The role of transcriptomes linked with responses to light environment on seedling mortality in a subtropical forest, China
Summary
- Differences in seedling survival in trees have a lasting imprint on seedling, juvenile and adult community structure. Identifying the drivers of these differences, therefore, is a critical research objective that ultimately requires knowledge regarding how organismal function interacts with the local environment to influence survival rates.
- In tree communities, differences in light use strategies are frequently invoked to explain differences in seedling demographic performance through growth and survival trade‐offs. For example, shade‐tolerant species grow slowly and have higher survival rates, whereas shade‐intolerant species grow quickly but have lower survival rates. Thus, functional traits related to photosynthesis should be strong predictors of demographic rates, but results in the literature are mixed indicating that additional or alternative information regarding organismal function should be considered.
- Here, we provide a community‐wide inventory of transcriptomes in a subtropical tree community. This information is utilized to determine the degree to which species share homologous genes related to gene ontologies for light use and harvesting. These species similarities are used in neighbourhood generalized linear mixed‐effects models of seedling survival that evaluated seedling survival as a function of the transcriptomic, functional trait and phylogenetic composition of the local neighbourhood. The results show neighbourhood similarity in three of the 15 gene ontologies evaluated are significantly related to survival rates based on neighbourhood composition. For two of these ontologies, survival rates increase when neighbours are similar in their gene tree composition indicating the importance of abiotic filtering and performance hierarchies.
- Synthesis. The present work takes a novel approach by sequencing the transcriptomes of naturally co‐occurring tree species in a subtropical forest in China. The results show that the transcriptomic similarity of species is a significant predictor of differential survival. The study demonstrates that exploring the functional genomic similarity of non‐model species in nature has the potential to increase the breadth and depth of our understanding of how gene function influences species co‐occurrence and population dynamics in communities.
Introduction
Determining the mechanisms underlying species diversity and co‐occurrence is considered one of the most challenging goals for community ecology (Sutherland et al. 2013). If all plant species share similar major requirements, light, water and macronutrients, how we can explain species co‐occurrence and diversity in plant communities particularly in subtropical and tropical regions where diversity reaches extraordinary levels? In other words, diverse plant communities seriously challenge classical and modern coexistence theory (e.g. MacArthur & Levins 1967; Chesson 2000). This has led to the development of theoretical approaches that assume demographic equivalence and dispersal limitation to predict emergent community properties such as the species abundance distribution (Hubbell 2001). In response to Hubbell's neutral theory, ecology redoubled its efforts to demonstrate non‐random patterns of species distributions and individual demographic rates. Functional traits are usually utilized to determine whether species have different resource capture strategies (Keddy 1992; Cornelissen et al. 2003). There is now robust evidence that prove subtropical and tropical tree community structure and dynamics are decidedly non‐neutral (e.g. Swenson & Enquist 2007, 2009; Swenson et al. 2012; Liu et al. 2013; Iida et al. 2014). However, these studies have focused on a relatively small number of functional traits that are indirectly related to resource acquisition and the statistical relationships reported are often weak. Thus, there is still a great deal unknown regarding how organismal functional diversity influences species diversity and co‐occurrence in these forests. Approaches that supplement and expand upon the traditional functional trait‐based approach may be required.
Among all resources required by plants, light is perhaps one of the most limiting in subtropical and tropical forests (Augspurger 1984; Nicotra, Chazdon & Iriarte 1999). Light variation has the potential to be a major axis of niche differentiation for tree species and was considered to be important in defining guilds in early neutral theory where demographic neutrality occurred within but not among guilds (Hubbell & Foster 1986). Co‐occurring tree species in forests often exhibit a broad range of light preferences that vary from shade tolerant to light demanding. Shade‐tolerant species are often described as slow growing and conservative species. Conversely, light‐demanding species tend to have rapid resource acquisition rates and fast growth. This variation among species along the light preference axis is thought to play a critical role in determining species co‐occurrence in tree communities (Kobe 1999; Poorter 1999). For example, under shaded conditions, species are expected to exhibit similar ecological strategies along a light preference axis that allows them to tolerate limiting light. They therefore have similar growth rates and would be expected co‐occur in similar light environments. On the other hand, co‐occurring species might also show high dissimilarity in light capture strategies in the dissimilar light environment which would allow them to partition niche space and therefore co‐occur.
As noted above, the role of ecological similarity among species in community assembly has been explored by integrating information on functional traits related to resource acquisition (e.g. Weiher, Clarke & Keddy 1998; Stubbs, Wilson & Bastow Wilson 2004; Swenson & Enquist 2007, 2009). This functional approach provides a connection between species and their local environment (McGill et al. 2006). In the case of light environments, previous studies have utilized a series of easily measured functional traits to indicate where species fall along the global leaf economics spectrum (e.g. Reich, Walters & Ellsworth 1997; Wright et al. 2004). For example, photosynthesis increases with higher specific leaf area (SLA) and %N and %P content in the leaves, but this comes at the cost of lower leaf life spans. However, many functional leaf traits likely do not capture the complexity of species responses to light environments and richer assays of the functions underlying light capture might prove useful for understanding the drivers of species co‐occurrence in shaded forest understories.
One promising way to overcome the limitations of the traditional functional trait‐based approach which relies on a few easily measured traits that loosely correlate with the physiology of interest is to incorporate information on functional genetic variation through the analysis of transcriptomes (Swenson 2012). Transcriptomes can now be generated for non‐model species through the sequencing of mRNA in a tissue sample and by de novo assembly of the resulting reads. These assembled transcripts can be compared to the annotated genomes of model species to infer their putative functions. Functional phylogenomics approaches can then be utilized to cluster and align putative homologous genes across species to generate gene trees (Yang & Smith 2014; Smith et al. 2015; Yang et al. 2015). It is therefore now possible to quantify the number of putatively homologous genes shared among species in a community and to categorize them by their putative functions. In sum, it is possible, for example, to identify how many homologous genes are found between pairs of species for light harvesting responses, photosystem I and II assemblies, photoinhibition, and response to red light among many others. This approach has the potential to greatly enrich our understanding of the similarity of species in relation to light and photosynthesis. Therefore, the complex responses of species to light gradients that may drive their differential demography and co‐occurrence could be further disentangled when information on functional genomic similarity is considered along with functional trait data.
Here, we take a functional phylogenomic approach to tree community ecology. We test alternative hypotheses regarding the functional similarity of species and their local light environments and implications these have for their co‐occurrence and performance. We generated transcriptomes for 101 non‐model tree species that naturally co‐occur in a long‐term forest dynamics plot in subtropical China. The transcriptomes were used in a functional phylogenomics framework. Homologous gene trees related to light and photosynthesis were analysed to evaluate the demographic rates of naturally occurring seedling individuals in the plot. Specifically, we evaluated different models of survival rates for focal seedlings as a function of the neighbourhood species composition. Heterospecific neighbours similarity to focal species survival was weighted by the number of homologous genes they share, their functional trait similarity and their phylogenetic relatedness. The analyses were partitioned by individual gene ontology (GO) terms to determine which ontologies have the greatest predictive power. The specific questions we ask are: (i) What are the most important light‐response GO terms influencing seedling survival in a subtropical forest in China? (ii) What is the effect of trascriptome similarity on seedling survival on shade‐tolerant species and light‐demanding species? (iii) What is the role of phylogenetic‐, functional‐ and transcriptome‐based similarity on predicting survival rates of seedlings?
Materials and methods
Study site
The study site is the 24 ha (400 m × 600 m) Gutianshan subtropical forest dynamics plot (GTS FDP) (29°15′6″–29°15′21″N, 118°07′1″–118°07′24″E) located in the Gutianshan Forest Reserve, Kaihua County, Zhejiang Province, China. In the plot, all trees with a diameter at breast height ≥1 cm (diameter at 1·3 m, DBH) were tagged, measured, mapped and identified to species (Legendre et al. 2009). The plot is censused every 5 years and the data from the 2010 census were used in this study. The topography of the plot is rugged with attitude varying from 446·3 to 714·9 m above sea level. The region experiences wet seasons from March to June and in September and two dry seasons from July to August and October to February (Lai et al. 2009).
Seedling census
This study used 285 5 × 5 m seedling plots distributed in a regular pattern in each 20 × 20 m subplot in the 24 ha GTS plot in 2012. Every seedling plot was set in the northeast corner of a 20 × 20 m subplot in the GTS FDP and 2 m away from the subplot edges. The total area of the 5 × 5 m seedling plots accounts for 2·96% of the GTS 24 ha FDP. In each of the 285 seedling plots, all individuals with a DBH <1 cm and height ≥10 cm were mapped, tagged, had their height measured and were identified to species. All 5 × 5 m seedling plots were recensused from May to August in 2014 checking for survival and recording new recruits.
Functional traits
We collected five functional traits from adult individuals for 126 species (Liu et al. 2013). The traits measured are leaf nitrogen content (LN), leaf phosphorus content (LP), leaf area (LA), leaf length to width ratio (LWR) and specific leaf area (SLA), which are associated with the photosynthetic capacity of plants (e.g. Reich, Walters & Ellsworth 1997; Wright et al. 2004). The LN and LP values were obtained using the Kjeldahl method and Mo‐Sb colour isometric method, respectively, following Cornelissen et al. (2003). Details regarding the measurement of LA and SLA are described in Liu et al. (2013).
DNA sequencing and phylogenetic inference
We used a species‐level molecular community phylogeny tree generated by Liu et al. (2013) to calculate the phylogenetic relatedness of neighbours. This phylogenetic tree, containing 156 species, was built with three DNA barcodes (rbcL, matK and trnH‐psbA) and was scaled to time. The phylogenetic tree contains all species for which we have transcriptome data.
Transcriptome sampling, sequencing, assembling and annotation
The project aimed to assemble transcriptomes for 101 angiosperm species that are found in the seedling layer in the GTS FDP. Tissue sampling was conducted in the GTS FDP by sampling fully expanded leaves where at least three seedling leaf samples per species were sampled from each of the five main habitats in the plot as described by Chen et al. (2010). However, the rarest species were limited to three samples taken from the only individuals known in the 24 ha plot. Leaf tissue excised from a seedling was flash frozen in liquid nitrogen in the field and ultimately stored in a −80 °C freezer. For RNA extraction, all conspecific individual samples were pooled to constitute one sample. This approach was used to maximize the breadth of the transcriptome sampled per species. Total RNA was extracted using RNeasy Plant Mini Kits, followed by RNA purification with the RNeasy Mini Elute Cleanup Kit (Qiagen, Düsseldorf, Germany), according to the manufacturer's instructions. RNA was quantified using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). The purity of all RNA samples was assessed at an absorbance ratio of OD260/280 and OD260/230. For mRNA library construction, RNA samples were prepared using the TruSeq RNA Sample Preparation Kit (Illumina company, San Diego, CA, USA) according to the manufacturer's protocol. Libraries were pooled and sequenced on an Illumina HiSeq 2500 platform (Illumina company) with paired‐end 2 × 125‐bp reads.
The raw reads were cleaned by trimming adapter sequences and low quality sequences by Trimmomatic software v0.31 (http://www.usadellab.org/cms/index.php?page=trimmomatic) (Bolger, Lohse & Usadel 2014). The trimmed reads for each species were assembled de novo using Trinity v2.2 (https://github.com/trinityrnaseq/trinityrnaseq/wiki) (Grabherr et al. 2011). The resulting assemblies were used in the functional phylogenomics informatics pipeline developed by Yang & Smith (2014) (https://bitbucket.org/yangya/phylogenomic_dataset_construction). The Yang & Smith (2014) pipeline is designed to cluster similar sequences. These clusters are then used to infer homologous gene trees. Specifically, homology inference was conducted by first translating assembled reads using TransDecoder and the Pfam database (Haas et al. 2013). This was followed by an all‐by‐all blastp search. Next, Markov clustering was performed (van Dongen 2000; Enright, van Dongen & Ouzounis 2002; van Dongen & Abreu‐Goodger 2012) to identify gene clusters. The gene clusters were then aligned and homologous gene trees were inferred using RAxML (Stamatakis 2006). All analyses used the default protocol in the pipeline with the exception that we identified all homologous gene clusters with 10 or greater taxa. Each gene tree was annotated using the output from TransDecoder and assigned to a GO grouping using the UniProt database (http://www.uniprot.org/).
For each gene tree, we quantified the number of tips per species and utilized this to represent the number of homologous per species. Because counting the number of homologous genes may have biased our inferences, we also scored species by their presence or absence in a given gene tree. We will, therefore, present the results from both of the analyses where we counted the number of tips and also just the presence or absence per species. This resulted in a matrix containing >1000 gene trees in rows and 101 species in the columns. Next, we selected from this matrix only those gene trees with GO annotations related to photosynthesis. This resulted in 135 rows (gene trees belonging to 47 light‐related GO terms (Table S1, Supporting Information). Next, for each of the 47 GO terms, we selected the corresponding gene trees from the matrix and calculated the Euclidean distance between species. This distance indicates the frequency at which two species are not found in the homologous gene trees for the GO. In other words, lower values indicate higher similarity in the gene composition for a given GO term. For the presence–absence matrix, the distance simply indicates the number of unshared gene between species per GO. Given that large gene families are often subject of gene duplication and loss that could bias the results obtained in this study, we subsampled our gene data to use 15 out of 47 light‐related GO terms where we were more confident that homologous were being identified.
Light data
Because the present work was interested in functions related to light, we quantified light availability at our study site. To do so, every 20 × 20 m subplot in the GTS FDP was divided into 4 10 × 10 m subplots. Hemispherical photographs were taken at the centre of each 10 × 10 m subplot within the seedling plot at 1·3 m above‐ground with a Canon 7D camera (Kyushu, Japan) from May to July in 2013. The camera was equipped with Sigma 8 mm Fisheye Converter lens (Fukushima‐ken, Japan) and arranged horizontally. Photographs were taken during either early dawn or late dusk to ensure the uniform overcast weather. Three replicate photos were taken and the photos showing the highest contrast between sky and foliage for each 10 × 10 m subplot were selected. Finally, the Gap Light Analyser software (version 2.0) (Frazer, Canham & Lertzman 1999) was used to convert photographs to a single canopy openness value, direct solar radiation and diffuse solar radiation (Beaudet & Messier 2002).
Neighbourhood model analysis
A total of 85 species in our seedling plots had enough individuals to reliably model their survival rates. Below, we describe the models for 85 species (Table S2) where we had information regarding the phylogenetic, functional trait and transcriptomic dissimilarity between species. We performed the analyses on three groups of species: all species (85), shade‐tolerant species (51 species) and light‐demanding species (34 species). Shade‐tolerant vs. light‐demanding species were classified according to Song (2013) (Table S2). In total, we had 7413 total seedling individuals, 5648 shade tolerance species individuals and 1765 light‐demanding seedling individuals.
In order to assess whether the transcriptome, functional traits, phylogenetic and neighbour composition influences the seedling survival, we evaluated a series of 24 neighbourhood models of individual survival. For the first and most basic model, we examined the importance of neighbourhood conspecific and heterospecific neighbour densities on seedling survival. In this model, focal seedling survival was modelled as a function of conspecific seedling neighbour density (SCON), heterospecific seedling neighbour density (SHET) conspecific, conspecific adult neighbour densities (ACON) and heterospecific adult neighbour densities (AHET). The SCON and SHET variables simply counted the number of neighbouring individuals in the seedling plot (Bai et al. 2012). However, the ACON and AHET variables used adult individuals varying in size. Thus, we calculated these variables using the basal area (BA) of conspecific (ACON) and heterospecific (AHET) adults within 20 m from a focal seedling and dividing that value by the distance between the adult and the focal seedling (Canham, LePage & Coates 2004). Eqn eqn 1 shows the formula used for this calculation:
(eqn 1)A second model considered the survival of a focal seedling given the canopy openness measured using the canopy photos. We refer to this model as ‘Canopy’ in the results. The Biotic and Canopy variables were used together in additional models. First, we combined only the Biotic and Canopy variables (i.e. 1 model). Then, we used the Biotic and Canopy variables with the trait dissimilarity in the neighbourhood for one trait at a time (i.e. 5 models) or the phylogenetic dissimilarity in the neighbourhood (i.e. 1 model). The neighbourhood trait and phylogenetic dissimilarities were calculated using a null model approach to control for neighbourhood heterospecific densities and richness as well as neighbourhood conspecific density. Specifically, we calculated an observed mean pairwise distance between all individuals in the seedling neighbourhood using trait or phylogenetic distance matrices. Next, we generated a null distribution of mean pairwise distances by randomizing the names of species on the distance matrices 999 times and recalculating the neighbourhood mean pairwise distances each time (Swenson 2014). Finally, we calculated an effect size as the observed value minus the mean of the null distribution and a standardized effect size (SES) was calculated by dividing the effect size by the standard deviation of the null distribution and multiplying by negative one. Thus, positive SES values in this study indicate a neighbourhood of functionally or phylogenetically similar individuals and negative SES values indicate a dissimilar neighbourhood.
Finally, we generated one neighbourhood survival model per GO term where we used the Biotic and Canopy variables with a GO SES value (i.e. 15 models). The GO SES value was calculated in the same manner as the trait and phylogenetic SES values with the exception that the Euclidean distance matrices between gene trees was utilized.
All the models were assessed using generalized linear mixed‐effects models (GLMMs) with binomial errors (Bolker et al. 2009). A logit transformation of seedling state: 1 (alive) or 0 (dead) was the response variable. Since the initial height of seedlings can affect the seedling survival significantly (Comita & Hubbell 2009), the log‐transformed values of initial seedling height (logH) were included in all the models. Also, all variables were standardized by subtracting the mean value of the variable from the observed value and dividing by the standard deviation. Species and plots were included as intercept specific random effects. We used the Akaike information criterion (AIC) to compare the three models. The ∆AIC was calculated by subtracting the minimum value of AIC from each of the models’ AIC values. All the analyses were conducted in the r software, the GLMMs were conducted with lme4 package (Bates et al. 2016). Because multiple models were considered, we adjusted our P‐values using a Holm correction.
Results
Neighbourhood model results – all species
Models were compared using AIC and ∆AIC between values ≤2 indicated that models were indistinguishable. When considering all species in the same model, we found two model constructs that performed similarly well (Fig. 1; Tables 1 and 2). In both models, high conspecific seedling densities in the neighbourhood (SCON) negatively impacted focal individual survival. Further, both models found adult densities (ACON and AHET) were positively related to focal individual survival. Canopy openness did not significantly influence survival in either model. The difference between the models was that one included the LWR SES value and the other included the GO:0010201 SES value. Both of the SES variables were positive effects indicating that similarity in LWR or gene trees related to GO:0010201 (‘response to continuous far red light stimulus’) enhanced survival.
| Candidate model | AIC | ||
|---|---|---|---|
| All species | Shade tolerance species | Light‐demanding species | |
| Null | 6197·37 | 4638·33 | 1568·15 |
| Biotic | 6178·22 | 4621·27 | 1570·22 |
| Canopy | 6196·49 | 4639·35 | 1568·55 |
| Biotic + Canopy | 6178·22 | 4621·27 | 1570·22 |
| Biotic + Canopy + lwr | 6175·44 | 4622·97 | 1571·04 |
| Biotic + Canopy + la | 6178·68 | 4623·71 | 1573·00 |
| Biotic + Canopy + sla | 6185·04 | 4627·35 | 1575·96 |
| Biotic + Canopy + fn | 6185·10 | 4627·30 | 1576·09 |
| Biotic + Canopy + fp | 6185·10 | 4627·25 | 1576·59 |
| Biotic + Canopy + phy | 6181·61 | 4623·34 | 1573·55 |
| Biotic + Canopy + GO.0010201 | 6173·81 | 4616·70 | 1572·99 |
| Biotic + Canopy + GO.0010362 | 6176·20 | 4619·09 | 1573·30 |
| Biotic + Canopy + GO.0009638 | 6176·24 | 4619·12 | 1573·31 |
| Biotic + Canopy + GO.0010196 | 6177·89 | 4616·91 | 1572·99 |
| Biotic + Canopy + GO.0009768 | 6178·30 | 4622·02 | 1573·17 |
| Biotic + Canopy + GO.0009785 | 6179·17 | 4621·72 | 1573·31 |
| Biotic + Canopy + GO.0043153 | 6180·20 | 4622·94 | 1573·01 |
| Biotic + Canopy + GO.0009643 | 6180·67 | 4623·60 | 1572·84 |
| Biotic + Canopy + GO.0010205 | 6181·44 | 4623·32 | 1573·23 |
| Biotic + Canopy + GO.0009585 | 6181·53 | 4621·39 | 1572·16 |
| Biotic + Canopy + GO.0010206 | 6181·62 | 4623·53 | 1573·17 |
| Biotic + Canopy + GO.0080005 | 6181·65 | 4623·56 | 1573·44 |
| Biotic + Canopy + GO.0009854 | 6182·09 | 4624·54 | 1573·69 |
| Biotic + Canopy + GO.0010117 | 6182·14 | 4624·52 | 1573·62 |
| Biotic + Canopy + GO.0009773 | 6182·16 | 4624·06 | 1572·70 |
| SES type | Total species | Shade tolerance species | Light‐demanding species | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Estimate | P‐value | P.adjust | Estimate | P‐value | P.adjust | Estimate | P‐value | P.adjust | |
| LN | 0·0058 | 0·8942 | 0·8974 | 0·0174 | 0·7512 | 0·8581 | −0·0575 | 0·4752 | 0·6825 |
| LP | −0·0056 | 0·8974 | 0·8974 | −0·0205 | 0·6933 | 0·8564 | −0·0036 | 0·9644 | 0·9644 |
| LA | 0·1092 | 0·0096 | 0·0571 | 0·1005 | 0·0499 | 0·1747 | 0·1526 | 0·0485 | 0·5093 |
| LWR | 0·1361 | 0·0028 | 0·0368 | 0·1187 | 0·0334 | 0·1403 | 0·1932 | 0·0166 | 0·3486 |
| SLA | −0·0131 | 0·775 | 0·8974 | 0·0124 | 0·8138 | 0·8581 | −0·0754 | 0·4244 | 0·6825 |
| Phylogeny | −0·0295 | 0·4395 | 0·6339 | −0·0483 | 0·2596 | 0·4484 | 0·0371 | 0·6758 | 0·7884 |
| GO:0009585 | −0·036 | 0·4108 | 0·6339 | −0·0917 | 0·0713 | 0·2139 | 0·1127 | 0·2051 | 0·6825 |
| GO:0009638 | 0·0956 | 0·0136 | 0·0571 | 0·105 | 0·0181 | 0·0950 | 0·0536 | 0·517 | 0·6825 |
| GO:0009643 | −0·0529 | 0·2114 | 0·4439 | −0·0498 | 0·3203 | 0·4484 | −0·0761 | 0·344 | 0·6825 |
| GO:0009768 | 0·0687 | 0·0467 | 0·1401 | 0·064 | 0·1067 | 0·2490 | 0·0547 | 0·454 | 0·6825 |
| GO:0009773 | −0·0086 | 0·8671 | 0·8974 | −0·041 | 0·4755 | 0·6241 | 0·1073 | 0·2964 | 0·6825 |
| GO:0009785 | 0·0631 | 0·0789 | 0·2071 | 0·0689 | 0·088 | 0·2310 | 0·053 | 0·52 | 0·6825 |
| GO:0009854 | 0·013 | 0·7479 | 0·8974 | 0·0081 | 0·8604 | 0·8604 | 0·0146 | 0·8726 | 0·9162 |
| GO:0010117 | −0·0097 | 0·8147 | 0·8974 | 0·0111 | 0·8172 | 0·8581 | −0·0257 | 0·7559 | 0·8355 |
| GO:0010196 | −0·0981 | 0·0373 | 0·1306 | −0·1517 | 0·0057 | 0·0599 | 0·0833 | 0·3872 | 0·6825 |
| GO:0010201 | 0·13 | 0·0035 | 0·0368 | 0·1455 | 0·0046 | 0·0599 | 0·08 | 0·3883 | 0·6825 |
| GO:0010205 | −0·0309 | 0·3747 | 0·6339 | −0·0455 | 0·2582 | 0·4484 | 0·0508 | 0·4799 | 0·6825 |
| GO:0010206 | −0·0268 | 0·4433 | 0·6339 | −0·0418 | 0·301 | 0·4484 | 0·0542 | 0·4537 | 0·6825 |
| GO:0010362 | 0·0959 | 0·0133 | 0·0571 | 0·1053 | 0·0176 | 0·0950 | 0·0545 | 0·51 | 0·6825 |
| GO:0043153 | 0·0678 | 0·1498 | 0·3495 | 0·0716 | 0·1908 | 0·4007 | 0·0801 | 0·3926 | 0·6825 |
| GO:0080005 | −0·0304 | 0·4528 | 0·6339 | −0·0476 | 0·3086 | 0·4484 | 0·0441 | 0·5937 | 0·7334 |
- LN, leaf nitrogen content; LP, leaf phosphorus content; LA, leaf area; LWR, leaf length to width ratio; SLA, specific leaf area.
The above results used the number of times a species was represented in a particular gene tree. When we considered simply the presence or absence of species from gene trees, thereby attempting to avoid issues with paralogous, two additional models were equally supported. These included GO:0010196 (‘non‐photochemical quenching’) or GO:0009638 (‘phototropism’) with negative and positive effects respectively (Tables 3 and 4). The model with phylogenetic information was not selected in either case.
| Candidate model | AIC | ||
|---|---|---|---|
| All species | Shade tolerance species | Light‐demanding species | |
| Null | 6197·37 | 4638·3 | 1568·15 |
| Biotic | 6178·22 | 4621·2 | 1570·22 |
| Canopy | 6196·49 | 4639·3 | 1568·55 |
| Biotic + Canopy | 6178·22 | 4621·2 | 1570·22 |
| Biotic + Canopy + lwr | 6175·44 | 4622·9 | 1571·04 |
| Biotic + Canopy + la | 6178·68 | 4623·7 | 1573 |
| Biotic + Canopy + sla | 6185·04 | 4627·3 | 1575·96 |
| Biotic + Canopy + fn | 6185·1 | 4627·3 | 1576·09 |
| Biotic + Canopy + fp | 6185·1 | 4627·2 | 1576·59 |
| Biotic + Canopy + phy | 6181·61 | 4623·3 | 1573·55 |
| Biotic + Canopy + GO:0010201 | 6174·82 | 4615·3 | 1573·709 |
| Biotic + Canopy + GO:0010196 | 6176·42 | 4614·8 | 1573·48 |
| Biotic + Canopy + GO:0009638 | 6176·61 | 4619·1 | 1573·225 |
| Biotic + Canopy + GO:0010362 | 6176·69 | 4619·2 | 1573·225 |
| Biotic + Canopy + GO:0009768 | 6177·99 | 4622·3 | 1572·646 |
| Biotic + Canopy + GO:0009585 | 6178·56 | 4612·5 | 1569·794 |
| Biotic + Canopy + GO:0009785 | 6180·01 | 4621·3 | 1573·712 |
| Biotic + Canopy + GO:0043153 | 6180·22 | 4622·9 | 1573·027 |
| Biotic + Canopy + GO:0009854 | 6181 | 4622·9 | 1573·71 |
| Biotic + Canopy + GO:0009643 | 6181·48 | 4624·4 | 1572·494 |
| Biotic + Canopy + GO:0080005 | 6181·53 | 4623·9 | 1573·704 |
| Biotic + Canopy + GO:0010205 | 6181·57 | 4623·4 | 1573·222 |
| Biotic + Canopy + GO:0010206 | 6181·73 | 4623·6 | 1573·128 |
| Biotic + Canopy + GO:0010117 | 6181·75 | 4624·4 | 1573·618 |
| Biotic + Canopy + GO:0009773 | 6182·16 | 4624 | 1572·757 |
| SES type | Total species | Shade tolerance species | Light‐demanding species | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Estimate | P‐value | P.adjust | Estimate | P‐value | P.adjust | Estimate | P‐value | P.adjust | |
| LN | 0·0058 | 0·8942 | 0·8974 | 0·0174 | 0·7512 | 0·8086 | −0·0575 | 0·4752 | 0·7707 |
| LP | −0·0056 | 0·8974 | 0·8974 | −0·0205 | 0·6933 | 0·8086 | −0·0036 | 0·9644 | 0·9644 |
| LA | 0·1092 | 0·0096 | 0·0616 | 0·1005 | 0·0499 | 0·1497 | 0·1526 | 0·0485 | 0·3395 |
| LWR | 0·1361 | 0·0028 | 0·0588 | 0·1187 | 0·0334 | 0·1169 | 0·1932 | 0·0166 | 0·3395 |
| SLA | −0·0131 | 0·7750 | 0·8974 | 0·0124 | 0·8138 | 0·8138 | −0·0754 | 0·4244 | 0·7707 |
| Phylogeny | −0·0295 | 0·4395 | 0·6153 | −0·0483 | 0·2596 | 0·4543 | 0·0371 | 0·6758 | 0·9461 |
| GO:0009585 | −0·086 | 0·0548 | 0·1439 | −0·1822 | 5·00E‐04 | 0·0105 | 0·1798 | 0·0446 | 0·3395 |
| GO:0009638 | 0·0877 | 0·0169 | 0·0616 | 0·1007 | 0·0184 | 0·0815 | 0·0534 | 0·4771 | 0·7707 |
| GO:0009643 | −0·0372 | 0·3928 | 0·6153 | −0·0187 | 0·7163 | 0·8086 | −0·0929 | 0·2630 | 0·7707 |
| GO:0009768 | 0·0735 | 0·0379 | 0·1137 | 0·0612 | 0·1355 | 0·3162 | 0·0767 | 0·2945 | 0·7707 |
| GO:0009773 | −0·0093 | 0·8557 | 0·8974 | −0·0414 | 0·4711 | 0·6183 | 0·1025 | 0·3097 | 0·7707 |
| GO:0009785 | 0·0536 | 0·1355 | 0·3152 | 0·0747 | 0·0680 | 0·1785 | −0·0042 | 0·9579 | 0·9644 |
| GO:0009854 | −0·0455 | 0·2698 | 0·5151 | −0·0600 | 0·1961 | 0·3744 | −0·0062 | 0·9471 | 0·9644 |
| GO:0010117 | −0·0305 | 0·4999 | 0·6175 | −0·0152 | 0·7701 | 0·8086 | −0·0289 | 0·7524 | 0·9644 |
| GO:0010196 | −0·1162 | 0·0157 | 0·0616 | −0·1775 | 0·0020 | 0·0140 | 0·0459 | 0·6221 | 0·9332 |
| GO:0010201 | 0·1288 | 0·0059 | 0·0616 | 0·1670 | 0·0020 | 0·0140 | 0·0076 | 0·9366 | 0·9644 |
| GO:0010205 | −0·0277 | 0·4260 | 0·6153 | −0·0427 | 0·2904 | 0·4691 | 0·0510 | 0·4757 | 0·7707 |
| GO:0010206 | −0·0241 | 0·4905 | 0·6175 | −0·0393 | 0·3317 | 0·4976 | 0·0561 | 0·4364 | 0·7707 |
| GO:0010362 | 0·0871 | 0·0176 | 0·0616 | 0·0998 | 0·0194 | 0·0815 | 0·0534 | 0·4771 | 0·7707 |
| GO:0043153 | 0·0676 | 0·1501 | 0·3152 | 0·0710 | 0·1934 | 0·3744 | 0·0793 | 0·3969 | 0·7707 |
| GO:0080005 | −0·0336 | 0·4093 | 0·6153 | −0·0391 | 0·4080 | 0·5712 | 0·0087 | 0·9145 | 0·9644 |
- LN, leaf nitrogen content; LP, leaf phosphorus content; LA, leaf area; LWR, leaf length to width ratio; SLA, specific leaf area.
Neighbourhood model results – shade‐tolerant species
Models considering only the 51 shade‐tolerant species were considered next. Two models were indistinguishable with both including a GO SES variable (Fig. 2; Tables 1 and 2). As in the models that considered all species, conspecific seedling density negatively influenced focal individual survival, while adult con‐ and heterospecific densities positively influenced survival. Canopy openness had a positive, but not significant, influence on survival rates. In one model, the SES for GO:0010196 (‘non‐photochemical quenching’) negatively impacted survival, while in the other model the SES for GO:0010201 (‘response to continuous far red light stimulus’) positively impacted survival (Fig. 2; Tables 1 and 2). When considering only the presence or absence of species in gene trees instead of their number of tips, the same models were supported (Tables 3 and 4). In all cases, the phylogenetic and trait information was not important.
Neighbourhood model results – light‐demanding species
Models that considered only the 34 light‐demanding species in our study were considered next. A total of 24 models were considered and the model including only seedling height and canopy openness (i.e. the ‘Canopy’ model) was the best supported (Tables 1 and 3) with trait, phylogenetic and transcriptomic SES values not being important (Tables 1–4).
Discussion
A key goal in community ecology is to determine the functional basis for species performance, abundance and co‐occurrence. In other words, community ecologists are still trying to establish the function–performance relationships that should lead to more robust predictions (McGill et al. 2006; Swenson 2013). A possible reason for previously reported weak or non‐existent relationships between individual performance and traits (e.g. Iida et al. 2014; Paine et al. 2015) is that trait information on the individual level is needed for strong inference (e.g. Liu et al. 2016). An additional reason may be that the traits frequently measured in trait‐based community ecology do not provide enough detail regarding the functional differences between species (Swenson 2012). Here, we explored an alternative approach to trait and phylogenetically based community ecology that integrates transcriptomic information on genes involved in photosynthesis. We evaluated how the survival of focal seedlings is related to the dissimilarity between species in their homologous gene content for 15 GO terms involved in species responses to light. We compared these models to models that included traditional trait and phylogenetic information. We were able to identify GO terms where having similar neighbours was positively related to survival as would be expected under an abiotic filtering or performance differences model of species co‐occurrence. We describe our results in detail below.
Conspecific and hererospecific density dependence
Conspecific negative density dependence is considered one of the most important forces operating in natural seedling communities (Janzen 1970; Comita & Hubbell 2009; Lebrija‐Trejos et al. 2014; Umana et al. 2016). Specifically, strong conspecific negative density dependence will regulate populations and prevent a single species from becoming dominant and reducing local species richness. Our results support the finding that the density of conspecific seedlings consistently negatively impacted the survival of focal individuals (Figs 1 and 2; Tables 1 and 2). Conversely, we found no evidence of heterospecific negative density dependence in the seedling layer. Thus, negative conspecific effects greatly outweighed heterospecific seedling effects, which should indicate large niche differences (Chesson 2000) and promote species coexistence.
We did find a strong positive effect of con‐ and heterospecific densities when considering adult neighbours. This may suggest that the negative density dependence detected in the seedling layer results from seedling competition or Janzen–Connell effects (Janzen 1970; Connell 1971) unrelated to adults. We also note that the data used in this study follows a large ice/snow storm that occurred in 2008 (Man, Mi & Ma 2011) where many adult trees died. It is possible that our results reflect a large recruitment of seedlings under adults after the canopy was opened by adult mortality occurring due to the storm. However, we caution that more detailed experimental analyses would be required to fully consider these alternative hypotheses.
Transcriptomic, functional trait and phylogenetic composition of neighbourhoods
The GO terms that had consistently significant effects on seedling survival were related to photosynthetic responses to different light wavelengths. For example, responses to red‐far red light (GO:0010201) was positively correlated with the seedling survival where species with different sets of genes involved in the responses to similar types of light tend to exhibit higher survival (Figs 1 and 2; Tables 1–4). This result suggests that the spectral composition of the light, also known as light quality, plays a critical role in determining photosynthetic responses in tree seedlings and ultimately species co‐occurrence by selecting for species with similar photosynthetic responses and performance and excluding dissimilar species.
Light quality has been frequently ignored in ecological studies with much more attention being paid to light quantity (e.g. Kobe 1999; Poorter 1999). However, the canopy interference with incoming light affects not only light quantity, but also quality. As a result, light in the understory is mainly composed of wavelengths falling within the red and green spectrum. To our knowledge, only one study has evaluated the effect of light quality on tropical or subtropical seedling species (Lee et al. 1996). Their findings are in agreement with our results suggesting that beyond light intensity, light quality also affects seedling development (Lee et al. 1996). Combined, these results may indicate why canopy openness is not always an important predictor of seedling demographic performance, as in this study, and measures of light quality should be considered more frequently.
We also found that neighbourhood similarity in GO:0009638 (phototropism) is positively related to survival (Tables 1–4). In other words, species with similar homolog composition for phototropism tend to survive at higher rates in the same location. As with the red‐far red results, from this we infer that abiotic filtering and or the exclusion of dissimilar species due to performance differences (Chesson 2000) drive the phototropism result. Lastly, we found that similarity in the GO term related to non‐photochemical quenching (GO:0010196) was negatively related to survival (Fig. 1; Tables 1 and 2). Photoprotective energy quenching can limit the production of active oxygen species and non‐photochemical quenching is vital in this process (Khan, Rai & Tripathi 1986; Horton, Ruban & Walters 1994; Holt, Fleming & Niyogi 2004). It is possible that this result is indicative of niche differentiation in non‐photochemical quenching and there is some evidence for niche differentiation related to response to changes light in conditions in the seedling layer in other systems (Scholes, Press & Zipperlen 1996). However, additional analyses and experimentation would be needed to address this hypothesis.
Across the different traits analysed in this study, the LWR was the only trait that showed a significant relationship with seedling survival. Co‐occurring species with similar LWR values exhibited higher survival (Figs 1 and 2; Tables 1–4). Previous work has shown that LWR is related to the light quality (far red or red:far red ratios) (Kasperbauer 1987). Thus, it may not be surprising that the LWR results were in the same direction as the GO:0010201 (response to red‐far red) results. The lack of significant results for the other traits (e.g. leaf %N, leaf %P and SLA) suggests that many of the plant traits frequently measured by community ecologists fail to predict differential demography (Paine et al. 2015) and a broader assay of plant function is likely needed (Swenson 2012, 2013).
The phylogenetic relatedness of neighbouring seedling species had no effect on focal seedling survival in any of our analyses. There are two possible reasons for this outcome. One is that there are no traits with phylogenetic signal that influence the population dynamics of species in this forest (Swenson 2013). Previous work has shown that phylogenetic information does not predict spatial patterns of trait diversity in this forest (Liu et al. 2013). A second possibility is that there are important functions that are correlated with phylogenetic relatedness, but that other functions more strongly predict survival than phylogenetic relatedness alone. We propose that it is likely the former than the latter possibility.
Shade‐tolerant and light‐demanding species
The results from our models including all species were similar to those including only the shade‐tolerant species (Figs 1 and 2; Tables 1–4). This may not be surprising given that 51 of the 85 species analysed were shade tolerant. The results for the models that included only light‐demanding species, however, deviated from the shade‐tolerant results. In particular, trait and transcriptomic information did not predict seedling survival in light‐demanding species. Rather, the best models of light‐demanding seedling survival included only initial seedling height and canopy openness information (Tables 1–4). While increased light is expected to positively affect seedling growth, the survival rates of shade‐tolerant species likely will not be as influenced by light. Light‐demanding species, however, do not tolerate low light levels and it is expected that their survival should be related to light quantity. The results therefore suggest that if transcriptomic and trait similarity does effect seedling survival, those effects are far outweighed by light quantity.
Caveats
The present work offers an alternative approach for examining the role of neighbourhood similarity on the survival of seedlings using transcriptomic information on homologous genes across co‐occurring species. While the results are potentially interesting and they indicate that transcriptomic data in some cases provides detail that cannot be obtained from functional trait or phylogenetic data, there are clear limitations to our approach that should be noted. First, the analyses utilized GO annotations that were originally generated for model species and used to annotate the transcriptomes of non‐model species in this study. Thus, we acknowledge that some annotations may be flawed as the genes in the non‐model species may not have the same function as those in the model species (Pavlidis et al. 2012). Furthermore, even in the best‐case scenario, where there are no annotation flaws, it is possible to over‐extend inferences from GO results (Pavlidis et al. 2012). In this work, we discussed where the GO output aligns with previous physiological literature for tree species, but we have tried to restrict our discussion for this reason. Lastly, our approach quantifies the presence and absence of species in putatively homologous gene trees and calculates a dissimilarity value between all species. This approach could be biased due to gene duplications and the presence of paralogous. Future research should aim to refine this approach by considering gene tree topology and branch lengths in more detail (Swenson 2012; Smith et al. 2015).
Conclusions
Community structure is the outcome of different demographic processes operating across individuals and species. Determining how organismal function predicts demography is therefore a major research objective. In recent years, functional traits have been popularized as a means to accomplish this linkage, but results have been mixed. Here, we have presented an alternative transcriptome‐based approach that yields deeper and broader information regarding organismal function than functional traits and phylogeny. We applied this approach to a tree seedling community in subtropical China in order to determine whether the transcriptomic similarity of neighbouring individuals influences focal individual survival rates. We found that only a few gene ontologies related to differential survival rates in seedlings. This indicates that transcriptomic information may be useful in community ecology, but there is still a great deal of unexplained variation. Future studies should consider other aspects of the (or the entire) transcriptome and should potentially also consider differential gene expression for photosynthesis‐related genes. Such work was not possible presently due to computational and financial barriers, but we expect these barriers will continue to be reduced allowing ecologists to leverage the full power of transcriptomes in the near future.
Author's contributions
K.P.M., X.C.M., W.W., B.C.H. and M.N.U. conceived the ideas and designed methodology; B.C.H., L.C., X.J.L., Y.L. and Y.Q.W. collected the data; B.C.H., M.N.U. and Y.Q.W. analysed the data; B.C.H. and M.N.U. led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.
Acknowledgements
We thank Dr. Kequan Pei, Dr. Yan Zhu, Jianyu Yang, Yin Guo, Wubing Xu, NingNing Wang and Yinan Liu for their helpful discussions and kind assistance for data collections. The research was financially supported by the National Natural Science Foundation of China (no. 31261120579) and M.N.U. was funded with US‐China Dimensions of Biodiversity grant (DEB‐1241136).
Data accessibility
Data available from the Dryad Digital Repository https://doi.org/10.5061/dryad.2cf46 (Han et al. 2017).
