Competition and site weakly explain tree growth variability in undisturbed Central African moist forests

Abstract

Identifying and quantifying factors that influence tree growth are crucial issues to ensure sustainable forest management, particularly in moist tropical forests. Tree growth depends on several factors comprising ontogenic stage, competition by neighbours and environmental conditions. Several studies have focused on one or two of them, but very few have considered all three, especially in Central Africa. We investigated the effects of diameter and competition on tree growth, in four Central African sites characterized by their soil physicochemical properties, at both tree community and population levels. We calibrated growth models using diameter data collected on 29,741 trees between 2015 and 2018, on twelve 4 or 9‐ha plots spread over the four sites. These models included diameter, wood density, competition indices and site effect as explainable variables at the community level and excluded wood density at the population level. At the community level, the best models explained 11% of growth variability with a decreasing effect of species wood density, diameter, site and competition. Our results show that even if low, site effect can result from different soil nutrients depending on both tree size and species wood density. We observed higher tree growth on sites with (i) high exchangeable K, organic C, total N and total P for low wood density species; (ii) high available P and C:N for small trees, high exchangeable Ca and Mg for medium to large trees, all belonging to medium and hard wood density species. At the population level, the best models explained between 0 to 43% of growth variability, with significant competition effect (resp. site effect) for 21 (resp. 9) of the 43 species studied. Site ranking varied greatly between the 9 species concerned, probably reflecting different sensitivities to the scarcity of particular soil nutrients. Synthesis. Our study provides original results on the factors influencing tree growth in Central Africa, showing that the potential effect of soil nutrients depends on tree size and species wood density. Remaining highly unpredictable at the population level, this effect makes it essential to increase the number of dynamics monitoring systems in logging concessions.

Using high-resolution images to analyze the importance of crown size and competition for the growth of tropical trees

“…It suggests that a general model might be devised for multiple sites. However, predicted growth variance was better explained for trees growing at Yoko than at Loundoungou, probably because of the varied growth conditions in the two sites, marked by differences in canopy structure, soil, and climatic characteristics (Gourlet-Fleury et al, 2023). Similar variation in predicted variance between sites of growth models based on crown measurements was also reported by Ma et al (2018) This would however require data collected in a larger number of sites.…”

“…Different crown competition indices were built from UAV data to predict tree growth. The performance of these predictors was generally significant, complementary to the indices built from The smallness of the contribution made by competition-based measures to explaining growth was nevertheless not surprising in tropical forests (Barros de Oliveira et al, 2021;Gourlet-Fleury et al, 2023;Laurans et al, 2014). It can be explained by the fact that the intrinsic characteristics of the tree (i.e., its diameter and crown size) already integrate the effects of past competition (Prévosto, 2005).…”

“…In addition, to homogenize the variance of the residuals, we used a logarithmic transformation of the response variable (DBHI). To avoid the problem of logarithms on negative increments, we used log(DBHI+1) instead of log(DBHI) (Gourlet-Fleury et al, 2023).…”

“…Delimiting the zone of influence to accurately assess competition experienced by a subject tree remains challenging in tropical forests. Several approaches have been used, including the fixed radius method (Gourlet-Fleury and Houllier, 2000;Gourlet-Fleury et al, 2023) and the crown overlap method (Zambrano et al, 2019). In this study, we calculated indices with a varying radius.…”

“…In others, the indices do not take into account the position of the measured trees (distance-independent) (Biging, 1995). Neighboring trees are usually defined as standing within a circular zone of influence of set radius, generally between 3 m and 30 m (Barros de Oliveira et al, 2021;Gourlet-Fleury and Houllier, 2000;Gourlet-Fleury et al, 2023).Competition indices can be a function of the size and position of neighboring tree crowns as demonstrated in temperate forests (…”