Effective adaptability of plants to new environments can be analysed in terms of survival rate. Analysing the traits that favour adaptation to environmental changes provides a more in-depth understanding of the mechanisms involved. Local adaptation occurs because different environmental factors exert selective pressure across habitats. Understanding the leaf mechanisms underlying plant survival and growth is crucial to determine why local adaptation involves trade-offs. A comparative provenance test on 29 eucalyptus species was conducted to improve our understanding of species adaptation strategies on coastal plains of Pointe-Noire, Republic of the Congo. We studied the different functional traits to determine how plants function and to highlight the different species’ adaptive strategies. For each species, survival, growth traits and leaf traits were measured, and the climatic factors of the origin area for each species was taken into account. Cluster analysis was performed on groups of species with a similar growth strategy. The results revealed general trends that explain the physiological mechanisms involved in the species’ local adaptation. Indeed, species have survived to current environmental changes by adjusting their specific leaf area plasticity. The 32 provenances of eucalyptus were subdivided into four groups by cluster analysis. The first cluster included two species (
The predictions suggest that Africa will be severely impacted by climate change. There is a significant risk that many forest ecosystems will not have the adaptive capacity to supply vital goods and services (
The study of functional traits, especially in plants, has a long tradition in ecological research (
The trade-offs between functional traits depends on how plants acquire, use and conserve the resources (
The current production context which involves unsustainable land use changes, as well as future climate change scenarios, call for more detailed studies on local varietal adaptation in order to reduce the use of land and optimize future timber production. In this context, the eucalyptus improvement programme in the Congo should move towards the selection of plastic genotypes with a more efficient use of resources.
Adapting to different environmental conditions entails costly fitness trade-offs (
In the Congo, the first attempts to introduce eucalyptus date back to 1953. Subsequently, several species and provenance trials have been conducted. Several studies on their productivity revealed that only some species were adapted to local conditions (
This study aims to understand the local adaptation mechanisms of 29 eucalyptus species using leaf traits in the Republic of Congo. We asked three research questions: (i) Do functional traits vary among eucalyptus species at different ages? (ii) What are the trade-offs among functional traits? (iii) Can the potential adaptation of eucalyptus species in the Pointe-Noire conditions be determined by examining the relationships between growth and functional traits?
This study is based on data from a provenance trial conducted at the Kissoko forestry station (04° 45′ 51″ S, 11° 59′ 21″ E) in the south of Republic of the Congo. Mean annual rainfall is approximately 1200 mm; daily temperature is 25-26 °C in the rainy season (October to May) and 22-23 °C in the dry season (June to September). The average monthly rainfall during the rainy season ranges from 83 to 92 mm, and during the dry season from 1 to 20 mm (
We conducted the present study in a Eucalyptus provenance trial which included 32 provenances belonging to 29 species (
The experimental design consisted of 4 blocks of 128 plots (32 per block); each plot contains 9 trees representing a single provenance. The plots were randomly distributed into the blocks. Originally, each plot was planted with spacing of 4.70 × 2.65 m (about 800 trees ha-1). Trees were measured at two dates: after 15 months (1.2 years) since their establishment (in February during the short dry season) and after 54 months (4.5 years, in April during the main rainy season).
We first evaluated the survival rate (SR, %) of the trees at each age at the plot level, and the difference in survival between 54 and 15 months (
where
Leaves were then dried at 65 °C to constant weight. The dry weight was used in conjunction with the area measurements to calculate specific leaf area (
where
We used data from the meteorological stations (http://www.bom.gov.au/) at each site where we collected the seeds of eucalyptus species in Australia and Indonesia. The following 18 climatic variables were considered (Tab. S1 in Supplementary material): 1- average annual rainfall (AAR); 2- maximum annual rainfall (MAR); 3- minimum annual rainfall (mAR); 4- maximum monthly average rainfall (MMAR); 5- minimum monthly average rainfall (mMAR); 6- number of months of rainfall less than 50 mm (NR50); 7- average annual temperature (AAT); 8- maximum monthly temperature (MMT); 9- minimum average temperature (mMT); 10- maximum monthly average temperature (MMAT); 11- minimum monthly average temperature (mMAT); 12- number of days below 40 °C (ND40); 13- number of freezing days (NFD); 14- annual potential evapotranspiration (PE); 15- annual solar radiation (SR); 16- altitude (A); 17- longitude (Long); 18- latitude (Lat). Data on the same 18 climatic variables were also collected at the planting site (Pointe-Noire).
The following linear mixed model was used to perform ANOVA for survival rate (at the plot level), functional traits and growth (at tree level -
where
where
To detect covariations among functional traits, we used pairwise Pearson’s multiple correlation tests to analyse bivariate and multivariate relationships. To analyse the multivariate relationships, a principal component analysis (PCA) was performed in order to identify the similarities between the climatic characteristics of the area of origin of the species and the area where the species were introduced. At provenances level, functional traits were estimated from the tree measurements at 15 and 54 months (Tab. S2 in Supplementary material). The
All statistical analyses were performed using the open-source software R v. 4.0.3.
The survival rate was significantly different between subgenera at 15 (P = 0.0003) and 54 months (P<0.001). Likewise, a similar effect was observed among species at 15 months (P<0.001) and at 54 months (P<0.001). At both ages, the subgenera Blakella and Idiogenes had the highest survival rates of 91% and 87%, respectively, whereas the subgenera Eucalyptus and Eudesmia had the lowest survival rates of 35% and 13%, respectively (
Growth traits increased according to ages from 15 months to 54 months. The ANOVA results showed significant differences between species of the same subgenus and between subgenera for all traits (
The juvenile-adult correlations (
The PCA results indicated that the main components (two first axes) account for 66% of the total variation (
The results of the cluster analysis suggest that the 32 provenances analyzed can be subdivided into four groups (
Survival is the first fundamental criterion to assess adaptation (
Species with a low survival rate but good growth, like
Our study revealed a difference of survival rate and growth both among species of the same subgenus and among subgenera. One of the characteristics in which species of the same or different habitats vary is their growth potential. In a common environment, trade-offs between functional traits are the basis for the observation of different growth strategies and finally different strategies of adaptation (
All growth traits were positively correlated between 15 and 54 months since tree establishment. The stability of leaf thickness and the decrease in leaf density led to an increase in
Our results showed negative and significant correlations between
Functional traits play a critical role in the adaptation process (
Leaf traits do not vary randomly, but depend on trade-offs from investing carbon in leaves (
The results of cluster analysis showed that the first group includes two species which are totally unsuited to the local conditions in Pointe-Noire. Conversely, species of the second cluster can adapt to the local conditions in Pointe-Noire because of their greater plasticity. Some species can rapidly exploit the environmental resources and allocate them to development, while others show a slow resource acquisition but a fairly efficient use. We found that species in the third cluster had strategies allowing them to acquire resources rapidly with a slow return in terms of investment on leaf economic spectrum (
Our study provides important advances for the understanding the adaptive strategies of
Following this study, it would be relevant to determine the differential expression of genes involved in growth in different provenances and contrasting environments. Further study should focus on species’ different strategies for acquiring and using resources in their area of origin and the area of introduction. A combined phenotypic and genomic approach (
We are grateful to Pacifique Ntadi and the CRDPI technical team for field harvesting. We would like to thank the Conservation Action Research Network (CARN) of the Congo Basin Forest Partnership (CBFP) for their help to support this study.
PhV and CGME conceived and designed the experiments; CBSVL and MPM performed the trials; CGME and GJLP carried out data analysis. CGME took the lead in writing the manuscript and all authors provided critical feedback.
Location of seed harvesting sites for the studied eucalypt species/provenances.
Mean values of survival rate by subgenera and species/provenances. Different letters above the bars indicate significant differences (p<0.05) between group means after Tukey’s test.
Difference in survival rate (%) of eucalyptus species/provenances between 54 and 15 months after tree establishment.
Correlations between juvenile and adult traits. (HT): total tree height (m); (CC): collar circumference (cm); (LT): leaf thickness (mm); (SLA): specific leaf area (m2 kg-1); (LD): leaf density (kg m-3); 15 and 54 indicate the age of tree (in months).
Principal component analysis biplot. Numbers refer to the different eucalypt species/provenances. (1):
Cluster dendrogram of species/provenances. Cluster 1: (11)
List of studied species and their subgenera.
Speciescode | Subgenus | Species | Provenance |
---|---|---|---|
1 | Alveolata |
|
Beerburrum (Australia) |
2 | Blakella |
|
Mareeba (Australia) |
3 | Corymbia |
|
Ord Irvinbanck (Australia) |
4 |
|
Rockhampton (Australia) | |
5 |
|
Jimmy’s Creek, Coboutg Peninsula (Australia) | |
6 |
|
Mantuan (Australia) | |
7 |
|
Est Gue Goyder river (Australia) | |
8 |
|
Flaggy Creek, QLD (Australia) | |
9 | Eucalyptus |
|
Wild Cattle (Australia) |
10 |
|
Noosa Heads (Australia) | |
11 |
|
Moleton (Australia) | |
12 |
|
Elliot river (Australia) | |
13 | Eudesmia |
|
SE Maningrida (Australia) |
14 | Idiogenes |
|
Gympie (Australia) |
15 | Minutifructus |
|
Exe Creek, Ouest Mckay (Australia) |
16 | Symphyomyrtus |
|
Sud Cooktown (Australia) |
17 |
|
Natar Bora (Timor) | |
18 |
|
Ouest Pentecost river, Gibb River (Australia) | |
19 |
|
S-O Katherine Kununurra river (Australia) | |
20 |
|
Cape York peninsula, Coen (Australia) | |
21 |
|
Mckay Rockhampton (Australia) | |
22 |
|
Est Atherton, QLD (Australia) | |
23 |
|
Nord Raymond Terrasse (Australia) | |
24 |
|
Pinnacle (Australia) | |
25 |
|
Clouds Creek SF Grafton (Australia) | |
26 |
|
Barakula SFNW Chinchilla (Australia) | |
27 |
|
10km Ravenshoe (Australia) | |
28 |
|
Noosa (Australia) | |
29 |
|
Helenvale (Australia) | |
30 |
|
Mte Lewotobi (Indonesia) | |
31 |
|
Flores Arbau Ulu (Indonesia) | |
32 |
|
Arafalaca, Ouest Alor (Timor) |
Descriptive statistics and Anova results of variables studied at 15 and 54 months. (SR): survival rate (%); (HT): tree height (m); (SLA): specific leaf area (m2 kg-1); (LT): leaf thickness (mm); (LD): leaf density (kg m-3); (SD): standard deviation; (Max): maximum; (Min): minimum. 15 and 54 indicate the age tree (in months).
Traits | Unit | Mean | SD | Max | Min | (Pr > F) | ||
---|---|---|---|---|---|---|---|---|
Block | Sub-genus | Species/Provenance | ||||||
SR15 | % | 68.93 | 21.07 | 100.00 | 17.85 | 0.273 | 0.0003 | <0.0001 |
SR54 | % | 61.24 | 23.40 | 91.83 | 0.00 | 0.177 | <0.0001 | <0.0001 |
HT15 | m | 2.02 | 0.87 | 5.55 | 0.55 | 0.195 | 0.023 | 0.003 |
HT54 | m | 7.99 | 3.26 | 18.60 | 1.50 | <0.0001 | <0.0001 | <0.0001 |
CC15 | cm | 7.27 | 3.87 | 26.12 | 0.94 | 0.188 | 0.068 | 0.003 |
CC54 | cm | 29.98 | 12.84 | 72.80 | 1.50 | <0.0001 | <0.0001 | <0.0001 |
SLA15 | m2 kg-1 | 80.43 | 16.79 | 148.84 | 45.76 | 0.392 | <0.0001 | <0.0001 |
SLA54 | m2 kg-1 | 259.73 | 102.00 | 529.46 | 38.36 | <0.0001 | <0.0001 | <0.0001 |
LT15 | mm | 0.28 | 0.05 | 0.46 | 0.14 | 0.0002 | <0.0001 | <0.0001 |
LT54 | mm | 0.27 | 0.05 | 0.42 | 0.16 | <0.0001 | <0.0001 | <0.0001 |
LD15 | kg m-3 | 0.46 | 0.06 | 0.60 | 0.32 | <0.0001 | <0.0001 | <0.0001 |
LD54 | kg m-3 | 0.18 | 0.11 | 1.15 | 0.08 | <0.0001 | <0.0001 | <0.0001 |
Tab. S1 - Climatic variables at the sites of seed origin and at the planting site.
Tab. S2 - Characteristics of the studied species indicating provenance and number of sampled trees at the beginning of the experiment.