Investigating how ecosystem impacts on climate evolve: simulations with the Tangled Nature + Climate (TaNC) model

These datasets are produced from the Tangled Nature + Climate (TaNC) model; a model designed to simulate and investigate how ecosystem impacts on the climate evolve on thousands- to million-year time scales, and how these impacts on the climate feedback on ecosystem properties. The TaNC is composed of an evolving ecosystem network model based on generalized Lotka-Volterra equations and a 1D energy balance climate model. The TaNC is fully described in the PhD dissertation entitled "Co-evolution of ecosystems and their environments: modeling the evolution of ecosystem-level responses to, and impacts on, environmental temperature" by Camille Febvre in 2025. There are three sets of model output presented in this dataset. In the first dataset, the TaNC was run with 7 different standard deviations of biotic impacts on a reservoir of atmospheric carbon, both in the configuration in which death and mutation are temperature-dependent (standard) and when death and mutation were held constant. This allows us to parameterize the relevant strength of species' impacts on the climate, at which ecosystems can cause climate change but are not most likely to drive themselves extinct. Additionally, this enables us to isolate the impacts of the thermal responses of birth, death, and mutation rates on ecosystem-climate co-evolution. In the second dataset, an intermediate standard deviation of biotic impacts is chosen and the TaNC model is then run with four different assumptions about species' thermal optima and climate impacts. Thermal optima are either uniformly distributed between 263 and 330K (~-10 degrees Celcius to ~53 degrees Celcius) or normally distributed around 290K (17 degrees Celcius) with a standard deviation of 10K (10 degrees Celcius). Species' impacts on the climate are normally distributed around zero with a standard deviation of 10^-8, and either this value is directly used, or the log transform of this value multiplied by a random choice of 1 or -1. This allows us to compare a normal to a bimodal distribution of climate impacts. The four possible combinations of these options for species' thermal optima and climate impacts are simulated and the model output is presented here. In the third dataset, the TaNC is run with and without inheritance of species' thermal optima and carbon impacts in four different configurations. In the first configuration, species' thermal optima and carbon impacts are not heritable, as in previous configurations of the TaNC. Thermal optima are drawn from a normal distribution, and carbon impacts from a binomial distribution centered at zero with a standard deviation of 6x10^-9. In the second configuration, species thermal optima are initially drawn from the normal distribution at the beginning of simulations, but subsequently, mutant species inherit the thermal optima of their ancestral species with a small modification drawn from a normal distribution centered at zero with a standard deviation of 1K. In the third configuration, thermal optima are not inherited by mutant species, but all species are assigned a climate impact which is the sum of their binary genome, so mutants are more likely to have similar climate impacts to their ancestors. In the fourth configuration, both thermal optima and climate impacts are heritable. The results of these simulations can be used to investigate how ecosystem-climate feedbacks emerge, and can give insight into ecosystem-scale niche construction. We have statistically analysed the results to understand the probabilities of different climate-ecosystem outcomes, but much more analysis could go into investigating single trajectories and how they progress.