Scaling up functional traits for ecosystem services with remote sensing: concepts and methods

Abstract

Ecosystem service‐based management requires an accurate understanding of how human modification influences ecosystem processes and these relationships are most accurate when based on functional traits. Although trait variation is typically sampled at local scales, remote sensing methods can facilitate scaling up trait variation to regional scales needed for ecosystem service management. We review concepts and methods for scaling up plant and animal functional traits from local to regional spatial scales with the goal of assessing impacts of human modification on ecosystem processes and services. We focus our objectives on considerations and approaches for (1) conducting local plot‐level sampling of trait variation and (2) scaling up trait variation to regional spatial scales using remotely sensed data. We show that sampling methods for scaling up traits need to account for the modification of trait variation due to land cover change and species introductions. Sampling intraspecific variation, stratification by land cover type or landscape context, or inference of traits from published sources may be necessary depending on the traits of interest. Passive and active remote sensing are useful for mapping plant phenological, chemical, and structural traits. Combining these methods can significantly improve their capacity for mapping plant trait variation. These methods can also be used to map landscape and vegetation structure in order to infer animal trait variation. Due to high context dependency, relationships between trait variation and remotely sensed data are not directly transferable across regions. We end our review with a brief synthesis of issues to consider and outlook for the development of these approaches. Research that relates typical functional trait metrics, such as the community‐weighted mean, with remote sensing data and that relates variation in traits that cannot be remotely sensed to other proxies is needed. Our review narrows the gap between functional trait and remote sensing methods for ecosystem service management.

Trait‐based approaches to analyze links between the drivers of change and ecosystem services: Synthesizing existing evidence and future challenges

“…Most of the studies were conducted in Europe, which is coherent with the extended application of the ecosystem services approach in this region (Seppelt, Dormann, Eppink, Lautenbach, & Schmidt, 2011). This geographical bias is particularly relevant given that the influence of traits in ecosystem functioning and the provision of ecosystem services are highly context dependent (Abelleira‐Martínez et al., 2016; Hooper et al., 2005; Srivastava & Vellend, 2005). Consequently, this bias largely hinders the global application of trait‐based approaches at present.…”

“…text dependent(Abelleira-Martínez et al, 2016;Hooper et al, 2005;Srivastava & Vellend, 2005). Consequently, this bias largely hinders the global application of trait-based approaches at present.…”

Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches

“…"Ecologists are increasingly looking at traits-rather than species-to measure the health of ecosystems" [115]. Furthermore can traits and their variations be monitored on all spatial-temporal scales [116]. As a result, FH indicators can be developed for all spatial and temporal scales.…”

Structural diversity as a predictor of ecosystem function

“…Here, we focus on the components of the structural diversity of ecosystems that can be measured rapidly with remote sensing tools over large spatial scales compared to traditional trait-based metrics that are time consuming to measure on a small spatial scale. Overall, the advent of new remote sensing technologies and the computational capacity to handle big datasets such as LiDAR present new opportunities to test how previously unexplored metrics of structural diversity influence ecosystem functions across scales (Martinez et al 2016, Cordell et al 2017, Tello et al 2018.…”