||Predicting species abundances in a grassland biodiversity experiment: Trade‐offs between model complexity and generality
||Clark, A.T.; Turnbull, L.A.; Tredennick, A.; Allan, E.; Harpole, W.S.
; Mayfield, M.M.; Soliveres, S.; Barry, K.; Eisenhauer, N.; de Kroon, H.; Rosenbaum, B.; Wagg, C.; Weigelt, A.; Feng, Y.; Roscher, C.; Schmid, B.
||Journal of Ecology
||bias‐variance trade‐off; cross‐validation; Gompertz population model; grasslands; interspecific competition; Jena experiment; over‐fitting; plant population and community dynamics
- Models of natural processes necessarily sacrifice
some realism for the sake of tractability. Detailed, parameter‐rich
models often provide accurate estimates of system behaviour but can be
data‐hungry and difficult to operationalize. Moreover, complexity
increases the danger of ‘over‐fitting’, which leads to poor performance
when models are applied to novel conditions. This challenge is typically
described in terms of a trade‐off between bias and variance (i.e. low
accuracy vs. low precision).
- In studies of ecological communities, this trade‐off
often leads to an argument about the level of detail needed to describe
interactions among species. Here, we used data from a grassland
biodiversity experiment containing nine locally abundant plant species
(the Jena ‘dominance experiment’) to parameterize models representing
six increasingly complex hypotheses about interactions. For each model,
we calculated goodness‐of‐fit across different subsets of the data based
on sown species richness levels, and tested how performance changed
depending on whether or not the same data were used to parameterize and
test the model (i.e. within vs. out‐of‐sample), and whether the range of
diversity treatments being predicted fell inside or outside of the
range used for parameterization.
- As expected, goodness‐of‐fit improved as a function
of model complexity for all within‐sample tests. In contrast, the best
out‐of‐sample performance generally resulted from models of intermediate
complexity (i.e. with only two interaction coefficients per species—an
intraspecific effect and a single pooled interspecific effect),
especially for predictions that fell outside the range of diversity
treatments used for parameterization. In accordance with other studies,
our results also demonstrate that commonly used selection methods based
on AIC of models fitted to the full dataset correspond more closely to
within‐sample than out‐of‐sample performance.
- Synthesis. Our results demonstrate that models
which include only general intra and interspecific interaction
coefficients can be sufficient for estimating species‐level abundances
across a wide range of contexts and may provide better out‐of‐sample
performance than do more complex models. These findings serve as a
reminder that simpler models may often provide a better trade‐off
between bias and variance in ecological systems, particularly when
applying models beyond the conditions used to parameterize them.
|Persistent UFZ Identifier
|Clark, A.T., Turnbull, L.A., Tredennick, A., Allan, E., Harpole, W.S., Mayfield, M.M., Soliveres, S., Barry, K., Eisenhauer, N., de Kroon, H., Rosenbaum, B., Wagg, C., Weigelt, A., Feng, Y., Roscher, C., Schmid, B. (2020):
Predicting species abundances in a grassland biodiversity experiment: Trade‐offs between model complexity and generality
J. Ecol. 108 (2), 774 - 787