[mixglm]: A tool for modelling ecosystem resilience

Aims: A number of modelling frameworks exist to aid in the identification and exploration of stable states and the assessment of resilience from ecological datasets. However, because such models are complex to implement there is a substantial barrier for the application in ecological research. Here we develop a flexible model of ecological resilience based on Bayesian approximation of the 'stability landscape'. We illustrate its usage on a tropical area where variation in tree cover has been previously interpreted as alternative stable states.Methods: The stability landscape, from which stable states and resilience parameters are computed, is modelled using a mixture of multiple distributions, each representing a regression between the system state variable and the environmental covariates. Our 'mixglm' model allows the mean, precision, and probability parameters of these distributions in the landscape to be dependent on multiple external covariates. 'Mixglm' is implemented as a function in R package with the same name, internally using Bayesian inference via NIMBLE. We also conducted a power analysis to provide guidance regarding required sample size.Results: We illustrate the use of the 'mixglm' on a published case of tree cover in South America which reports a stability landscape with three distinct stable states. Using 'mixglm', we were able to replicate the identification of these states. Moreover, we quantified uncertainty of our estimates, and computed resilience of South America's forests.Conclusions: 'Mixglm' can be readily used for description of stability landscapes and identification of stable states in most spatial datasets of system state variables, and it is accompanied by tools for calculation of resilience metrics. It can also be further expanded using regression framework to account for more complex data structures such as spatio-temporal data.