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.
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.
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.
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.
The conservation and restoration of forest ecosystems require detailed knowledge of the native plant compositions. Here, we map global forest tree composition and assess the impacts of historical ...forest cover loss and climate change on trees. The global occupancy of 10,590 tree species reveals complex taxonomic and phylogenetic gradients determining a local signature of tree lineage assembly. Species occupancy analyses indicate that historical forest loss has significantly restricted the potential suitable range of tree species in all forest biomes. Nevertheless, tropical moist and boreal forest biomes display the lowest level of range restriction and harbor extremely large ranged tree species, albeit with a stark contrast in richness and composition. Climate change simulations indicate that forest biomes are projected to differ in their response to climate change, with the highest predicted species loss in tropical dry and Mediterranean ecoregions. Our findings highlight the need for preserving the remaining large forest biomes while regenerating degraded forests in a way that provides resilience against climate change.
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.
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.
In northern Mongolia, at the southern boundary of the Siberian boreal forest
belt, the distribution of steppe and forest is generally linked to climate
and topography, making this region highly ...sensitive to climate change and
human impact. Detailed investigations on the limiting parameters of forest
and steppe in different biomes provide necessary information for
paleoenvironmental reconstruction and prognosis of potential landscape
change. In this study, remote sensing data and gridded climate data were
analyzed in order to identify main distribution patterns of forest and steppe
in Mongolia and to detect environmental factors driving forest development.
Forest distribution and vegetation vitality derived from the normalized
differentiated vegetation index (NDVI) were investigated for the three types
of boreal forest present in Mongolia (taiga, subtaiga and forest–steppe),
which cover a total area of 73 818 km2. In addition to the
forest type areas, the analysis
focused on subunits of forest and nonforested areas at the upper and lower
treeline, which represent ecological borders between vegetation types.
Climate and NDVI data were analyzed for a reference period of 15 years from
1999 to 2013. The presented approach for treeline delineation by identifying representative
sites mostly bridges local forest disturbances like fire or tree cutting.
Moreover, this procedure provides a valuable tool to distinguish the
potential forested area. The upper treeline generally rises from 1800 m
above sea level (a.s.l.) in the northeast to 2700 m a.s.l. in the south.
The lower treeline locally emerges at 1000 m a.s.l. in the northern taiga
and rises southward to 2500 m a.s.l. The latitudinal gradient of both
treelines turns into a longitudinal one on the eastern flank of mountain
ranges due to higher aridity caused by rain-shadow effects. Less productive
trees in terms of NDVI were identified at both the upper and lower treeline
in relation to the respective total boreal forest type area. The mean growing
season temperature (MGST) of 7.9–8.9 ∘C and a minimum MGST of
6 ∘C are limiting parameters at the upper treeline but are
negligible for the lower treeline. The minimum of the mean annual
precipitation (MAP) of 230–290 mm yr−1 is a limiting parameter at the
lower treeline but also at the upper treeline in the forest–steppe ecotone.
In general, NDVI and MAP are lower in grassland, and MGST is higher compared
to the corresponding boreal forest. One exception occurs at the upper
treeline of the subtaiga and taiga, where the alpine vegetation consists of
mountain meadow mixed with shrubs. The relation between NDVI and climate data
corroborates that more precipitation and higher temperatures generally lead
to higher greenness in all ecological subunits. MGST is positively correlated
with MAP of the total area of forest–steppe, but this correlation turns
negative in the taiga. The limiting factor in the forest–steppe is the
relative humidity and in the taiga it is the snow cover distribution. The
subtaiga represents an ecological transition zone of approximately
300 mm yr−1 precipitation, which occurs independently from the MGST. Since the treelines are mainly determined by climatic parameters, the rapid
climate change in inner Asia will lead to a spatial relocation of tree communities, treelines and
boreal forest types. However, a direct deduction of future tree vitality,
forest composition and biomass trends from the recent relationships between
NDVI and climate parameters is challenging. Besides human impact, it must
consider bio- and geoecological issues like, for example, tree rejuvenation,
temporal lag of climate adaptation and disappearing permafrost.
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.
Understanding the drivers of species richness gradients is a central challenge of ecological and biodiversity research in freshwater science. Species richness along elevational gradients reveals a ...great variety of patterns. Here, we investigate elevational changes in species richness and turnover between microhabitats in near-natural spring habitats across Switzerland. Species richness was determined for 175 subsamples from 71 near-natural springs, and Poisson regression was applied between species richness and environmental predictors. Compositional turnover was calculated between the different microhabitats within single springs using the Jaccard index based on observed species and the Chao index based on estimated species numbers. In total, 539 diatom species were identified. Species richness increased monotonically with elevation. Habitat diversity and elevation explaining some of the species richness per site. The Jaccard index for the measured compositional turnover showed a mean similarity of 70% between microhabitats within springs, whereas the Chao index which accounts for sampling artefacts estimated a turnover of only 37%. Thus, the commonly applied method of counting 500 valves led to an undersampling of the rare species and might need to be reconsidered when assessing diatom biodiversity.
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