Inferring Alternative Ecosystem States with Field Survey Data

Abstract Many ecosystems potentially exhibit alternative stable states, where distinct states can coexist under identical environmental conditions. While simulation models have generated key hypotheses in alternative stable states theory, they often rely on scale‐free parameters disconnected from real ecosystems. Controlled experiments remain the gold standard for identifying alternative stable states, but are time‐intensive due to transient dynamics. Observational data have become increasingly important due to their rich and flexible insights into community structure and ecosystem processes, as well as their rapidly expanding availability. However, current methodological frameworks using field survey data—particularly snapshot surveys—have not yet incorporated recent advances and best practices, which limits their practical utility. To address this gap, we summarized recent progress and propose an updated, practicable framework that links alternative stable states theory directly to empirical data. Specifically, we focused on (i) sampling strategy, (ii) selecting state variables, (iii) environmental factors, (iv) statistical evidence of alternative stable states, (v) the procedure of determining drivers and (vi) inferring regulating mechanisms. We aim to provide an easy‐to‐use template for studying alternative stable states with snapshot field survey data, which can be tailored to individual study systems.