Search

Theory predicts that rising CO2 increases global photosynthesis, a process known as CO2 fertilization, and that this is responsible for much of the current terrestrial carbon sink. The estimated magnitude of the historic CO2 fertilization, however, differs by an order of magnitude between long-term proxies, remote sensing-based estimates and terrestrial biosphere models. Here we constrain the likely historic effect of CO2 on global photosynthesis by combining terrestrial biosphere models, ecological optimality theory, remote sensing approaches and an emergent constraint based on global carbon budget estimates. Our analysis suggests that CO2 fertilization increased global annual terrestrial photosynthesis by 13.5 ± 3.5% or 15.9 ± 2.9 PgC (mean ± s.d.) between 1981 and 2020. Our results help resolve conflicting estimates of the historic sensitivity of global terrestrial photosynthesis to CO2 and highlight the large impact anthropogenic emissions have had on ecosystems worldwide.

Leaf-level gas exchange data support the mechanistic understanding of plant fluxes of carbon and water. These fluxes inform our understanding of ecosystem function, are an important constraint on parameterization of terrestrial biosphere models, are necessary to understand the response of plants to global environmental change, and are integral to efforts to improve crop production. Collection of these data using gas analyzers can be both technically challenging and time consuming, and individual studies generally focus on a small range of species, restricted time periods, or limited geographic regions. The high value of these data is exemplified by the many publications that reuse and synthesize gas exchange data, however the lack of metadata and data reporting conventions make full and efficient use of these data difficult. Here we propose a reporting format for leaf-level gas exchange data and metadata to provide guidance to data contributors on how to store data in repositories to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format specifies data table variable naming and unit conventions, as well as metadata characterizing experimental conditions and protocols. For common data types that were the focus of this initial version of the reporting format, i.e., survey measurements, dark respiration, carbon dioxide and light response curves, and parameters derived from those measurements, we took a further step of defining required additional data and metadata that would maximize the potential reuse of those data types. To aid data contributors and the development of data ingest tools by data repositories we provided a translation table comparing the outputs of common gas exchange instruments. Extensive consultation with data collectors, data users, instrument manufacturers, and data scientists was undertaken in order to ensure that the reporting format met community needs. The reporting format presented here is intended to form a foundation for future development that will incorporate additional data types and variables as gas exchange systems and measurement approaches advance in the future. The reporting format is published in the U.S. Department of Energy's ESS-DIVE data repository, with documentation and future development efforts being maintained in a version control system.

Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth system model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a GPP (photosynthesis per unit ground area) model, the P-model, that combines the Farquhar-von Caemmerer-Berry model for C3 photosynthesis with an optimality principle for the carbon assimilation-Transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C3 vegetation type. The model builds on the theory developed in Prentice et al. (2014) and Wang et al. (2017a) and is extended to include low temperature effects on the intrinsic quantum yield and an empirical soil moisture stress factor. The model is forced with site-level data of the fraction of absorbed photosynthetically active radiation (fAPAR) and meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs, the R2 is reduced to 0.70 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R2 for the P-model-predicted LUE is 0.32 (means by site) and 0.48 (means by vegetation type). Applying this model for global-scale simulations yields a total global GPP of 106-122 Pg C yr-1 (mean of 2001-2011), depending on the fAPAR forcing data. The P-model provides a simple but powerful method for predicting-rather than prescribing-light use efficiency and simulating terrestrial photosynthesis across a wide range of conditions. The model is available as an R package (rpmodel).

There is an increasing demand within the humanities and social sciences to use computers to analyze material culture and discover patterns of historical and anthropological significance. Using southern Levantine Iron Age (ca. 1200-500 BCE) ceramics as a test case, the Pottery Informatics Query Database (PIQD) provides a novel solution for constructing regional ceramic typologies. Beyond digitally archiving 2D/3D-scanned ceramics, the PIQD encodes ceramic profiles as mathematical representations. This method of digital preservation enables rapid queries to be conducted in a mathematically grounded approach. In this sense, the queries are similar to online Basic Local Alignment Search Tool searches developed in the field of genetics by rapidly associating large quantities of digital vessel profiles to each other based on similar morphological traits. The PIQD is an open-source online tool that enables scholars and students to test humanities-related hypotheses against ceramic data in ways that conventional publications or other databases cannot provide. Regional spatial patterning of the ceramic data is delivered over a Google Earth-based user interface. In this paper, we present the PIQD as an objective method for developing a comprehensive ceramic typology of an entire region of archaeological study and provide an arena to conduct novel scientific research. We then demonstrate through a case study its analytical capabilities to handle large datasets of 3D scans and digitized 2D ceramic profiles and generate cultural inferences with the ceramic assemblages of the Iron Age II "Edomite" region located in modern southern Jordan. PIQD adds an important methodological tool to the post-excavation cyber-archaeology tool box. © 2012 Springer Science+Business Media, LLC.

Cyber-archaeology represents the marriage of archaeology, computer science, engineering, and the natural sciences with the aim of taking advantage of constantly evolving technologies for digital data capture, curation, analyses and dissemination. Digital data collection tools are perhaps the most rapidly changing arenas of development in cyber-archaeology and are becoming affordable tools for every archaeologist. In this paper, we examine two users’ approaches to produce point cloud models of archaeological sites using structure from motion (SfM) photography. The experiment took place at the Fountain of Peirene in ancient Corinth, Greece. Their implementation of the technology and their results are compared to highlight the very important role the photo-shooting session can play in the final outcome of the SfM reconstruction. We correlate the users’ approaches to the applied algorithms’ robust features and known limitations to provide a technical explanation of how archaeologists can significantly improve their success in SfM. As new algorithms and software emerge making SfM a common tool in archaeological documentation the methodology presented in this paper will enable archaeologists to meet the high demand for digital documentation on a global scale.

In late 2013, a joint archaeological and computer vision project was initiated to digitally capture the archaeological remains in the al-Ula valley, Saudi Arabia. The goal of our team of archeologists and computer scientists is to integrate 3D scanning technologies to produce 3D reconstructions of archaeological sites. Unmanned Aerial Vehicles (UAVs) serve as the vehicle which makes this scanning possible. UAVs allow the acquisition of 3D data as easily from the air as from the ground. This project focuses on the recent excavations carried out in ancient Dedan by King Saud University and the country's conservation of the Lihyanite "lion tombs" carved into the ancient city's cliff faces. Over the next several years this site will be used as a test bed to validate the potential of this emerging technology for rapid cultural heritage documentation. We additionally scanned several areas in Mada'in Saleh, an ancient Nabatean city filled with monumental carved sandstone tomb facades, rivaled only by the capital of the Nabatean empire: Petra.

Archaeological excavations are the last stronghold of analog recording in this era of digital archaeology. Around the world perhaps at this very moment excavations are occurring where artifacts, architecture, stratigraphic layers, and other archaeological datasets are recorded using only the paper and pen. Every evening after an excavation, supervisors and their volunteers huddle over their illuminated graph paper to convert their written notes and drawings into daily top plans. It is these paper top plans, journal notes, and overstuffed binders filled with printed spreadsheets that much later are meticulously entered into a computer. At this point, long after the season of excavation has been wrapped up, that the drudgery of data entry begins, which with large excavations can take months to digitize, process, and produce data that can be digitally analyzed. As multiple seasons of excavations compound, the delay between field excavation, ability to analyze and to publish increases exponentially.