Towards a New Generation of Trait-Flexible Vegetation Models

Berzaghi, Fabio; Wright, Ian J.; Krämer, K.; Oddou‐Muratorio, Sylvie; Bohn, Friedrich J.; Reyer, Christopher; Sabaté, Santiago; Sanders, Tanja G. M.; Härtig, Florian

doi:10.1016/j.tree.2019.11.006

Cited by 77 publications

(60 citation statements)

References 168 publications

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“…Uncertainty in these trait measurements can have large consequences for model outputs, such as the carbon sink of the terrestrial biosphere (Verheijen et al., 2015). There has been substantial progress on estimating geographic patterns of functional traits, but the role of ITV in vegetation models has largely been ignored (Butler et al., 2017; Berzaghi et al., 2020, but see Sakschewski et al., 2015). Therefore, there is a need to understand ITV across climate gradients, but it is often infeasible to collect these kinds of measurements for large numbers of species and traits over the large geographic areas required.…”

Section: Introductionmentioning

confidence: 99%

Predicting intraspecific trait variation among California's grasses

et al. 2021

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Plant species can show considerable morphological and functional variation along environmental gradients. This intraspecific trait variation (ITV) can have important consequences for community assembly, biotic interactions, ecosystem functions and responses to global change. However, directly measuring ITV across many species and wide geographic areas is often infeasible. Thus, a method to predict spatial variation in a species’ functional traits could be valuable. We measured specific leaf area (SLA), height and leaf area (LA) of grasses across California, covering 59 species at 230 sampling locations. We asked how these traits change along climate gradients within each species and used machine learning to predict local trait values for any species at any location based on phylogenetic position, local climate and that species’ mean traits. We then examined how much these local predictions alter patterns of assemblage‐level trait variation across the state. Most species exhibited higher SLA and grew taller at higher temperatures and produced larger leaves in drier conditions. The random forests predicted spatial variation in functional traits very accurately, with correlations up to 0.97. Because trait records were spatially biased towards warmer areas, and these areas tend to have higher SLA individuals within each species, species means of SLA were upwardly biased. As a result, using species means over‐estimates SLA in the cooler regions of the state. Our results also suggest that height may be substantially under‐predicted in the warmest areas. Synthesis. Using only species mean traits to characterize the functional composition of communities risks introducing substantial error into trait‐based estimates of ecosystem properties including decomposition rates or NPP. The high performance of random forests in predicting local trait values provides a way forward for estimating high‐resolution patterns of ITV without a massive data collection effort.

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Section: Introductionmentioning

confidence: 99%

Predicting intraspecific trait variation among California's grasses

et al. 2021

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“…Moreover, as for the vast majority of other DGVMs, we assumed that species‐specific parameters are identical across their range, despite ample evidence for intraspecific variability of functional traits within species (Moran, Hartig, & Bell, ). Such a strong assumption simplifies the calibration process, but may also lead to inaccurate predictions about climate responses and forest resilience (see also Berzaghi et al, ). Thus, it will be beneficial to apply spatially variable parameterizations, despite the substantial computational cost.…”

Section: Discussionmentioning

confidence: 99%

Assessing the response of forest productivity to climate extremes in Switzerland using model–data fusion

Trotsiuk

Härtig

Cailleret

et al. 2020

Global Change Biology

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The response of forest productivity to climate extremes strongly depends on ambient environmental and site conditions. To better understand these relationships at a regional scale, we used nearly 800 observation years from 271 permanent long‐term forest monitoring plots across Switzerland, obtained between 1980 and 2017. We assimilated these data into the 3‐PG forest ecosystem model using Bayesian inference, reducing the bias of model predictions from 14% to 5% for forest stem carbon stocks and from 45% to 9% for stem carbon stock changes. We then estimated the productivity of forests dominated by Picea abies and Fagus sylvatica for the period of 1960–2018, and tested for productivity shifts in response to climate along elevational gradient and in extreme years. Simulated net primary productivity (NPP) decreased with elevation (2.86 ± 0.006 Mg C ha−1 year−1 km−1 for P. abies and 0.93 ± 0.010 Mg C ha−1 year−1 km−1 for F. sylvatica). During warm–dry extremes, simulated NPP for both species increased at higher and decreased at lower elevations, with reductions in NPP of more than 25% for up to 21% of the potential species distribution range in Switzerland. Reduced plant water availability had a stronger effect on NPP than temperature during warm‐dry extremes. Importantly, cold–dry extremes had negative impacts on regional forest NPP comparable to warm–dry extremes. Overall, our calibrated model suggests that the response of forest productivity to climate extremes is more complex than simple shift toward higher elevation. Such robust estimates of NPP are key for increasing our understanding of forests ecosystems carbon dynamics under climate extremes.

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“…Based on the strength of simultaneous modeling and predictions, high computational efficiency and data requirement commonalities, our integrated Bayesian approach offers a generalizable foundation for powerful modeling of trait-environment linkages under changing climate and for predicting their consequences on ecosystem functions and services in other critical forest ecosystems of the world such as tundra, the most rapidly warming biome on the planet (Bjorkman et al 2018a). The increasing contribution of collaborators to the global trait databases (Kattge et al 2020) and fast expansion of the spatially explicit global data sets for species distributions (Hudson et al 2014), and high-resolution environmental layers (Shangguan et al 2014, Basher et al 2018 provide opportunities for such applications, especially within dynamic global vegetation models (Scheiter et al 2013, Berzaghi et al 2020.…”

Section: Practical Applicationsmentioning

confidence: 99%