Non‐additive biotic interactions improve predictions of tropical tree growth and impact community size structure

Abstract

Growth in individual size or biomass is a key demographic component in population models, with wide-ranging applications from quantifying species performance across abiotic or biotic conditions to assessing landscape-level dynamics under global change. In forest ecology, the responses of tree growth to biotic interactions are widely held to be crucial for understanding forest diversity, function, and structure. To date, most studies on plant-plant interactions only examine the additive competitive or facilitative interactions between species pairs; however, there is increasing evidence of non-additive, higher-order interactions (HOIs) impacting species demographic rates. When HOIs are present, the dynamics of a multispecies community cannot be fully understood or accurately predicted solely from pairwise outcomes because of how additional species "interfere" with the direct, pairwise interactions. Such HOIs should be particularly prevalent when species show non-linear functional responses to resource availability and resource-acquisition traits themselves are density dependent. With this in mind, we used data from a tropical secondary forest-a system that fulfills both of these conditions-to build an ontogenetic diameter growth model for individuals across 10 woody-plant species. We allowed both direct and indirect interactions within communities to influence the species-specific growth parameters in a generalized Lotka-Volterra model. Specifically, indirect interactions entered the model as higher-order quadratic terms, i.e., non-additive effects of conspecific and heterospecific neighbor size on the focal individual's growth. For the whole community and for four out of 10 focal species, the model that included HOIs had more statistical support than the model that included only direct interactions, despite the former containing a far greater number of parameters. HOIs had comparable effect sizes to direct interactions, and tended to further reduce the diameter growth rates of most species beyond what direct interactions had already reduced. In a simulation of successional stand dynamics, the inclusion of HOIs led to rank swaps in species' diameter hierarchies, even when community-level size distributions remained qualitatively similar. Our study highlights the implications, and discusses possible mechanisms, of non-additive density dependence in highly diverse and light-competitive tropical forests.

Biotic interactions among temperate rainforest trees are non-additive and environmentally dependent

“…Second, compared to the Gaussian distribution, the Student-t is a thick-tailed distribution that can accommodate more extreme-valued observations and reduce their influence on parameter estimation (hence a form of "robust regression," McElreath, 2020). Previous studies that fitted a Gaussian distribution on diameter growth (e.g., Lai et al, 2022) had to perform variable transformation, whereas the Student-t distribution allows us to analyse the response in its original scale. We also explored the Gaussian regression on transformed growth here, but the residual diagnostics were much more problematic than the present Student-t regression (Appendix S3).…”

“…In this model, the pairwise interaction coefficients, α i j , quantify the per-SD-basal-area main effects of species j on the growth of focal individual m (of species i). To accommodate non-additive density dependence among tree species (Lai et al, 2022;Li et al, 2021), we also included non-additive biotic interaction terms, ∑ j, k≥ j β i jk N jpq N kpq , where each parameter β i jk quantifies the moderating effect of the density of the kth intermediary neighbour species, N kpq , on the main effect of direct neighbour species j in the same subplot. By modifying the pairwise interaction α i j between focal species i and direct neighbour j, the parameters β i jk are referred to as the higher-order interaction effects of neighbour species k on focal species i .…”

“…In a forest, for instance, an intermediary-neighbour tree that shades a directly competing tree can cause the direct neighbour's phototropic crown to grow closer to the focal tree, thereby further depleting light for the focal tree (Aiba & Kohyama, 1997). Regardless of the mechanism, accounting for non-additive biotic interactions has been shown to improve the prediction of tree growth in natural forest ecosystems (Lai et al, 2022;Li et al, 2021).…”

“…An inability to do so will prevent us from correctly identifying the drivers of community patterns and the relative importance of abiotic and biotic drivers under different circumstances (Catford et al, 2022;Mayfield & Levine, 2010). There have been empirical efforts to detect indirect abiotic effects (Bimler et al, 2018;Coomes & Allen, 2007;Kunstler et al, 2011;Soliveres et al, 2015) or indirect biotic effects (Lai et al, 2022;Li et al, 2021;Uriarte et al, 2004;Xiao et al, 2020) in plant-plant interactions, but rarely both. A key challenge when simultaneously disentangling non-additive abiotic and biotic effects lies in the large number of parameters that need to be fitted to and interpreted from what is often sparse observational data.…”

Environmental context, parameter sensitivity and structural sensitivity impact predictions of annual-plant coexistence

“…Such predictions are possible because of the implicit assumption that models that reproduce the observed data faithfully also capture how the studied system operates (Marquet et al, 2015;Klir, 1985;Zeigler et al, 2000;Stouffer, 2022). For example, models that describe the effects neighboring plants have on each other can be used to make quantitative predictions about changes of biomass in the system (Godoy et al, 2020;Lai et al, 2022) or qualitative predictions such as whether or not co-occurring plant species can coexist (Levine & HilleRisLambers, 2009;Zepeda & Martorell, 2019).…”