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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. 

1. The geodiv r package calculates gradient surface metrics from imagery and other gridded datasets to provide continuous measures of landscape heterogeneity for landscape pattern analysis. 2. geodiv is the first open-source, command line toolbox for calculating many gradient surface metrics and easily integrates parallel computing for applications with large images or rasters (e.g. remotely sensed data). All functions may be applied either globally to derive a single metric for an entire image or locally to create a texture image over moving windows of a user-defined extent. 3. We present a comprehensive description of the functions available through geodiv. A supplemental vignette provides an example application of geodiv to the fields of landscape ecology and biogeography. 4. geodiv allows users to easily retrieve estimates of spatial heterogeneity for a variety of purposes, enhancing our understanding of how environmental structure influences ecosystem processes. The package works with any continuous imagery and may be widely applied in many fields where estimates of surface complexity are useful. 

To create a comprehensive view of ecosystem resource use, we integrated parallel resource use efficiency observations into a multiple-resource use efficiency (mRUE) framework using a dynamic factor analysis model. Results from 56 site-years of eddy covariance data and mRUE factors for a site in the US Midwest show temporal dynamics and coherence (using Pearson’s R) among resources are associated with interannual variation in precipitation. Loading factors are derived from mRUE observations and quantify how strongly data are connected to the underlying ecosystem state. Water and light resource use loading factors are coherent at annual timescales (Pearson’s R of 0.86), whereas declining patterns of carbon use efficiency loading factors highlight the ecosystem’s trade-off between carbon uptake and respiration during the growing season. At annual and monthly timescales, influence decreases from ~ 85 to ~ 65% for loading factors for carbon use, while influence of light use loading factors peaks to ~ 60% at growing season timescales. Quantifying variation in ecosystem function provides novel insights into the temporal dynamics of changing importance of multiple resources to ecosystem function. 

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).