Distribution Of Biomes Research Articles

The distribution and drivers of tree cover in savannas and forests across India

The distribution of forest and savanna biomes and the role of resources (climate and soil) and disturbances (fire and herbivory) in determining tree-grass dynamics remains elusive and variable across geographies. This is especially problematic in Indian savannas which have been historically misclassified as degraded forests and are targeted for tree-planting. Here, we examine biome distribution and determinants through the lens of tree cover across India. Our analyses reveal four distinct zones of differing tree cover, with intermediate zones containing savanna vegetation. Rainfall seasonality determines maximum possible tree cover non-linearly. Once rainfall seasonality is factored out, soil sand fraction and topography partially explain residual variation of tree cover. High domestic livestock herbivory and other anthropogenic pressures reduce tree cover. Lastly, lack of detectable fires precludes robust conclusions about the relationship between fire and tree cover. By considering these environmental drivers in restoration planning, we can improve upon simplistic tree planting initiatives that may be detrimental to Indian savannas.

The impacts of modelling prescribed vs. dynamic land cover in a high-CO2 future scenario – greening of the Arctic and Amazonian dieback

Abstract. Terrestrial biosphere models are a key tool in investigating the role played by land surface in the global climate system. However, few models simulate the geographic distribution of biomes dynamically, opting instead to prescribe them using remote sensing products. While prescribing land cover still allows for the simulation of the impacts of climate change on vegetation growth and the impacts of land use change, it prevents the simulation of climate-change-driven biome shifts, with implications for the projection of future terrestrial carbon sink. Here, we isolate the impacts of prescribed vs. dynamic land cover implementations in a terrestrial biosphere model. We first introduce a new framework for evaluating dynamic land cover (i.e., the spatial distribution of plant functional types across the land surface), which can be applied across terrestrial biosphere models alongside standard benchmarking of energy, water, and carbon cycle variables in model intercomparison projects. After validating simulated land cover, we then show that the simulated terrestrial carbon sink differs significantly between simulations with dynamic vs. prescribed land cover for a high-CO2 future scenario. This is because of important range shifts that are only simulated when dynamic land cover is implemented: tree expansion into the Arctic and Amazonian transition from forest to grassland. In particular, the projected change in net land–atmosphere CO2 flux at the end of the 21st century is twice as large in simulations with dynamic land cover than in simulations with prescribed land cover. Our results illustrate the importance of climate-change-driven biome shifts for projecting future terrestrial carbon sink.

Predicting habitat suitability for Asian elephants in non-analog ecosystems with Bayesian models

Rewilding is an ambitious approach to conservation aiming at restoring and protecting natural processes. As the world is rapidly transitioning into conditions that have not been observed before, we need to be able to extrapolate and predict how natural processes would act under new conditions. Species distribution models have a good potential to inform rewilding decisions by the predictive modeling of potential species presence under various habitat conditions. A critical requirement when utilizing these models is to be able to express the uncertainty in the environment or its predictions. This study demonstrates the use of Bayesian statistical models to address this challenge. As a case study, we explore Bayesian logistic regression and Bayesian generalized additive models in order to predict suitable habitats for Asian elephants (Elephas maximus) until the year 2070 under the worst case working scenario of climate change. In this comparative study predictions of habitat suitability are solely based on climatic conditions. The results of the two Bayesian models are compared to two benchmark models, maximum-likelihood estimated logistic regression and random forest. We analyze and discuss trade-offs, relative advantages, and limitations of these modeling choices. The results of our analysis suggest that one configuration of Bayesian logistic regression gives the most robust predictions in this setting, which tend to correspond with the distribution of woodland biomes broadly similar to those in the species' historical range.

Positive feedbacks and alternative stable states in forest leaf types

The emergence of alternative stable states in forest systems has significant implications for the functioning and structure of the terrestrial biosphere, yet empirical evidence remains scarce. Here, we combine global forest biodiversity observations and simulations to test for alternative stable states in the presence of evergreen and deciduous forest types. We reveal a bimodal distribution of forest leaf types across temperate regions of the Northern Hemisphere that cannot be explained by the environment alone, suggesting signatures of alternative forest states. Moreover, we empirically demonstrate the existence of positive feedbacks in tree growth, recruitment and mortality, with trees having 4–43% higher growth rates, 14–17% higher survival rates and 4–7 times higher recruitment rates when they are surrounded by trees of their own leaf type. Simulations show that the observed positive feedbacks are necessary and sufficient to generate alternative forest states, which also lead to dependency on history (hysteresis) during ecosystem transition from evergreen to deciduous forests and vice versa. We identify hotspots of bistable forest types in evergreen-deciduous ecotones, which are likely driven by soil-related positive feedbacks. These findings are integral to predicting the distribution of forest biomes, and aid to our understanding of biodiversity, carbon turnover, and terrestrial climate feedbacks.

A new species and new records of the spider genus Dubiaranea (Araneae, Linyphiidae) from southern Brazil and Uruguay, with an analysis of the potential distribution of the species

Dubiaranea magatama Cajade, Hagopián & Rodrigues n. sp. is described from Uruguay, and southern Brazil based on males and females from several types of native forests, and a potential distribution for this species is modeled. The predicted model for this species indicates a Pampean province and southern Atlantic Forest biome distribution. Three variables contributed significantly to the model with positive influence: the spatial component, the proximity to large rivers, and the tree cover. Also, we provide new records of Dubiaranea difficilis (Mello-Leitão 1944) for Uruguay and a model of its potential distribution was performed. It showed that tree cover, the proximity to large rivers, the spatial component and precipitation/evapotranspiration constraints were recovered for the model with positive influence, expanding the potential distribution area previously proposed.

Spatial phylogenetics of Fagales: Investigating drivers of temperate forest distributions

AbstractAimQuantifying the phylogenetic diversity of temperate trees is essential for understanding the processes that have shaped the modern distribution of temperate broadleaf forest and other major forest biomes. Here, we focus on Fagales, an iconic member of forests worldwide, to uncover global diversity and endemism patterns and investigate the distribution of root nodule symbiosis (RNS), an important morphological specialisation in this clade, as a key factor behind these patterns.LocationGlobal.TaxonFagales.MethodsWe combined phylogenetic data covering 60.2% of living species, fine‐scale distribution models covering 90% of species, and nodulation data covering all species to investigate the distribution of species richness and phylogenetic diversity at fine spatial scales compared to the distribution of RNS. We identify abiotic environmental factors associated with RNS and with Fagales diversity in general.ResultsWe find the highest species richness in temperate east Asia, eastern North America, and equatorial montane regions of Asia and Central America. By contrast, relative phylogenetic diversity (RPD) is highest at higher latitudes, where RNS also predominates. We found a strong spatial structuring of regionalisations of Fagales floras, reflecting distinct Northern and Southern Hemisphere floras (except a unique Afro‐Boreal region), each with distinct RNS‐environment relationships.Main ConclusionsAlthough species richness and phylogenetic regionalisation for Fagales accord well with traditional biogeographic concepts for temperate forests, this is not the case for RPD. RNS is almost universal in the highest RPD regions, which may reflect ecological filtering promoting RNS in these regions. Our results highlight the utility of global‐scale, clade‐specific spatial phylogenetics and its utility for understanding drivers of diversity in species‐rich clades.

The role of dispersal limitation in the forest biome shifts of Europe in the last 18,000 years

AbstractAimHow the ability of plants to move towards newly favourable habitats (dispersal limitation) impacts the change of biome distribution and transition under fast climate warming is still debated. Analysing vegetation change in the past may help to clarify the relative importance of underlying ecological processes such as climate, biotic interactions, and dispersal. In this study, we investigated how dispersal limitation affected the distribution of European forests in the last 18,000 years.LocationSouthern and Central Europe.TaxonSpermatophyta.MethodsUsing the LPJ‐GM 2.0 model (an extension of LPJ‐GUESS), we simulated European vegetation from the end of the Last Glacial Maximum (18.5 ka) to the current time (0 ka). Using biome reconstructions from pollen data as reference, we compared the performance of two dispersal modes: with no migration constraints or seed limitation (free dispersal mode), and with plant establishment depending on seed dynamics and dispersal (dispersal limitation mode).ResultsThe model run, including migration processes, was better at capturing the post‐glacial expansion of European temperate forests (and the longer persistence of boreal forests) than the setting assuming free dispersal, especially during periods of rapid warming. This suggests that a number of (temperate) tree taxa experienced delayed occupancy of climatically suitable habitats due to a limited dispersal capacity, i.e., post‐glacial migration lags.Main ConclusionsOur results show that including migration processes in model simulations allows for more realistic reconstructions of forest patterns under rapid climate change, with consequences for future projections of carbon sequestration and climate reconstructions with vegetation feedback, assisted migration and forest conservation.

Biome classification influences current and projected future biome distributions

AbstractAimBiome classification schemes are widely used to map biogeographic patterns of vegetation formations on large spatial scales. Future climate change will influence biome patterns, and vegetation models can be used to assess the susceptibility of biomes to experience transitions. However, biome classification is not unique, and various classification schemes and biome maps exist. Here, we aimed to assess how the choice of biome classification schemes influences current and projected future biome patterns.LocationAfrica, Australia, Tropical Asia.Time period2000–2099.Major taxa studiedTropical vegetation.MethodsWe used the adaptive dynamic global vegetation model version 2 (aDGVM2) to simulate vegetation in the study region. We classified vegetation into biomes using (1) a classification scheme based on the cover of functional types, (2) a cluster analysis based on the cover of functional types and (3) a cluster analysis based on trait patterns simulated by the aDGVM2. We compared the resulting biome maps to multiple observation‐based biome maps and quantified differences in projected biome changes under the RCP8.5 scenario for the different classification schemes.ResultsAs expected, biome patterns were strongly related to the scheme used for biome classification. The highest data‐model agreement was derived for a cluster analysis using 21 simulated traits. Traits related to size were most important for classification. Considering all classification schemes, the area projected to undergo biome transitions under climate change varied between 16.5% and 32.1%. Despite this variability, different schemes consistently showed that grassland and savanna areas are most susceptible to climate change, whereas tropical forests and deserts are stable. Our results demonstrate that traits simulated by aDGVM2 are appropriate to delimit biomes.Main conclusionsStudies projecting biome patterns and transitions under current and future climate should consider applying different biome classification schemes to avoid biases in such projections caused by biome classification schemes.

The Database of European Forest Insect and Disease Disturbances: DEFID2.

Insect and disease outbreaks in forests are biotic disturbances that can profoundly alter ecosystem dynamics. In many parts of the world, these disturbance regimes are intensifying as the climate changes and shifts the distribution of species and biomes. As a result, key forest ecosystem services, such as carbon sequestration, regulation of water flows, wood production, protection of soils, and the conservation of biodiversity, could be increasingly compromised. Despite the relevance of these detrimental effects, there are currently no spatially detailed databases that record insect and disease disturbances on forests at the pan-European scale. Here, we present the new Database of European Forest Insect and Disease Disturbances (DEFID2). It comprises over 650,000 harmonized georeferenced records, mapped as polygons or points, of insects and disease disturbances that occurred between 1963 and 2021 in European forests. The records currently span eight different countries and were acquired through diverse methods (e.g., ground surveys, remote sensing techniques). The records in DEFID2 are described by a set of qualitative attributes, including severity and patterns of damage symptoms, agents, host tree species, climate-driven trigger factors, silvicultural practices, and eventual sanitary interventions. They are further complemented with a satellite-based quantitative characterization of the affected forest areas based on Landsat Normalized Burn Ratio time series, and damage metrics derived from them using the LandTrendr spectral-temporal segmentation algorithm (including onset, duration, magnitude, and rate of the disturbance), and possible interactions with windthrow and wildfire events. The DEFID2 database is a novel resource for many large-scale applications dealing with biotic disturbances. It offers a unique contribution to design networks of experiments, improve our understanding of ecological processes underlying biotic forest disturbances, monitor their dynamics, and enhance their representation in land-climate models. Further data sharing is encouraged to extend and improve the DEFID2 database continuously. The database is freely available at https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/FOREST/DISTURBANCES/DEFID2/.

Biomes of the world under climate change scenarios: increasing aridity and higher temperatures lead to significant shifts in natural vegetation.

The global potential distribution of biomes (natural vegetation) was modelled using 8,959 training points from the BIOME 6000 dataset and a stack of 72 environmental covariates representing terrain and the current climatic conditions based on historical long term averages (1979-2013). An ensemble machine learning model based on stacked regularization was used, with multinomial logistic regression as the meta-learner and spatial blocking (100 km) to deal with spatial autocorrelation of the training points. Results of spatial cross-validation for the BIOME 6000 classes show an overall accuracy of 0.67 and R2logloss of 0.61, with "tropical evergreen broadleaf forest" being the class with highest gain in predictive performances (R2logloss = 0.74) and "prostrate dwarf shrub tundra" the class with the lowest (R2logloss = -0.09) compared to the baseline. Temperature-related covariates were the most important predictors, with the mean diurnal range (BIO2) being shared by all the base-learners (i.e.,random forest, gradient boosted trees and generalized linear models). The model was next used to predict the distribution of future biomes for the periods 2040-2060 and 2061-2080 under three climate change scenarios (RCP 2.6, 4.5 and 8.5). Comparisons of predictions for the three epochs (present, 2040-2060 and 2061-2080) show that increasing aridity and higher temperatures will likely result in significant shifts in natural vegetation in the tropical area (shifts from tropical forests to savannas up to 1.7 ×105 km2 by 2080) and around the Arctic Circle (shifts from tundra to boreal forests up to 2.4 ×105 km2 by 2080). Projected global maps at 1 km spatial resolution are provided as probability and hard classes maps for BIOME 6000 classes and as hard classes maps for the IUCN classes (six aggregated classes). Uncertainty maps (prediction error) are also provided and should be used for careful interpretation of the future projections.

Global photosynthetic capacity of C3 biomes retrieved from solar-induced chlorophyll fluorescence and leaf chlorophyll content

The maximum Ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) carboxylation rate (Vcmax) has been widely used to help determine the photosynthetic capacity for carbon cycle modelling. Field measurements of Vcmax are very limited, so fixed Vcmax values over growing seasons are often used, causing considerable uncertainties in modelled results. Also, the lack of a global Vcmax dataset with seasonal variations limits our understanding of the spatial and temporal patterns of Vcmax, and hence photosynthesis. This study proposes a refined data assimilation scheme to retrieve daily Vcmax using remotely sensed solar-induced chlorophyll fluorescence (SIF) and leaf chlorophyll content (LCC) data. Using the refined method, we produced a global Vcmax dataset with daily 0.1° × 0.1° resolutions. The seasonal variations in Vcmax have been successfully retrieved. Vcmax retrieved in this study was compared with field-measured Vcmax from past literature. Good agreements were achieved in relation to Vcmax measured at 201 sites (R2 is 0.78, and relative RMSE is 34%) and the seasonal variation in Vcmax measured at 9 sites (R2 ranges from 0.57 to 0.99, and the relative RMSE ranged from 6% to 28%). Our method effectively utilizes the information from both SIF and LCC, showing advantages over previous approaches that estimate Vcmax based solely on using SIF or LCC. Our results suggest that the spatial distribution of Vcmax depends on the spatial distribution of biomes, along with air temperature and absorbed photosynthetically active radiation (APAR). Higher seasonal mean air temperature and APAR tend to lead to higher seasonal mean Vcmax. The seasonal variation in Vcmax is driven by the seasonal variations in air temperature and APAR, rather than their seasonal means. We also derived and analyzed Vcmax25, which is Vcmax normalized to 25 °C (Vcmax25) and has been found to be more closely related to the Rubisco content. Vcmax25 also has considerable spatiotemporal variations, and the variations differ to that in Vcmax. Different biomes have different ratios of Vcmax25 to LCC. Air temperature and APAR exert weak but positive effects on the seasonal variation in Vcmax25. Vcmax retrieved in this study enhances our understanding of the spatiotemporal pattern of leaf photosynthetic capacity on the global scale and is potentially useful to terrestrial carbon cycle modelling.

A Pleistocene legacy structures variation in modern seagrass ecosystems

Distribution of Earth's biomes is structured by the match between climate and plant traits, which in turn shape associated communities and ecosystem processes and services. However, that climate-trait match can be disrupted by historical events, with lasting ecosystem impacts. As Earth's environment changes faster than at any time in human history, critical questions are whether and how organismal traits and ecosystems can adjust to altered conditions. We quantified the relative importance of current environmental forcing versus evolutionary history in shaping the growth form (stature and biomass) and associated community of eelgrass (Zostera marina), a widespread foundation plant of marine ecosystems along Northern Hemisphere coastlines, which experienced major shifts in distribution and genetic composition during the Pleistocene. We found that eelgrass stature and biomass retain a legacy of the Pleistocene colonization of the Atlantic from the ancestral Pacific range and of more recent within-basin bottlenecks and genetic differentiation. This evolutionary legacy in turn influences the biomass of associated algae and invertebrates that fuel coastal food webs, with effects comparable to or stronger than effects of current environmental forcing. Such historical lags in phenotypic acclimatization may constrain ecosystem adjustments to rapid anthropogenic climate change, thus altering predictions about the future functioning of ecosystems.

Global variation in diversification rate and species richness are unlinked in plants

Species richness varies immensely around the world. Variation in the rate of diversification (speciation minus extinction) is often hypothesized to explain this pattern, while alternative explanations invoke time or ecological carrying capacities as drivers. Focusing on seed plants, the world's most important engineers of terrestrial ecosystems, we investigated the role of diversification rate as a link between the environment and global species richness patterns. Applying structural equation modeling to a comprehensive distribution dataset and phylogenetic tree covering all circa 332,000 seed plant species and 99.9% of the world's terrestrial surface (excluding Antarctica), we test five broad hypotheses postulating that diversification serves as a mechanistic link between species richness and climate, climatic stability, seasonality, environmental heterogeneity, or the distribution of biomes. Our results show that the global patterns of species richness and diversification rate are entirely independent. Diversification rates were not highest in warm and wet climates, running counter to the Metabolic Theory of Ecology, one of the dominant explanations for global gradients in species richness. Instead, diversification rates were highest in edaphically diverse, dry areas that have experienced climate change during the Neogene. Meanwhile, we confirmed climate and environmental heterogeneity as the main drivers of species richness, but these effects did not involve diversification rates as a mechanistic link, calling for alternative explanations. We conclude that high species richness is likely driven by the antiquity of wet tropical areas (supporting the "tropical conservatism hypothesis") or the high ecological carrying capacity of warm, wet, and/or environmentally heterogeneous environments.

Understanding climate change impacts on biome and plant distributions in the Andes: Challenges and opportunities.

AimClimate change is expected to impact mountain biodiversity by shifting species ranges and the biomes they shape. The extent and regional variation in these impacts are still poorly understood, particularly in the highly biodiverse Andes. Regional syntheses of climate change impacts on vegetation are pivotal to identify and guide research priorities. Here we review current data, knowledge and uncertainties in past, present and future climate change impacts on vegetation in the Andes. Location: Andes. Taxon: Plants.MethodsWe (i) conducted a literature review on Andean vegetation responses to past and contemporary climatic change, (ii) analysed future climate projections for different elevations and slope orientations at 19 Andean locations using an ensemble of model outputs from the Coupled Model Intercomparison Project 5, and (iii) calculated changes in the suitable climate envelope area of Andean biomes and compared these results to studies that used species distribution models.ResultsFuture climatic changes (2040–2070) are projected to be stronger at high‐elevation areas in the tropical Andes (up to 4°C under RCP 8.5), while in the temperate Andes temperature increases are projected to be up to 2°C. Under this worst‐case scenario, temperate deciduous forests and the grasslands/steppes from the Central and Southern Andes are predicted to show the greatest losses of suitable climatic space (30% and 17%–23%, respectively). The high vulnerability of these biomes contrasts with the low attention from researchers modelling Andean species distributions. Critical knowledge gaps include a lack of an Andean wide plant checklist, insufficient density of weather stations at high‐elevation areas, a lack of high‐resolution climatologies that accommodates the Andes' complex topography and climatic processes, insufficient data to model demographic and ecological processes, and low use of palaeo data for distribution modelling.Main conclusionsClimate change is likely to profoundly affect the extent and composition of Andean biomes. Temperate Andean biomes in particular are susceptible to substantial area contractions. There are, however, considerable challenges and uncertainties in modelling species and biome responses and a pressing need for a region‐wide approach to address knowledge gaps and improve understanding and monitoring of climate change impacts in these globally important biomes.

The Seasonality of Surface Urban Heat Islands across Climates

In this work, we investigate how the seasonal hysteresis of the Surface Urban Heat Island Intensity (SUHII) differs across climates and provide a detailed typology of the daytime and nighttime SUHII hysteresis loops. Instead of the typical tropical/dry/temperate/continental grouping, we describe Earth’s climate using the Köppen–Geiger system that empirically maps Earth’s biome distribution into 30 climate classes. Our thesis is that aggregating multi-city data without considering the biome of each city results in temporal means that fail to reflect the actual SUHII characteristics. This is because the SUHII is a function of both urban and rural features and the phenology of the rural surroundings can differ considerably between cities, even in the same climate zone. Our investigation covers all the densely populated areas of Earth and uses 18 years (2000–2018) of land surface temperature and land cover data from the European Space Agency’s Climate Change Initiative. Our findings show that, in addition to concave-up and -down shapes, the seasonal hysteresis of the SUHII also exhibits twisted, flat, and triangle-like patterns. They also suggest that, in wet climates, the daytime SUHII hysteresis is almost universally concave-up, but they paint a more complex picture for cities in dry climates.

Coastal Zone Management of West Bengal – A Review

Sea level rise due to global warming leading to changing coastal environmental scenarios is a very recent phenomenon. As a result, coastal climate like relative humidity, river flow rates and run off, fertility of coastal soils, distribution of coastal biomes, characters of tidal flow regime and wave actions, pattern of sedimentation and sedimentary environment, nutrient upwelling and downwelling, chemical parameters of coastal waters, frequency of cyclonic storms, storm surges and coastal inundation thereon have been changing with time. Monitoring integrated coastal zone management including proper steps of coastal protection applying useful and befitted coastal engineering structures, and the proper implementation of the roles of exclusive economic zone along with the coastal zone regulations may mitigate such problems.

Predicting global terrestrial biomes with the LeNet convolutional neural network

Abstract. A biome is a major regional ecological community characterized by distinctive life forms and principal plants. Many empirical schemes such as the Holdridge life zone (HLZ) system have been proposed and implemented to predict the global distribution of terrestrial biomes. Knowledge of physiological climatic limits has been employed to predict biomes, resulting in more precise simulation; however, this requires different sets of physiological limits for different vegetation classification schemes. Here, we demonstrate an accurate and practical method to construct empirical models for biome mapping: a convolutional neural network (CNN) was trained by an observation-based biome map, as well as images depicting air temperature and precipitation. Unlike previous approaches, which require assumption(s) of environmental constrain for each biome, this method automatically extracts non-linear seasonal patterns of climatic variables that are relevant in biome classification. The trained model accurately simulated a global map of current terrestrial biome distribution. Then, the trained model was applied to climate scenarios toward the end of the 21st century, predicting a significant shift in global biome distribution with rapid warming trends. Our results demonstrate that the proposed CNN approach can provide an efficient and objective method to generate preliminary estimations of the impact of climate change on biome distribution. Moreover, we anticipate that our approach could provide a basis for more general implementations to build empirical models of other climate-driven categorical phenomena.

Estimating relative sunshine duration from commonly available meteorological variables for simulating biome distribution in the Carpathian Region

Bright sunshine duration (BSD) data are required for simulating biomes using process-based vegetation models. However, monthly global paleoclimate datasets that can be used in paleo data–model comparisons do not necessarily contain BSD or radiation data. Considering the theoretical and practical aspects, the scheme of Yin, X. (1999) is here recommended to estimate monthly time series of relative BSD using only monthly climate and location data. As a case study for the Carpathian Region, the efficiency of both the original and a variant of that scheme is analysed in this paper. The alternative scheme has high applicability in paleoenvironmental studies. Comparison of the estimated and observed BSD data shows that from May to August, the value of relative root mean squared error in more than 90 percent of the study area does not exceed the threshold of 20 percent, indicating an excellent performance of the original estimation scheme. It is also found that though the magnitude of overestimation for the alternative algorithm is significant in the winter period, the proposed method performs similarly well in the growing season as the original. Furthermore, concerning modelling the distribution of biomes, simulation experiments are performed to assess the effects of modifying some configuration settings: (a) the generation of relative BSD data, and (b) the algorithm used to create quasi-daily weather data from the monthly values. Under both the recent humidity conditions of the study region and the spatial resolution of the climate dataset used, the results can be considered sufficiently robust, regardless of the configuration settings tested. Thus, using monthly temperature and precipitation climatologies, the spatial distribution of biomes can be properly simulated with the configuration settings proposed here.

Evaluating refugia in recent human evolution in Africa.

Homo sapiens have adapted to an incredible diversity of habitats around the globe. This capacity to adapt to different landscapes is clearly expressed within Africa, with Late Pleistocene Homo sapiens populations occupying savannahs, woodlands, coastlines and mountainous terrain. As the only area of the world where Homo sapiens have clearly persisted through multiple glacial-interglacial cycles, Africa is the only continent where classic refugia models can be formulated and tested to examine and describe changing patterns of past distributions and human phylogeographies. The potential role of refugia has frequently been acknowledged in the Late Pleistocene palaeoanthropological literature, yet explicit identification of potential refugia has been limited by the patchy nature of palaeoenvironmental and archaeological records, and the low temporal resolution of climate or ecological models. Here, we apply potential climatic thresholds on human habitation, rooted in ethnographic studies, in combination with high-resolution model datasets for precipitation and biome distributions to identify persistent refugia spanning the Late Pleistocene (130–10 ka). We present two alternate models suggesting that between 27% and 66% of Africa may have provided refugia to Late Pleistocene human populations, and examine variability in precipitation, biome and ecotone distributions within these refugial zones.This article is part of the theme issue ‘Tropical forests in the deep human past’.

Representing plant diversity in land models: An evolutionary approach to make "Functional Types" more functional.

Plants are critical mediators of terrestrial mass and energy fluxes, and their structural and functional traits have profound impacts on local and global climate, biogeochemistry, biodiversity, and hydrology. Yet, Earth System Models (ESMs), our most powerful tools for predicting the effects of humans on the coupled biosphere-atmosphere system, simplify the incredible diversity of land plants into a handful of coarse categories of "Plant Functional Types" (PFTs) that often fail to capture ecological dynamics such as biome distributions. The inclusion of more realistic functional diversity is a recognized goal for ESMs, yet there is currently no consistent, widely accepted way to add diversity to models, that is, to determine what new PFTs to add and with what data to constrain their parameters. We review approaches to representing plant diversity in ESMs and draw on recent ecological and evolutionary findings to present an evolution-based functional type approach for further disaggregating functional diversity. Specifically, the prevalence of niche conservatism, or the tendency of closely related taxa to retain similar ecological and functional attributes through evolutionary time, reveals that evolutionary relatedness is a powerful framework for summarizing functional similarities and differences among plant types. We advocate that Plant Functional Types based on dominant evolutionary lineages ("Lineage Functional Types") will provide an ecologically defensible, tractable, and scalable framework for representing plant diversity in next-generation ESMs, with the potential to improve parameterization, process representation, and model benchmarking. We highlight how the importance of evolutionary history for plant function can unify the work of disparate fields to improve predictive modeling of the Earth system.