Biology subjects

Caldararu, S.

Publications and source records attributed to Caldararu, S..

3 recordsLinked to original sources

bioRxiv2025

Phenological benchmarking with a land surface model: an in-silico experiment for temperate forests

O_LIPlant phenology affects biotic interactions, water, carbon and nutrient cycling, ultimately influencing global climate, and making understanding and quantifying phenological dynamics central for ecological forecasting. This however varies depending on data source, smoothing methodology or date-extraction method. The selection of an appropriate phenological processing pipeline for the system of study thus requires accurate benchmarking in a controlled environment. C_LIO_LIIn this study we use a Land Surface Model (LSM) to simulate ecosystem dynamics at four temperate forest sites under five lesser studied climate perturbations. We use the generated synthetic dataset to control for environmental conditions while testing commonly used phenological detection methods (relative thresholds at 10%, 20% and 50%, highest rate of change, detection of inflection points in the curvatures angle, detection of recovery and senescence points). C_LIO_LIAmongst tested methods, we find general agreement in terms of response to climate alterations. Inflection and low thresholds (10%) methods provide the closest match to the models predicted start of season, with a maximum discrepancy of 4 days between the two. Higher thresholds (20%, 50%) are closer to the models predicted end of season, although with discrepancies of up to 10 days. In addition, high thresholds and methods based on the rate of change of vegetation parameters successfully detect the peak of season. We also show how some methods can pick up on effects that are ignored by temperature-only type growing seasons, thereby supporting the need for increased realism of phenological representations in land surface models. C_LIO_LIThis is, to our knowledge, the first time an LSM is used to generate an in silico experiment. This study not only tests phenological extraction methodologies within a common framework, but demonstrates how land surface models can be used as tools for ecological testing beyond their current use as predictive tools. C_LI

ecology↗

bioRxiv2021

Long-term ecosystem nitrogen limitation from foliar δ15N data and a land surface model

The effect of nutrient availability on plant growth and the terrestrial carbon sink under climate change and elevated CO2 remains one of the main uncertainties of the terrestrial carbon cycle. This is partially due to the difficulty of assessing nutrient limitation at large scales over long periods of time. Consistent declines in leaf nitrogen (N) content and leaf {delta}15N have been used to suggest that nitrogen limitation has increased in recent decades, most likely due to the concurrent increase in atmospheric CO2. However, such datasets are often not straightforward to interpret due to the complex factors that contribute to the spatial and temporal variation in leaf N and isotope concentration. We use the land surface model QUINCY, which has the unique capacity to represent N isotopic processes, in conjunction with two large datasets of foliar N and N isotope content. We run the model with different scenarios to test whether foliar {delta}15N isotopic data can be used to infer large scale nitrogen limitation and if the observed trends are caused by increasing atmospheric CO2, changes in climate or changes in sources of anthropogenic N deposition. We show that while the model can capture the observed change in leaf N content and predicts widespread increases in N limitation, it does not capture the pronounced, but very spatially heterogeneous, decrease in foliar {delta}15N observed in the data across the globe. The addition of an observed temporal trend in isotopic composition of N deposition leads to a more pronounced decrease in simulated leaf {delta}15N. Our results show that leaf {delta}15N observations should not, on their own, be used to assess global scale N limitation and that using such a dataset in conjunction with a land surface model can reveal the drivers behind the observed patterns.

ecology↗

bioRxiv2019

Whole-plant optimality predicts changes in leaf nitrogen under variable CO2 and nutrient availability

O_LIVegetation nutrient limitation is essential for understanding ecosystem responses to global change. In particular, leaf nitrogen (N) is known to be plastic under changed nutrient limitation. However, models can often not capture these observed changes, leading to erroneous predictions of whole-ecosystem stocks and fluxes.\nC_LIO_LIWe hypothesise that an optimality approach can improve representation of leaf N content compared to existing empirical approaches. Unlike previous optimality-based approaches, which adjust foliar N concentrations based on canopy carbon export, we use a maximisation criteria based on whole-plant growth and allow for a lagged response of foliar N to this maximisation criterion to account for the limited plasticity of this plant trait. We test these model variants at a range of Free-Air CO2 Enrichment (FACE) and N fertilisation experimental sites.\nC_LIO_LIWe show a model solely based on canopy carbon export fails to reproduce observed patterns and predicts decreasing leaf N content with increased N availability. However, an optimal model which maximises total plant growth can correctly reproduce the observed patterns.\nC_LIO_LIThe optimality model we present here is a whole-plant approach which reproduces biologically realistic changes in leaf N and can thereby improve ecosystem-level predictions under transient conditions.\nC_LI

ecology↗