Capturing ecological processes in dynamic forest models: why there is no silver bullet to cope with complexity

Huber, Nica; Bugmann, Harald; Lafond, Valentine

doi:10.1002/ecs2.3109

Cited by 29 publications

(77 citation statements)

References 184 publications

(308 reference statements)

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“…Some forest gap models have alternative links between aboveground inputs and belowground pools (Friend et al, 1997), but it is unclear if more complex processes or different processes would reduce forecast uncertainty. In order to improve the link between aboveground inputs and belowground accumulation we agree with the sentiment in (Huber et al, 2020) that multiple model representations of unclear mechanistic processes should be used for predictions. We suggest that future directions focus on incorporating a variety processes known to affect the evolution of soil carbon beyond aboveground inputs using ensemble based methods.…”

Section: Discussionsupporting

confidence: 66%

Towards understanding predictability in ecology: A forest gap model case study

Raiho

Dietze

Dawson

et al. 2020

Preprint

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11Predictions from ecological models necessarily include five different uncertainties: demographic stochas-12 ticity, initial conditions, external forcing (i.e., drivers/covariates), parameters, and modeled processes. 13 However, most predictions from process-based ecological models only account for a subset of these un-14 certainties (e.g. only demographic stochasticity). This underestimation of uncertainty runs the risk of 15 producing precise, but inaccurate predictions. To address these limitations, we created a new generaliz-16 able ensemble state data assimilation algorithm that accommodates two common features of ecological 17 data, zero-truncation and zero-inflation, and allows the estimation of process error and its covariance 18 among multiple ecological variables. We then demonstrate the use of this novel algorithm by assimilating 19 50 years of tree-ring-estimated species-level biomass at Harvard Forest into a process-based forest gap 20 model. Finally, we partitioned the variance in this hindcast to test long-standing assumptions in the 21 ecological modeling community about which uncertainties dominate our ability to forecast forest com-22 munity and carbon dynamics. Contrary to >40 years of research relying on stochastic forest gap models, 23 we found that demographic stochasticity alone massively underestimated forecast uncertainty (0.09% 24 of the total) and resulted in overconfident and biased model predictions. Similarly, despite decades of 25 reliance on unconstrained "spin ups" to initialize models, constraining initial conditions with data led to 26 the largest increases in prediction accuracy. Counter to conventional wisdom from modeling other Earth 27 1 system process, initial condition uncertainty declined very little over the forecast time period. Process 28 variance, which heretofore had been difficult to estimate in mechanistic ecosystem model projections, 29 dominated the prediction uncertainty over the forecast time period (49.1%), followed by meteorological 30 uncertainty (32.5%). Parameter uncertainty, which has recently been the focus of the modeling commu-31 nity, contributed a modest 18.3%. These findings call into question much of our conventional wisdom 32 about how to improve forest community and carbon cycle projections on multi-decadal to centennial time 33 scales. This foundation can be used to test long standing modeling assumptions across fields in global 34 change biology and suggests a fairly significant reorientation of the modeling community toward better 35 initialization of models with current observations and efforts to better quantify, propagate, and reduce 36 process error. These approaches have the potential to improve both the accuracy of ecological forecasts 37 and our understanding of the predictability of ecological processes. 38 Running Head: Drivers of multi-decadal forecast uncertainty 39 nity ecology, Tobit Wishart ensemble filter (TWEnF) 41 1 Introduction 42 Understanding how different component uncertainties contribute to the overall uncertai...

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Section: Discussionsupporting

confidence: 66%

Towards understanding predictability in ecology: A forest gap model case study

Raiho

Dietze

Dawson

et al. 2020

Preprint

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“…Here, we used a highly complex simulation model to investigate the interactions between disturbance dynamics, management, vegetation feedbacks, and climate change. Although such an approach allowed identifying and attributing the effects of different management actions, the uncertainty related to the representation of individual processes and model assumptions needs to be carefully considered (Huber et al., 2020). Although the model's use is supported by numerous testing exercises that particularly addressed forest productivity, regeneration, and natural mortality (e.g., Dobor et al., 2018), reproducing complex disturbance regimes in models remains challenging (Seidl et al., 2011).…”

Section: Discussionmentioning

confidence: 99%

Contrasting vulnerability of monospecific and species‐diverse forests to wind and bark beetle disturbance: The role of management

Dobor

Hlásny

Zimová

2020

Ecology and Evolution

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“…ForClim has been applied across various temperate forests in Europe and other parts of the world (e.g., Gutierrez et al, 2016;Mina et al, 2017;Huber 2019) and undergone thorough evaluation under a wide range of species and site conditions (Rasche et al 2012, Huber 2019.…”

Section: Forest Model Forclimmentioning

confidence: 99%

“…Over the past decade, the capacities of ForClim to represent forest management have been continuously expanded and evaluated in several mountain regions across Europe (Rasche et al 2011, Mina et al 2017a. All simulations of this study were conducted with ForClim Version 4.0.1 (Huber 2019); see Online Resource for further details.…”

Section: Forest Model Forclimmentioning

confidence: 99%

See 1 more Smart Citation

Climate change impacts across a large forest enterprise in the Northern Pre-Alps: dynamic forest modelling as a tool for decision support

Thrippleton

Lüscher²,

Bugmann

2020

Eur J Forest Res

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We thank Dominic Michel for support in all IT-related questions and the employees of the OAK Schwyz for conducting the measurements of forest structure in the field. David I. Forrester and the Experimental Forest Management (EFM) Project of the Swiss Federal Research Institute WSL are gratefully acknowledged for providing data on the current stand structure of the site FW (Riemenstalden). Two anonymous reviewers are gratefully acknowledged for their thoughtful comments and helpful suggestions.

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Capturing ecological processes in dynamic forest models: why there is no silver bullet to cope with complexity

Cited by 29 publications

References 184 publications

Towards understanding predictability in ecology: A forest gap model case study

Towards understanding predictability in ecology: A forest gap model case study

Contrasting vulnerability of monospecific and species‐diverse forests to wind and bark beetle disturbance: The role of management

Climate change impacts across a large forest enterprise in the Northern Pre-Alps: dynamic forest modelling as a tool for decision support

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