scholarly journals Unifying ecosystem resistance, resilience, and recovery from extreme stress into a single statistical framework

2019 ◽  
Author(s):  
Nathan P. Lemoine
Keyword(s):  
Time Series ◽  
Global Change ◽  
Observation Error ◽  
Impulse Response Functions ◽  
Legacy Effects ◽  
Ecosystem Responses ◽  
Ecosystem Sensitivity ◽  
Extreme Stress ◽  
Statistical Framework ◽  
Biotic Change

AbstractNatural communities and ecosystems are currently experiencing unprecedented rates of environmental and biotic change. While gradual shifts in average conditions, such as rising mean air temperatures, can significantly alter ecosystem function, ecologists recently acknowledged that the most damaging consequences of global change will probably emanate from both a higher prevalence and increased intensity of extreme climatic stress events. Given the potential ecological and societal ramifications of more frequent disturbances, it is imperative that we identify which ecosystems are most vulnerable to global change by accurately quantifying ecosystem responses to extreme stress. Unfortunately, the lack of a standardized method for estimating ecosystem sensitivity to drought makes drawing general conclusions difficult. There is a need for estimates of resistance/resilience/legacy effects that are free of observation error, not biased by stochasticity in production or rainfall, and standardizes stress magnitude among many disparate ecosystems relative to normal interannual variability. Here, I propose a statistical framework that estimates all three components of ecosystem response to stress using standardized language (resistance, resilience, recovery, and legacy effects) while resolving all of the issues described above. Coupling autoregressive time series with exogenous predictors (ARX) models with impulse response functions (IRFs) allows researchers to statistically subject all ecosystems to similar levels of stress, estimate legacy effects, and obtain a standardized estimate of ecosystem resistance and resilience to drought free from observation error and stochastic processes inherent in raw data. This method will enable researchers to rigorously compare resistance and resilience among locations using long-term time series, thereby improving our knowledge of ecosystem responses to extreme stress.

Impacts of long-term warming and enhanced nitrogen and sulfur deposition on carbon sink potential in a boreal peatland

2021 ◽  
Author(s):  
Tuula Larmola ◽  
Liisa Maanavilja ◽  
Heikki Kiheri ◽  
Mats Nilsson ◽  
Matthias Peichl
Keyword(s):  
Global Change ◽  
Wet Deposition ◽  
Carbon Sink ◽  
Ecosystem Respiration ◽  
N Deposition ◽  
Ecosystem Responses ◽  
Gross Photosynthesis ◽  
Poor Fen ◽  
The Individual

<p>In order to assess peatland carbon sink potential under multiple global change perturbations, we examined the individual and combined effects of long-term warming and enhanced nitrogen (N) and sulfur (S) deposition on ecosystem CO<sub>2 </sub>exchange at one of the longest-running experiments on peatlands, Degerö Stormyr poor fen, Sweden. The site has been treated with NH<sub>4</sub>NO<sub>3</sub> (15 times ambient annual wet deposition), Na<sub>2</sub>SO<sub>4</sub> (6 times ambient annual wet deposition) and elevated temperature (air +3.6 C) for 23 years. Gross photosynthesis, ecosystem respiration and net CO<sub>2</sub> exchange were measured weekly during June-August using chambers. After 23 years, two of the experimental perturbations: N addition and warming individually reduced net CO<sub>2</sub> uptake potential down to 0.3-0.4 fold compared to the control mainly due to lower gross photosynthesis. Under S only treatment ecosystem CO<sub>2</sub> fluxes were largely unaltered. In contrast, the combination of S and N deposition and warming led to a more pronounced effect and close to zero net CO<sub>2</sub> uptake potential or net C source. Our study emphasizes the value of the long-term multifactor experiments in examining the ecosystem responses: simultaneous perturbations can have nonadditive interactions that cannot be predicted based on individual responses and thus, must be studied in combination when evaluating feedback mechanisms to ecosystem C sink potential under global change.</p>

Education and Literature for Development in Responsibility

2014 ◽  
pp. 526-584 ◽  
Author(s):  
Gilbert Ahamer ◽  
Karl A. Kumpfmüller
Keyword(s):  
Higher Education ◽  
Time Series ◽  
Quality Assurance ◽  
Global Change ◽  
Global Studies ◽  
Bibliometric Data ◽  
Higher Education Management ◽  
Publish Or Perish ◽  
Education Management ◽  
Transnational Higher Education

In order to propose quality assurance for cutting-edge transnational higher education management, this chapter first analyzes data on academic developmental journals while making use of the three widely known literature databases ISI Thomson, Scopus, and Google Scholar; the latter analyzed by the software Publish or Perish (PoP). Time series of data for documents and their citations provide indices; this chapter provides as most helpful indices the ISI impact factor, Scopus SNIP, and PoP AW index. A dozen of the most influential developmental journals are heuristically ranked by taking into account all available indices from all three literature databases. The series of historic bibliometric data since the 1950s shows the dynamics of the global emergence of developmental journals and developmental thought. Secondly, and as a possible template for similar initiatives in global higher education management, this chapter presents the recently established “Global Studies” (GS) Master’s curriculum at Graz University, Austria. Details on this novel curriculum’s targets, modules, courses, and practicals are given. GS embraces six modules and courses from different schools at university. Emphasis is placed on dialogic interdisciplinary understanding and interparadigmatic integration of multiple disciplines and perspectives, when managing education for the purpose of responsibly hedging and managing globalization and socio-economic global change in responsible partnership.

Time series analyses of global change data

1994 ◽  
Vol 83 (1-2) ◽  
pp. 63-68 ◽  
Author(s):  
L.J. Lane ◽  
M.H. Nichols ◽  
H.B. Osborn
Keyword(s):  
Time Series ◽  
Global Change ◽  
Time Series Analyses

Marine Ecosystem Responses to Cenozoic Global Change

Science ◽  
2013 ◽  
Vol 341 (6145) ◽  
pp. 492-498 ◽  
Author(s):  
R. D. Norris ◽  
S. K. Turner ◽  
P. M. Hull ◽  
A. Ridgwell
Keyword(s):  
Global Change ◽  
Marine Ecosystem ◽  
Ecosystem Responses

AnaEE: a European infrastructure for future-oriented experimental ecosystem research

2020 ◽  
Author(s):  
Hans De Boeck ◽  
Simon Reynaert ◽  
Ivan Nijs ◽  
Karel Klem ◽  
Klaus Steenberg Larsen ◽  
...  
Keyword(s):  
Ecosystem Services ◽  
Global Change ◽  
Integrated Approach ◽  
Global Changes ◽  
Precipitation Regime ◽  
Environmental Pressures ◽  
Ecosystem Responses ◽  
Ecosystem Research ◽  
Change Factors ◽  
First Results

<p>Human activities are directly and indirectly generating major environmental pressures on ecosystems worldwide through climate change, pollution and other global changes. Altogether, these changes result in a rapid erosion of biodiversity and a perturbation of ecological and agricultural systems and services, prompting urgent societal questions on how to retain or promote sustainable ecosystem services in a global change context. Understanding the responses of ecosystems to such pressures and perturbations, and developing adaptation strategies critically requires state-of-the-art experimental facilities that are able to simulate multiple global change factors. AnaEE (Analysis and Experimentation on Ecosystems) brings together such facilities in a European-wide infrastructure for experimental research on managed and unmanaged terrestrial and aquatic ecosystems. It assists and integrates four types of national platforms (Open-air, Enclosed, Analytical, and Modelling) and provides support to scientists who wish to engage in research projects using these platforms or the data they generate. These services are organised through the Central Hub and three Service Centres (Technology, Data and Modelling, Interface and Synthesis). This integrated approach improves the quality and availability of data and projections on ecosystem responses to global changes, enabling policy makers and stakeholders to make fact-based  decisions on how to sustainably manage ecosystem services. As an example, we shortly discuss the new open air FATI-platform (UAntwerp) in which ecosystems can be exposed to various combinations of precipitation change and warming, and present first results of a study on the impacts of precipitation regime changes on temperate grassland.</p>

Estimating the Vegetation phenology Time of recovery after a critical perturbation from Landsat time series within the frame of a Bayesian Harmonic model

2020 ◽  
Author(s):  
saverio vicario ◽  
Maria adamo ◽  
Cristina tarantino ◽  
palma blonda
Keyword(s):  
Time Series ◽  
National Park ◽  
Vegetation Index ◽  
Carbon Sink ◽  
Atmospheric Correction ◽  
Harmonic Model ◽  
Statistical Framework ◽  
Functional Attributes ◽  
The Stability ◽  
Index Time Series

<p>In Murgia Alta National Park the repeated fire perturb the stability of the environment and it s capacity to be a carbon sink. Thanks to the Landsat archive we can observed change in phenology t over the two decade (2000-2019). Unfortunately the phenological signal extracted from Landsat time series bear several uncertainties caused by missing data and error in atmospheric correction that makes difficult to reconstruct the trajectory of each pixel. Applying a Bayesian Harmonic model we can obtain not only expected values for the vegetation index time series but also confidence interval both for vegetation index and derived statistics. We took the phenological statistical framework of the Ecological Functional Attributes (EFA) to obtain annual statics and evaluate the time of recovery to obtain EFA with no statistical difference from the pre-perturbation time.</p><p>The results highlighted that only of subset of burned forest recover EFA values after 10 years of critical events. In particular the values of intra year variability tend to be higher due to the different trajectory of young shoots. The burned grassland time of recovery is much shorter given that the vast majority of pixel recover pre-event EFA in less than 4 year.</p>

Hilbert Transformation Impulse Response

2014 ◽  
Vol 19 (4) ◽  
pp. 27-35
Author(s):  
Mariusz Sulima
Keyword(s):  
Time Series ◽  
Impulse Response ◽  
Response Function ◽  
Signal To Noise Ratio ◽  
Impulse Response Function ◽  
Equation System ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Signal To Noise ◽  
Input Time

Abstract This work presents a new DHT impulse response function based on the proposed nonlinear equation system obtained as a result of combining the DHT and IDHT equation systems. In the case of input time series with selected characteristics, the DHT results obtained using this impulse response function are characterised by a higher accuracy compared to the DHT results obtained based on the convolution using other known DHT impulse response functions. The results are also characterised by a higher accuracy than the DHT results obtained using the popular indirect DHT method based on discrete Fourier transform (DFT). Analysis of these example time series with selected characteristics was performed based on the signal-to-noise ratio.

A simple plankton model for the Oregon upwelling ecosystem: Sensitivity and validation against time-series ocean data

2011 ◽  
Vol 222 (6) ◽  
pp. 1222-1235 ◽  
Author(s):  
James J. Ruzicka ◽  
Thomas C. Wainwright ◽  
William T. Peterson
Keyword(s):  
Time Series ◽  
Ecosystem Sensitivity ◽  
Plankton Model

Dynamical Analysis of Time Series by Statistical Tests

1997 ◽  
Vol 07 (12) ◽  
pp. 2629-2652 ◽  
Author(s):  
Gustavo Deco ◽  
Christian Schittenkopf ◽  
Bernd Schürmann
Keyword(s):  
Time Series ◽  
Information Flow ◽  
Statistical Tests ◽  
Statistical Test ◽  
Dynamical Analysis ◽  
Fourier Space ◽  
Statistical Framework ◽  
Temporal Structures ◽  
Analysis Of Time Series

In this review we deal with the application of statistical test techniques for the extraction of structures in time series. Two kinds of questions are answered in this statistical framework: Are there any temporal dependences in the data? and Which kind of dynamics generate these temporal dependences? The first question is known as the problem of predictability and also considers the aspect of stationarity. The second question is deeper in the sense that it deals with the dynamical characterization of the detected temporal structures. Central to our approach is a cumulant-based measure of statistical dependences in Fourier space. The dynamical aspects are studied by means of the information flow. The theory is illustrated by artificial and real-world, stochastic and chaotic examples.

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