Search for: All records

ABSTRACT BackgroundClimate‐change‐induced shifts in the timing of leaf emergence during spring have been widely documented and have important ecological consequences. However, mechanistic knowledge regarding what controls the timing of spring leaf emergence is incomplete. Field‐based studies under natural conditions suggest that climate‐warming‐induced decreases in cold temperature accumulation (chilling) have expanded the dormancy duration or reduced the sensitivity of plants to warming temperatures (thermal forcing) during spring, thereby slowing the rate at which the timing of leaf emergence is shifting earlier in response to ongoing climate change. However, recent studies have argued that the apparent reductions in temperature sensitivity may arise from artefacts in the way that temperature sensitivity is calculated, while other studies based on statistical and mechanistic models specifically designed to quantify the role of chilling have shown conflicting results. MethodsWe analysed four commonly used combinations of phenology and temperature datasets obtained from remote sensing and ground observations to elucidate whether current model‐based approaches robustly quantify how chilling, in concert with thermal forcing, controls the timing of leaf emergence during spring under current climate conditions. ResultsWe show that widely used modeling approaches that are calibrated using field‐based observations misspecify the role of chilling under current climate conditions as a result of statistical artefacts inherent to the way that chilling is parameterised. Our results highlight the limitations of existing modelling approaches and observational data in quantifying how chilling affects the timing of spring leaf emergence and suggest that decreasing chilling arising from climate warming may not constrain near‐future shifts towards earlier leaf emergence in extra‐tropical ecosystems worldwide. 

Leaf area index (LAI) is an important biophysical indicator of forest health that is linearly related to productivity, serving as a key criterion for potential nutrient management. A single equation was produced to model surface reflectance values captured from the Sentinel-2 Multispectral Instrument (MSI) with a robust dataset of field observations of loblolly pine (Pinus taeda L.) LAI collected with a LAI-2200C plant canopy analyzer. Support vector machine (SVM)-supervised classification was used to improve the model fit by removing plots saturated with aberrant radiometric signatures that would not be captured in the association between Sentinel-2 and LAI-2200C. The resulting equation, LAI = 0.310SR − 0.098 (where SR = the simple ratio between near-infrared (NIR) and red bands), displayed good performance ( R 2 = 0.81, RMSE = 0.36) at estimating the LAI for loblolly pine within the analyzed region at a 10 m spatial resolution. Our model incorporated a high number of validation plots (n = 292) spanning from southern Virginia to northern Florida across a range of soil textures (sandy to clayey), drainage classes (well drained to very poorly drained), and site characteristics common to pine forest plantations in the southeastern United States. The training dataset included plot-level treatment metrics—silviculture intensity, genetics, and density—on which sensitivity analysis was performed to inform model fit behavior. Plot density, particularly when there were ≤618 trees per hectare, was shown to impact model performance, causing LAI estimates to be overpredicted (to a maximum of X i + 0.16). Silviculture intensity (competition control and fertilization rates) and genetics did not markedly impact the relationship between SR and LAI. Results indicate that Sentinel-2’s improved spatial resolution and temporal revisit interval provide new opportunities for managers to detect within-stand variance and improve accuracy for LAI estimation over current industry standard models.