10.5061/DRYAD.S1RN8PK85
Muñoz, Rodrigo
0000-0001-9434-0126
Wageningen University & Research
Bongers, Frans
Wageningen University & Research
Rozendaal, Danae
Wageningen University & Research
González, Edgar Javier
National Autonomous University of Mexico
Dupuy, Juan Manuel
Centro de Investigación Científica de Yucatán
Meave, Jorge
National Autonomous University of Mexico
Autogenic regulation and resilience in tropical dry forest
Dryad
dataset
2021
Autogenic regulation
demographic processes
State variables
Community dynamics
recovery
constancy
FOS: Biological sciences
Dutch Research Council
https://ror.org/04jsz6e67
ALWOP.457
DGAPA*
PAPIIT IN217620
Universidad Nacional Autónoma de México
https://ror.org/01tmp8f25
PAPIIT IN218416
Universidad Nacional Autónoma de México
https://ror.org/01tmp8f25
PAPIIT IN221503
Consejo Nacional de Humanidades, Ciencias y Tecnologías
https://ror.org/059ex5q34
SEMARNAT-2002-C01-0267 and CB-2009-01-128136
Universidad Nacional Autónoma de México
https://ror.org/01tmp8f25
PAPIIT IN216007
DGAPA
PAPIIT IN217620
2021-07-09T00:00:00Z
2021-07-09T00:00:00Z
en
46521 bytes
2
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
1. Engineering resilience, a forest’s ability to maintain its properties
in the event of disturbance, comprises two components: resistance and
recovery. In human-dominated landscapes, forest resilience depends mostly
on recovery. Forest recovery largely depends on autogenic regulation,
which entails a negative feedback loop between rates of change of forest
state variables and state variables themselves. Hence community dynamics
changes in response to deviations from forest equilibrium state. Based on
the premise that autogenic regulation is a key aspect of the recovery
process, here we tested the hypothesis that combined old-growth forest
(OGF) and secondary forest (SF) dynamics should show autogenic regulation
in state variables, and thus convergence towards OGF-based reference
points, indicating forest resilience. 2. We integrated dynamic data for
OGF (11-year monitoring) and SF (16-year monitoring) to analyse three key
state variables (basal area, tree density, species richness), their annual
rates of change, and their underlying demographic processes (recruitment,
growth, mortality). We examined autogenic regulation through generalized
linear mixed-effects models (GLMMs) to quantify functional relationships
between rates of change of state variables (and underlying demographic
processes), and their respective state variables. 3. State variables in
OGF decreased moderately over time, against our prediction of OGF
constancy. In turn, the three state variables analysed showed negative
relationships with their respective rates of change, which allows the
return of SF to OGF values after disturbance. In all cases, recruitment
decreased with increasing values in state variables, while mortality
increased. 4. The observed negative relationships between state variables,
their rates of change and their underlying demographic processes support
our hypothesis of integrated OGF and SF dynamics showing autogenic
regulation for state variables. Competition seems to be a major driver of
autogenic regulation given its dependence on a resource availability that
declines as forest structure develops. 5. Synthesis. Based on a
straightforward and comprehensive approach to quantify the extent to which
tropical forest dynamics is self-regulated, this study highlights the role
of autogenic regulation in tropical dry forest as a basic component of its
resilience. This approach is potentially valuable for a generalised
assessment of engineering resilience of forests worldwide.
Data comes from 28 tropical dry forest plots of secondary forest (SF) and
old-growth forest (OGF). SF plots were established in 2003 and OGF plots
were established in 2008. Within these plots, woody stems meeting
inclusion criteria (see Supplementary Material Figure S1) were tagged
and identified to species. Diameter at breast height and survival were
recorded annually for all tagged individuals. Plant diameter, survival and
taxonomical identity were used to estimate basal area, tree density and
species richness (state variables of the study) at plot level for all
plots on a yearly basis. We used survival information to describe the
ongoing demographic process (recruitment, growth, mortality) per
individual and annual period. Further, for each individual and period, we
computed the difference in state variable values. Then, we estimated the
contribution of each demographic process towards each of the three state
variables by grouping individuals going through the same demographic
process in each year, and then adding their changes in state variable
values. This database contains information on state variable values,
annual changes, and contributions per demographic process on a plot-year
resolution.
For some plot-year datapoints, tree density's sum of recruitment and
mortality do not add up to the net change of the state variable. This is
because we used a stratified sampling: trees shifting from a size category
to another change their scaling factor, although they do not
undergo recruitment or mortality. Therefore, their tree density value
might differ from one year to the next one. These differences are
negligible and were not accounted for in the analysis.