Climate regulation processes are linked to the functional composition of plant communities in European forests, shrublands, and grasslands.

Terrestrial ecosystems affect climate by reflecting solar irradiation, evaporative cooling, and carbon sequestration. Yet very little is known about how plant traits affect climate regulation processes (CRPs) in different habitat types. Here, we used linear and random forest models to relate the community-weighted mean and variance values of 19 plant traits (summarized into eight trait axes) to the climate-adjusted proportion of reflected solar irradiation, evapotranspiration, and net primary productivity across 36,630 grid cells at the European extent, classified into 10 types of forest, shrubland, and grassland habitats. We found that these trait axes were more tightly linked to log evapotranspiration (with an average of 6.2% explained variation) and the proportion of reflected solar irradiation (6.1%) than to net primary productivity (4.9%). The highest variation in CRPs was explained in forest and temperate shrubland habitats. Yet, the strength and direction of these relationships were strongly habitat-dependent. We conclude that any spatial upscaling of the effects of plant communities on CRPs must consider the relative contribution of different habitat types.

CA-discharge: Geo-Located Discharge Time Series for Mountainous Rivers in Central Asia.

We present a collection of 295 gauge locations in mountainous Central Asia with norm discharge as well as time series of river discharge from 135 of these locations collected from hydrological yearbooks in Central Asia. Time series have monthly, 10-day and daily temporal resolution and are available for different duration. A collection of third-party data allows basin characterization for all gauges. The time series data is validated using standard quality checks. Norm discharge is validated against literature values and by using a water balance approach. The novelty of the data consists in the combination of discharge time series and gauge locations for mountainous rivers in Central Asia which is not available anywhere else. The geo-located discharge time series can be used for water balance modelling and training of forecast models for river runoff in mountainous Central Asia.

Interannual climate variability improves niche estimates for ectothermic but not endothermic species.

Climate is an important limiting factor of species' niches and it is therefore regularly included in ecological applications such as species distribution models (SDMs). Climate predictors are often used in the form of long-term mean values, yet many species experience wide climatic variation over their lifespan and within their geographical range which is unlikely captured by long-term means. Further, depending on their physiology, distinct groups of species cope with climate variability differently. Ectothermic species, which are directly dependent on the thermal environment are expected to show a different response to temporal or spatial variability in temperature than endothermic groups that can decouple their internal temperature from that of their surroundings. Here, we explore the degree to which spatial variability and long-term temporal variability in temperature and precipitation change niche estimates for ectothermic (730 amphibian, 1276 reptile), and endothermic (1961 mammal) species globally. We use three different species distribution modelling (SDM) algorithms to quantify the effect of spatial and temporal climate variability, based on global range maps of all species and climate data from 1979 to 2013. All SDMs were cross-validated and accessed for their performance using the Area under the Curve (AUC) and the True Skill Statistic (TSS). The mean performance of SDMs using only climatic means as predictors was TSS = 0.71 and AUC = 0.90. The inclusion of spatial variability offers a significant gain in SDM performance (mean TSS = 0.74, mean AUC = 0.92), as does the inclusion of temporal variability (mean TSS = 0.80, mean AUC = 0.94). Including both spatial and temporal variability in SDMs shows the highest scores in AUC and TSS. Accounting for temporal rather than spatial variability in climate improved the SDM prediction especially in ectotherm groups such as amphibians and reptiles, while for endothermic mammals no such improvement was observed. These results indicate that including long term climate interannual climate variability into niche estimations matters most for ectothermic species that cannot decouple their physiology from the surrounding environment as endothermic species can.

Climate-trait relationships exhibit strong habitat specificity in plant communities across Europe.

Ecological theory predicts close relationships between macroclimate and functional traits. Yet, global climatic gradients correlate only weakly with the trait composition of local plant communities, suggesting that important factors have been ignored. Here, we investigate the consistency of climate-trait relationships for plant communities in European habitats. Assuming that local factors are better accounted for in more narrowly defined habitats, we assigned > 300,000 vegetation plots to hierarchically classified habitats and modelled the effects of climate on the community-weighted means of four key functional traits using generalized additive models. We found that the predictive power of climate increased from broadly to narrowly defined habitats for specific leaf area and root length, but not for plant height and seed mass. Although macroclimate generally predicted the distribution of all traits, its effects varied, with habitat-specificity increasing toward more narrowly defined habitats. We conclude that macroclimate is an important determinant of terrestrial plant communities, but future predictions of climatic effects must consider how habitats are defined.

Global models and predictions of plant diversity based on advanced machine learning techniques.

Despite the paramount role of plant diversity for ecosystem functioning, biogeochemical cycles, and human welfare, knowledge of its global distribution is still incomplete, hampering basic research and biodiversity conservation. Here, we used machine learning (random forests, extreme gradient boosting, and neural networks) and conventional statistical methods (generalized linear models and generalized additive models) to test environment-related hypotheses of broad-scale vascular plant diversity gradients and to model and predict species richness and phylogenetic richness worldwide. To this end, we used 830 regional plant inventories including c. 300 000 species and predictors of past and present environmental conditions. Machine learning showed a superior performance, explaining up to 80.9% of species richness and 83.3% of phylogenetic richness, illustrating the great potential of such techniques for disentangling complex and interacting associations between the environment and plant diversity. Current climate and environmental heterogeneity emerged as the primary drivers, while past environmental conditions left only small but detectable imprints on plant diversity. Finally, we combined predictions from multiple modeling techniques (ensemble predictions) to reveal global patterns and centers of plant diversity at multiple resolutions down to 7774 km2 . Our predictive maps provide accurate estimates of global plant diversity available at grain sizes relevant for conservation and macroecology.

Postglacial species arrival and diversity buildup of northern ecosystems took millennia.

What drives ecosystem buildup, diversity, and stability? We assess species arrival and ecosystem changes across 16 millennia by combining regional-scale plant sedimentary ancient DNA from Fennoscandia with near-complete DNA and trait databases. We show that postglacial arrival time varies within and between plant growth forms. Further, arrival times were mainly predicted by adaptation to temperature, disturbance, and light. Major break points in ecological trait diversity were seen between 13.9 and 10.8 calibrated thousand years before the present (cal ka BP), as well as break point in functional diversity at 12.0 cal ka BP, shifting from a state of ecosystem buildup to a state where most habitat types and biotic ecosystem components were in place. Trait and functional diversity stabilized around 8 cal ka BP, after which both remained stable, although changes in climate took place and species inflow continued. Our ecosystem reconstruction indicates a millennial-scale time phase of formation to reach stable and resilient levels of diversity and functioning.

Identifying climate refugia for high-elevation Alpine birds under current climate warming predictions.

Identifying climate refugia is key to effective biodiversity conservation under a changing climate, especially for mountain-specialist species adapted to cold conditions and highly threatened by climate warming. We combined species distribution models (SDMs) with climate forecasts to identify climate refugia for high-elevation bird species (Lagopus muta, Anthus spinoletta, Prunella collaris, Montifringilla nivalis) in the European Alps, where the ecological effects of climate changes are particularly evident and predicted to intensify. We considered future (2041-2070) conditions (SSP585 scenario, four climate models) and identified three types of refugia: (1) in-situ refugia potentially suitable under both current and future climate conditions, ex-situ refugia suitable (2) only in the future according to all future conditions, or (3) under at least three out of four future conditions. SDMs were based on a very large, high-resolution occurrence dataset (2901-12,601 independent records for each species) collected by citizen scientists. SDMs were fitted using different algorithms, balancing statistical accuracy, ecological realism and predictive/extrapolation ability. We selected the most reliable ones based on consistency between training and testing data and extrapolation over distant areas. Future predictions revealed that all species (with the partial exception of A. spinoletta) will undergo a range contraction towards higher elevations, losing 17%-59% of their current range (larger losses in L. muta). We identified ~15,000 km2 of the Alpine region as in-situ refugia for at least three species, of which 44% are currently designated as protected areas (PAs; 18%-66% among countries). Our findings highlight the usefulness of spatially accurate data collected by citizen scientists, and the importance of model testing by extrapolating over independent areas. Climate refugia, which are only partly included within the current PAs system, should be priority sites for the conservation of Alpine high-elevation species and habitats, where habitat degradation/alteration by human activities should be prevented to ensure future suitability for alpine species.

Global daily 1 km land surface precipitation based on cloud cover-informed downscaling.

High-resolution climatic data are essential to many questions and applications in environmental research and ecology. Here we develop and implement a new semi-mechanistic downscaling approach for daily precipitation estimate that incorporates high resolution (30 arcsec, ≈1 km) satellite-derived cloud frequency. The downscaling algorithm incorporates orographic predictors such as wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. We apply the method to the ERA5 precipitation archive and MODIS monthly cloud cover frequency to develop a daily gridded precipitation time series in 1 km resolution for the years 2003 onward. Comparison of the predictions with existing gridded products and station data from the Global Historical Climate Network indicates an improvement in the spatio-temporal performance of the downscaled data in predicting precipitation. Regional scrutiny of the cloud cover correction from the continental United States further indicates that CHELSA-EarthEnv performs well in comparison to other precipitation products. The CHELSA-EarthEnv daily precipitation product improves the temporal accuracy compared with a large improvement in the spatial accuracy especially in complex terrain.

Root traits explain plant species distributions along climatic gradients yet challenge the nature of ecological trade-offs.

Ecological theory is built on trade-offs, where trait differences among species evolved as adaptations to different environments. Trade-offs are often assumed to be bidirectional, where opposite ends of a gradient in trait values confer advantages in different environments. However, unidirectional benefits could be widespread if extreme trait values confer advantages at one end of an environmental gradient, whereas a wide range of trait values are equally beneficial at the other end. Here, we show that root traits explain species occurrences along broad gradients of temperature and water availability, but model predictions only resembled trade-offs in two out of 24 models. Forest species with low specific root length and high root tissue density (RTD) were more likely to occur in warm climates but species with high specific root length and low RTD were more likely to occur in cold climates. Unidirectional benefits were more prevalent than trade-offs: for example, species with large-diameter roots and high RTD were more commonly associated with dry climates, but species with the opposite trait values were not associated with wet climates. Directional selection for traits consistently occurred in cold or dry climates, whereas a diversity of root trait values were equally viable in warm or wet climates. Explicit integration of unidirectional benefits into ecological theory is needed to advance our understanding of the consequences of trait variation on species responses to environmental change.

Climate sensitivity and drought seasonality determine post-drought growth recovery of Quercus petraea and Quercus robur in Europe.

Recent studies have identified strong relationships between delayed recovery of tree growth after drought and tree mortality caused by subsequent droughts. These observations raise concerns about forest ecosystem services and post-drought growth recovery given the projected increase in drought frequency and extremes. For quantifying the impact of extreme droughts on tree radial growth, we used a network of tree-ring width data of 1689 trees from 100 sites representing most of the distribution of two drought tolerant, deciduous oak species (Quercus petraea and Quercus robur). We first examined which climatic factors and seasons control growth of the two species and if there is any latitudinal, longitudinal or elevational trend. We then quantified the relative departure from pre-drought growth during droughts, and how fast trees were able to recover the pre-drought growth level. Our results showed that growth was more related to precipitation and climatic water balance (precipitation minus potential evapotranspiration) than to temperature. However, we did not detect any clear latitudinal, longitudinal or elevational trends except a decreasing influence of summer water balance on growth of Q. petraea with latitude. Neither species was able to maintain the pre-drought growth level during droughts. However, both species showed rapid recovery or even growth compensation after summer droughts but displayed slow recovery in response to spring droughts where none of the two species was able to fully recover the pre-drought growth-level over the three post-drought years. Collectively, our results indicate that oaks which are considered resilient to extreme droughts have also shown vulnerability when droughts occurred in spring especially at sites where long-term growth is not significantly correlated with climatic factors. This improved understanding of the role of drought seasonality and climate sensitivity of sites is key to better predict trajectories of post-drought growth recovery in response to the drier climate projected for Europe.

Limited protection and ongoing loss of tropical cloud forest biodiversity and ecosystems worldwide.

Tropical cloud forests (TCFs) are one of the world's most species- and endemism-rich terrestrial ecosystems. TCFs are threatened by direct human pressures and climate change, yet the fate of these extraordinary ecosystems remains insufficiently quantified. With discussions of the post-2020 biodiversity framework underway, TCFs are a defining test case of the success and promise of recent policy targets and their associated mechanisms to avert the global biodiversity crisis. Here we present a global assessment of the recent status and trends of TCFs and their biodiversity and evaluate the efficacy of current protection measures. We find that cloud forests occupied 0.4% of the global land surface in 2001 and harboured ~3,700 species of birds, mammal, amphibians and tree ferns (~15% of the global diversity of those groups), with half of those species entirely restricted to cloud forests. Worldwide, ~2.4% of cloud forests (in some regions, more than 8%) were lost between 2001 and 2018, especially in readily accessible places. While protected areas have slowed this decline, a large proportion of loss in TCF cover is still occurring despite formal protection. Increased conservation efforts are needed to avert the impending regional or global demise of TCFs and their unique biodiversity.

High-resolution monthly precipitation and temperature time series from 2006 to 2100.

Predicting future climatic conditions at high spatial resolution is essential for many applications and impact studies in science. Here, we present monthly time series data on precipitation, minimum- and maximum temperature for four downscaled global circulation models. We used model output statistics in combination with mechanistic downscaling (the CHELSA algorithm) to calculate mean monthly maximum and minimum temperatures, as well as monthly precipitation at ~5 km spatial resolution globally for the years 2006-2100. We validated the performance of the downscaling algorithm by comparing model output with the observed climate of the historical period 1950-1969.

Crop and forest pest metawebs shift towards increased linkage and suitability overlap under climate change.

Global changes pose both risks and opportunities to agriculture and forestry, and biological forecasts can inform future management strategies. Here, we investigate potential land-use opportunities arising from climate change for these sectors in Europe, and risks associated with the introduction and establishment of novel insect pests. Adopting a metaweb approach including all interaction links between 126 crops and forest tree species and 89 black-listed insect pest species, we show that the metawebs shift toward increased numbers of links and overlap of suitable area under climate change. Decomposing the metaweb across regions shows large saturation in southern Europe, while many novel interactions are expected for northern Europe. In light of the rising consumer awareness about human health and environmental impacts of food and wood production, the challenge will be to effectively exploit new opportunities to create diverse local agriculture and forestry while controlling pest species and reducing risks from pesticide use.

End of an enigma: Aenigmopteris belongs in Tectaria (Tectariaceae: Polypodiopsida).

The phylogenetic affinities of the fern genus Aenigmopteris have been the subject of considerable disagreement, but until now, no molecular data were available from the genus. Based on the analysis of three chloroplast DNA regions (rbcL, rps16-matK, and trnL-F) we demonstrate that Aenigmopteris dubia (the type species of the genus) and A. elegans are closely related and deeply imbedded in Tectaria. The other three species of genus are morphologically very similar; we therefore transfer all five known species into Tectaria. Detailed morphological comparison further shows that previously proposed diagnostic characters of Aenigmopteris fall within the range of variation of a broadly circumscribed Tectaria.

Climatologies at high resolution for the earth's land surface areas.

High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth's land surface areas) data of downscaled model output temperature and precipitation estimates of the ERA-Interim climatic reanalysis to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. The resulting data consist of a monthly temperature and precipitation climatology for the years 1979-2013. We compare the data derived from the CHELSA algorithm with other standard gridded products and station data from the Global Historical Climate Network. We compare the performance of the new climatologies in species distribution modelling and show that we can increase the accuracy of species range predictions. We further show that CHELSA climatological data has a similar accuracy as other products for temperature, but that its predictions of precipitation patterns are better.

Environmentally driven extinction and opportunistic origination explain fern diversification patterns.

Combining palaeontological and neontological data offers a unique opportunity to investigate the relative roles of biotic and abiotic controls of species diversification, and the importance of origination versus extinction in driving evolutionary dynamics. Ferns comprise a major terrestrial plant radiation with an extensive evolutionary history providing a wealth of modern and fossil data for modelling environmental drivers of diversification. Here we develop a novel Bayesian model to simultaneously estimate correlations between diversification dynamics and multiple environmental trajectories. We estimate the impact of different factors on fern diversification over the past 400 million years by analysing a comprehensive dataset of fossil occurrences and complement these findings by analysing a large molecular phylogeny. We show that origination and extinction rates are governed by fundamentally different processes: originations depend on within-group diversity but are largely unaffected by environmental changes, whereas extinctions are strongly affected by external factors such as climate and geology. Our results indicate that the prime driver of fern diversity dynamics is environmentally driven extinction, with origination being an opportunistic response to diminishing ecospace occupancy.

Global patterns and drivers of phylogenetic structure in island floras.

Islands are ideal for investigating processes that shape species assemblages because they are isolated and have discrete boundaries. Quantifying phylogenetic assemblage structure allows inferences about these processes, in particular dispersal, environmental filtering and in-situ speciation. Here, we link phylogenetic assemblage structure to island characteristics across 393 islands worldwide and 37,041 vascular plant species (representing angiosperms overall, palms and ferns). Physical and bioclimatic factors, especially those impeding colonization and promoting speciation, explained more variation in phylogenetic structure of angiosperms overall (49%) and palms (52%) than of ferns (18%). The relationships showed different or contrasting trends among these major plant groups, consistent with their dispersal- and speciation-related traits and climatic adaptations. Phylogenetic diversity was negatively related to isolation for palms, but unexpectedly it was positively related to isolation for angiosperms overall. This indicates strong dispersal filtering for the predominantly large-seeded, animal-dispersed palm family whereas colonization from biogeographically distinct source pools on remote islands likely drives the phylogenetic structure of angiosperm floras. We show that signatures of dispersal limitation, environmental filtering and in-situ speciation differ markedly among taxonomic groups on islands, which sheds light on the origin of insular plant diversity.