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The Acheulean technological tradition, characterized by a large (>10 cm) flake-based component, represents a significant technological advance over the Oldowan. Although stone tool assemblages attributed to the Acheulean have been reported from as early as circa 1.6–1.75 Ma, the characteristics of these earliest occurrences and comparisons with later assemblages have not been reported in detail. Here, we provide a newly established chronometric calibration for the Acheulean assemblages of the Konso Formation, southern Ethiopia, which span the time period ∼1.75 to <1.0 Ma. The earliest Konso Acheulean is chronologically indistinguishable from the assemblage recently published as the world’s earliest with an age of ∼1.75 Ma at Kokiselei, west of Lake Turkana, Kenya. This Konso assemblage is characterized by a combination of large picks and crude bifaces/unifaces made predominantly on large flake blanks. An increase in the number of flake scars was observed within the Konso Formation handaxe assemblages through time, but this was less so with picks. The Konso evidence suggests that both picks and handaxes were essential components of the Acheulean from its initial stages and that the two probably differed in function. The temporal refinement seen, especially in the handaxe forms at Konso, implies enhanced function through time, perhaps in processing carcasses with long and stable cutting edges. The documentation of the earliest Acheulean at ∼1.75 Ma in both northern Kenya and southern Ethiopia suggests that behavioral novelties were being established in a regional scale at that time, paralleling the emergence of Homo erectus- like hominid morphology.

Soils are warming as air temperatures rise across the Arctic and Boreal region concurrent with the expansion of tall-statured shrubs and trees in the tundra. Changes in vegetation structure and function are expected to alter soil thermal regimes, thereby modifying climate feedbacks related to permafrost thaw and carbon cycling. However, current understanding of vegetation impacts on soil temperature is limited to local or regional scales and lacks the generality necessary to predict soil warming and permafrost stability on a pan-Arctic scale. Here we synthesize shallow soil and air temperature observations with broad spatial and temporal coverage collected across 106 sites representing nine different vegetation types in the permafrost region. We showed ecosystems with tall-statured shrubs and trees (>40 cm) have warmer shallow soils than those with short-statured tundra vegetation when normalized to a constant air temperature. In tree and tall shrub vegetation types, cooler temperatures in the warm season do not lead to cooler mean annual soil temperature indicating that ground thermal regimes in the cold-season rather than the warm-season are most critical for predicting soil warming in ecosystems underlain by permafrost. Our results suggest that the expansion of tall shrubs and trees into tundra regions can amplify shallow soil warming, and could increase the potential for increased seasonal thaw depth and increase soil carbon cycling rates and lead to increased carbon dioxide loss and further permafrost thaw.

AIM: Advancement in ecological methods predicting species distributions is a crucial precondition for deriving sound management actions. Maximum entropy (MaxEnt) models are a popular tool to predict species distributions, as they are considered able to cope well with sparse, irregularly sampled data and minor location errors. Although a fundamental assumption of MaxEnt is that the entire area of interest has been systematically sampled, in practice, MaxEnt models are usually built from occurrence records that are spatially biased towards better‐surveyed areas. Two common, yet not compared, strategies to cope with uneven sampling effort are spatial filtering of occurrence data and background manipulation using environmental data with the same spatial bias as occurrence data. We tested these strategies using simulated data and a recently collated dataset on Malay civet Viverra tangalunga in Borneo. LOCATION: Borneo, Southeast Asia. METHODS: We collated 504 occurrence records of Malay civets from Borneo of which 291 records were from 2001 to 2011 and used them in the MaxEnt analysis (baseline scenario) together with 25 environmental input variables. We simulated datasets for two virtual species (similar to a range‐restricted highland and a lowland species) using the same number of records for model building. As occurrence records were biased towards north‐eastern Borneo, we investigated the efficacy of spatial filtering versus background manipulation to reduce overprediction or underprediction in specific areas. RESULTS: Spatial filtering minimized omission errors (false negatives) and commission errors (false positives). We recommend that when sample size is insufficient to allow spatial filtering, manipulation of the background dataset is preferable to not correcting for sampling bias, although predictions were comparatively weak and commission errors increased. MAIN CONCLUSIONS: We conclude that a substantial improvement in the quality of model predictions can be achieved if uneven sampling effort is taken into account, thereby improving the efficacy of species conservation planning.

Importance of evaluation of global hydrological models (gHMs) before doing climate impact assessment was underlined in several studies. The main objective of this study is to evaluate the performance of six gHMs in simulating observed discharge for a set of 57 large catchments applying common metrics with thresholds for the monthly and seasonal dynamics and summarize them estimating an aggregated index of model performance for each model in each basin. One model showed a good performance, and other five showed a weak or poor performance in most of the basins. In 15 catchments, evaluation results of all models were poor. The model evaluation was supplemented by climate impact assessment for a subset of 12 representative catchments using (1) usual ensemble mean approach and (2) weighted mean approach based on model performance, and the outcomes were compared. The comparison of impacts in terms of mean monthly and mean annual discharges using two approaches has shown that in four basins, differences were negligible or small, and in eight catchments, differences in mean monthly, mean annual discharge or both were moderate to large. The spreads were notably decreased in most cases when the second method was applied. It can be concluded that for improving credibility of projections, the model evaluation and application of the weighted mean approach could be recommended, especially if the mean monthly (seasonal) impacts are of interest, whereas the ensemble mean approach could be applied for projecting the mean annual changes. The calibration of gHMs could improve their performance and, consequently, the credibility of projections.

DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptationAn exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registrationPractical discussions of regression, fitting, parameter retrieval, forecasting and interpolationAn examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.

We collected the available relative pollen productivity estimates (PPEs) for 27 major pollen taxa from Eurasia and applied them to estimate plant abundances during the last 40 ka cal BP (calibrated thousand years before present) using pollen counts from 203 fossil pollen records in northern Asia (north of 40∘ N). These pollen records were organized into 42 site groups and regional mean plant abundances calculated using the REVEALS (Regional Estimates of Vegetation Abundance from Large Sites) model. Time-series clustering, constrained hierarchical clustering, and detrended canonical correspondence analysis were performed to investigate the regional pattern, time, and strength of vegetation changes, respectively. Reconstructed regional plant functional type (PFT) components for each site group are generally consistent with modern vegetation in that vegetation changes within the regions are characterized by minor changes in the abundance of PFTs rather than by an increase in new PFTs, particularly during the Holocene. We argue that pollen-based REVEALS estimates of plant abundances should be a more reliable reflection of the vegetation as pollen may overestimate the turnover, particularly when a high pollen producer invades areas dominated by low pollen producers. Comparisons with vegetation-independent climate records show that climate change is the primary factor driving land-cover changes at broad spatial and temporal scales. Vegetation changes in certain regions or periods, however, could not be explained by direct climate change, e.g. inland Siberia, where a sharp increase in evergreen conifer tree abundance occurred at ca. 7–8 ka cal BP despite an unchanging climate, potentially reflecting their response to complex climate–permafrost–fire–vegetation interactions and thus a possible long-term lagged climate response.

Geology is a subject of low interest for many pupils and teachers. The present study aims at examining the organizational conditions for geology education using the model of the Czech Republic and Slovakia, drawing from the national curricula. The study discusses the possible reasons for the unpopularity of the field worldwide and proposes general recommendations that would contribute to increasing interest in geoscience. The main drawbacks of geology education seem to be the large volume of required knowledge, its thematic structure, and a lack of links to real life. The Czech curriculum is vaguely and theoretically defined, placing demand on pupils, especially in the area of memorizing given information and practically pays no attention to recommended teaching methods. In contrast, the Slovak curriculum better reflects current trends. In general, it is necessary to implement continuous educational support for geology teachers and restructure the geology syllabus so that individual sub-fields are interlinked. Moreover, the learning outcome definition should include action-based education, fieldwork, experimenting, and similar elements. (DIPF/Orig.)

Knowledge of the evolutionary history of birds has much improved in recent decades. Fossils from critical time periods are being described at unprecedented rates and modern phylogenetic analyses have provided a framework for the interrelationships of the extant groups. This book gives an overview of the avian fossil record and its paleobiological significance, and it is the only up-to-date textbook that covers both Mesozoic and more modern-type Cenozoic birds in some detail. The reader is introduced to key features of basal avians and the morphological transformations that have occurred in the evolution towards modern birds. An account of the Cenozoic fossil record sheds light on the biogeographic history of the extant avian groups and discusses fossils in the context of current phylogenetic hypotheses. This review of the evolutionary history of birds not only addresses students and established researchers, but it may also be a useful source of information for anyone else with an interest in the evolution of birds and a moderate background in biology and geology.

Regional flood frequency analysis (RFFA) is a powerful method for interrogating hydrological series since it combines observational time series from several sites within a region to estimate risk-relevant statistical parameters with higher accuracy than from single-site series. Since RFFA extreme value estimates depend on the shape of the selected distribution of the data-generating stochastic process, there is need for a suitable goodness-of-distributional-fit measure in order to optimally utilize given data. Here we present a novel, least-squares-based measure to select the optimal fit from a set of five distributions, namely Generalized Extreme Value (GEV), Generalized Logistic, Gumbel, Log-Normal Type III and Log-Pearson Type III. The fit metric is applied to annual maximum discharge series from six hydrological stations along the Sava River in South-eastern Europe, spanning the years 1961 to 2020. Results reveal that (1) the Sava River basin can be assessed as hydrologically homogeneous and (2) the GEV distribution provides typically the best fit. We offer hydrological-meteorological insights into the differences among the six stations. For the period studied, almost all stations exhibit statistically insignificant trends, which renders the conclusions about flood risk as relevant for hydrological sciences and the design of regional flood protection infrastructure.