BII-Implementation: The causes and consequences of plant biodiversity across scales in a rapidly changing world

Integrating remote sensing with ecology and evolution to advance biodiversity conservation

Remote sensing has transformed the monitoring of life on Earth by revealing spatial and temporal dimensions of biological diversity through structural, compositional and functional measurements of ecosystems. Yet, many aspects of Earth's biodiversity are not directly quantified by reflected or emitted photons. Inclusive integration of remote sensing with field-based ecology and evolution is needed to fully understand and preserve Earth's biodiversity. In this Perspective, we argue that multiple data types are necessary for almost all draft targets set by the Convention on Biological Diversity. We examine five key topics in biodiversity science that can be advanced by integrating remote sensing with in situ data collection from field sampling, experiments and laboratory studies to benefit conservation. Lowering the barriers for bringing these approaches together will require global-scale collaboration.

Predicting species distributions and community composition using satellite remote sensing predictors

Biodiversity is rapidly changing due to changes in the climate and human related activities; thus, the accurate predictions of species composition and diversity are critical to developing conservation actions and management strategies. In this paper, using satellite remote sensing products as covariates, we constructed stacked species distribution models (S-SDMs) under a Bayesian framework to build next-generation biodiversity models. Model performance of these models was assessed using oak assemblages distributed across the continental United States obtained from the National Ecological Observatory Network (NEON). This study represents an attempt to evaluate the integrated predictions of biodiversity models-including assemblage diversity and composition-obtained by stacking next-generation SDMs. We found that applying constraints to assemblage predictions, such as using the probability ranking rule, does not improve biodiversity prediction models. Furthermore, we found that independent of the stacking procedure (bS-SDM versus pS-SDM versus cS-SDM), these kinds of next-generation biodiversity models do not accurately recover the observed species composition at the plot level or ecological-community scales (NEON plots are 400 m2). However, these models do return reasonable predictions at macroecological scales, i.e., moderately to highly correct assignments of species identities at the scale of NEON sites (mean area ~ 27 km2). Our results provide insights for advancing the accuracy of prediction of assemblage diversity and composition at different spatial scales globally. An important task for future studies is to evaluate the reliability of combining S-SDMs with direct detection of species using image spectroscopy to build a new generation of biodiversity models that accurately predict and monitor ecological assemblages through time and space.

Open science in practice: 300 published research ideas and outcomes illustrate how RIO Journal facilitates engagement with the research process

Since Research Ideas and Outcomes was launched in late 2015, it has stimulated experimentation around the publication of and engagement with research processes, especially those with a strong open science component. Here, we zoom in on the first 300 RIO articles that have been published and elucidate how they relate to the different stages and variants of the research cycle, how they help address societal challenges and what forms of engagement have evolved around these resources, most of which have a nature and scope that would prevent them from entering the scholarly record via more traditional journals. Building on these observations, we describe some changes we recently introduced in the policies and peer review process at RIO to further facilitate engagement with the research process, including the establishment of an article collections feature that allows us to bring together research ideas and outcomes from within one research cycle or across multiple ones, irrespective of where they have been published.