Extinction-immigration dynamics lag behind environmental filtering in shaping the composition of tropical dry forests within a changing landscape
Cite this dataset
Blanchard, Gregoire; Birnbaum, Philippe; Munoz, François (2020). Extinction-immigration dynamics lag behind environmental filtering in shaping the composition of tropical dry forests within a changing landscape [Dataset]. Dryad. https://doi.org/10.5061/dryad.jwstqjq5r
New Caledonia is an archipelago located in southwest Pacific (20–23° S, 164–167° E). The study area is a 20x20km landscape located in a plain surrounded by low-elevation mountains (500-1000m), on the west coast of the main island (Kone-Pouembout plain). It receives ~1000-1400 mm·yr-1 rainfall and the mean annual temperature is ~23-24 °C (WorldClim2 database, Fick & Hijmans 2017). The vegetation is a mosaic of evergreen dry forest patches (“tropical dry forest”, Holdridge 1947) surrounded by secondary thickets and farmlands.
We designed a random sampling scheme stratified by patch area. Based on a present landscape map, we selected 36 accessible patches spanning a wide range of area (0.5-272.2 ha, median 23.6). We excluded areas recently colonized by the forest (i.e., areas currently covered by forest, but which were open in 1954). Then, we applied a 25*25m square grid and, in each patch, we randomly selected a number of grid nodes proportionally to the logarithm of patch area. Within a circular 400m² plot (i.e., 11.30m radius) at each selected point, we identified all trees with diameter at breast height (DBH, 1.3 meters above ground) above 10cm. We ensured that the entire plots were actually within forest patches by relocating the center of plots away from the edge if necessary. All the plots were established during years 2017 and 2018.
For all sampled species, we measured five wood and leaf functional traits involved in resource-use strategies and stress resistance, following standardized protocols in Pérez-Harguindeguy et al. (2013). Trait were collected during years 2017 and 2018. For leaf traits, we collected five leaves per individual and sampled five individuals per species. For compound leaves, we considered a leaflet as the laminar unit. Petioles and petiolules were removed from leaves before measurement. We measured leaf area (the area of a leaf in cm²), Specific Leaf Area (the leaf area per dry mass in cm²·g- 1), and Leaf Dry-Matter Content (the leaf dry mass per fresh mass in mg·g- 1). Specific leaf area and leaf dry-matter content capture species investment in leaves, and represent a trade-off between acquisitive (high specific leaf area) and conservative (high leaf dry-matter content) strategies along the leaf economic spectrum (Wright et al. 2004). Leaf area represents the light-capturing and transpiration surface and is thus related to water-use efficiency (Moles 2018). We measured the wood density of one wood sample per individual and five individuals per species. Wood density is a key trait of the wood economic spectrum, from water use efficiency and high growth rate to lower growth rate and drought resistance (Chave et al. 2009). Furthermore, we measured bark thickness. Thick bark provides stem protection from heat and fire (Pausas 2015), as well as from other damages (Rosell 2016). For bark thickness, we calculated the mean of two bark measures on individual trees, and sampled at least 15 individuals per species (mean = 17.8). For wood density and bark thickness of 38 species, data were complemented from the New Caledonian plant inventory and permanent plot network (NC-PIPPN) database, in which trait measurements were carried out using identical protocols (Ibanez et al. 2017a). In subsequent analyses, we used the mean trait value per species for leaf traits and wood density. As bark thickness varies with DBH (Pausas 2015), we used the maximum value per species, to better approximate an upper bound reached during tree growth. We used a Box-Cox transformation (Box & Cox 1964) on each trait to get distributions closer to normality. The five functional traits were finally informed for 99 species, representing 90% of the identified species (n = 110), and more than 99% of the identified individuals (n = 3069). We used this subset of 99 species in further analyses.
We evaluated trait covariation to identify main functional dimensions (trait spectra) across species by performing a Principal Component Analysis (PCA) of species traits values, with varimax orthogonal rotation of the three first components. Trait values were centered and scaled to unit variance before performing PCA. We considered species scores on the first three rotated PCA (RPCA) axes as synthetic trait values representing species ecological strategies along the main functional dimensions. The synthetic trait values where standardized between 0 and 1.
We quantified summary statistics representing (i) taxonomic composition, i.e., species richness (S, the number of species) and Shannon diversity (ES), and (ii) functional composition, i.e., community weighted mean (CWM) and community weighted variance (CWV) of each trait and each synthetic trait (RPCA axes).