Linking theory with empirical data: Improving prediction through mechanistic understanding of lake ecosystem complexity under global change

TY - JOUR

T1 - Linking theory with empirical data

T2 - Improving prediction through mechanistic understanding of lake ecosystem complexity under global change

AU - Adrian, Rita

AU - Gsell, Alena S.

AU - Shatwell, Tom

AU - Scharfenberger, Ulrike

N1 - Data archiving: no NIOO data.

PY - 2023/7/14

Y1 - 2023/7/14

N2 - 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, largescale data has the unique ability to test the general validity of a theory, thus giving valuable feedback to theory.

AB - 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, largescale data has the unique ability to test the general validity of a theory, thus giving valuable feedback to theory.

KW - experimental data

KW - long-term monitoring

KW - scaling

KW - Theory

KW - theory-data synergy

U2 - 10.1127/fal/2022/1457

DO - 10.1127/fal/2022/1457

M3 - Article

AN - SCOPUS:85167983469

SN - 1863-9135

VL - 196

SP - 179

EP - 194

JO - Fundamental and Applied Limnology

JF - Fundamental and Applied Limnology

IS - 3-4

ER -