaway, 1994; Coyle et al., 2014). However, three additional species exerted direct competitive pressures at maximum drought index on average (Fig. 4c), and more net interactions became competitive (Fig. 5; Table 1). Hence, while the majority of direct competitive interactions was weakened by positive HOIs, the strengthening of some competitors may have led to a destabilized system where more species were being outcompeted under longer drought (Table 1; Fig. 5). This is in agreement with some studies (Maestre et al., 2005; Olsen et al., 2016; Saccone et al., 2009), but contradicted by others (Brooker et al., 2008; He et al., 2013). Having more species in negative direct interactions (and more net competitive interactions) under longer drought indeed suggests that some species (e.g. Phleum pratense ) could become more competitive under water shortage because of, for example, favorable resource acquisition traits (Kraft et al., 2015; Olsen et al., 2016).
In that regard, we observed that direct interactions became more negative with increasing Ellenberg’s nutrient indices under longer drought (Fig. S5b), indicating that species with an affinity for resource rich environments became involved in more direct competitive interactions. Hence, increasing weather persistence seemed to not only affect species interaction networks directly through intrinsic negative responses to changes in soil water availability (i.e. Ellenberg’s moisture indices; Fig. S5a), but also indirectly through effects of drought on nutrient availability. These intensified direct competitive effects may thus also reflect differences in plant resilience during post-drought rewetting and recovery phases, when the flush of nutrients related to Birch effects induces transient periods of high nutrient availability (Birch, 1958; Borken & Matzner, 2009), allowing rapid recovery of more resilient species.
However, positive interactions still made up half of all interactions under extreme drought (Fig. 5b), and the average strength of net interactions per functional group did not change noticeably with drought (Table 1). Together with the striking productivity declines (Fig. S2 & S3) and diversity losses related to the intrinsic drought sensitivity of individual species (Reynaert et al., 2021), these observations may indicate a primary role of environmental filtering (i.e. drought), and only to a lesser extent shifts in species interactions as the primary cause for aboveground productivity declines under drought (Coyle et al., 2014; Elst et al., 2017). Following Maestre et al. (2009), we pose that under extreme environmental stress (e.g., prolonged drought), species could potentially also stop growing due to exceedance of intrinsic ecological thresholds, mostly independently of the community dynamics (i.e. facilitative or competitive interactions) in their growing environment (De Boeck et al., 2018; Reynaert et al., 2021; Soliveres et al., 2015). The non-linearity in survival responses (Fig. S5; Reynaert et al. (2021)), could indeed suggest such a mechanism. Similar negative threshold responses of aboveground plant productivity have been observed in many terrestrial ecosystems, particularly in relation to the duration of dry spell anomalies (Felton et al., 2021). Taken together, these results indicate that both intrinsic responses to increasing drought and intensifying interspecific competitive effects (Olsen et al., 2016) lead to the observed changes in community dynamics by the end of the first 120 days of the experiment.
In line with our third hypothesis, forbs (particularly N-fixers) tended to be more involved in facilitative interactions on average, while grasses seemed the strongest competitors (Table 1; Fig. 5). This role of species identity (Martyn et al., 2021; Weigelt et al., 2002) was confirmed by the relationships with Ellenberg’s moisture and nutrient indices at maximum drought index, with species with an affinity for wetter and more resource rich environments experiencing or exerting more negative HOIs or direct interactions on average, respectively (Fig. S5a,b). Additionally, the disproportionate contribution of N-fixers to positive interactions and grasses to negative interactions suggests that some species (or species groups) are crucial in defining the dynamics of complex species networks (Singh & Baruah, 2021). With the available information, we cannot fully determine whether N-fixer importance is because of active facilitative traits (e.g., improving water or nutrient supply), or due to passive competitive release as these smaller species rapidly disappeared with increasing drought stress (i.e. were more susceptible to environmental filtering), or because they prevent other more competitive neighbors from colonizing (e.g., via vegetative propagation) (Maestre et al., 2003).
Some aspects of this study’s methodology should be considered. First, the presented direct vs three-way higher-order counteracting interaction system may not fully reveal the nature of net community dynamics. Inclusion of HOIs beyond simple three-way terms may shed more light on the overall strength of facilitation and competition, but was not feasible in this study because of insufficient data (Martyn et al., 2021). Nonetheless, previous studies have indicated that inclusion of more complex (i.e., 4-way, 5-way, etc.) HOIs leads to progressively diminished returns in explaining community dynamics (Li et al., 2021), highlighting the importance of the modelled interactions. Second, the exclusion of intraspecific and density effects likely perturbed the estimation of interaction coefficients, since intraspecific interactions (both direct and HOIs) and competitor density influence net community dynamics in diverse ecosystems (Chesson, 2000; Li et al., 2021; Xiao et al., 2020). However, because individuals of the same species were never planted directly next to one-another and the initial planting density was identical in every mesocosm, we believe that the observed changes primarily reflect plant responses in function of neighbor performance and drought (Grant et al., 2014). Finally, the species interactions investigated here represent short-term responses to altered persistence in summer precipitation (Reynaert et al., 2021). Since we already observed strong differences in species performance related to the extremity of the precipitation regime, later assessment of interactions would likely disproportionally reflect the effects of only a few single species since many had died already under extreme summer drought after 120 days (Reynaert et al., 2021). Consequently, the identified interactions likely represent the most important ones that contributed to the observed declines of species under the imposed climate trend.
Conclusion
Modelling and disentangling the role of HOIs in community dynamics remains challenging because of their complexity, extensive dataset requirements and a lack of widely applicable methodologies. More targeted manipulation experiments filtering out the environmental noise that perturbs the estimation of interaction coefficients could aid in bridging the gap between theoretical modelling and meaningful ecological understanding of diverse communities. We took a first step in that direction, by experimentally demonstrating that species interactions, including HOIs, can significantly change along a gradient of increasingly persistent precipitation regimes. Inclusion of HOIs did not only improve model explanatory power, but also shed more light on potential stabilizing mechanisms for increasing competition under drought. Hence, HOI approaches could further improve the accuracy of species performance models under rapidly changing environmental conditions, an imperative prerequisite for targeted and efficient ecosystem management.
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data availability
The corresponding data of this study will be made publicly available online in a Zenodo repository at [INSERT DOI] and in the previously published Dryad repository at https://doi.org/10.5061/dryad.k98sf7m52.
References
Bairey, E., Kelsic, E. D., & Kishony, R. (2016). High-order species interactions shape ecosystem diversity. Nat Commun, 7 (1), 12285. doi:10.1038/ncomms12285
Ball, K. R., Power, S. A., Brien, C., Woodin, S., Jewell, N., Berger, B., & Pendall, E. (2020). High-throughput, image-based phenotyping reveals nutrient-dependent growth facilitation in a grass-legume mixture. PLoS One, 15 (10), e0239673. doi:10.1371/journal.pone.0239673
Bertness, M. D., & Callaway, R. (1994). Positive interactions in communities. Trends Ecol Evol, 9 (5), 191-193. doi:10.1016/0169-5347(94)90088-4
Billick, I., & Case, T. J. (1994). Higher order interactions in ecological communities: what are they and how can they be detected?Ecology, 75 (6), 1529-1543. doi:/10.2307/1939614
Bimler, M. D., Stouffer, D. B., Lai, H. R., & Mayfield, M. M. (2018). Accurate predictions of coexistence in natural systems require the inclusion of facilitative interactions and environmental dependency.Journal of Ecology, 106 (5), 1839-1852. doi:10.1111/1365-2745.13030
Birch, H. (1958). The effect of soil drying on humus decomposition and nitrogen availability. Plant and soil, 10 (1), 9-31.
Borken, W., & Matzner, E. (2009). Reappraisal of drying and wetting effects on C and N mineralization and fluxes in soils. Global Change Biology, 15 (4), 808-824. doi:10.1111/j.1365-2486.2008.01681.x
Brooker, R. W., Maestre, F. T., Callaway, R. M., Lortie, C. L., Cavieres, L. A., Kunstler, G., . . . Michalet, R. (2008). Facilitation in plant communities: the past, the present, and the future.Journal of Ecology, 96 (1), 18-34. doi:10.1111/j.1365-2745.2007.01295.x
Chesson, P. (2000). Mechanisms of maintenance of species diversity.Annual review of Ecology and Systematics, 31 (1), 343-366. doi:DOI 10.1146/annurev.ecolsys.31.1.343
Ciais, P., Reichstein, M., Viovy, N., Granier, A., Ogee, J., Allard, V., . . . Valentini, R. (2005). Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature, 437 (7058), 529-533. doi:10.1038/nature03972
Collins, M., Knutti, R., Arblaster, J., Dufresne, J.-L., Fichefet, T., Friedlingstein, P., . . . Krinner, G. (2013). Long-term climate change: projections, commitments and irreversibility. In Climate Change 2013-The Physical Science Basis: Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 1029-1136): Cambridge University Press.
Corbin, J. D., & D’Antonio, C. M. (2004). Competition between native perennial and exotic annual grasses: Implications for an historical invasion. Ecology, 85 (5), 1273-1283. doi:10.1890/02-0744
Coyle, J. R., Halliday, F. W., Lopez, B. E., Palmquist, K. A., Wilfahrt, P. A., & Hurlbert, A. H. (2014). Using trait and phylogenetic diversity to evaluate the generality of the stress-dominance hypothesis in eastern North American tree communities. Ecography, 37 (9), 814-826. doi:10.1111/ecog.00473
Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, complex systems, 1695 (5), 1-9.
De Boeck, H. J., Bloor, J. M. G., Kreyling, J., Ransijn, J. C. G., Nijs, I., Jentsch, A., . . . Wardle, D. (2018). Patterns and drivers of biodiversity-stability relationships under climate extremes.Journal of Ecology, 106 (3), 890-902. doi:10.1111/1365-2745.12897
Elst, E. M., De Boeck, H. J., Vanmaele, L., Verlinden, M., Dhliwayo, P., & Nijs, I. (2017). Impact of climate extremes modulated by species characteristics and richness. Perspectives in Plant Ecology Evolution and Systematics, 24 , 80-92. doi:10.1016/j.ppees.2016.12.007
Fay, P. A., Carlisle, J. D., Knapp, A. K., Blair, J. M., & Collins, S. L. (2003). Productivity responses to altered rainfall patterns in a C-4-dominated grassland. Oecologia, 137 (2), 245-251. doi:10.1007/s00442-003-1331-3
Felton, A. J., Knapp, A. K., & Smith, M. D. (2021). Precipitation-productivity relationships and the duration of precipitation anomalies: An underappreciated dimension of climate change. Glob Chang Biol, 27 (6), 1127-1140. doi:10.1111/gcb.15480
Grant, K., Kreyling, J., Heilmeier, H., Beierkuhnlein, C., & Jentsch, A. (2014). Extreme weather events and plant-plant interactions: shifts between competition and facilitation among grassland species in the face of drought and heavy rainfall. Ecological Research, 29 (5), 991-1001. doi:10.1007/s11284-014-1187-5
Grilli, J., Barabas, G., Michalska-Smith, M. J., & Allesina, S. (2017). Higher-order interactions stabilize dynamics in competitive network models. Nature, 548 (7666), 210-213. doi:10.1038/nature23273
He, Q., Bertness, M. D., & Altieri, A. H. (2013). Global shifts towards positive species interactions with increasing environmental stress.Ecol Lett, 16 (5), 695-706. doi:10.1111/ele.12080
Klaus, V. H., Friedritz, L., Hamer, U., & Kleinebecker, T. (2020). Drought boosts risk of nitrate leaching from grassland fertilisation.Sci Total Environ, 726 , 137877. doi:10.1016/j.scitotenv.2020.137877
KMI. (2019). Klimatologisch jaaroverzicht, jaar 2019. [WWW document] URL https://www.meteo.be/resources/climatology/pdf/klimatologisch_jaaroverzicht_2019.pdf [accessed 5 October 2021].
Knapp, A. K., Beier, C., Briske, D. D., Classen, A. T., Luo, Y., Reichstein, M., . . . Weng, E. (2008). Consequences of More Extreme Precipitation Regimes for Terrestrial Ecosystems. BioScience, 58 (9), 811-821. doi:10.1641/B580908
Kraft, N. J., Godoy, O., & Levine, J. M. (2015). Plant functional traits and the multidimensional nature of species coexistence.Proc Natl Acad Sci U S A, 112 (3), 797-802. doi:10.1073/pnas.1413650112
Levine, J. M., Bascompte, J., Adler, P. B., & Allesina, S. (2017). Beyond pairwise mechanisms of species coexistence in complex communities. Nature, 546 (7656), 56-64. doi:10.1038/nature22898
Li, Y., Bearup, D., & Liao, J. (2020). Habitat loss alters effects of intransitive higher-order competition on biodiversity: a new metapopulation framework. Proc Biol Sci, 287 (1940), 20201571. doi:10.1098/rspb.2020.1571
Li, Y., Mayfield, M. M., Wang, B., Xiao, J., Kral, K., Janik, D., . . . Chu, C. (2021). Beyond direct neighbourhood effects: higher-order interactions improve modelling and predicting tree survival and growth.Natl Sci Rev, 8 (5), nwaa244. doi:10.1093/nsr/nwaa244
Lubbe, S., Filzmoser, P., & Templ, M. (2021). Comparison of zero replacement strategies for compositional data with large numbers of zeros. Chemometrics and Intelligent Laboratory Systems, 210 , 104248. doi:10.1016/j.chemolab.2021.104248
Maestre, F. T., Bautista, S., & Cortina, J. (2003). Positive, negative, and net effects in grass-shrub interactions in mediterranean semiarid grasslands. Ecology, 84 (12), 3186-3197. doi:10.1890/02-0635
Maestre, F. T., Callaway, R. M., Valladares, F., & Lortie, C. J. (2009). Refining the stress-gradient hypothesis for competition and facilitation in plant communities. Journal of Ecology, 97 (2), 199-205. doi:10.1111/j.1365-2745.2008.01476.x
Maestre, F. T., & Cortina, J. (2004). Do positive interactions increase with abiotic stress? A test from a semi-arid steppe. Proceedings of the Royal Society of London. Series B: Biological Sciences, 271 (suppl_5), S331-S333. doi:10.1098/rsbl.2004.0181
Maestre, F. T., Valladares, F., & Reynolds, J. F. (2005). Is the change of plant–plant interactions with abiotic stress predictable? A meta‐analysis of field results in arid environments. Journal of Ecology, 93 (4), 748-757. doi:10.1111/j.1365-2745.2005.01017.x
Martyn, T. E., Stouffer, D. B., Godoy, O., Bartomeus, I., Pastore, A. I., & Mayfield, M. M. (2021). Identifying ”Useful” Fitness Models: Balancing the Benefits of Added Complexity with Realistic Data Requirements in Models of Individual Plant Fitness. Am Nat, 197 (4), 415-433. doi:10.1086/713082
Mayfield, M. M., & Stouffer, D. B. (2017). Higher-order interactions capture unexplained complexity in diverse communities. Nat Ecol Evol, 1 (3), 62. doi:10.1038/s41559-016-0062
Metz, J., & Tielborger, K. (2016). Spatial and temporal aridity gradients provide poor proxies for plant-plant interactions under climate change: a large-scale experiment. Functional Ecology, 30 (1), 20-29. doi:10.1111/1365-2435.12599
Olsen, S. L., Topper, J. P., Skarpaas, O., Vandvik, V., & Klanderud, K. (2016). From facilitation to competition: temperature-driven shift in dominant plant interactions affects population dynamics in seminatural grasslands. Glob Chang Biol, 22 (5), 1915-1926. doi:10.1111/gcb.13241
Pendergrass, A. G., Knutti, R., Lehner, F., Deser, C., & Sanderson, B. M. (2017). Precipitation variability increases in a warmer climate.Sci Rep, 7 (1), 17966. doi:10.1038/s41598-017-17966-y
Pfleiderer, P., Schleussner, C. F., Kornhuber, K., & Coumou, D. (2019). Summer weather becomes more persistent in a 2 degrees C world.Nature Climate Change, 9 (9), 666-+. doi:10.1038/s41558-019-0555-0
R Core Team. (2019). R: a language and environment for statistical computing, R v.3.6.1 . Vienna, Austria: R Foundation for Statistical Computing. [WWW document] URL https://www.R-project.org [accessed 5 October 2021].
Reynaert, S., De Boeck, H. J., Verbruggen, E., Verlinden, M., Flowers, N., & Nijs, I. (2021). Risk of short-term biodiversity loss under more persistent precipitation regimes. Glob Chang Biol, 27 (8), 1614-1626. doi:10.1111/gcb.15501
Reynaert, S., Zi, L., AbdElgawad, H., De Boeck, H. J., Vinduskova, O., Nijs, I., . . . Asard, H. (2022). Does previous exposure to extreme precipitation regimes result in acclimated grassland communities?Sci Total Environ, 838 (Pt 3), 156368. doi:10.1016/j.scitotenv.2022.156368
Saccone, P., Delzon, S., Pages, J. P., Brun, J. J., & Michalet, R. (2009). The role of biotic interactions in altering tree seedling responses to an extreme climatic event. Journal of vegetation science, 20 (3), 403-414. doi:10.1111/j.1654-1103.2009.01012.x
Schmitz, O. J., Grabowski, J. H., Peckarsky, B. L., Preisser, E. L., Trussell, G. C., & Vonesh, J. R. (2008). From individuals to ecosystem function: toward an integration of evolutionary and ecosystem ecology.Ecology, 89 (9), 2436-2445. doi:10.1890/07-1030.1
Siepielski, A. M., Morrissey, M. B., Buoro, M., Carlson, S. M., Caruso, C. M., Clegg, S. M., . . . MacColl, A. D. (2017). Precipitation drives global variation in natural selection. Science, 355 (6328), 959-962. doi:10.1126/science.aag2773
Singh, P., & Baruah, G. (2021). Higher order interactions and species coexistence. Theoretical Ecology, 14 (1), 71-83. doi:10.1007/s12080-020-00481-8
Soliveres, S., Smit, C., & Maestre, F. T. (2015). Moving forward on facilitation research: response to changing environments and effects on the diversity, functioning and evolution of plant communities.Biol Rev Camb Philos Soc, 90 (1), 297-313. doi:10.1111/brv.12110
Spinoni, J., Vogt, J. V., Naumann, G., Barbosa, P., & Dosio, A. (2018). Will drought events become more frequent and severe in Europe?International Journal of Climatology, 38 (4), 1718-1736. doi:10.1002/joc.5291
Suttle, K. B., Thomsen, M. A., & Power, M. E. (2007). Species interactions reverse grassland responses to changing climate.Science, 315 (5812), 640-642. doi:10.1126/science.1136401
Van den Berge, J., Naudts, K., De Boeck, H., Ceulemans, R., & Nijs, I. (2014). Do interactions with neighbours modify the above-ground productivity response to drought? A test with two grassland species.Environmental and Experimental Botany, 105 , 18-24. doi:10.1016/j.envexpbot.2014.04.002
Van Sundert, K., Arfin Khan, M. A. S., Bharath, S., Buckley, Y. M., Caldeira, M. C., Donohue, I., . . . Vicca, S. (2021). Fertilized graminoids intensify negative drought effects on grassland productivity.Glob Chang Biol . doi:10.1111/gcb.15583
Vicca, S., Gilgen, A. K., Serrano, M. C., Dreesen, F. E., Dukes, J. S., Estiarte, M., . . . Granier, A. (2012). Urgent need for a common metric to make precipitation manipulation experiments comparable. New Phytologist, 195 (3), 518-522. doi:10.1111/j.1469-8137.2012.04224.x
Walker, L. R., Clarkson, B. D., Silvester, W. B., & Clarkson, B. R. (2003). Colonization dynamics and facilitative impacts of a nitrogen-fixing shrub in primary succession. Journal of vegetation science, 14 (2), 277-290. doi:DOI 10.1111/j.1654-1103.2003.tb02153.x
Weigelt, A., Steinlein, T., & Beyschlag, W. (2002). Does plant competition intensity rather depend on biomass or on species identity?Basic and Applied Ecology, 3 (1), 85-94. doi:Doi 10.1078/1439-1791-00080
Weiher, E., & Keddy, P. A. (1995). Assembly rules, null models, and trait dispersion: new questions from old patterns. Oikos , 159-164.
Wickham, H. (2016). ggplot2: elegant graphics for data analysis.New York, NY, USA: Springer.
Wickham, H., Francois, R., Henry, L., & Müller, K. (2015). dplyr: A grammar of data manipulation. [WWW document] URL https://dplyr.tidyverse.org/ [accessed 5 October 2021].
Wootton, J. T. (1993). Indirect Effects and Habitat Use in an Intertidal Community - Interaction Chains and Interaction Modifications.American Naturalist, 141 (1), 71-89. doi:10.1086/285461
Xiao, J. L., Li, Y. Z., Chu, C. J., Wang, Y. S., Meiners, S. J., & Stouffer, D. B. (2020). Higher-order interactions mitigate direct negative effects on population dynamics of herbaceous plants during succession. Environmental Research Letters, 15 (7), 074023. doi:10.1088/1748-9326/ab8a88
Zhao, W. N., & Khalil, M. A. K. (1993). The Relationship between Precipitation and Temperature over the Contiguous United-States.Journal of Climate, 6 (6), 1232-1236. doi:10.1175/1520-0442(1993)006<1232:Trbpat>2.0.Co;2