An operational definition of the biome for global change research

Predicting the responses of European grassland communities to climate and land cover change

European grasslands are among the most species-rich ecosystems on small spatial scales. However, human-induced activities like land use and climate change pose significant threats to this diversity. To explore how climate and land cover change will affect biodiversity and community composition in grassland ecosystems, we conducted joint species distribution models (SDMs) on the extensive vegetation-plot database sPlotOpen to project distributions of 1178 grassland species across Europe under current conditions and three future scenarios. We further compared model accuracy and computational efficiency between joint SDMs (JSDMs) and stacked SDMs, especially for rare species. Our results show that: (i) grassland communities in the mountain ranges are expected to suffer high rates of species loss, while those in western, northern and eastern Europe will experience substantial turnover; (ii) scaling anomalies were observed in the predicted species richness, reflecting regional differences in the dominant drivers of assembly processes; (iii) JSDMs did not outperform stacked SDMs in predictive power but demonstrated superior efficiency in model fitting and predicting; and (iv) incorporating co-occurrence datasets improved the model performance in predicting the distribution of rare species. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

Fire-modulated fluctuations in nutrient availability stimulate biome-scale floristic turnover in time, and elevated species richness, in low-nutrient fynbos heathland

BACKGROUND AND AIMS

In many systems, postfire vegetation recovery is characterized by temporal changes in plant species composition and richness. We attribute this to changes in resource availability with time since fire, with the magnitude of species turnover determined by the degree of resource limitation. Here, we test the hypothesis that postfire species turnover in South African fynbos heathland is powered by fire-modulated changes in nutrient availability, with the magnitude of turnover in nutrient-constrained fynbos being greater than in fertile renosterveld shrubland. We also test the hypothesis that floristic overlaps between fynbos and renosterveld are attributable to nutritional augmentation of fynbos soils immediately after fire.

METHODS

We use vegetation survey data from two sites on the Cape Peninsula to compare changes in species richness and composition with time since fire.

KEY RESULTS

Fynbos communities display a clear decline in species richness with time since fire, whereas no such decline is apparent in renosterveld. In fynbos, declining species richness is associated with declines in the richness of plant families having high foliar concentrations of nitrogen, phosphorus and potassium and possessing attributes that are nutritionally costly. In contrast, families that dominate late-succession fynbos possess adaptations for the acquisition and retention of sparse nutrients. At the family level, recently burnt fynbos is compositionally more similar to renosterveld than is mature fynbos.

CONCLUSIONS

Our data suggest that nutritionally driven species turnover contributes significantly to fynbos community richness. We propose that the extremely low baseline fertility of fynbos soils serves to lengthen the nutritional resource axis along which species can differentiate and coexist, thereby providing the opportunity for low-nutrient extremophiles to coexist spatially with species adapted to more fertile soil. This mechanism has the potential to operate in any resource-constrained system in which episodic disturbance affects resource availability.

Reassessment of the risks of climate change for terrestrial ecosystems

Forecasting the risks of climate change for species and ecosystems is necessary for developing targeted conservation strategies. Previous risk assessments mapped the exposure of the global land surface to changes in climate. However, this procedure is unlikely to robustly identify priority areas for conservation actions because nonlinear physiological responses and colimitation processes ensure that ecological changes will not map perfectly to the forecast climatic changes. Here, we combine ecophysiological growth models of 135,153 vascular plant species and plant growth-form information to transform ambient and future climatologies into phytoclimates, which describe the ability of climates to support the plant growth forms that characterize terrestrial ecosystems. We forecast that 33% to 68% of the global land surface will experience a significant change in phytoclimate by 2070 under representative concentration pathways RCP 2.6 and RCP 8.5, respectively. Phytoclimates without present-day analogue are forecast to emerge on 0.3-2.2% of the land surface and 0.1-1.3% of currently realized phytoclimates are forecast to disappear. Notably, the geographic pattern of change, disappearance and novelty of phytoclimates differs markedly from the pattern of analogous trends in climates detected by previous studies, thereby defining new priorities for conservation actions and highlighting the limits of using untransformed climate change exposure indices in ecological risk assessments. Our findings suggest that a profound transformation of the biosphere is underway and emphasize the need for a timely adaptation of biodiversity management practices.

Empirical delineation of the forest-steppe zone is supported by macroclimate

Eurasian forest-steppes form a 9000-km-long transitional zone between temperate forests and steppes, featuring a complex mosaic of herbaceous and woody habitats. Due to its heterogeneity regarding climate, topography and vegetation, the forest-steppe zone has been divided into several regions. However, a continental-scale empirical delineation of the zone and its regions was missing until recently. Finally, a map has been proposed by Erdős et al. based on floristic composition, physiognomy, relief, and climate. By conducting predictive distribution modeling and hierarchical clustering, here we compared this expert delineation with the solely macroclimate-based predictions and clusters. By assessing the discrepancies, we located the areas where refinement of the delineation or the inclusion of non-macroclimatic predictors should be considered. Also, we identified the most important variables for predicting the existence of the Eurasian forest-steppe zone and its regions. The predicted probability of forest-steppe occurrence showed a very high agreement with the expert delineation. The previous delineation of the West Siberia region was confirmed by our results, while that of the Inner Asia region was the one least confirmed by the macroclimate-based model predictions. The appropriate delineation of the Southeast Europe region from the East Europe region should be refined by further research, and splitting the Far East region into a southern and northern subregion should also be considered. The main macroclimatic predictors of the potential distribution of the zone and its regions were potential evapotranspiration (zone and regions), annual mean temperature (regions), precipitation of driest quarter (regions) and precipitation of warmest quarter (zone), but the importance of climatic variables for prediction showed great variability among the fitted predictive distribution models.

Limited climatic space for alternative ecosystem states in Africa

One of the foundational premises of ecology is that climate determines ecosystems. This has been challenged by alternative ecosystem state models, which illustrate that internal ecosystem dynamics acting on the initial ecosystem state can overwhelm the influence of climate, and by observations suggesting that climate cannot reliably discriminate forest and savanna ecosystem types. Using a novel phytoclimatic transform, which estimates the ability of climate to support different types of plants, we show that climatic suitability for evergreen trees and C4 grasses are sufficient to discriminate between forest and savanna in Africa. Our findings reassert the dominant influence of climate on ecosystems and suggest that the role of feedbacks causing alternative ecosystem states is less prevalent than has been suggested.

Assessing protected area vulnerability to climate change in a case study of South African national parks

Climate change is challenging the ability of protected areas (PAs) to meet their objectives. To improve PA planning, we developed a framework for assessing PA vulnerability to climate change based on consideration of potential climate change impacts on species and their habitats and resource use. Furthermore, the capacity of PAs to adapt to these climate threats was determined through assessment of PA management effectiveness, adjacent land use, and financial resilience. Users reach a PA-specific vulnerability score and rank based on scoring of these categories. We applied the framework to South Africa's 19 national parks. Because the 19 parks are managed as a national network, we explored how resources might be best allocated to address climate change. Each park's importance to the network's biodiversity conservation and revenue generation was estimated and used to weight overall vulnerability scores and ranks. Park vulnerability profiles showed distinct combinations of potential impacts of climate change and adaptive capacities; the former had a greater influence on vulnerability. Mapungubwe National Park emerged as the most vulnerable to climate change, despite its relatively high adaptive capacity, largely owing to large projected changes in species and resource use. Table Mountain National Park scored the lowest in overall vulnerability. Climate change vulnerability rankings differed markedly once importance weightings were applied; Kruger National Park was the most vulnerable under both importance scenarios. Climate change vulnerability assessment is fundamental to effective adaptation planning. Our PA assessment tool is the only tool that quantifies PA vulnerability to climate change in a comparative index. It may be used in data-rich and data-poor contexts to prioritize resource allocation across PA networks and can be applied from local to global scales.

Identifying climate thresholds for dominant natural vegetation types at the global scale using machine learning: Average climate versus extremes

The global distribution of vegetation is largely determined by climatic conditions and feeds back into the climate system. To predict future vegetation changes in response to climate change, it is crucial to identify and understand key patterns and processes that couple vegetation and climate. Dynamic global vegetation models (DGVMs) have been widely applied to describe the distribution of vegetation types and their future dynamics in response to climate change. As a process-based approach, it partly relies on hard-coded climate thresholds to constrain the distribution of vegetation. What thresholds to implement in DGVMs and how to replace them with more process-based descriptions remain among the major challenges. In this study, we employ machine learning using decision trees to extract large-scale relationships between the global distribution of vegetation and climatic characteristics from remotely sensed vegetation and climate data. We analyse how the dominant vegetation types are linked to climate extremes as compared to seasonally or annually averaged climatic conditions. The results show that climate extremes allow us to describe the distribution and eco-climatological space of the vegetation types more accurately than the averaged climate variables, especially those types which occupy small territories in a relatively homogeneous ecological space. Future predicted vegetation changes using both climate extremes and averaged climate variables are less prominent than that predicted by averaged climate variables and are in better agreement with those of DGVMs, further indicating the importance of climate extremes in determining geographic distributions of different vegetation types. We found that the temperature thresholds for vegetation types (e.g. grass and open shrubland) in cold environments vary with moisture conditions. The coldest daily maximum temperature (extreme cold day) is particularly important for separating many different vegetation types. These findings highlight the need for a more explicit representation of the impacts of climate extremes on vegetation in DGVMs.

Climatic niche lability but growth form conservatism in the African woody flora

Climatic niche evolution during the diversification of tropical plants has received little attention in Africa. To address this, we characterised the climatic niche of >4000 tropical African woody species, distinguishing two broad bioclimatic groups (forest vs. savanna) and six subgroups. We quantified niche conservatism versus lability at the genus level and for higher clades, using a molecular phylogeny of >800 genera. Although niche stasis at speciation is prevalent, numerous clades individually cover vast climatic spaces suggesting a general ease in transcending ecological limits, especially across bioclimatic subgroups. The forest biome was the main source of diversity, providing many lineages to savanna, but reverse shifts also occurred. We identified clades that diversified in savanna after shifts from forest. The forest-savanna transition was not consistently associated with a growth form change, though we found evolutionarily labile clades whose presence in forest or savanna is associated respectively with climbing or shrubby species diversification.

Defining Biologically Meaningful Biomes Through Floristic, Functional, and Phylogenetic Data

While we have largely improved our understanding on what biomes are and their utility in global change ecology, conservation planning, and evolutionary biology is clear, there is no consensus on how biomes should be delimited or mapped. Existing methods emphasize different aspects of biomes, with different strengths and limitations. We introduce a novel approach to biome delimitation and mapping, based upon combining individual regionalizations derived from floristic, functional, and phylogenetic data linked to environmentally trained species distribution models. We define “core Biomes” as areas where independent regionalizations agree and “transition zones” as those whose biome identity is not corroborated by all analyses. We apply this approach to delimiting the neglected Caatinga seasonally dry tropical forest biome in northeast Brazil. We delimit the “core Caatinga” as a smaller and more climatically limited area than previous definitions, and argue it represents a floristically, functionally, and phylogenetically coherent unit within the driest parts of northeast Brazil. “Caatinga transition zones” represent a large and biologically important area, highlighting that ecological and evolutionary processes work across environmental gradients and that biomes are not categorical variables. We discuss the differences among individual regionalizations in an ecological and evolutionary context and the potential limitations and utility of individual and combined biome delimitations. Our integrated ecological and evolutionary definition of the Caatinga and associated transition zones are argued to best describe and map biologically meaningful biomes.

Large uncertainties in future biome changes in Africa call for flexible climate adaptation strategies

Anthropogenic climate change is expected to impact ecosystem structure, biodiversity and ecosystem services in Africa profoundly. We used the adaptive Dynamic Global Vegetation Model (aDGVM), which was originally developed and tested for Africa, to quantify sources of uncertainties in simulated African potential natural vegetation towards the end of the 21st century. We forced the aDGVM with regionally downscaled high-resolution climate scenarios based on an ensemble of six general circulation models (GCMs) under two representative concentration pathways (RCPs 4.5 and 8.5). Our study assessed the direct effects of climate change and elevated CO₂ on vegetation change and its plant-physiological drivers. Total increase in carbon in aboveground biomass in Africa until the end of the century was between 18% to 43% (RCP4.5) and 37% to 61% (RCP8.5) and was associated with woody encroachment into grasslands and increased woody cover in savannas. When direct effects of CO₂ on plants were omitted, woody encroachment was muted and carbon in aboveground vegetation changed between -8 to 11% (RCP 4.5) and -22 to -6% (RCP8.5). Simulated biome changes lacked consistent large-scale geographical patterns of change across scenarios. In Ethiopia and the Sahara/Sahel transition zone, the biome changes forecast by the aDGVM were consistent across GCMs and RCPs. Direct effects from elevated CO₂ were associated with substantial increases in water use efficiency, primarily driven by photosynthesis enhancement, which may relieve soil moisture limitations to plant productivity. At the ecosystem level, interactions between fire and woody plant demography further promoted woody encroachment. We conclude that substantial future biome changes due to climate and CO₂ changes are likely across Africa. Because of the large uncertainties in future projections, adaptation strategies must be highly flexible. Focused research on CO₂ effects, and improved model representations of these effects will be necessary to reduce these uncertainties.

Predictive ability of a process-based versus a correlative species distribution model

Species distribution modeling is a widely used tool in many branches of ecology and evolution. Evaluations of the transferability of species distribution models-their ability to predict the distribution of species in independent data domains-are, however, rare. In this study, we contrast the transferability of a process-based and a correlative species distribution model. Our case study uses 664 Australian eucalypt and acacia species. We estimate models for these species using data from their native Australia and then assess whether these models can predict the adventive range of these species. We find that the correlative model-MaxEnt-has a superior ability to describe the data in the training data domain (Australia) and that the process-based model-TTR-SDM-has a superior ability to predict the distribution of the study species outside of Australia. The implication of this analysis, that process-based models may be more appropriate than correlative models when making projections outside of the domain of the training data, needs to be tested in other case studies.

Climate change promotes transitions to tall evergreen vegetation in tropical Asia

Vegetation in tropical Asia is highly diverse due to large environmental gradients and heterogeneity of landscapes. This biodiversity is threatened by intense land use and climate change. However, despite the rich biodiversity and the dense human population, tropical Asia is often underrepresented in global biodiversity assessments. Understanding how climate change influences the remaining areas of natural vegetation is therefore highly important for conservation planning. Here, we used the adaptive Dynamic Global Vegetation Model version 2 (aDGVM2) to simulate impacts of climate change and elevated CO₂ on vegetation formations in tropical Asia for an ensemble of climate change scenarios. We used climate forcing from five different climate models for representative concentration pathways RCP4.5 and RCP8.5. We found that vegetation in tropical Asia will remain a carbon sink until 2099, and that vegetation biomass increases of up to 28% by 2099 are associated with transitions from small to tall woody vegetation and from deciduous to evergreen vegetation. Patterns of phenology were less responsive to climate change and elevated CO₂ than biomes and biomass, indicating that the selection of variables and methods used to detect vegetation changes is crucial. Model simulations revealed substantial variation within the ensemble, both in biomass increases and in distributions of different biome types. Our results have important implications for management policy, because they suggest that large ensembles of climate models and scenarios are required to assess a wide range of potential future trajectories of vegetation change and to develop robust management plans. Furthermore, our results highlight open ecosystems with low tree cover as most threatened by climate change, indicating potential conflicts of interest between biodiversity conservation in open ecosystems and active afforestation to enhance carbon sequestration.