Data collection
Vegetation sampling: I obtained the data on tree
communities from an online repository (Osuri and Sankaran, 2016a). The
dataset comprised of 50 square plots of 30 x 30 m – 31 plots at eight
locations within contiguous forest and 19 plots in eight forest
fragments. All plots were censused during Jan-Dec 2011. Sampled
fragments ranged from 5-10 ha in size, chosen to avoid ongoing or recent
disturbance or human-use.
Vegetation plots were placed at least 50-m from fragment edges and 50-m
apart from each other. The contiguous forest plots were located within a
complex of structurally connected nature reserves adjacent to the
fragments. Shade coffee plantations dominated the land-use in the
human-modified matrix. For more details of the plot locations and
placement, see Osuri and Sankaran (2016b). Species were identified using
field keys, regional floras (Pascal and Ramesh 1997; Ramesh et al. 2007)
and with input from experienced botanists. We excluded about 3% of
individuals as could not be identified to genus or species because they
had no leaves or the canopy was obscured by climbers.
Traits: I used four key functional traits of
plants—seed size, wood density, specific leaf area (SLA) and maximum
height—that were available in the supplementary information included
with Osuri & Sankaran (2016b). Trait data was collected between
November 2011 and January 2013 following standard protocols (Cornelissen
et al. 2003) from trees within or adjacent to the vegetation plots. The
data collection methods have been detailed in Osuri and Sankaran
(2016b). Here, I briefly repeat the main aspects.
For SLA, five mature, healthy, sun-exposed canopy leaves were collected
per tree at the end of the wet season (October–December) for 358 trees
comprising 79 species. Leaf areas were estimated using the Black Spot
Leaf Area Calculator (Varma and Osuri, 2013), after which leaves were
oven-dried at 60°C for 72 h to obtain dry weights. Wood density was
estimated by dividing dry weight by fresh volume of trunk wood cores
collected with an increment borer, for 352 trees representing 74
species. Thirty-six species for which adequate primary data could not be
collected, wood density was also collated from secondary sources (Chave
et al., 2009). Seed size was quantified as the length of the primary
seed axis, for 34 species (1879 seeds in total). Additionally, seed size
data were obtained from two other mid-elevation evergreen forests in the
Western Ghats (D. Mudappa, unpublished data). For species without
primary measurements, seed lengths were collated from published
secondary sources (Matthew 1983; Saldanha & Ramesh 1984; Ramesh et al.
2007). Maximum heights for each species were obtained in the field and
from secondary sources (Matthew 1983; Saldanha & Ramesh 1984; Ramesh et
al. 2007).
Environmental data: I used mean Climatological Water
Deficit (Chave et al., 2014) to quantify site-level variation in
seasonal water stress (Condit et al., 2013; Vicente-Serrano et al.,
2013). CWD, measured in mm/year, is always a negative number since it is
the difference between rainfall and evapotranspiration during dry months
only, and more negative values indicate greater water deficit. Data were
downloaded from the source cited in Chave et al. (2014;
http://chave.ups-tlse.fr/pantropical_allometry.htm#CWD). I also
characterized site-level climate aridity with Annual Evapotranspiration
(AET) data, available at a spatial resolution of 1
km2, and can be downloaded from the CGIAR-CSI
GeoPortal (https://cgiarcsi.community). I also used data from
WorldClim, collated per 1 km2, to characterize
gradients in precipitation and temperature across the study region
(Hijmans et al., 2005). I used mean annual precipitation, mean
precipitation of the driest quarter, mean precipitation of the warmest
quarter, mean annual temperature, mean temperature of the warmest
quarter, and the mean temperature of the driest quarter.