Differential drivers of benthic foraminiferal and molluscan community composition from a multivariate record of early Miocene environmental change

Paleobiology ◽  
10.1666/13019 ◽  
2014 ◽  
Vol 40 (3) ◽  
pp. 398-416 ◽  
Author(s):  
Christina L. Belanger ◽  
Marites Villarosa Garcia
Keyword(s):  
Time Series ◽  
Environmental Change ◽  
Multivariate Time Series ◽  
Environmental Data ◽  
Early Miocene ◽  
Environmental Drivers ◽  
Proxy Data ◽  
Organic Carbon Flux ◽  
Taxonomic Groups ◽  
Environmental Proxies

Climate changes are multivariate in nature, and disentangling the proximal drivers of biotic responses to paleoclimate events requires time series of multiple environmental proxies. We reconstruct a multivariate time series of local environmental change for the early Miocene Newport Member of the Astoria Formation (20.26–18 Ma), using proxies for temperature (δ18O), productivity (δ13C), organic carbon flux (Δδ13C), oxygenation (δ15N), and sedimentary grain size (% mud). Our data suggest increases in productivity and declines in oxygenation on the Oregon shelf during this interval of global warming. We evaluate the association of individual environmental factors, and combinations of factors, with changes in faunal composition observed in benthic foraminiferal and molluscan communities collected from the exact same sediments as the environmental data. The δ15N values are the most parsimonious correlates with major changes in foraminiferal composition, whereas molluscan composition is most closely related to δ13C values, suggesting that different components of the environment are influencing each group. When the proxies that have the best supported relationships with the faunal gradients are removed from the analyses to simulate the absence of those proxy data, significant relationships between the faunal gradients and the remaining environmental proxies can still be found. This suggests that environmental drivers can be incorrectly attributed to faunal changes when key proxy data are missing. Paleoecological studies of biotic response that test multiple environmental drivers for multiple taxonomic groups are powerful tools for identifying the ecological consequences of past warming events and the regional drivers of ecological changes.

Reconstructing missing data sequences in multivariate time series: an application to environmental data

2018 ◽  
Vol 28 (2) ◽  
pp. 359-383
Author(s):  
Maria Lucia Parrella ◽  
Giuseppina Albano ◽  
Michele La Rocca ◽  
Cira Perna
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Multivariate Time Series ◽  
Environmental Data

scholarly journals State tagging for improved earth and environmental data quality assurance

2020 ◽  
Author(s):  
Michael Tso ◽  
Peter Henrys ◽  
Susannah Rennie ◽  
John Watkins
Keyword(s):  
Quality Assurance ◽  
Environmental Change ◽  
Data Science ◽  
Contextual Information ◽  
Environmental Data ◽  
Prediction Intervals ◽  
Environmental Drivers ◽  
System State ◽  
System States

<p>Long-term monitoring data that considers a wide array of environmental variables provides key insights to environmental change because responses of ecosystem functions and services to environmental drivers are inherently long-term and strongly interlinked. To ensure that the data are reliable for analysis and interpretation, they must undergo quality assurance procedures. However, the expected or acceptable range of data values vary greatly as the state of the ecosystem changes. Current quality assurance procedures for environmental data take no consideration of the system state at which each measurement is made, and provide the user with little contextual information on the probable cause for a measurement to be flagged out of range. We propose the use of data science techniques to tag each measurement with an identified system state. The term “state” here is defined loosely and they are identified using k-means clustering, an unsupervised machine learning method. The meaning of the states is open to specialist interpretation. Once the states are identified, state-dependent prediction intervals can be calculated for each observational variable. This approach provides the user with more contextual information to resolve out-of-range flags and derive prediction intervals for observational variables that considers the changes in system states. Our highly flexible and efficient approach is applicable to any point data time series in earth and environmental sciences, regardless of their sub-discipline. Such advantage is particularly relevant when conducting simultaneous analysis of multiple processes and feedbacks, where a wide variety of data is used.</p><p>We illustrate our approach using the moth and butterfly data from the UK Environmental Change Network (ECN), where meteorological variables are used to define system states. A web application is publicly available to allow users to explore the method on various ECN site, while a generic is also available for users to upload their own data files. Our work contributes to the ongoing development of a better data science framework that allows researchers and other stakeholders to find and use the data they need more readily and reliably.</p><p> </p>

Integration of long-term collocated ecological datasets: examples from the UK Environmental Change Network (ECN)

2021 ◽  
Author(s):  
Chak-Hau Michael Tso ◽  
Aaron Lowther ◽  
Don Monteith ◽  
Linsay Flynn Banin ◽  
William Simm ◽  
...  
Keyword(s):  
Time Series ◽  
Cloud Computing ◽  
Environmental Change ◽  
Confidence Intervals ◽  
System Approach ◽  
Data Science ◽  
Environmental Data ◽  
Environmental Sciences ◽  
Environmental Change Network ◽  
The Uk

<p>It is increasingly recognized that a whole-system approach is needed to address many challenging environmental research questions. While the whole-system approach is increasingly adopted by integrating data and models from various sub-systems, the ambition to apply this approach more widely across the environmental sciences requires infrastructure, methodologies, and a culture shift in order to facilitate seamless collaboration and re-deployment of workflows. </p><p>We report our recent progress in addressing some of these issues. We focus our examples here on work related to the UK Environmental Change Network (ECN, an eLTER member network). A transdisciplinary project team comprised of environmental scientists, statisticians, and computer scientists collaborated through the medium of a virtual research platform (DataLabs). Within the DataLabs platform, all data and analysis code are centrally stored via a cloud service and easily accessible via an internet browser from any operating system. Access to cloud computing resources for analyses are also available. More importantly, all users have access to the same versions of the data and software running on the same hardware throughout the collaboration process.</p><p>Such close collaboration allows us to co-develop statistical/data science algorithms that are suitable for a wide range of environmental data. These algorithms are not domain-specific and are generic enough to be used on any environmental datasets. Here we demonstrate how they are used to highlight periods of data with significant change. The first example is a "state tagging" algorithm, where each point in time of a dataset is classified as belonging to an arbitrary state based on clustering of covariates. Subsequently, confidence intervals, based on the statistics of each state, are computed and any data points that lie outside the confidence intervals are flagged for further investigation. A second example is the development of an algorithm for the identification of changepoints across multiple time series comprising different sampling frequencies or misaligned sampling times.  Existing multivariate changepoint algorithms assume that each time series is sampled at the same time (a situation not commonly applicable to environmental data). Our method removes this assumption, and emerged after consultation and collaboration with domain scientists. It has many potential applications, such as confirming whether changepoints occur across sites or across multiple variables within sites, or combinations thereof. In the final example, we show how DataLabs can facilitate the acquisition and application of third-party data to improve understanding of ECN atmospheric deposition chemistry data. Specifically, it allows users to take advantage of cloud computing and storage and collaborate seamlessly; where each collaborator is not required to have independent versions of software and data, saving time and effort. </p><p>The developments reported herein highlight the benefits of collaborative research using DataLabs to advance the integration of data, models, and methods across the environmental sciences. It provides the infrastructure, data, and culture to allow scientists to work more closely together. This in turn allows rapid incorporation of novel data science methods. It also allows the data integration workflows developed to be more readily applied elsewhere, while stakeholders can view and manipulate resultant data products.</p><p> </p>

scholarly journals Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

2021 ◽  
Vol 13 (16) ◽  
pp. 3069
Author(s):  
Yadong Liu ◽  
Junhwan Kim ◽  
David H. Fleisher ◽  
Kwang Soo Kim
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Satellite Data ◽  
Growing Season ◽  
Environmental Data ◽  
Area Index ◽  
Current Season ◽  
Wide Range

Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

Sign in / Sign up

Export Citation Format

Share Document