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General statistical scaling laws for stability in ecological systems
Author(s) -
Clark Adam Thomas,
Arnoldi JeanFrancois,
Zelnik Yuval R.,
Barabas György,
Hodapp Dorothee,
Karakoç Canan,
König Sara,
Radchuk Viktoriia,
Donohue Ian,
Huth Andreas,
Jacquet Claire,
Mazancourt Claire,
Mentges Andrea,
Nothaaß Dorian,
Shoemaker Lauren G.,
Taubert Franziska,
Wiegand Thorsten,
Wang Shaopeng,
Chase Jonathan M.,
Loreau Michel,
Harpole Stanley
Publication year - 2021
Publication title -
ecology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.852
H-Index - 265
eISSN - 1461-0248
pISSN - 1461-023X
DOI - 10.1111/ele.13760
Subject(s) - stability (learning theory) , ecology , scaling , statistical physics , ecological systems theory , context (archaeology) , dynamical systems theory , range (aeronautics) , ecological stability , psychological resilience , scaling law , econometrics , temporal scales , complex system , mathematics , environmental science , computer science , ecosystem , geography , physics , biology , artificial intelligence , machine learning , psychology , geometry , materials science , composite material , psychotherapist , archaeology , quantum mechanics
Ecological stability refers to a family of concepts used to describe how systems of interacting species vary through time and respond to disturbances. Because observed ecological stability depends on sampling scales and environmental context, it is notoriously difficult to compare measurements across sites and systems. Here, we apply stochastic dynamical systems theory to derive general statistical scaling relationships across time, space, and ecological level of organisation for three fundamental stability aspects: resilience, resistance, and invariance. These relationships can be calibrated using random or representative samples measured at individual scales, and projected to predict average stability at other scales across a wide range of contexts. Moreover deviations between observed vs. extrapolated scaling relationships can reveal information about unobserved heterogeneity across time, space, or species. We anticipate that these methods will be useful for cross‐study synthesis of stability data, extrapolating measurements to unobserved scales, and identifying underlying causes and consequences of heterogeneity.