BACKGROUND : Canopy structure, defined by leaf area index (LAI), fractional vegetation cover (FCover) and fraction of
absorbed photosynthetically active radiation (fAPAR), regulates a wide range of forest functions and ecosystem
services. Spatially consistent field-measurements of canopy structure are however lacking, particularly for the tropics.
METHODS : Here, we introduce the Global LAI database: a global dataset of field-based canopy structure
measurements spanning tropical forests in four continents (Africa, Asia, Australia and the Americas). We use these
measurements to test for climate dependencies within and across continents, and to test for the potential of
anthropogenic disturbance and forest protection to modulate those dependences.
RESULTS : Using data collected from 887 tropical forest plots, we show that maximum water deficit, defined across
the most arid months of the year, is an important predictor of canopy structure, with all three canopy attributes
declining significantly with increasing water deficit. Canopy attributes also increase with minimum temperature, and
with the protection of forests according to both active (within protected areas) and passive measures (through
topography). Once protection and continent effects are accounted for, other anthropogenic measures (e.g. human
population) do not improve the model.
CONCLUSIONS : We conclude that canopy structure in the tropics is primarily a consequence of forest adaptation to
the maximum water deficits historically experienced within a given region. Climate change, and in particular
changes in drought regimes may thus affect forest structure and function, but forest protection may offer some
resilience against this effect.
Additional file 1: Table S1. Attributes of each dataset used in the
analyses. Locations of each plot are provided as *.pdf file (Additional file 2).
N - Number of plots used for the analyses (we excluded plots that
measured at less than eight sampling points). Year - Year of field
measurements. Researcher - AB, Andrew Burt; ACS, Aida Cuni-Sanchez; AG,
Alemu Gonsamo; AL, Alicia Ledo; ARM, Andrew R Marshall; BW, Beatrice
Wedeux; DD, Dereje Denu; DS, Deo Shirima; HS, Hamidu Seki; JGT, Jose
Gonzalez de Tanago Menaca; KC, Kim Calders; LC, Luis Cayuela; LAS, Lau
Alvaro Sarmiento; MJM, Manuel J Macia; MP, Marion Pfeifer; ND, Nicolas
Deere; PO, Pieter Olivier; PKEP, Petri Pellikka; PJP, Philip J Platts; RT, Rebecca
Trevithick; RH, Robin Hayward; RM, Robert Marchant; TP, Timothy Paine;
WW, Woodgate William. Figure S1. Example maps of human population
pressure, calculated from human population density grids using a range of
sigma values (σ = 5, 15, 25, 50). Colours are graduated on a log base 2 scale
(light colours, low pressure; dark colours, high pressure). The maps provide
scope for capturing human-driven pressures at a variety of spatial scales
(Platts 2012). For example, if σ = 5 then the weight given to remote
populations (relative to the local population) halves over a distance of
~4 km, nearing zero by ~15 km, whereas if σ = 25 then the weight halves
over a distance of ~20 km, nearing zero by ~60 km. We imposed a
maximum distance of 100 km, beyond which no pressure is exerted.
Figure S2. Relationships between Annual Moisture Index (AMI) and Mean
Annual Precipitation (MAP) and canopy attributes LAI, fAPAR and FCover.
We fitted linear, polynomial and nonlinear (nls model 1: y ~ a + b * I(x^z);
nls model 2: y~a/(1 + exp.(−(b + c*x))) models. Upper panel: polynomial
models fitted to LAI ~ MAP, FCover - MAP and fAPAR - MAP relationships.
The polynomial (RSS 1.464) and sigmoidal growth models (RSS 1.464)
produced slightly better fits to the LAI data compared to the fits produced
by the linear (RSS 1.47) and exponential (RSS 1.467) models. The polynomial
model produced the best fit to the FCover (RSS 24.76) and fAPAR (RSS
0.2371) data. Lower panel: nls model 2 fitted to LAI ~ MAP, FCover - MAP
and fAPAR - MAP relationships. The logistic growth model produced the
best fit to the LAI data (RSS 1.347), the FCover data (RSS 22.95) and the
fAPAR data (RSS 0.2191).