---
_id: '12217'
abstract:
- lang: eng
text: 'In this study dedicated to Winfried Lampert, we present a suite of case studies
which successfully combined empirical long-term and experimental data with theory
to identify mechanisms driving the non-linear dynamics and critical transitions
in a lake ecosystem under environmental change. The theoretical concepts used
include Probability Theory, Regime Shift Theory, Intraguild Predation Theory,
Metabolic Theory of Ecology, and Early Warning Indicators. Only by linking theory
with data do we gain a mechanistic understanding of the dynamics and long-term
changes observed in the case study sites – allowing for realistic projections
under different climate change scenarios. If this combined approach correctly
identifies the mechanisms governing change in case studies, then upscaling beyond
the case study at hand is likely feasible. Indeed, for most of the presented case
studies, identified mechanisms were confirmed by explicitly linking them to relevant
recent studies based on large-scale global data sets. These include the rise in
lake ice intermittency, shifts in thermal regime and the amplification of lake’s
trophic state in a warmer world. This link also documents the importance and value
of re-using long-term records under the FAIR data principles in international
initiatives. Further, in the context of linking theory and data, large-scale data
has the unique ability to test the general validity of a theory, thus giving valuable
feedback to theory. '
author:
- first_name: Rita
full_name: Adrian, Rita
last_name: Adrian
- first_name: Alena S.
full_name: Gsell, Alena S.
last_name: Gsell
- first_name: Tom
full_name: Shatwell, Tom
id: '86424'
last_name: Shatwell
orcid: 0000-0002-4520-7916
- first_name: Ulrike
full_name: Scharfenberger, Ulrike
last_name: Scharfenberger
citation:
ama: 'Adrian R, Gsell AS, Shatwell T, Scharfenberger U. Linking theory with empirical
data: Improving prediction through mechanistic understanding of lake ecosystem
complexity under global change. Fundamental and applied limnology : formerly:
Archiv für Hydrobiologie . 2022;196(3/4):179-194. doi:10.1127/fal/2022/1457'
apa: 'Adrian, R., Gsell, A. S., Shatwell, T., & Scharfenberger, U. (2022). Linking
theory with empirical data: Improving prediction through mechanistic understanding
of lake ecosystem complexity under global change. Fundamental and Applied Limnology :
Formerly: Archiv Für Hydrobiologie , 196(3/4), 179–194. https://doi.org/10.1127/fal/2022/1457'
bjps: 'Adrian R et al. (2022) Linking Theory with Empirical Data:
Improving Prediction through Mechanistic Understanding of Lake Ecosystem Complexity
under Global Change. Fundamental and applied limnology : formerly: Archiv für
Hydrobiologie 196, 179–194.'
chicago: 'Adrian, Rita, Alena S. Gsell, Tom Shatwell, and Ulrike Scharfenberger.
“Linking Theory with Empirical Data: Improving Prediction through Mechanistic
Understanding of Lake Ecosystem Complexity under Global Change.” Fundamental
and Applied Limnology : Formerly: Archiv Für Hydrobiologie 196, no. 3/4 (2022):
179–94. https://doi.org/10.1127/fal/2022/1457.'
chicago-de: 'Adrian, Rita, Alena S. Gsell, Tom Shatwell und Ulrike Scharfenberger.
2022. Linking theory with empirical data: Improving prediction through mechanistic
understanding of lake ecosystem complexity under global change. Fundamental
and applied limnology : formerly: Archiv für Hydrobiologie 196, Nr. 3/4:
179–194. doi:10.1127/fal/2022/1457,
.'
din1505-2-1: 'Adrian, Rita ; Gsell, Alena S. ; Shatwell,
Tom ; Scharfenberger, Ulrike:
Linking theory with empirical data: Improving prediction through mechanistic understanding
of lake ecosystem complexity under global change. In: Fundamental and applied
limnology : formerly: Archiv für Hydrobiologie Bd. 196. Stuttgart, Schweizerbart
(2022), Nr. 3/4, S. 179–194'
havard: 'R. Adrian, A.S. Gsell, T. Shatwell, U. Scharfenberger, Linking theory with
empirical data: Improving prediction through mechanistic understanding of lake
ecosystem complexity under global change, Fundamental and Applied Limnology :
Formerly: Archiv Für Hydrobiologie . 196 (2022) 179–194.'
ieee: 'R. Adrian, A. S. Gsell, T. Shatwell, and U. Scharfenberger, “Linking theory
with empirical data: Improving prediction through mechanistic understanding of
lake ecosystem complexity under global change,” Fundamental and applied limnology :
formerly: Archiv für Hydrobiologie , vol. 196, no. 3/4, pp. 179–194, 2022,
doi: 10.1127/fal/2022/1457.'
mla: 'Adrian, Rita, et al. “Linking Theory with Empirical Data: Improving Prediction
through Mechanistic Understanding of Lake Ecosystem Complexity under Global Change.”
Fundamental and Applied Limnology : Formerly: Archiv Für Hydrobiologie ,
vol. 196, no. 3/4, 2022, pp. 179–94, https://doi.org/10.1127/fal/2022/1457.'
short: 'R. Adrian, A.S. Gsell, T. Shatwell, U. Scharfenberger, Fundamental and Applied
Limnology : Formerly: Archiv Für Hydrobiologie 196 (2022) 179–194.'
ufg: 'Adrian, Rita u. a.: Linking theory with empirical data: Improving prediction
through mechanistic understanding of lake ecosystem complexity under global change,
in: Fundamental and applied limnology : formerly: Archiv für Hydrobiologie
196 (2022), H. 3/4, S. 179–194.'
van: 'Adrian R, Gsell AS, Shatwell T, Scharfenberger U. Linking theory with empirical
data: Improving prediction through mechanistic understanding of lake ecosystem
complexity under global change. Fundamental and applied limnology : formerly:
Archiv für Hydrobiologie . 2022;196(3/4):179–94.'
date_created: 2024-12-08T19:44:54Z
date_updated: 2024-12-11T13:40:42Z
department:
- _id: DEP8022
doi: 10.1127/fal/2022/1457
extern: '1'
intvolume: ' 196'
issue: 3/4
keyword:
- Theory
- experimental data
- scaling
- long-term monitoring
- theory-data synergy
language:
- iso: eng
page: 179 - 194
place: Stuttgart
publication: 'Fundamental and applied limnology : formerly: Archiv für Hydrobiologie '
publication_identifier:
eissn:
- 2363-7110
issn:
- 1863-9135
publication_status: published
publisher: Schweizerbart
quality_controlled: '1'
status: public
title: 'Linking theory with empirical data: Improving prediction through mechanistic
understanding of lake ecosystem complexity under global change'
type: scientific_journal_article
user_id: '83781'
volume: 196
year: '2022'
...