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Global declines in biodiversity have the potential to affect ecosystem function, and vice versa, in both terrestrial and aquatic ecological realms. While many studies have considered biodiversity-ecosystem function (BEF) relationships at local scales within single realms, there is a critical need for more studies examining BEF linkages among ecological realms, across scales, and across trophic levels. We present a framework linking abiotic attributes, productivity, and biodiversity across terrestrial and inland aquatic realms. We review examples of the major ways that BEF linkages form across realms–cross-system subsidies, ecosystem engineering, and hydrology. We then formulate testable hypotheses about the relative strength of these connections across spatial scales, realms, and trophic levels. While some studies have addressed these hypotheses individually, to holistically understand and predict the impact of biodiversity loss on ecosystem function, researchers need to move beyond local and simplified systems and explicitly investigate cross-realm and trophic interactions and large-scale patterns and processes. Recent advances in computational power, data synthesis, and geographic information science can facilitate studies spanning multiple ecological realms that will lead to a more comprehensive understanding of BEF connections. 

Abstract. The fortedata R package is an open data notebook from the Forest Resilience ThresholdExperiment (FoRTE) – a modeling and manipulative field experiment that teststhe effects of disturbance severity and disturbance type on carbon cyclingdynamics in a temperate forest. Package data consist of measurements ofcarbon pools and fluxes and ancillary measurements to help analyze andinterpret carbon cycling over time. Currently the package includes data andmetadata from the first three FoRTE field seasons, serves as a central,updatable resource for the FoRTE project team, and is intended as a resourcefor external users over the course of the experiment and in perpetuity.Further, it supports all associated FoRTE publications, analyses, andmodeling efforts. This increases efficiency, consistency, compatibility, and productivity while minimizing duplicated effort and error propagation thatcan arise as a function of a large, distributed and collaborative effort.More broadly, fortedata represents an innovative, collaborative way of approachingscience that unites and expedites the delivery of complementary datasets tothe broader scientific community, increasing transparency andreproducibility of taxpayer-funded science. The fortedata package is available via GitHub:https://github.com/FoRTExperiment/fortedata (last access: 19 February 2021), and detaileddocumentation on the access, used, and applications of fortedata are available athttps://fortexperiment.github.io/fortedata/ (last access: 19 February 2021). The first publicrelease, version 1.0.1 is also archived athttps://doi.org/10.5281/zenodo.4399601 (Atkins et al., 2020b). All data products are also available outside of thepackage as .csv files: https://doi.org/10.6084/m9.figshare.13499148.v1 (Atkins et al., 2020c). 

Abstract AimRapid global change is impacting the diversity of tree species and essential ecosystem functions and services of forests. It is therefore critical to understand and predict how the diversity of tree species is spatially distributed within and among forest biomes. Satellite remote sensing platforms have been used for decades to map forest structure and function but are limited in their capacity to monitor change by their relatively coarse spatial resolution and the complexity of scales at which different dimensions of biodiversity are observed in the field. Recently, airborne remote sensing platforms making use of passive high spectral resolution (i.e., hyperspectral) and active lidar data have been operationalized, providing an opportunity to disentangle how biodiversity patterns vary across space and time from field observations to larger scales. Most studies to date have focused on single sites and/or one sensor type; here we ask how multiple sensor types from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) perform across multiple sites in a single biome at the NEON field plot scale (i.e., 40 m × 40 m). LocationEastern USA. Time period2017–2018. Taxa studiedTrees. MethodsWith a fusion of hyperspectral and lidar data from the NEON AOP, we assess the ability of high resolution remotely sensed metrics to measure biodiversity variation across eastern US temperate forests. We examine how taxonomic, functional, and phylogenetic measures of alpha diversity vary spatially and assess to what degree remotely sensed metrics correlate with in situ biodiversity metrics. ResultsModels using estimates of forest function, canopy structure, and topographic diversity performed better than models containing each category alone. Our results show that canopy structural diversity, and not just spectral reflectance, is critical to predicting biodiversity. Main conclusionsWe found that an approach that jointly leverages spectral properties related to leaf and canopy functional traits and forest health, lidar derived estimates of forest structure, fine‐resolution topographic diversity, and careful consideration of biogeographical differences within and among biomes is needed to accurately map biodiversity variation from above.