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The drivers of plant richness at fine spatial scales in steppe ecosystems are still not sufficiently understood. Our main research questions were: (i) How rich in plant species are the natural steppes of Southern Siberia compared to natural and semi-natural grasslands in other regions of the Palaearctic? (ii) What are the main environmental drivers of the diversity patterns in these steppes? (iii) What are the diversity–environment relationships and do they vary between spatial scales and among different taxonomic groups? We sampled the steppe vegetation (vascular plants, bryophytes and lichens) in Khakassia (Russia) with 39 nested-plot series (0.0001–100-m 2 plot size) and 54 additional 10-m 2 quadrats across the regional range of steppe types and measured various environmental variables. We measured β -diversity using z -values of power-law species–area relationships. GLM analyses were performed to assess the importance of environmental variables as predictors of species richness and z -value. Khakassian steppes showed both high α - and β -diversity. We found significant scale dependence for the z -values, which had their highest values at small spatial scales and then decreased exponentially. Total species richness was controlled predominantly by heat load index, mean annual precipitation, humus content and soil skeleton content. The positive role of soil pH was evident only for vascular plant species richness. Similar to other studies, we found that the importance of environmental factors strongly differed among taxonomic groups and across spatial scales, thus highlighting the need to study more than one taxon and more than one plot size to get a reliable picture.

Accurately quantifying species’ area requirements is a prerequisite for effective area-based conservation. This typically involves collecting tracking data on species of interest and then conducting home-range analyses. Problematically, autocorrelation in tracking data can result in space needs being severely underestimated. Based on the previous work, we hypothesized the magnitude of nderestimation varies with body mass, a relationship that could have serious conservation implications. To evaluate this hypothesis for terrestrial mammals, we estimated home-range areas with global positioning system (GPS) locations from 757 individuals across 61 globally distributed mammalian species with body masses ranging from 0.4 to 4000 kg. We then applied block cross-validation to quantify bias in empirical home-range estimates. Area requirements of mammals <10 kg were underestimated by a mean approximately15%, and species weighing approximately100 kg were underestimated by approximately 50% on average. Thus, we found area estimation was subject to autocorrelation-induced bias that was worse for large species. Combined with the fact that extinction risk increases as body mass increases, the allometric scaling of bias we observed suggests the most threatened species are also likely to be those with the least accurate home-range estimates. As a correction, we tested whether data thinning or autocorrelation-informed home-range estimation minimized the scaling effect of autocorrelation on area estimates. Data thinning required an approximately93% data loss to achieve statistical independence with 95% confidence and was, therefore, not a viable solution. In contrast, autocorrelation-informed home-range estimation resulted in consistently accurate estimates irrespective of mass. When relating body mass to home range size, we detected that correcting for autocorrelation resulted in a scaling exponent significantly >1, meaning the scaling of the relationship changed substantially at the upper end of the mass spectrum.

Citizen science has a long history in the ecological sciences and has made substantial contributions to science, education, and society. Developments in information technology during the last few decades have created new opportunities for citizen science to engage ever larger audiences of volunteers to help address some of ecology’s most pressing issues, such as global environmental change. Using online tools, volunteers can find projects that match their interests and learn the skills and protocols required to develop questions, collect data, submit data, and help process and analyze data online. Citizen science has become increasingly important for its ability to engage large numbers of volunteers to generate observations at scales or resolutions unattainable by individual researchers. As a coupled natural and human approach, citizen science can also help researchers access local knowledge and implement conservation projects that might be impossible otherwise. In Japan, however, the value of citizen science to science and society is still underappreciated. Here we present case studies of citizen science in Japan, the United States, and the United Kingdom, and describe how citizen science is used to tackle key questions in ecology and conservation, including spatial and macro-ecology, management of threatened and invasive species, and monitoring of biodiversity. We also discuss the importance of data quality, volunteer recruitment, program evaluation, and the integration of science and human systems in citizen science projects. Finally, we outline some of the primary challenges facing citizen science and its future.

Ruppia cirrhosa and R. maritima are two widely used names, each applied respectively to a long- and coiled-pedunculate species or a short- and non-coiled pedunculate species of Ruppia. The nomenclatural history of the two names is outlined here. A lectotype for the name R. cirrhosa is designated and the name is shown to be a homotypic synonym of R. maritima. Consequently, R. spiralis has nomenclatural priority over R. cirrhosa for the long- and coiled-pedunculate Ruppia.

The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers. Examining drivers of the latitudinal biodiversity gradient in a global database of local tree species richness, the authors show that co-limitation by multiple environmental and anthropogenic factors causes steeper increases in richness with latitude in tropical versus temperate and boreal zones.

Things without names are difficult to rationalise, and so species that go without names are difficult to conserve or protect. This is a case study in resolving conflicts in historical taxonomy and ‘real’ species (identifiable and evolutionarily relevant groupings) using an approach including population genetics, natural history, and pragmatism. We report the observation that populations of a shallow-water chiton species from Washington and British Columbia demonstrate extremely high site fidelity and patchy distribution. Their limited dispersal potential and isolation could be explained by a brooding life history. This stands in direct contrast with the supposedly wide distribution of this “species”, Leptochiton rugatus (Carpenter in Pilsbry, 1892) sensu lato, from the Sea of Japan to Baja California. But this lineage has previously been suggested to comprise several cryptic species. Indeed, a haplotype network analysis using 61 individual sequences of the cytochrome oxidase c subunit I gene for L. rugatus s.l. revealed four discrete clusters which correspond to different parts of the geographic range. We infer these to represent four distinct species, at least two of which are likely novel. Leptochiton rugatus sensu stricto is herein reinterpreted as restricted to California and Baja California, and the new name L. cascadiensis sp. nov. is established for the lineage with a distribution in the Cascadia coastal bioregion from the panhandle of Alaska to Oregon. There are minor morphological differences among these species in the L. rugatus species complex, but genetic data or morphological observations alone would not have been sufficient to definitively recognise these groups as species-level lineages. The observation that different species within the complex may have different life history strategies provides important support for interpreting different populations as genuinely separate species.

Theory predicts that deleterious mutations accumulate more readily in small populations. As a consequence, mutation load is expected to be elevated in species where life-history strategies and geographic or historical contingencies reduce the number of reproducing individuals. Yet, few studies have empirically tested this prediction using genome-wide data in a comparative framework. We collected whole-genome sequencing data for 147 individuals across seven crow species (Corvus spp.). For each species, we estimated the distribution of fitness effects of deleterious mutations and compared it with proxies of the effective population size Ne. Island species with comparatively smaller geographic range sizes had a significantly increased mutation load. These results support the view that small populations have an elevated risk of mutational meltdown, which may contribute to the higher extinction rates observed in island species.

Despite substantial progress in understanding global biodiversity loss, major taxonomic and geographic knowledge gaps remain. Decision makers often rely on expert judgement to fill knowledge gaps, but are rarely able to engage with sufficiently large and diverse groups of specialists. To improve understanding of the perspectives of thousands of biodiversity experts worldwide, we conducted a survey and asked experts to focus on the taxa and freshwater, terrestrial, or marine ecosystem with which they are most familiar. We found several points of overwhelming consensus (for instance, multiple drivers of biodiversity loss interact synergistically) and important demographic and geographic differences in specialists’ perspectives and estimates. Experts from groups that are underrepresented in biodiversity science, including women and those from the Global South, recommended different priorities for conservation solutions, with less emphasis on acquiring new protected areas, and provided higher estimates of biodiversity loss and its impacts. This may in part be because they disproportionately study the most highly threatened taxa and habitats.

FLUXNET comprises globally distributed eddy-covariance-based estimates of carbon fluxes between the biosphere and the atmosphere. Since eddy covariance flux towers have a relatively small footprint and are distributed unevenly across the world, upscaling the observations is necessary to obtain global-scale estimates of biosphere–atmosphere exchange. Based on cross-consistency checks with atmospheric inversions, sun-induced fluorescence (SIF) and dynamic global vegetation models (DGVMs), here we provide a systematic assessment of the latest upscaling efforts for gross primary production (GPP) and net ecosystem exchange (NEE) of the FLUXCOM initiative, where different machine learning methods, forcing data sets and sets of predictor variables were employed. Spatial patterns of mean GPP are consistent across FLUXCOM and DGVM ensembles (R2>0.94 at 1∘ spatial resolution) while the majority of DGVMs show, for 70 % of the land surface, values outside the FLUXCOM range. Global mean GPP magnitudes for 2008–2010 from FLUXCOM members vary within 106 and 130 PgC yr−1 with the largest uncertainty in the tropics. Seasonal variations in independent SIF estimates agree better with FLUXCOM GPP (mean global pixel-wise R2∼0.75) than with GPP from DGVMs (mean global pixel-wise R2∼0.6). Seasonal variations in FLUXCOM NEE show good consistency with atmospheric inversion-based net land carbon fluxes, particularly for temperate and boreal regions (R2>0.92). Interannual variability of global NEE in FLUXCOM is underestimated compared to inversions and DGVMs. The FLUXCOM version which also uses meteorological inputs shows a strong co-variation in interannual patterns with inversions (R2=0.87 for 2001–2010). Mean regional NEE from FLUXCOM shows larger uptake than inversion and DGVM-based estimates, particularly in the tropics with discrepancies of up to several hundred grammes of carbon per square metre per year. These discrepancies can only partly be reconciled by carbon loss pathways that are implicit in inversions but not captured by the flux tower measurements such as carbon emissions from fires and water bodies. We hypothesize that a combination of systematic biases in the underlying eddy covariance data, in particular in tall tropical forests, and a lack of site history effects on NEE in FLUXCOM are likely responsible for the too strong tropical carbon sink estimated by FLUXCOM. Furthermore, as FLUXCOM does not account for CO2 fertilization effects, carbon flux trends are not realistic. Overall, current FLUXCOM estimates of mean annual and seasonal cycles of GPP as well as seasonal NEE variations provide useful constraints of global carbon cycling, while interannual variability patterns from FLUXCOM are valuable but require cautious interpretation. Exploring the diversity of Earth observation data and of machine learning concepts along with improved quality and quantity of flux tower measurements will facilitate further improvements of the FLUXCOM approach overall.

Mapping in situ eddy covariance measurements of terrestrial land–atmosphere fluxes to the globe is a key method for diagnosing the Earth system from a data-driven perspective. We describe the first global products (called X-BASE) from a newly implemented upscaling framework, FLUXCOM-X, representing an advancement from the previous generation of FLUXCOM products in terms of flexibility and technical capabilities. The X-BASE products are comprised of estimates of CO2 net ecosystem exchange (NEE), gross primary productivity (GPP), evapotranspiration (ET), and for the first time a novel, fully data-driven global transpiration product (ETT), at high spatial (0.05°) and temporal (hourly) resolution. X-BASE estimates the global NEE at −5.75 ± 0.33 Pg C yr−1 for the period 2001–2020, showing a much higher consistency with independent atmospheric carbon cycle constraints compared to the previous versions of FLUXCOM. The improvement of global NEE was likely only possible thanks to the international effort to increase the precision and consistency of eddy covariance collection and processing pipelines, as well as to the extension of the measurements to more site years resulting in a wider coverage of bioclimatic conditions. However, X-BASE global net ecosystem exchange shows a very low interannual variability, which is common to state-of-the-art data-driven flux products and remains a scientific challenge. With 125 ± 2.1 Pg C yr−1 for the same period, X-BASE GPP is slightly higher than previous FLUXCOM estimates, mostly in temperate and boreal areas. X-BASE evapotranspiration amounts to 74.7×103 ± 0.9×103 km3 globally for the years 2001–2020 but exceeds precipitation in many dry areas, likely indicating overestimation in these regions. On average 57 % of evapotranspiration is estimated to be transpiration, in good agreement with isotope-based approaches, but higher than estimates from many land surface models. Despite considerable improvements to the previous upscaling products, many further opportunities for development exist. Pathways of exploration include methodological choices in the selection and processing of eddy covariance and satellite observations, their ingestion into the framework, and the configuration of machine learning methods. For this, the new FLUXCOM-X framework was specifically designed to have the necessary flexibility to experiment, diagnose, and converge to more accurate global flux estimates.