TY  - JOUR
A1  - Lin, Yue
A1  - Berger, Uta
A1  - Grimm, Volker
A1  - Huth, Franka
A1  - Weiner, Jacob
T1  - Plant interactions alter the predictions of metabolic scaling theory
JF  - PLoS one
N2  - Metabolic scaling theory (MST) is an attempt to link physiological processes of individual organisms with macroecology. It predicts a power law relationship with an exponent of -4/3 between mean individual biomass and density during density-dependent mortality (self-thinning). Empirical tests have produced variable results, and the validity of MST is intensely debated. MST focuses on organisms' internal physiological mechanisms but we hypothesize that ecological interactions can be more important in determining plant mass-density relationships induced by density. We employ an individual-based model of plant stand development that includes three elements: a model of individual plant growth based on MST, different modes of local competition (size-symmetric vs. -asymmetric), and different resource levels. Our model is consistent with the observed variation in the slopes of self-thinning trajectories. Slopes were significantly shallower than -4/3 if competition was size-symmetric. We conclude that when the size of survivors is influenced by strong ecological interactions, these can override predictions of MST, whereas when surviving plants are less affected by interactions, individual-level metabolic processes can scale up to the population level. MST, like thermodynamics or biomechanics, sets limits within which organisms can live and function, but there may be stronger limits determined by ecological interactions. In such cases MST will not be predictive.
Y1  - 2013
U6  - https://doi.org/10.1371/journal.pone.0057612
SN  - 1932-6203
VL  - 8
IS  - 2
PB  - PLoS
CY  - San Fransisco
ER  - 
TY  - JOUR
A1  - Grimm, Volker
A1  - Revilla, Eloy
A1  - Berger, Uta
A1  - Jeltsch, Florian
A1  - Mooij, Wolf M.
A1  - Railsback, Steven Floyd
A1  - Thulke, Hans-Hermann
A1  - Weiner, Jacob
A1  - Wiegand, Thorsten
A1  - DeAngelis, Donald L.
T1  - Pattern-oriented modeling of agend-based complex systems : lessons from ecology
N2  - Agent-based complex systems are dynamic networks of many interacting agents; examples include ecosystems, financial markets, and cities. The search for general principles underlying the internal organization of such systems often uses bottom-up simulation models such as cellular automata and agent-based models. No general framework for designing, testing, and analyzing bottom-up models has yet been established, but recent advances in ecological modeling have come together in a general strategy we call pattern-oriented modeling. This strategy provides a unifying framework for decoding the internal organization of agent-based complex systems and may lead toward unifying algorithmic theories of the relation between adaptive behavior and system complexity
Y1  - 2005
ER  -