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Article ; Online: Functional thresholds alter the relationship of plant resistance and recovery to drought.

Ingrisch, Johannes / Umlauf, Nikolaus / Bahn, Michael

2023 Volume 104, Issue 2, Page(s) e3907

Abstract: The ecological consequences of future droughts are difficult to predict due to a limited understanding of the nonlinear responses of plants to increasing drought intensity, which can change abruptly when critical thresholds of drought intensity are ... ...

Abstract	The ecological consequences of future droughts are difficult to predict due to a limited understanding of the nonlinear responses of plants to increasing drought intensity, which can change abruptly when critical thresholds of drought intensity are crossed. Drought responses are composed of resistance and postdrought recovery. Although it is well established that higher drought intensity increases the impact and, thus, reduces plant resistance, less is known about how drought intensity affects recovery and how resistance and recovery are related. In this study, we tested the hypothesis that resistance, recovery, and their relationship change abruptly upon crossing critical thresholds of drought intensity. We exposed mesocosms of two monospecific stands of the common grassland species Dactylis glomerata and Plantago lanceolata to a large gradient of drought intensity and quantified the resistance and recovery of multiple measures of plant productivity, including gross-primary productivity, vegetative height, Normalized Difference Vegetation Index, and aboveground biomass production. Drought intensity had nonlinear and contrasting effects on plant productivity during drought and recovery, which differed between the two species. Increasing drought intensity decreased the resistance of plant productivity and caused rapid compensatory growth during postdrought recovery, the degree of which was highly dependent on drought intensity. Across multiple response parameters two thresholds of drought intensity emerged, upon which we observed abrupt changes in plant resistance and recovery, as well as their relationship. We conclude that across gradients of drought intensity resistance and recovery are tightly coupled and that both the magnitude and the direction of drought effects on resistance and recovery can change abruptly upon specific thresholds of stress intensity. These findings highlight the urgent need to account for nonlinear responses of resistance and recovery to drought intensity as critical drivers of productivity in a changing climate.
MeSH term(s)	Biomass ; Climate Change ; Droughts ; Ecosystem ; Grassland ; Plants
Language	English
Publishing date	2023-01-03
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2010140-5
ISSN	1939-9170 ; 0012-9658
ISSN (online)	1939-9170
ISSN	0012-9658
DOI	10.1002/ecy.3907
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Article ; Online: Functional thresholds alter the relationship of plant resistance and recovery to drought

Ingrisch, Johannes / Umlauf, Nikolaus / Bahn, Michael

Ecology. 2023 Feb., v. 104, no. 2 p.e3907-

2023

Abstract	The ecological consequences of future droughts are difficult to predict due to a limited understanding of the nonlinear responses of plants to increasing drought intensity, which can change abruptly when critical thresholds of drought intensity are crossed. Drought responses are composed of resistance and postdrought recovery. Although it is well established that higher drought intensity increases the impact and, thus, reduces plant resistance, less is known about how drought intensity affects recovery and how resistance and recovery are related. In this study, we tested the hypothesis that resistance, recovery, and their relationship change abruptly upon crossing critical thresholds of drought intensity. We exposed mesocosms of two monospecific stands of the common grassland species Dactylis glomerata and Plantago lanceolata to a large gradient of drought intensity and quantified the resistance and recovery of multiple measures of plant productivity, including gross‐primary productivity, vegetative height, Normalized Difference Vegetation Index, and aboveground biomass production. Drought intensity had nonlinear and contrasting effects on plant productivity during drought and recovery, which differed between the two species. Increasing drought intensity decreased the resistance of plant productivity and caused rapid compensatory growth during postdrought recovery, the degree of which was highly dependent on drought intensity. Across multiple response parameters two thresholds of drought intensity emerged, upon which we observed abrupt changes in plant resistance and recovery, as well as their relationship. We conclude that across gradients of drought intensity resistance and recovery are tightly coupled and that both the magnitude and the direction of drought effects on resistance and recovery can change abruptly upon specific thresholds of stress intensity. These findings highlight the urgent need to account for nonlinear responses of resistance and recovery to drought intensity as critical drivers of productivity in a changing climate.
Keywords	Dactylis glomerata ; Plantago lanceolata ; aboveground biomass ; biomass production ; climate ; compensatory growth ; drought ; ecology ; grasslands ; vegetation index
Language	English
Dates of publication	2023-02
Publishing place	John Wiley & Sons, Inc.
Document type	Article ; Online
Note	JOURNAL ARTICLE
ZDB-ID	1797-8
ISSN	0012-9658
ISSN	0012-9658
DOI	10.1002/ecy.3907
Database	NAL-Catalogue (AGRICOLA)

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Article: Amplification of annual and diurnal cycles of alpine lightning.

Simon, Thorsten / Mayr, Georg J / Morgenstern, Deborah / Umlauf, Nikolaus / Zeileis, Achim

Climate dynamics

2023 Volume 61, Issue 9-10, Page(s) 4125–4137

Abstract: The response of lightning to a changing climate is not fully understood. Historic trends of proxies known for fostering convective environments suggest an increase of lightning over large parts of Europe. Since lightning results from the interaction of ... ...

Abstract	The response of lightning to a changing climate is not fully understood. Historic trends of proxies known for fostering convective environments suggest an increase of lightning over large parts of Europe. Since lightning results from the interaction of processes on many scales, as many of these processes as possible must be considered for a comprehensive answer. Recent achievements of decade-long seamless lightning measurements and hourly reanalyses of atmospheric conditions including cloud micro-physics combined with flexible regression techniques have made a reliable reconstruction of cloud-to-ground lightning down to its seasonally varying diurnal cycle feasible. The European Eastern Alps and their surroundings are chosen as reconstruction region since this domain includes a large variety of land-cover, topographical and atmospheric circulation conditions. The most intense changes over the four decades from 1980 to 2019 occurred over the high Alps where lightning activity doubled in the 2010 s compared to the 1980 s. There, the lightning season reaches a higher maximum and starts one month earlier. Diurnally, the peak is up to 50% stronger with more lightning strikes in the afternoon and evening hours. Signals along the southern and northern alpine rim are similar but weaker whereas the flatlands surrounding the Alps have no significant trend.
Language	English
Publishing date	2023-04-27
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1471747-5
ISSN	1432-0894 ; 0930-7575
ISSN (online)	1432-0894
ISSN	0930-7575
DOI	10.1007/s00382-023-06786-8
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Article: LASSO-type penalization in the framework of generalized additive models for location, scale and shape

Groll, Andreas / Hambuckers, Julien / Kneib, Thomas / Umlauf, Nikolaus

Computational statistics & data analysis. 2019 Dec., v. 140

2019

Abstract: For numerous applications, it is of interest to provide full probabilistic forecasts, which are able to assign plausibilities to each predicted outcome. Therefore, attention is shifting constantly from conditional mean models to probabilistic ... ...

Abstract	For numerous applications, it is of interest to provide full probabilistic forecasts, which are able to assign plausibilities to each predicted outcome. Therefore, attention is shifting constantly from conditional mean models to probabilistic distributional models capturing location, scale, shape and other aspects of the response distribution. One of the most established models for distributional regression is the generalized additive model for location, scale and shape (GAMLSS). In high-dimensional data set-ups, classical fitting procedures for GAMLSS often become rather unstable and methods for variable selection are desirable. Therefore, a regularization approach for high-dimensional data set-ups in the framework of GAMLSS is proposed. It is designed for linear covariate effects and is based on L1-type penalties. The following three penalization options are provided: the conventional least absolute shrinkage and selection operator (LASSO) for metric covariates, and both group and fused LASSO for categorical predictors. The methods are investigated both for simulated data and for two real data examples, namely Munich rent data and data on extreme operational losses from the Italian bank UniCredit.
Keywords	banking ; data analysis ; economic forecasting ; least squares ; probabilistic models ; probability distribution ; simulation models
Language	English
Dates of publication	2019-12
Size	p. 59-73.
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	1478763-5
ISSN	0167-9473
ISSN	0167-9473
DOI	10.1016/j.csda.2019.06.005
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Nonlinear association structures in flexible Bayesian additive joint models.

Köhler, Meike / Umlauf, Nikolaus / Greven, Sonja

Statistics in medicine

2018 Volume 37, Issue 30, Page(s) 4771–4788

Abstract: Joint models of longitudinal and survival data have become an important tool for modeling associations between longitudinal biomarkers and event processes. The association between marker and log hazard is assumed to be linear in existing shared random ... ...

Abstract	Joint models of longitudinal and survival data have become an important tool for modeling associations between longitudinal biomarkers and event processes. The association between marker and log hazard is assumed to be linear in existing shared random effects models, with this assumption usually remaining unchecked. We present an extended framework of flexible additive joint models that allows the estimation of nonlinear covariate specific associations by making use of Bayesian P-splines. Our joint models are estimated in a Bayesian framework using structured additive predictors for all model components, allowing for great flexibility in the specification of smooth nonlinear, time-varying, and random effects terms for longitudinal submodel, survival submodel, and their association. The ability to capture truly linear and nonlinear associations is assessed in simulations and illustrated on the widely studied biomedical data on the rare fatal liver disease primary biliary cirrhosis. All methods are implemented in the R package bamlss to facilitate the application of this flexible joint model in practice.
MeSH term(s)	Bayes Theorem ; Biomarkers ; Data Interpretation, Statistical ; Humans ; Likelihood Functions ; Linear Models ; Longitudinal Studies ; Models, Statistical ; Nonlinear Dynamics ; Survival Analysis ; Time Factors
Chemical Substances	Biomarkers
Language	English
Publishing date	2018-10-10
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	843037-8
ISSN	1097-0258 ; 0277-6715
ISSN (online)	1097-0258
ISSN	0277-6715
DOI	10.1002/sim.7967
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Article ; Online: Climatic legacy effects on the drought response of the Amazon rainforest.

Van Passel, Johanna / de Keersmaecker, Wanda / Bernardino, Paulo N / Jing, Xin / Umlauf, Nikolaus / Van Meerbeek, Koenraad / Somers, Ben

Global change biology

2022 Volume 28, Issue 19, Page(s) 5808–5819

Abstract: Extreme precipitation and drought events are predicted to become more intense and more frequent over the Amazon rainforest. Because changes in forest dynamics could prompt strong feedback loops to the global climate, it is of crucial importance to gain ... ...

Abstract	Extreme precipitation and drought events are predicted to become more intense and more frequent over the Amazon rainforest. Because changes in forest dynamics could prompt strong feedback loops to the global climate, it is of crucial importance to gain insight into the response of tropical forests to these recurring extreme climatic events. Here, we evaluated the Amazon forest stability (resistance and resilience) to drought in the context of past dry and wet climatic events using MODIS EVI satellite imagery and cumulative water deficit anomalies. We observed large spatial differences in the occurrence of extreme climatic events from 1980 to 2019, with an increase in drought frequency in the central and northern Amazon and drought intensity in the southern Amazon basin. An increasing trend in the occurrence of wet events was found in the western, southern, and eastern Amazon. Furthermore, we found significant legacy effects of previous climatic events on the forest drought response. An extreme drought closely preceding another drought decreased forest resilience, whereas the occurrence of a recent drier-than-usual event also decreased the forest resistance to later droughts. Both wetter-than-usual and extreme wet events preceding an extreme drought increased the resistance of the forest, and with similar effects sizes as dry events, indicating that wet and dry events have similarly sized legacy effects on the drought response of tropical forests. Our results indicate that the predicted increase in drought frequency and intensity can have negative consequences for the functioning of the Amazon forest. However, more frequent wet periods in combination with these droughts could counteract their negative impact. Finally, we also found that more stable forests according to the alternative stable states theory are also more resistant and resilient to individual droughts, showing a positive relationship between the equilibrium and non-equilibrium stability dynamics.
MeSH term(s)	Climate Change ; Droughts ; Forests ; Rainforest ; Satellite Imagery ; Trees/physiology ; Water
Chemical Substances	Water (059QF0KO0R)
Language	English
Publishing date	2022-07-17
Publishing country	England
Document type	Journal Article
ZDB-ID	1281439-8
ISSN	1365-2486 ; 1354-1013
ISSN (online)	1365-2486
ISSN	1354-1013
DOI	10.1111/gcb.16336
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Climatic legacy effects on the drought response of the Amazon rainforest

Van Passel, Johanna / de Keersmaecker, Wanda / Bernardino, Paulo N. / Jing, Xin / Umlauf, Nikolaus / Van Meerbeek, Koenraad / Somers, Ben

Global change biology. 2022 Oct., v. 28, no. 19

2022

Abstract	Extreme precipitation and drought events are predicted to become more intense and more frequent over the Amazon rainforest. Because changes in forest dynamics could prompt strong feedback loops to the global climate, it is of crucial importance to gain insight into the response of tropical forests to these recurring extreme climatic events. Here, we evaluated the Amazon forest stability (resistance and resilience) to drought in the context of past dry and wet climatic events using MODIS EVI satellite imagery and cumulative water deficit anomalies. We observed large spatial differences in the occurrence of extreme climatic events from 1980 to 2019, with an increase in drought frequency in the central and northern Amazon and drought intensity in the southern Amazon basin. An increasing trend in the occurrence of wet events was found in the western, southern, and eastern Amazon. Furthermore, we found significant legacy effects of previous climatic events on the forest drought response. An extreme drought closely preceding another drought decreased forest resilience, whereas the occurrence of a recent drier‐than‐usual event also decreased the forest resistance to later droughts. Both wetter‐than‐usual and extreme wet events preceding an extreme drought increased the resistance of the forest, and with similar effects sizes as dry events, indicating that wet and dry events have similarly sized legacy effects on the drought response of tropical forests. Our results indicate that the predicted increase in drought frequency and intensity can have negative consequences for the functioning of the Amazon forest. However, more frequent wet periods in combination with these droughts could counteract their negative impact. Finally, we also found that more stable forests according to the alternative stable states theory are also more resistant and resilient to individual droughts, showing a positive relationship between the equilibrium and non‐equilibrium stability dynamics.
Keywords	Biological Sciences ; basins ; climate ; drought ; forest dynamics ; forests ; global change ; remote sensing ; Amazonia
Language	English
Dates of publication	2022-10
Size	p. 5808-5819.
Publishing place	John Wiley & Sons, Ltd
Document type	Article
Note	JOURNAL ARTICLE
ZDB-ID	1281439-8
ISSN	1365-2486 ; 1354-1013
ISSN (online)	1365-2486
ISSN	1354-1013
DOI	10.1111/gcb.16336
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Bayesian Gaussian distributional regression models for more efficient norm estimation.

Voncken, Lieke / Kneib, Thomas / Albers, Casper J / Umlauf, Nikolaus / Timmerman, Marieke E

The British journal of mathematical and statistical psychology

2020 Volume 74, Issue 1, Page(s) 99–117

Abstract: A test score on a psychological test is usually expressed as a normed score, representing its position relative to test scores in a reference population. These typically depend on predictor(s) such as age. The test score distribution conditional on ... ...

Abstract	A test score on a psychological test is usually expressed as a normed score, representing its position relative to test scores in a reference population. These typically depend on predictor(s) such as age. The test score distribution conditional on predictors is estimated using regression, which may need large normative samples to estimate the relationships between the predictor(s) and the distribution characteristics properly. In this study, we examine to what extent this burden can be alleviated by using prior information in the estimation of new norms with Bayesian Gaussian distributional regression. In a simulation study, we investigate to what extent this norm estimation is more efficient and how robust it is to prior model deviations. We varied the prior type, prior misspecification and sample size. In our simulated conditions, using a fixed effects prior resulted in more efficient norm estimation than a weakly informative prior as long as the prior misspecification was not age dependent. With the proposed method and reasonable prior information, the same norm precision can be achieved with a smaller normative sample, at least in empirical problems similar to our simulated conditions. This may help test developers to achieve cost-efficient high-quality norms. The method is illustrated using empirical normative data from the IDS-2 intelligence test.
MeSH term(s)	Bayes Theorem ; Computer Simulation ; Normal Distribution ; Psychological Tests ; Sample Size
Language	English
Publishing date	2020-07-20
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	218109-5
ISSN	2044-8317 ; 0007-1102
ISSN (online)	2044-8317
ISSN	0007-1102
DOI	10.1111/bmsp.12206
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: BAMLSS

Umlauf, Nikolaus / Klein, Nadja / Zeileis, Achim

Bayesian additive models for location, scale and shape (and beyond)

(Working papers in economics and statistics ; 2017, 04)

2017

Abstract: Bayesian analysis provides a convenient setting for the estimation of complex generalized additive regression models (GAMs). Since computational power has tremendously increased in the past decade it is now possible to tackle complicated inferential ... ...

Author's details	Nikolaus Umlauf, Nadja Klein, Achim Zeileis
Series title	Working papers in economics and statistics ; 2017, 04
Abstract	Bayesian analysis provides a convenient setting for the estimation of complex generalized additive regression models (GAMs). Since computational power has tremendously increased in the past decade it is now possible to tackle complicated inferential problems, e.g., with Markov chain Monte Carlo simulation, on virtually any modern computer. This is one of the reasons why Bayesian methods have become increasingly popular, leading to a number of highly specialized and optimized estimation engines and with attention shifting from conditional mean models to probabilistic distributional models capturing location, scale, shape (and other aspects) of the response distribution. In order to embed many different approaches suggested in literature and software, a unified modeling architecture for distributional GAMs is established that exploits the general structure of these models and encompasses many different response distributions, estimation techniques (posterior mode or posterior mean), and model terms (fixed, random, smooth, spatial, . . . ). It is shown that within this framework implementing algorithms for complex regression problems, as well as the integration of already existing software, is relatively straightforward. The usefulness is emphasized with two complex and computationally demanding application case studies: a large daily precipitation climatology based on more than 1.2 million observations from more than 50 meteorological stations, as well as a Cox model for continuous time with space-time interactions on a data set with over five thousand "individuals".
Keywords	GAMLSS ; distributional regression ; MCMC ; BUGS ; R ; software
Language	English
Dates of publication	2017-9999
Size	1 Online-Ressource (circa 42 Seiten), Illustrationen
Publisher	Research platform Empirical and Experimental Economics, University of Innsbruck
Publishing place	Innsbruck, Austria
Document type	Book ; Online
Note	Die Zählung sollte lauten: 2017, 05
Database	ECONomics Information System

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Article ; Online: Pedestrian exposure to black carbon and PM

Alas, Honey Dawn / Stöcker, Almond / Umlauf, Nikolaus / Senaweera, Oshada / Pfeifer, Sascha / Greven, Sonja / Wiedensohler, Alfred

Journal of exposure science & environmental epidemiology

2021 Volume 32, Issue 4, Page(s) 604–614

Abstract: Background: Data from extensive mobile measurements (MM) of air pollutants provide spatially resolved information on pedestrians' exposure to particulate matter (black carbon (BC) and PM: Objective: We present a distributional regression model in a ... ...

Abstract	Background: Data from extensive mobile measurements (MM) of air pollutants provide spatially resolved information on pedestrians' exposure to particulate matter (black carbon (BC) and PM Objective: We present a distributional regression model in a Bayesian framework that estimates the effects of spatiotemporal factors on the pollutant concentrations influencing pedestrian exposure. Methods: We modeled the mean and variance of the pollutant concentrations obtained from MM in two cities and extended commonly used lognormal models with a lognormal-normal convolution (logNNC) extension for BC to account for instrument measurement error. Results: The logNNC extension significantly improved the BC model. From these model results, we found local sources and, hence, local mitigation efforts to improve air quality, have more impact on the ambient levels of BC mass concentrations than on the regulated PM Significance: Firstly, this model (logNNC in bamlss package available in R) could be used for the statistical analysis of MM data from various study areas and pollutants with the potential for predicting pollutant concentrations in urban areas. Secondly, with respect to pedestrian exposure, it is crucial for BC mass concentration to be monitored and regulated in areas dominated by traffic-related air pollution.
MeSH term(s)	Air Pollutants/analysis ; Air Pollution/analysis ; Bayes Theorem ; Carbon/analysis ; Environmental Exposure/analysis ; Environmental Monitoring/methods ; Humans ; Particulate Matter/analysis ; Pedestrians ; Soot/analysis ; Vehicle Emissions/analysis
Chemical Substances	Air Pollutants ; Particulate Matter ; Soot ; Vehicle Emissions ; Carbon (7440-44-0)
Language	English
Publishing date	2021-08-28
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2218551-3
ISSN	1559-064X ; 1559-0631
ISSN (online)	1559-064X
ISSN	1559-0631
DOI	10.1038/s41370-021-00379-5
Database	MEDical Literature Analysis and Retrieval System OnLINE

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