No abstract

The Ciénaga Grande de Santa Marta (CGSM), Colombia is possibly the wetland that has experienced the largest mangrove mortality on record due to modification of hydrologic connectivity and consequent hypersaline conditions. We used hydroclimatic, salinity and mangrove basal area data collected in five stations from 1993 to 2015 to study the relation between ongoing mangrove recovery, changes in salinity in the wetland and hydroclimatic changes in precipitation, potential evapotranspiration and freshwater inputs. We found that until 2015, the mangrove ecosystems in CGSM are in general terms in a path of recovery due to the combined effect of favorable hydroclimatic conditions and management operations to increase freshwater inputs into the wetland. We observed in three stations that the annual growth of mangrove basal area increased as pore water salinity decreased. Regarding surface water salinity, El Niño/Southern Oscillation explained most of the inter-annual variability in the wet season by regulating freshwater and in the dry season by regulating potential evaporation from the wetland. However, persistent channel reopening appeared to be the cause for the largest salinity decreases, whereas lack of persistent dredging slowed recovery in other areas. The monitoring of the mangrove-salinity-hydroclimate system must continue in order to increase its understanding and to avoid more recurring episodes of mangrove mortality.

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.