Spectral asynchrony as a measure of ecosystem response diversity

Abstract

Species diversity is crucial for promoting ecosystem resilience and stability. Species diversity promotes complementarity in resource use, resulting in a wider range of responses to adverse conditions. This enables populations of different species to fluctuate asynchronously, maintaining ecosystem functioning during extreme climatic events. However, incorporating such mechanisms into conservation decisions and ecosystem modelling requires scalable metrics that represent species diversity, which is currently lacking. To address this, we introduce spectral asynchrony, a metric that captures the spatial heterogeneity of species’ functional responses occurring in distinct pixels. Here, we use remote sensing datasets to investigate the relationship between spectral asynchrony and productivity responses of seasonally dry tropical forests (SDTF) to climatic fluctuations. Our findings reveal that spectral asynchrony is associated with increased resistance and recovery of SDTF productivity in following extreme drought years, as well as greater productivity stability over two decades. Furthermore, higher spectral asynchrony was associated with relatively wetter regions, suggesting that increasing aridity across SDTF could potentially reduce landscape heterogeneity and limit ecosystem resilience to increasing droughts in the future. Spectral asynchrony provides an easily measurable and monitorable metric for assessing ecosystem responses to global changes, reflecting and scaling‐up the effects of species diversity at the local level.

Parcel level temporal variance of remotely sensed spectral reflectance predicts plant diversity

“…The resulting spectral diversity components on a parcel level were then averaged across the three VIs. In doing so, the spectral diversity components were calculated as the spectral variance accounting for temporal and/or spatial variations (supplement: equations (S1)-( S3)): (i) the spatial component of a parcel accounted for the spectral variance between pixels after averaging their temporal variability, (ii) the temporal component calculated the spectral variance of the average parcel value over time and (iii) the spatio-temporal component quantified the spectral variance among pixels over both space and time within a parcel, encompassing the variance not captured by the other two components, thus serving as a measurement of spectral asynchrony (Mazzochini et al 2024). As a metric for spectral diversity that is normalized by the sample size, the spectral variance is less sensitive to the spatial extent of plant communities (Laliberté et al 2020), allowing for its applicability and comparability across parcels of differing sizes.…”