Open AccessA MATHEMATICAL REVIEW OF RESILIENCE IN ECOLOGY
Author(s) -
MEYER KATHERINE
Publication year - 2016
Publication title -
natural resource modeling
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.28
H-Index - 32
eISSN - 1939-7445
pISSN - 0890-8575
DOI - 10.1111/nrm.12097
Subject(s) - resilience (materials science) , context (archaeology) , ecological resilience , ecology , state space , state variable , meaning (existential) , socio ecological system , state (computer science) , variable (mathematics) , psychological resilience , dynamical systems theory , ecological systems theory , computer science , mathematics , geography , epistemology , ecosystem , psychology , statistics , biology , physics , social psychology , philosophy , thermodynamics , software engineering , algorithm , dependability , mathematical analysis , archaeology , quantum mechanics
Rising interest in the resilience of ecological systems has spawned diverse interpretations of the term's precise meaning, particularly in the context of resilience quantification. The purpose of this paper is twofold. The first aim is to use the language of dynamical systems to organize and scrutinize existing resilience definitions within a unified framework. The second aim is to provide an introduction for mathematicians to the ecological concept of resilience, a potential area for expanded quantitative research. To frame the discussion of resilience in dynamical systems terms, a model consisting of ordinary differential equations is assumed to represent the ecological system. The question “resilience of what to what?” posed by Carpenter et al. [2001] informs two broad categories of definitions, based on resilience to state variable perturbations and to parameter changes, respectively. Definitions of resilience to state variable perturbations include measures of basin size (relevant to one‐time perturbations) and basin steepness (relevant to repeated perturbations). Resilience to parameter changes has been quantified by viewing parameters as state variables but has also considered the reversibility of parameter shifts. Quantifying this reversibility and fully describing how recovery rates determine resilience to repeated state‐space perturbations emerge as two opportunities for mathematics research.