Using functional traits to predict species growth trajectories, and cross‐validation to evaluate these models for ecological prediction

Thomas, Freya; Yen, Jian D. L.; Vesk, Peter A.

doi:10.1002/ece3.4693

Cited by 9 publications

(8 citation statements)

References 76 publications

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“…It is worth asking whether those models transfer. Early work on transferring trait–height growth models has not proved highly successful (Thomas and Vesk 2017), yet interestingly a theory‐driven trait‐parameter subset produced the most generalizable models (Thomas et al 2019).…”

Section: Discussionmentioning

confidence: 99%

“…The only study of trait‐SDM that we are aware of employing internal, between‐species cross‐validation of trait‐SDM showed good performance between species, within one single dataset of insects, but still within only one region (Brown et al 2014). The closest analogues of our approach here can be found in trait‐based models of height growth which employed internal, between‐species cross‐validation (Thomas et al 2019) and between‐region transfer testing (Thomas and Vesk 2017).…”

Section: Introductionmentioning

confidence: 99%

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Transferability of trait‐based species distribution models

et al. 2020

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The need for reliable prediction of species distributions dependent upon traits has been hindered by a lack of model transferability testing. We tested the predictive capacity of trait-SDMs by fitting hierarchical generalised linear models with three trait and four environmental predictors for 20 eucalypt taxa in a reference region. We used these models to predict occurrence for a much larger set of taxa and target areas (82 taxa across 18 target regions) in southeastern Australia. Median predictive performance for new species in target regions was 0.65 (area under receiver operating curve) and 1.24 times random (area under precision recall curve). Prediction in target regions did not worsen with increasing geographic, environmental or community compositional distance from the reference region, and was improved with reliable trait-environment relationships. Transfer testing also identified trait-environment relationships that did not transfer. These results give confidence that traits and transfer testing can assist in the hard problem of predicting environmental responses for new species, environmental conditions and regions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Transferability of trait‐based species distribution models

et al. 2020

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“…Those processes ultimately influence the carbon cycle by changing values of elements in the matrix K as well as allocation coefficients in vector B , carbon influx μ ( t ), and, potentially, transfer coefficients A . Recent modeling efforts linking plant traits to tree demography, or microbial traits to carbon processes, make K a function of traits (Thomas et al., 2019). When the decomposition rates of soil organic matter are represented by Michaelis‐Menten equations, as formulated in some microbial models, it generates a nonlinearity (Sierra & Müller, 2015; Wieder et al., 2015).…”

Section: Unifying Land Carbon Cycle Modelsmentioning

confidence: 99%

“…Nitrogen and phosphorus cycle processes primarily modify the rates of photosynthesis μ ( t ), allocation B , transfer A , and mortality and decomposition rates K (Q. Zhu et al., 2019; Wang et al., 2010), but do not directly affect the environmental modifier ξ ( t ). Trait‐based modeling has the potential to link plant and microbial traits to photosynthesis, allocation, mortality, decomposition, transfer, rooting depth, and plant and microbial responses to environmental variables (Fry et al., 2019; Thomas et al., 2019). Thus, it may influence all five components (six if vertical mixing is resolved) of the matrix equation.…”

Section: Unifying Land Carbon Cycle Modelsmentioning

confidence: 99%

Matrix Approach to Land Carbon Cycle Modeling

Luo

Huang

Sierra

et al. 2022

J Adv Model Earth Syst

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Land ecosystems contribute to climate change mitigation by taking up approximately 30% of anthropogenically emitted carbon. However, estimates of the amount and distribution of carbon uptake across the world's ecosystems or biomes display great uncertainty. The latter hinders a full understanding of the mechanisms and drivers of land carbon uptake, and predictions of the future fate of the land carbon sink. The latter is needed as evidence to inform climate mitigation strategies such as afforestation schemes. To advance land carbon cycle modeling, we have developed a matrix approach. Land carbon cycle models use carbon balance equations to represent carbon exchanges among pools. Our approach organizes this set of equations into a single matrix equation without altering any processes of the original model. The matrix equation enables the development of a theoretical framework for understanding the general, transient behavior of the land carbon cycle. While carbon input and residence time are used to quantify carbon storage capacity at steady state, a third quantity, carbon storage potential, integrates fluxes with time to define dynamic disequilibrium of the carbon cycle under global change. The matrix approach can help address critical contemporary issues in modeling, including pinpointing sources of model uncertainty and accelerating spin‐up of land carbon cycle models by tens of times. The accelerated spin‐up liberates models from the computational burden that hinders comprehensive parameter sensitivity analysis and assimilation of observational data to improve model accuracy. Such computational efficiency offered by the matrix approach enables substantial improvement of model predictions using ever‐increasing data availability. Overall, the matrix approach offers a step change forward for understanding and modeling the land carbon cycle.

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“…We followed a hypothesis-driven selection of traits, rather than a model selection approach. A priori selection of predictor variables based on previous knowledge has been found to improve model predictive capacity, at least for trait-based vegetation growth models (Thomas et al 2019). Models were specified as follows:…”

Section: Statistical Analysesmentioning

confidence: 99%

Plant functional traits reflect different dimensions of species invasiveness

Palma

Vesk

White

et al. 2021

Ecology

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Trait-based invasiveness studies typically categorize exotic species as invasive or noninvasive, implicitly assuming species form two homogenous groups. However, species can become invasive in different ways (e.g., high abundance, fast spread), likely relying on different functional traits to do so. As such, binary classification may obscure traits associated with invasiveness. We tested whether (1) the way in which invasiveness is quantified influences its correlation with functional traits and (2) different demography-based metrics are related to different sets of traits. Using a case study of 251 herbs exotic to Victoria, Australia, we quantified species' invasiveness using 10 metrics: four continuous, demography-based dimensions of invasiveness (spread rate, local abundance, geographic and environmental range sizes) and six binary classifications of invasiveness (based on alternative sources and invasion criteria). We examined the correlation between species' invasiveness and a set of four traits known to relate to plant demography and invasion. Then, we examined whether different demographic dimensions of invasiveness were better explained by different sets of traits. We found that the way invasiveness was quantified was important: different traits were linked with different invasiveness metrics, and some traits showed opposite effects across metrics. Species with fast spread were either tall with small seeds (i.e., good colonizers), or had heavy, animal-dispersed seeds. Plants with a large environmental range had greater plasticity for some traits. Locally abundant plants had low SLA and heavy seeds (i.e., strong competitors). Animal dispersal was also key to reach a large geographic range. No traits were consistently related to the six binary classifications. Our results indicate that exotic plants are invasive in different ways and rely on different combinations of traits to be so. Some traits (e.g., seed mass) had complex relationships with invasion: they apparently promote, hampered, or had no influence on different dimensions of invasiveness. Our findings are consistent with the notion that plant species use strategies that may be near optimal under some, but not all, ecological conditions. Compared to binary classifications of invasiveness, the use of invasiveness dimensions advances clearer hypothesis testing in invasion science.Key words: demography metrics; dimensions of invasiveness; environmental and geographic range sizes; exotic plant invasion; invasiveness and invasive species; local abundance; plant functional traits; spread rate.

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Using functional traits to predict species growth trajectories, and cross‐validation to evaluate these models for ecological prediction

Cited by 9 publications

References 76 publications

Transferability of trait‐based species distribution models

Transferability of trait‐based species distribution models

Matrix Approach to Land Carbon Cycle Modeling

Plant functional traits reflect different dimensions of species invasiveness

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