Community trait assembly in highly diverse tropical rainforests is still poorly understood. Based on more than a decade of field measurements in a biodiversity hotspot of southern Ecuador, we implemented plant trait variation and improved soil organic matter dynamics in a widely used dynamic vegetation model (the Lund-Potsdam-Jena General Ecosystem Simulator, LPJ-GUESS) to explore the main drivers of community assembly along an elevational gradient. In the model used here (LPJ-GUESS-NTD, where NTD stands for nutrient-trait dynamics), each plant individual can possess different trait combinations, and the community trait composition emerges via ecological sorting. Further model developments include plant growth limitation by phosphorous (P) and mycorrhizal nutrient uptake. The new model version reproduced the main observed community trait shift and related vegetation processes along the elevational gradient, but only if nutrient limitations to plant growth were activated. In turn, when traits were fixed, low productivity communities emerged due to reduced nutrient-use efficiency. Mycorrhizal nutrient uptake, when deactivated, reduced net primary production (NPP) by 61–72% along the gradient. Our results strongly suggest that the elevational temperature gradient drives community assembly and ecosystem functioning indirectly through its effect on soil nutrient dynamics and vegetation traits. This illustrates the importance of considering these processes to yield realistic model predictions.
Aim: Recent studies in southern Africa identified past biome stability as an important predictor of biodiversity. We aimed to assess the extent to which past biome stability predicts present global biodiversity patterns, and the extent to which projected climatic changes may lead to eventual biome changes in areas with constant past biome.
Location: Global.
Taxon: Spermatophyta; terrestrial vertebrates.
Methods: Biome constancy was assessed and mapped using results from 89 dynamic global vegetation model simulations, driven by outputs of palaeoclimate experiments spanning the past 140 ka. We tested the hypothesis that terrestrial vertebrate diversity is predicted by biome constancy. We also simulated potential future vegetation, and hence potential future biome patterns, and quantified and mapped the extent of projected eventual future biome change in areas of past constant biome.
Results: Approximately 11% of global ice-free land had a constant biome since 140 ka. Apart from areas of constant Desert, many areas with constant biome support high species diversity. All terrestrial vertebrate groups show a strong positive relationship between biome constancy and vertebrate diversity in areas of greater diversity, but no relationship in less diverse areas. Climatic change projected by 2100 commits 46%–66% of global ice-free land, and 34%–52% of areas of past constant biome (excluding areas of constant Desert) to eventual biome change.
Main conclusions: Past biome stability strongly predicts vertebrate diversity in areas of higher diversity. Future climatic changes will lead to biome changes in many areas of past constant biome, with profound implications for biodiversity conservation. Some projected biome changes will result in substantial reductions in biospheric carbon sequestration and other ecosystem services.
The establishment and maintenance of protected areas (PAs) is viewed as a key action in delivering post-2020 biodiversity targets. PAs often need to meet multiple objectives, ranging from biodiversity protection to ecosystem service provision and climate change mitigation, but available land and conservation funding is limited. Therefore, optimizing resources by selecting the most beneficial PAs is vital. Here, we advocate for a flexible and transparent approach to selecting protected areas based on multiple objectives, and illustrate this with a decision support tool on a global scale. The tool allows weighting and prioritization of different conservation objectives according to user-specified preferences, as well as real-time comparison of the selected areas that result from such different priorities. We apply the tool across 1347 terrestrial PAs and highlight frequent trade-offs among different objectives, e.g., between species protection and ecosystem integrity. Outputs indicate that decision makers frequently face trade-offs among conflicting objectives. Nevertheless, we show that transparent decision-support tools can reveal synergies and trade-offs associated with PA selection, thereby helping to illuminate and resolve land-use conflicts embedded in divergent societal and political demands and values.
The establishment and maintenance of protected areas(PAs) is viewed as a key action in delivering post-2020 biodiversity targets. PAs often need to meet a multitude of objectives, ranging from biodiversity protection to ecosystem service provision and climate change mitigation. As available land and conservation funding are limited, optimizing resources by selecting the most beneficial PAs is vital. Here we present a decision support tool that enables a flexible approach to PA selection on a global scale, allowing different conservation objectives to be weighted and prioritized according to user-specified preferences. We apply the tool across 1347 terrestrial PAs and highlight frequent trade-offs among different objectives, e.g., between biodiversity protection and ecosystem integrity. These results indicate that decision makers must usually decide among conflicting objectives. To assist this our decision support tool provides an explicitly value-based approach that can help resolve such conflicts by considering divergent societal and political demands and values.
The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols
(2016)
The important role of fire in regulating vegetation community composition and contributions to emissions of greenhouse gases and aerosols make it a critical component of dynamic global vegetation models and Earth system models. Over two decades of development, a wide variety of model structures and mechanisms have been designed and incorporated into global fire models, which have been linked to different vegetation models. However, there has not yet been a systematic examination of how these different strategies contribute to model performance. Here we describe the structure of the first phase of the Fire Model Intercomparison Project (FireMIP), which for the first time seeks to systematically compare a number of models. By combining a standardized set of input data and model experiments with a rigorous comparison of model outputs to each other and to observations, we will improve the understanding of what drives vegetation fire, how it can best be simulated, and what new or improved observational data could allow better constraints on model behavior. Here we introduce the fire models used in the first phase of FireMIP, the simulation protocols applied, and the benchmarking system used to evaluate the models. The works published in this journal are distributed under the Creative Commons Attribution 3.0 License. This license does not affect the Crown copy-right work, which is re-usable under the Open Government Licence (OGL). The Creative Commons Attribution 3.0 License and the OGL are interoperable and do not conflict with, reduce, or limit each other.
Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, using either well-founded empirical relationships or process-based models with good predictive skill. While a large variety of models exist today, it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central question underpinning the creation of the Fire Model Intercomparison Project (FireMIP), an international initiative to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions. In this paper we review how fires have been represented in fire-enabled dynamic global vegetation models (DGVMs) and give an overview of the current state of the art in fire-regime modelling. We indicate which challenges still remain in global fire modelling and stress the need for a comprehensive model evaluation and outline what lessons may be learned from FireMIP.
Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, either using well-founded empirical relationships or process-based models with good predictive skill. A large variety of models exist today and it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central question underpinning the creation of the Fire Model Intercomparison Project - FireMIP, an international project to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions. In this paper we summarise the current state-of-the-art in fire regime modelling and model evaluation, and outline what lessons may be learned from FireMIP.
The important role of fire in regulating vegetation community composition and contributions to emissions of greenhouse gases and aerosols make it a critical component of dynamic global vegetation models and Earth system models. Over 2 decades of development, a wide variety of model structures and mechanisms have been designed and incorporated into global fire models, which have been linked to different vegetation models. However, there has not yet been a systematic examination of how these different strategies contribute to model performance. Here we describe the structure of the first phase of the Fire Model Intercomparison Project (FireMIP), which for the first time seeks to systematically compare a number of models. By combining a standardized set of input data and model experiments with a rigorous comparison of model outputs to each other and to observations, we will improve the understanding of what drives vegetation fire, how it can best be simulated, and what new or improved observational data could allow better constraints on model behavior. In this paper, we introduce the fire models used in the first phase of FireMIP, the simulation protocols applied, and the benchmarking system used to evaluate the models. We have also created supplementary tables that describe, in thorough mathematical detail, the structure of each model.
Establishing and maintaining protected areas (PAs) is a key action in delivering post-2020 biodiversity targets. PAs often need to meet multiple objectives, ranging from biodiversity protection to ecosystem service provision and climate change mitigation, but available land and conservation funding is limited. Therefore, optimizing resources by selecting the most beneficial PAs is vital. Here, we advocate for a flexible and transparent approach to selecting PAs based on multiple objectives, and illustrate this with a decision support tool on a global scale. The tool allows weighting and prioritization of different conservation objectives according to user-specified preferences as well as real-time comparison of the outcome. Applying the tool across 1,346 terrestrial PAs, we demonstrate that decision makers frequently face trade-offs among conflicting objectives, e.g., between species protection and ecosystem integrity. Nevertheless, we show that transparent decision support tools can reveal synergies and trade-offs associated with PA selection, thereby helping to illuminate and resolve land-use conflicts embedded in divergent societal and political demands and values.
Highlights
• Northern and eastern grassland-savanna boundary defined by minimum temperature.
• Dynamics of fire, frost and growing season temperatures combine to produce this limit.
• Western limit is related to moisture availability.
• Modern, high-resolution climate data enables refinement of bioclimatic limits.
• Reparameterisation improves global model performance at regional scale.
Abstract
Understanding the controls of biome distributions is crucial for assessing terrestrial ecosystem functioning and its response to climate change. We analysed to what extent differences in climate factors (minimum temperatures, water availability, and growing season temperatures (degree days above 5 °C (GDD5)) might explain the poorly understood borders between grasslands, savannas and shrublands in eastern South Africa. The results were used to improve bioclimatic limits in the dynamic global vegetation model (DGVM) LPJ-GUESS. The vegetation model was also used to explore the role of fire in the biome borders. Results show no clear differences between the adjacent biomes in water availability. Treeless grasslands primarily occur in areas with minimum temperatures and GDD5 values below that of savannas. The standard fire module in LPJ-GUESS is not able to reproduce observed burned area patterns in the study region, but simulations with prescribed fire return intervals show that a combination of low temperatures and fire can explain the treeless state of the grassland biome. These results confirm earlier hypotheses that a combination of low winter temperatures, causing frost damage to trees, and low growing season temperatures that impede tree sapling growth and recruitment, particularly under re-occurring fires, drive the grassland-savanna border. With these insights implemented, the LPJ-GUESS simulation results substantially improved grass distribution in the grassland biome, but challenges remain concerning the grassland-shrubland boundary, tree-grass competition and prognostic fire modelling.
