Khushboo Gurung

Mechanistic eco‐evolutionary models are powerful tools for understanding how life diversified over geological timescales, yet estimating their parameters remains a key challenge. The high computational requirements of these models make probabilistic inversion methods unfeasible. We introduce INDITEK‐2.0, a novel Bayesian inverse modelling framework built upon the cost‐efficient and spatially explicit INDITEK diversification engine. To validate this framework, we conducted a proof‐of‐concept study. We first generated synthetic global distributions of current biodiversity of marine invertebrates using INDITEK with known parameter values. To mimic field data, some realistic random Gaussian errors were added. We then used our Bayesian inverse modelling framework to recover these original (true) parameter values from the dataset of current biodiversity distributions after 500 million years of diversification. We also conducted a suite of sensitivity analyses by disabling specific mechanisms of the model. Our solution is a probability distribution on the parameter space, and we show that the method successfully recovered the target parameter values with a relatively low degree of uncertainty. INDITEK‐2.0's ability to probabilistically infer underlying evolutionary processes provides a new, spatially explicit modelling tool for reconstructing the evolutionary history of biodiversity across Earth's ecosystems and taxonomic groups.

The Carboniferous presents a period of dynamic changes in climate, tectonics and ecology. Plant evolution and expansion during this time allowed for large-scale coal deposit formation and enhanced biological input within biogeochemical cycles which exerted a stronger influence on Earth's climate. Using fossil data and vegetation–climate modelling, we explore the gradual changes of vegetation as they change from globally uniform ecosystems to fragmented and distinct provinces. This paper also discusses the potential missing link between vegetation strategies and localized environmental conditions, and climate modelling over the Carboniferous, which may explain the increase in organic sediment and coal forest extent despite lower global temperatures. As changes in palaeogeography and palaeoclimate created drier and more seasonal environments, the trajectory of plant evolution favoured seasonality- and drought-tolerant plants, bringing us closer to present-day floral distributions.

Knowing the past temperature of the Earth is crucial for understanding the mechanisms driving climate change and biosphere evolution, but there is significant debate about the range of past temperature variation. Previous interpretations, largely based on oxygen isotope records, suggest that global temperature has generally declined over the past 539 million years, but substantial uncertainties persist. In this study, we introduce an independent estimate for long-term Phanerozoic temperature trends based on a large database of chemical weathering indices from siliciclastic sedimentary rocks, globally upscaled using a state-of-the-art general circulation paleoclimate model. Our results imply that Phanerozoic global temperatures remained within 10-30 °C, and that Paleozoic oceans had comparable temperatures to Mesozoic and Cenozoic oceans, in contrast to previous work suggesting that they were anomalously hot. This finding supports the idea that negative feedback processes, such as silicate weathering, have maintained long-term global average temperatures within a relatively tight range, contributing to the continued long-term evolution of the biosphere.

During the Neoproterozoic, early land biota consisted of cyanobacteria, microalgae and various fungi or fungi-like communities. Although called micro-organisms, their role in stabilising environments, and driving and controlling nutrient cycles [1], creates a macro-scale impact. Photosynthetic microbial mats are predicted to have been present ~3 billion years ago, creating microcosms of oxygen-rich environments that contribute towards global net primary productivity, weathering and nitrogen fixation [2]. However due to the lack of fossil evidence and understanding of their role in a non-vegetated environment, it is unclear what their impact is on biogeochemical cycling and thus the shaping of Neoproterozoic climate. Building on the new process based spatial vegetation model [3], we try to understand the role of expanding microbial communities on events such as the Neoproterozic Oxygenation Event and Snowball Earth. [1] Taylor, T.N., Krings, M. (2005) Fossil microorganisms and land plants: Associations and interactions. Symbiosis 40:119-135[2] Lenton, T.M., Daines, S.J. (2016) Matworld- the biogeochemical effects of early life on land. New Phytologist 215: 505-507[3] Gurung, K., Field, K.J, et al. (2024) Geographic range of plants drives long-term climate change. Nature Comms 15: 1805

Mechanistic eco-evolutionary models are powerful tools for understanding how life diversified over geological timescales, yet estimating their parameters remains a key challenge. The high computational requirements of these models make probabilistic inversion methods unfeasible. We address this challenge with INDITEK-2.0, a new, cost-efficient eco-evolutionary model that integrates a Bayesian inverse modelling framework for probabilistic estimation of parameters. To validate this framework, we conducted a proof-of-concept study. We first generated synthetic biodiversity data for marine invertebrates using INDITEK-2.0 with known parameter values. To mimic field data, some realistic random Gaussian errors were added to the data. We then used our Bayesian inverse modelling framework to recover these original (true) parameter values from the dataset of current biodiversity distributions after 500 million years of diversification. Our solution is a probability distribution on the parameter space, and we show that the method successfully recovered the target parameter values with a relatively low degree of uncertainity. INDITEK-2.0’s ability to probabilistically infer underlying eco-evolutionary processes provides a new modelling tool for reconstructing the evolutionary history of biodiversity across Earth’s ecosystems and taxonomic groups.

The Permian–Triassic Mass Extinction (PTME), the most severe crisis of the Phanerozoic, has been attributed to intense global warming triggered by Siberian Traps volcanism. However, it remains unclear why super-greenhouse conditions persisted for around five million years after the volcanic episode, with one possibility being that the slow recovery of plants limited carbon sequestration. Here we use fossil occurrences and lithological indicators of climate to reconstruct spatio-temporal maps of plant productivity changes through the PTME and employ climate-biogeochemical modelling to investigate the Early Triassic super-greenhouse. Our reconstructions show that terrestrial vegetation loss during the PTME, especially in tropical regions, resulted in an Earth system with low levels of organic carbon sequestration and restricted chemical weathering, resulting in prolonged high CO ₂ levels. These results support the idea that thresholds exist in the climate-carbon system whereby warming can be amplified by vegetation collapse.

Long computation times in vegetation and climate models hamper our ability to evaluate the potentially powerful role of plants on weathering and carbon sequestration over the Phanerozoic Eon. Simulated vegetation over deep time is often homogenous, and disregards the spatial distribution of plants and the impact of local climatic variables on plant function. Here we couple a fast vegetation model (FLORA) to a spatially-resolved long-term climate-biogeochemical model (SCION), to assess links between plant geographical range, the long-term carbon cycle and climate. Model results show lower rates of carbon fixation and up to double the previously predicted atmospheric CO₂ concentration due to a limited plant geographical range over the arid Pangea supercontinent. The Mesozoic dispersion of the continents increases modelled plant geographical range from 65% to > 90%, amplifying global CO₂ removal, consistent with geological data. We demonstrate that plant geographical range likely exerted a major, under-explored control on long-term climate change.

Earth’s long-term climate may have profoundly influenced plant evolution. Local climatic factors, including water availability, light, and temperature, play a key role in plant physiology and growth, and have fluctuated substantially over geological time. However, the impact of these key climate variables on global plant biomass across the Phanerozoic has not yet been established. Linking climate and dynamic vegetation modelling, we identify two key ‘windows of opportunity’ during the Ordovician and Jurassic-Paleogene capable of supporting dramatic expansions of potential plant biomass. These conditions are driven by continental dispersion, paleolatitude of continental area and a lack of glaciation, allowing for an intense hydrological cycle and greater water availability. These windows coincide with the initial expansion of land plants and the later angiosperm radiation. Our findings suggest that the timing and expansion of habitable space for plants played an important role in plant evolution and diversification.

Plants are likely to have had a profound influence on Earth’s long-term climate through their role in drawing down CO2 and emitting O2 into the atmosphere and their interactions with soils and minerals. Local climatic factors, including water availability, light, and temperature, play a key role in plant physiology and growth and have fluctuated substantially over geological time. However, the impact of these key climate variables on global plant biomass across the Phanerozoic have not yet been established.